import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data python version used : 3 #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 7218 run mask_detect Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.12507295608520508 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Thu May 29 11:20:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 7218 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 /home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp 2025-05-29 11:20:32.148348: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-29 11:20:32.175186: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-29 11:20:32.177238: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fa7b4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-29 11:20:32.177294: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-29 11:20:32.181296: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-29 11:20:32.412929: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x14973850 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-29 11:20:32.412983: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-29 11:20:32.414237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-29 11:20:32.414655: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:20:32.417705: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:20:32.420510: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-29 11:20:32.420909: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-29 11:20:32.423492: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-29 11:20:32.424752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-29 11:20:32.429816: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:20:32.431179: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-29 11:20:32.431265: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:20:32.431969: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-29 11:20:32.431987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-29 11:20:32.431996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-29 11:20:32.433181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6642 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 2025-05-29 11:20:33.217519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-29 11:20:33.217594: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:20:33.217611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:20:33.217627: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-29 11:20:33.217642: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-29 11:20:33.217657: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-29 11:20:33.217671: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-29 11:20:33.217686: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:20:33.218788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-29 11:20:33.219846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-29 11:20:33.219883: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:20:33.219899: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:20:33.219914: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-29 11:20:33.219935: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-29 11:20:33.219950: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-29 11:20:33.219964: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-29 11:20:33.219978: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:20:33.221015: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-29 11:20:33.221040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-29 11:20:33.221048: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-29 11:20:33.221056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-29 11:20:33.222124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6642 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-05-29 11:20:43.511945: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:20:43.701484: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (480, 640, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 1921542 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 932 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1929 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl454 Catched exception ! Connect or reconnect ! thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0005865097045898438 nb_pixel_total : 15551 time to create 1 rle with old method : 0.03550457954406738 length of segment : 256 time for calcul the mask position with numpy : 0.002777576446533203 nb_pixel_total : 145330 time to create 1 rle with old method : 0.34569883346557617 length of segment : 371 time for calcul the mask position with numpy : 0.0002472400665283203 nb_pixel_total : 14254 time to create 1 rle with old method : 0.03247785568237305 length of segment : 151 time for calcul the mask position with numpy : 0.00012540817260742188 nb_pixel_total : 5613 time to create 1 rle with old method : 0.013281822204589844 length of segment : 48 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 1825 time to create 1 rle with old method : 0.005284309387207031 length of segment : 39 time spent for convertir_results : 1.3247027397155762 time spend for datou_step_exec : 21.92324924468994 time spend to save output : 3.4809112548828125e-05 total time spend for step 1 : 21.92328405380249 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 3336 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.018597126007080078 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[(957285035, 492601069, 445, 0, 186, 22, 282, 0.9954875, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (127, 30, 27), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.99238014, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 311), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), (89, 111, 449), (89, 112, 450), (89, 113, 451), (89, 114, 453), (89, 115, 454), (89, 116, 455), (88, 117, 456), (88, 118, 457), (87, 119, 459), (87, 120, 459), (86, 121, 461), (85, 122, 462), (85, 123, 463), (84, 124, 464), (84, 125, 465), (83, 126, 466), (82, 127, 468), (82, 128, 468), (81, 129, 470), (80, 130, 471), (78, 131, 473), (76, 132, 476), (75, 133, 477), (73, 134, 480), (71, 135, 482), (70, 136, 484), (68, 137, 486), (67, 138, 488), (65, 139, 490), (64, 140, 492), (63, 141, 493), (61, 142, 496), (60, 143, 497), (59, 144, 499), (58, 145, 501), (58, 146, 501), (57, 147, 503), (57, 148, 504), (57, 149, 505), (56, 150, 507), (56, 151, 507), (55, 152, 509), (55, 153, 510), (54, 154, 511), (54, 155, 512), (54, 156, 513), (53, 157, 514), (53, 158, 514), (52, 159, 515), (52, 160, 516), (52, 161, 516), (51, 162, 517), (51, 163, 517), (50, 164, 518), (50, 165, 518), (49, 166, 519), (49, 167, 520), (48, 168, 521), (48, 169, 521), (47, 170, 522), (47, 171, 522), (46, 172, 523), (46, 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(39, 298, 399), (39, 299, 397), (41, 300, 394), (42, 301, 392), (43, 302, 389), (44, 303, 387), (45, 304, 385), (46, 305, 382), (47, 306, 380), (47, 307, 378), (48, 308, 376), (49, 309, 373), (50, 310, 370), (51, 311, 368), (51, 312, 367), (52, 313, 365), (54, 314, 362), (55, 315, 360), (56, 316, 359), (58, 317, 356), (61, 318, 352), (64, 319, 349), (67, 320, 345), (70, 321, 341), (73, 322, 338), (75, 323, 335), (78, 324, 332), (80, 325, 329), (82, 326, 327), (84, 327, 324), (86, 328, 322), (88, 329, 320), (90, 330, 317), (93, 331, 314), (96, 332, 311), (99, 333, 307), (102, 334, 304), (105, 335, 300), (108, 336, 297), (111, 337, 294), (113, 338, 291), (115, 339, 289), (117, 340, 286), (119, 341, 283), (121, 342, 281), (123, 343, 278), (125, 344, 275), (127, 345, 272), (129, 346, 269), (132, 347, 266), (135, 348, 262), (137, 349, 259), (141, 350, 255), (143, 351, 252), (145, 352, 250), (147, 353, 247), (149, 354, 245), (151, 355, 242), (152, 356, 241), (154, 357, 239), (156, 358, 237), 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['321,407,296,403,263,401,215,388,178,371,168,363,140,349,110,336,90,330,77,323,56,316,39,299,31,273,31,236,34,199,58,145,79,131,89,116,89,101,104,88,115,72,159,49,180,43,199,41,237,41,272,38,339,37,382,39,402,43,417,43,481,55,504,76,543,116,556,143,566,156,568,167,566,186,554,199,548,216,528,235,496,256,471,275,420,309,407,327,403,339,392,355,389,371,383,385,369,400,358,405']), (957285035, 492601069, 445, 485, 636, 23, 174, 0.97115636, [(540, 24, 21), (626, 24, 3), (531, 25, 49), (594, 25, 40), (527, 26, 107), (523, 27, 111), (520, 28, 114), (517, 29, 118), (516, 30, 119), (515, 31, 120), (513, 32, 122), (512, 33, 123), (510, 34, 125), (509, 35, 126), (507, 36, 128), (506, 37, 129), (504, 38, 131), (503, 39, 132), (501, 40, 134), (500, 41, 135), (499, 42, 136), (498, 43, 137), (497, 44, 138), (496, 45, 139), (496, 46, 139), (495, 47, 140), (495, 48, 140), (494, 49, 141), (493, 50, 142), (492, 51, 143), (491, 52, 144), (491, 53, 144), (490, 54, 145), (490, 55, 145), (490, 56, 145), 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['598,172,591,172,586,170,578,168,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,501,40,514,32,517,29,531,25,539,25,540,24,560,24,561,25,579,25,580,26,593,26,594,25,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161,604,166']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8299479, [(292, 3, 128), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 44), (432, 38, 41), (434, 39, 40), (436, 40, 39), (438, 41, 37), (441, 42, 35), (444, 43, 32), (448, 44, 29), (452, 45, 25), (454, 46, 23), (459, 47, 17), (463, 48, 12), (468, 49, 5)], ['472,49,468,49,467,48,459,47,458,46,454,46,451,44,448,44,447,43,444,43,440,41,438,41,428,36,424,36,423,35,419,35,418,34,409,34,408,33,401,33,400,32,371,32,370,31,337,31,336,30,283,31,281,29,281,6,284,4,291,4,292,3,419,3,420,4,429,4,430,5,432,5,436,7,441,11,445,12,453,16,456,19,457,19,465,27,465,29,472,37,476,44,476,46']), (957285035, 492601069, 445, 456, 547, 6, 45, 0.7402921, [(482, 8, 19), (463, 9, 4), (481, 9, 44), (457, 10, 12), (479, 10, 50), (457, 11, 13), (476, 11, 56), (457, 12, 15), (475, 12, 65), (457, 13, 84), (457, 14, 85), (457, 15, 89), (457, 16, 89), (458, 17, 88), (459, 18, 87), (460, 19, 86), (461, 20, 80), (464, 21, 71), (466, 22, 63), (467, 23, 59), (468, 24, 55), (469, 25, 52), (469, 26, 51), (470, 27, 48), (471, 28, 46), (471, 29, 44), (472, 30, 42), (473, 31, 39), (473, 32, 38), (474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1748510428_1921311_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1776 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2072582244873047 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Thu May 29 11:20:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3000 l 3632 free memory gpu now : 1698 wait 20 seconds l 3637 free memory gpu now : 1698 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-29 11:21:18.934153: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-29 11:21:18.963183: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-29 11:21:18.965424: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fa7b4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-29 11:21:18.965487: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-29 11:21:18.971100: I tensorflow/strInside mask_sub_process Inside mask_detect About to load cache.load_thcl_param FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-05-29 11:21:29.548050: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:21:29.773734: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:21:31.517141: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.517208: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.523888: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.523924: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.580376: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.580453: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.623915: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.623987: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.672020: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.672094: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-05-29 11:21:31.701351: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature. 2025-05-29 11:21:31.715985: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.00G (1073741824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.716885: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 921.60M (966367744 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.717783: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 829.44M (869731072 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.718704: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 746.50M (782758144 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.882196: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature. 2025-05-29 11:21:31.900393: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.901357: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.907478: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.908828: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.914961: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.916063: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.928334: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.928915: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.933073: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.933637: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.955057: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.955643: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.956225: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.956765: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.957303: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.957842: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.994965: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.995029: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-05-29 11:21:31.995622: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:31.996177: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.003886: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.004428: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.012628: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.013175: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.028790: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.029511: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.030207: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.030887: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.035369: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.035957: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.036505: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.037056: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.038117: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.048713: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.049300: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.060406: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.061030: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.061653: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.062251: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.062868: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-05-29 11:21:32.063466: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 1.02G (1092485120 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 1924884 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 736 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1929 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0007691383361816406 nb_pixel_total : 16902 time to create 1 rle with old method : 0.039803504943847656 length of segment : 107 time for calcul the mask position with numpy : 0.05013775825500488 nb_pixel_total : 480752 time to create 1 rle with new method : 0.035863637924194336 length of segment : 632 time for calcul the mask position with numpy : 0.0004744529724121094 nb_pixel_total : 36640 time to create 1 rle with old method : 0.07913827896118164 length of segment : 133 time for calcul the mask position with numpy : 0.0001590251922607422 nb_pixel_total : 4793 time to create 1 rle with old method : 0.01600337028503418 length of segment : 51 time spent for convertir_results : 0.48732447624206543 time spend for datou_step_exec : 41.39690089225769 time spend to save output : 5.698204040527344e-05 total time spend for step 1 : 41.396957874298096 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 428 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.015233755111694336 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917855882': [[(917855882, 492601069, 445, 1092, 1280, 0, 108, 0.99883944, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.99774665, [(711, 22, 21), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (489, 47, 589), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (413, 103, 677), (410, 104, 680), (405, 105, 685), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (368, 113, 722), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (348, 120, 742), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (286, 134, 803), (279, 135, 810), (273, 136, 816), (266, 137, 823), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (233, 148, 856), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 876), (207, 153, 882), (200, 154, 889), (193, 155, 896), (187, 156, 902), (183, 157, 906), (181, 158, 908), (178, 159, 911), (176, 160, 913), (174, 161, 915), (172, 162, 917), (170, 163, 919), (168, 164, 921), (167, 165, 922), (165, 166, 924), (164, 167, 925), (162, 168, 927), (161, 169, 928), (159, 170, 930), (157, 171, 932), (155, 172, 934), (153, 173, 935), (151, 174, 937), (148, 175, 940), (146, 176, 942), (144, 177, 944), (142, 178, 946), (140, 179, 948), (139, 180, 949), (137, 181, 951), (136, 182, 952), (134, 183, 954), (133, 184, 955), (132, 185, 956), (131, 186, 957), (130, 187, 958), (129, 188, 959), (128, 189, 960), (127, 190, 960), (126, 191, 961), (126, 192, 961), (125, 193, 962), (124, 194, 963), (123, 195, 964), (122, 196, 965), (122, 197, 965), (121, 198, 966), (120, 199, 967), (119, 200, 968), (118, 201, 969), (117, 202, 970), (116, 203, 971), (114, 204, 973), (113, 205, 973), (112, 206, 974), (111, 207, 975), (109, 208, 977), (108, 209, 978), 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492601069, 445, 390, 550, 0, 54, 0.93922865, [(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (420, 25, 99), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1748510454_1921311_917855882_da0fa7b7e6b5b551fe26c0ba8713276d.jpg']} ############################### TEST POLYGON ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.16833090782165527 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Thu May 29 11:21:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3000 l 3632 free memory gpu now : 1929 wait 20 seconds l 3637 free memory gpu now : 1929 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-05-29 11:22:01.848476: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-29 11:22:01.919188: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-29 11:22:01.922064: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fa7bc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-29 11:22:01.922135: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-29 11:22:01.927355: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-29 11:22:02.195733: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x13dc3670 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-29 11:22:02.195837: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-29 11:22:02.196901: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-29 11:22:02.197400: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:22:02.206367: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:22:02.210075: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-29 11:22:02.210812: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-29 11:22:02.253631: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-29 11:22:02.256866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-29 11:22:02.279997: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:22:02.281657: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-29 11:22:02.281852: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:22:02.282707: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-29 11:22:02.282732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-29 11:22:02.282744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-29 11:22:02.284565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5221 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-05-29 11:22:02.479917: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-29 11:22:02.480029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:22:02.480050: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:22:02.480068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-29 11:22:02.480085: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-29 11:22:02.480101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-29 11:22:02.480118: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-29 11:22:02.480135: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:22:02.481155: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-29 11:22:02.482307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-29 11:22:02.482371: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:22:02.482389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:22:02.482405: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-29 11:22:02.482422: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-29 11:22:02.482438: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-29 11:22:02.482454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-29 11:22:02.482470: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:22:02.483512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-29 11:22:02.483546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-29 11:22:02.483554: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-29 11:22:02.483562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-29 11:22:02.484626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5221 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-05-29 11:22:08.610801: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 51380224 exceeds 10% of free system memory. 2025-05-29 11:22:14.344761: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:22:14.633748: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 1926648 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 2323 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 7544 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.22549033164978027 nb_pixel_total : 3688897 time to create 1 rle with new method : 0.942518949508667 length of segment : 2036 time spent for convertir_results : 3.0524258613586426 time spend for datou_step_exec : 46.539066791534424 time spend to save output : 4.7206878662109375e-05 total time spend for step 1 : 46.539113998413086 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 722 chid ids of type : 445 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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.012861251831054688 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 7, 2264, 122, 2237, 0.9848778, [(522, 124, 446), (1147, 124, 278), (501, 125, 949), (480, 126, 993), (461, 127, 1035), (443, 128, 1096), (427, 129, 1153), (411, 130, 1178), (396, 131, 1201), (382, 132, 1223), (369, 133, 1244), (367, 134, 1254), (364, 135, 1264), (361, 136, 1274), (359, 137, 1283), (356, 138, 1292), (354, 139, 1300), (351, 140, 1309), (349, 141, 1316), (347, 142, 1322), (345, 143, 1328), (343, 144, 1334), (341, 145, 1340), (339, 146, 1346), (337, 147, 1351), (336, 148, 1356), (334, 149, 1361), (332, 150, 1367), (331, 151, 1371), (329, 152, 1376), (328, 153, 1380), (326, 154, 1385), (325, 155, 1389), (323, 156, 1393), (322, 157, 1397), (321, 158, 1401), (319, 159, 1405), (318, 160, 1409), (317, 161, 1412), (316, 162, 1416), (314, 163, 1421), (313, 164, 1425), (312, 165, 1428), (311, 166, 1431), (309, 167, 1435), (308, 168, 1438), (306, 169, 1443), (305, 170, 1446), (303, 171, 1450), (302, 172, 1453), (300, 173, 1458), (298, 174, 1462), (297, 175, 1466), (295, 176, 1471), (293, 177, 1475), (291, 178, 1480), (289, 179, 1485), (286, 180, 1491), (284, 181, 1496), (281, 182, 1502), (279, 183, 1508), (276, 184, 1514), (273, 185, 1521), (271, 186, 1526), (268, 187, 1533), (265, 188, 1540), (262, 189, 1547), (260, 190, 1554), (257, 191, 1561), (254, 192, 1569), (251, 193, 1579), (249, 194, 1587), (246, 195, 1596), (243, 196, 1605), (240, 197, 1615), (237, 198, 1624), (234, 199, 1633), (231, 200, 1642), (229, 201, 1650), (226, 202, 1658), (223, 203, 1667), (220, 204, 1676), (217, 205, 1684), (214, 206, 1690), (211, 207, 1694), (208, 208, 1699), (206, 209, 1703), (204, 210, 1707), (202, 211, 1710), (201, 212, 1713), (199, 213, 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(89, 326, 1941), (88, 327, 1943), (88, 328, 1944), (88, 329, 1944), (87, 330, 1946), (87, 331, 1946), (87, 332, 1947), (87, 333, 1948), (86, 334, 1950), (86, 335, 1950), (86, 336, 1951), (86, 337, 1952), (85, 338, 1953), (85, 339, 1954), (85, 340, 1955), (84, 341, 1957), (84, 342, 1957), (84, 343, 1958), (84, 344, 1959), (83, 345, 1961), (83, 346, 1962), (83, 347, 1963), (82, 348, 1964), (82, 349, 1965), (82, 350, 1966), (82, 351, 1967), (81, 352, 1969), (81, 353, 1970), (81, 354, 1971), (80, 355, 1973), (80, 356, 1974), (80, 357, 1975), (79, 358, 1977), (79, 359, 1978), (79, 360, 1979), (79, 361, 1981), (78, 362, 1983), (78, 363, 1984), (78, 364, 1985), (77, 365, 1987), (77, 366, 1988), (77, 367, 1989), (76, 368, 1991), (76, 369, 1992), (76, 370, 1993), (75, 371, 1995), (75, 372, 1996), (75, 373, 1997), (75, 374, 1998), (74, 375, 2000), (74, 376, 2001), (74, 377, 2002), (73, 378, 2003), (73, 379, 2004), (73, 380, 2005), (72, 381, 2007), (72, 382, 2007), (72, 383, 2008), (71, 384, 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pixel non reg : 3692295 nb pixel common : 3682639 proportion of common points : 0.997384824343667 #&_# TEST FAILED #&_# : tests/mask_test #&_# #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : sam list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.27719879150390625 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:sam Thu May 29 11:22:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0021669864654541016 nb_pixel_total : 3781 time to create 1 rle with old method : 0.012308835983276367 time for calcul the mask position with numpy : 0.0016868114471435547 nb_pixel_total : 8611 time to create 1 rle with old method : 0.02816009521484375 time for calcul the mask position with numpy : 0.0017609596252441406 nb_pixel_total : 16472 time to create 1 rle with old method : 0.045302391052246094 time for calcul the mask position with numpy : 0.0016183853149414062 nb_pixel_total : 14721 time to create 1 rle with old method : 0.033262014389038086 time for calcul the mask position with numpy : 0.0015642642974853516 nb_pixel_total : 7651 time to create 1 rle with old method : 0.016943931579589844 time for calcul the mask position with numpy : 0.001361846923828125 nb_pixel_total : 5517 time to create 1 rle with old method : 0.01234745979309082 time for calcul the mask position with numpy : 0.0016682147979736328 nb_pixel_total : 38816 time to create 1 rle with old method : 0.08601808547973633 time for calcul the mask position with numpy : 0.0019421577453613281 nb_pixel_total : 83775 time to create 1 rle with old method : 0.21819114685058594 time for calcul the mask position with numpy : 0.0020110607147216797 nb_pixel_total : 5797 time to create 1 rle with old method : 0.020646333694458008 time for calcul the mask position with numpy : 0.00185394287109375 nb_pixel_total : 5630 time to create 1 rle with old method : 0.016510009765625 time for calcul the mask position with numpy : 0.0018088817596435547 nb_pixel_total : 29424 time to create 1 rle with old method : 0.07640457153320312 time for calcul the mask position with numpy : 0.0019061565399169922 nb_pixel_total : 13916 time to create 1 rle with old method : 0.041373252868652344 time for calcul the mask position with numpy : 0.001979351043701172 nb_pixel_total : 3112 time to create 1 rle with old method : 0.008501529693603516 time for calcul the mask position with numpy : 0.0018737316131591797 nb_pixel_total : 2942 time to create 1 rle with old method : 0.010299921035766602 time for calcul the mask position with numpy : 0.0018684864044189453 nb_pixel_total : 2454 time to create 1 rle with old method : 0.00693058967590332 time for calcul the mask position with numpy : 0.0018622875213623047 nb_pixel_total : 4264 time to create 1 rle with old method : 0.011354684829711914 time for calcul the mask position with numpy : 0.0018472671508789062 nb_pixel_total : 10830 time to create 1 rle with old method : 0.031993865966796875 time for calcul the mask position with numpy : 0.0016384124755859375 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0030755996704101562 time for calcul the mask position with numpy : 0.0018579959869384766 nb_pixel_total : 3951 time to create 1 rle with old method : 0.009678125381469727 time for calcul the mask position with numpy : 0.0016164779663085938 nb_pixel_total : 6633 time to create 1 rle with old method : 0.02352738380432129 time for calcul the mask position with numpy : 0.002231597900390625 nb_pixel_total : 2077 time to create 1 rle with old method : 0.008873939514160156 time for calcul the mask position with numpy : 0.0021271705627441406 nb_pixel_total : 16351 time to create 1 rle with old method : 0.04990744590759277 time for calcul the mask position with numpy : 0.0018553733825683594 nb_pixel_total : 2726 time to create 1 rle with old method : 0.00748133659362793 time for calcul the mask position with numpy : 0.0017323493957519531 nb_pixel_total : 861 time to create 1 rle with old method : 0.002547740936279297 time for calcul the mask position with numpy : 0.0018596649169921875 nb_pixel_total : 1335 time to create 1 rle with old method : 0.003750324249267578 time for calcul the mask position with numpy : 0.0017473697662353516 nb_pixel_total : 4276 time to create 1 rle with old method : 0.012954473495483398 time for calcul the mask position with numpy : 0.0021576881408691406 nb_pixel_total : 11928 time to create 1 rle with old method : 0.0315699577331543 time for calcul the mask position with numpy : 0.001657724380493164 nb_pixel_total : 2448 time to create 1 rle with old method : 0.006880760192871094 time for calcul the mask position with numpy : 0.001966238021850586 nb_pixel_total : 3535 time to create 1 rle with old method : 0.009324789047241211 time for calcul the mask position with numpy : 0.002215862274169922 nb_pixel_total : 886 time to create 1 rle with old method : 0.0023241043090820312 time for calcul the mask position with numpy : 0.002003908157348633 nb_pixel_total : 13044 time to create 1 rle with old method : 0.03563642501831055 time for calcul the mask position with numpy : 0.0016651153564453125 nb_pixel_total : 9878 time to create 1 rle with old method : 0.02507805824279785 time for calcul the mask position with numpy : 0.0015819072723388672 nb_pixel_total : 970 time to create 1 rle with old method : 0.002378702163696289 time for calcul the mask position with numpy : 0.0015654563903808594 nb_pixel_total : 10491 time to create 1 rle with old method : 0.02682781219482422 time for calcul the mask position with numpy : 0.0015912055969238281 nb_pixel_total : 2781 time to create 1 rle with old method : 0.007466793060302734 time for calcul the mask position with numpy : 0.0016429424285888672 nb_pixel_total : 5414 time to create 1 rle with old method : 0.01446080207824707 time for calcul the mask position with numpy : 0.0016245841979980469 nb_pixel_total : 1244 time to create 1 rle with old method : 0.003166675567626953 time for calcul the mask position with numpy : 0.0015780925750732422 nb_pixel_total : 1022 time to create 1 rle with old method : 0.002737283706665039 time for calcul the mask position with numpy : 0.0016283988952636719 nb_pixel_total : 343 time to create 1 rle with old method : 0.0011334419250488281 time for calcul the mask position with numpy : 0.0019974708557128906 nb_pixel_total : 3330 time to create 1 rle with old method : 0.009538888931274414 time for calcul the mask position with numpy : 0.0017549991607666016 nb_pixel_total : 1654 time to create 1 rle with old method : 0.004415035247802734 time for calcul the mask position with numpy : 0.0016016960144042969 nb_pixel_total : 4028 time to create 1 rle with old method : 0.01110529899597168 time for calcul the mask position with numpy : 0.0016522407531738281 nb_pixel_total : 596 time to create 1 rle with old method : 0.0015149116516113281 time for calcul the mask position with numpy : 0.0015225410461425781 nb_pixel_total : 3844 time to create 1 rle with old method : 0.011065006256103516 time for calcul the mask position with numpy : 0.00157928466796875 nb_pixel_total : 2028 time to create 1 rle with old method : 0.005671501159667969 time for calcul the mask position with numpy : 0.0017726421356201172 nb_pixel_total : 4188 time to create 1 rle with old method : 0.010942935943603516 time for calcul the mask position with numpy : 0.0016582012176513672 nb_pixel_total : 2324 time to create 1 rle with old method : 0.006140708923339844 time for calcul the mask position with numpy : 0.0015947818756103516 nb_pixel_total : 577 time to create 1 rle with old method : 0.0017726421356201172 time for calcul the mask position with numpy : 0.0016014575958251953 nb_pixel_total : 875 time to create 1 rle with old method : 0.002453327178955078 time for calcul the mask position with numpy : 0.0015912055969238281 nb_pixel_total : 2384 time to create 1 rle with old method : 0.006561279296875 time for calcul the mask position with numpy : 0.0017139911651611328 nb_pixel_total : 13221 time to create 1 rle with old method : 0.03328824043273926 time for calcul the mask position with numpy : 0.001789093017578125 nb_pixel_total : 1673 time to create 1 rle with old method : 0.0044748783111572266 time for calcul the mask position with numpy : 0.001611471176147461 nb_pixel_total : 693 time to create 1 rle with old method : 0.0018792152404785156 time for calcul the mask position with numpy : 0.0017673969268798828 nb_pixel_total : 27890 time to create 1 rle with old method : 0.0722200870513916 time for calcul the mask position with numpy : 0.0016644001007080078 nb_pixel_total : 586 time to create 1 rle with old method : 0.0015571117401123047 time for calcul the mask position with numpy : 0.0015857219696044922 nb_pixel_total : 337 time to create 1 rle with old method : 0.0010228157043457031 time for calcul the mask position with numpy : 0.0016884803771972656 nb_pixel_total : 2411 time to create 1 rle with old method : 0.006353855133056641 time for calcul the mask position with numpy : 0.0015976428985595703 nb_pixel_total : 298 time to create 1 rle with old method : 0.0009698867797851562 time for calcul the mask position with numpy : 0.0016269683837890625 nb_pixel_total : 1706 time to create 1 rle with old method : 0.004384279251098633 time for calcul the mask position with numpy : 0.001615762710571289 nb_pixel_total : 2770 time to create 1 rle with old method : 0.0072786808013916016 time for calcul the mask position with numpy : 0.0015115737915039062 nb_pixel_total : 1206 time to create 1 rle with old method : 0.00316619873046875 time for calcul the mask position with numpy : 0.0016007423400878906 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0031490325927734375 time for calcul the mask position with numpy : 0.0016891956329345703 nb_pixel_total : 8606 time to create 1 rle with old method : 0.02139902114868164 time for calcul the mask position with numpy : 0.0016279220581054688 nb_pixel_total : 9666 time to create 1 rle with old method : 0.02435612678527832 time for calcul the mask position with numpy : 0.0015931129455566406 nb_pixel_total : 1078 time to create 1 rle with old method : 0.002770662307739258 time for calcul the mask position with numpy : 0.0015964508056640625 nb_pixel_total : 915 time to create 1 rle with old method : 0.0022602081298828125 time for calcul the mask position with numpy : 0.0016760826110839844 nb_pixel_total : 16681 time to create 1 rle with old method : 0.042464256286621094 time for calcul the mask position with numpy : 0.0016636848449707031 nb_pixel_total : 199 time to create 1 rle with old method : 0.0005779266357421875 time for calcul the mask position with numpy : 0.0015811920166015625 nb_pixel_total : 1740 time to create 1 rle with old method : 0.004542827606201172 time for calcul the mask position with numpy : 0.001470327377319336 nb_pixel_total : 1526 time to create 1 rle with old method : 0.0037848949432373047 time for calcul the mask position with numpy : 0.001589059829711914 nb_pixel_total : 268 time to create 1 rle with old method : 0.0007398128509521484 time for calcul the mask position with numpy : 0.0016236305236816406 nb_pixel_total : 8442 time to create 1 rle with old method : 0.020606517791748047 time for calcul the mask position with numpy : 0.0016789436340332031 nb_pixel_total : 9077 time to create 1 rle with old method : 0.02237105369567871 time for calcul the mask position with numpy : 0.0017132759094238281 nb_pixel_total : 712 time to create 1 rle with old method : 0.0019655227661132812 time for calcul the mask position with numpy : 0.0016334056854248047 nb_pixel_total : 3167 time to create 1 rle with old method : 0.008249282836914062 time for calcul the mask position with numpy : 0.0015425682067871094 nb_pixel_total : 615 time to create 1 rle with old method : 0.001590728759765625 time for calcul the mask position with numpy : 0.0016009807586669922 nb_pixel_total : 248 time to create 1 rle with old method : 0.0006518363952636719 time for calcul the mask position with numpy : 0.001596212387084961 nb_pixel_total : 972 time to create 1 rle with old method : 0.0025200843811035156 time for calcul the mask position with numpy : 0.0015528202056884766 nb_pixel_total : 222 time to create 1 rle with old method : 0.0006229877471923828 time for calcul the mask position with numpy : 0.0015027523040771484 nb_pixel_total : 1354 time to create 1 rle with old method : 0.003579854965209961 time for calcul the mask position with numpy : 0.0015289783477783203 nb_pixel_total : 1515 time to create 1 rle with old method : 0.00396728515625 time for calcul the mask position with numpy : 0.0015206336975097656 nb_pixel_total : 740 time to create 1 rle with old method : 0.001994609832763672 time for calcul the mask position with numpy : 0.0016148090362548828 nb_pixel_total : 7405 time to create 1 rle with old method : 0.01896071434020996 time for calcul the mask position with numpy : 0.0015621185302734375 nb_pixel_total : 1633 time to create 1 rle with old method : 0.004200935363769531 time for calcul the mask position with numpy : 0.0015411376953125 nb_pixel_total : 5013 time to create 1 rle with old method : 0.012804985046386719 time for calcul the mask position with numpy : 0.0016567707061767578 nb_pixel_total : 7496 time to create 1 rle with old method : 0.01913142204284668 time for calcul the mask position with numpy : 0.0017170906066894531 nb_pixel_total : 594 time to create 1 rle with old method : 0.0016677379608154297 time for calcul the mask position with numpy : 0.0016627311706542969 nb_pixel_total : 1443 time to create 1 rle with old method : 0.0039479732513427734 time for calcul the mask position with numpy : 0.0016281604766845703 nb_pixel_total : 917 time to create 1 rle with old method : 0.002680063247680664 time for calcul the mask position with numpy : 0.0015909671783447266 nb_pixel_total : 1127 time to create 1 rle with old method : 0.0029790401458740234 time for calcul the mask position with numpy : 0.0016965866088867188 nb_pixel_total : 2217 time to create 1 rle with old method : 0.005947589874267578 time for calcul the mask position with numpy : 0.0017275810241699219 nb_pixel_total : 890 time to create 1 rle with old method : 0.0023670196533203125 time for calcul the mask position with numpy : 0.002073049545288086 nb_pixel_total : 947 time to create 1 rle with old method : 0.0033605098724365234 time for calcul the mask position with numpy : 0.0024454593658447266 nb_pixel_total : 1615 time to create 1 rle with old method : 0.006264925003051758 time for calcul the mask position with numpy : 0.0016658306121826172 nb_pixel_total : 481 time to create 1 rle with old method : 0.0012798309326171875 time for calcul the mask position with numpy : 0.0016217231750488281 nb_pixel_total : 883 time to create 1 rle with old method : 0.002303600311279297 time for calcul the mask position with numpy : 0.0015709400177001953 nb_pixel_total : 1438 time to create 1 rle with old method : 0.003729104995727539 time for calcul the mask position with numpy : 0.0016319751739501953 nb_pixel_total : 830 time to create 1 rle with old method : 0.0022668838500976562 batch 1 Loaded 98 chid ids of type : 4677 Number RLEs to save : 8704 TO DO : save crop sub photo not yet done ! Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] Looping around the photos to save general results len do output : 1 /1189321094Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4573', None, None, None, None, None, None, None, None) ('4573', None, '1189321094', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.013786792755126953 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.387539386749268 time spend to save output : 0.01418924331665039 total time spend for step 1 : 11.401728630065918 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1748510551_1921311_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 98 ############################### TEST frcnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : frcnn list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.16126585006713867 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:frcnn Thu May 29 11:22:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1748510563_1921311_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg image_size (600, 800, 3) [[[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] ... [[ 14 16 16] [ 13 15 15] [ 11 13 13] ... [198 206 205] [198 206 205] [198 206 205]] [[ 16 18 18] [ 14 16 16] [ 11 13 13] ... [206 214 213] [206 214 213] [206 214 213]] [[ 13 15 15] [ 12 14 14] [ 9 11 11] ... [210 218 217] [210 218 217] [210 218 217]]] Detection took 0.118s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 3.088930368423462 time spend to save output : 0.00012826919555664062 total time spend for step 1 : 3.0890586376190186 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False Inside saveFrcnn : final : True verbose : False threshold to save the result : 0.1 Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.013901233673095703 [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.01484537124633789 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.063893355, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05221738, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012276415, None)], 'temp/1748510563_1921311_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.11254310607910156 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Thu May 29 11:22:46 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 355 time to import caffe and check if the image exist : 0.01228952407836914 time to convert the images to numpy array : 0.0009834766387939453 total time to convert the images to numpy array : 0.013771533966064453 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 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'506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3121 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : car_360_1027 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3121 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.010071277618408203 time used to do the prediction : 0.0631563663482666 save descriptor for thcl : 355 time to traite the descriptors : 0.06111550331115723 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.6188068389892578 Inside saveOutput : final : False verbose : False time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 1.1682510375976562e-05 save missing photos in datou_result : time spend for datou_step_exec : 5.7868897914886475 time spend to save output : 1.8302414417266846 total time spend for step 1 : 7.617131233215332 step2:argmax Thu May 29 11:22:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 355 Inside saveOutput : final : True verbose : False photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.017708488, 332, '355'), 'temp/1748510566_1921311_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.02343297004699707 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.018705129623413086 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.015842437744140625 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 7.62939453125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0016546249389648438 time spend to save output : 0.05841326713562012 total time spend for step 2 : 0.06006789207458496 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.017708488, 332, '355'), 'temp/1748510566_1921311_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12835 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12836 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.2004997730255127 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Thu May 29 11:22:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 106 wait 20 seconds inside check gpu memory 2025-05-29 11:23:22.734954: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-05-29 11:23:22.735578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-29 11:23:22.735698: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:23:22.735753: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:23:22.738076: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-29 11:23:22.738152: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-29 11:23:22.740435: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-29 11:23:22.741529: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-29 11:23:22.745563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:23:22.746572: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-29 11:23:22.747128: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-05-29 11:23:22.779450: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-05-29 11:23:22.781517: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fa518000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-05-29 11:23:22.781548: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-05-29 11:23:22.785074: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x255355b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-05-29 11:23:22.785106: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-05-29 11:23:22.786027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-05-29 11:23:22.786157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:23:22.786189: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-05-29 11:23:22.786281: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-05-29 11:23:22.786319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-05-29 11:23:22.786366: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-05-29 11:23:22.786417: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-05-29 11:23:22.786468: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-05-29 11:23:22.787824: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-05-29 11:23:22.787903: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-05-29 11:23:22.787964: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-05-29 11:23:22.787980: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-05-29 11:23:22.787998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-05-29 11:23:22.789393: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) l 3637 free memory gpu now : 3121 max_wait_temp : 5 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update ERROR in datou_step_exec, will save and exit ! assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3139, in datou_step_tfhub2 this_model = model_evaluator(model_name, model_type=model_type, fc_size=fc_size,use_multi_inputs=use_multi_inputs) File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 156, in __init__ self.model, _, _ = create_tfhub_model(module_handle=self.tfhub_module, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 77, in create_tfhub_model hub.KerasLayer(module_handle, trainable=do_fine_tuning, name="module"), File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 152, in __init__ self._func = load_module(handle, tags, self._load_options) File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/keras_layer.py", line 421, in load_module return module_v2.load(handle, tags=tags, options=set_load_options) File "/home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_v2.py", line 106, in load obj = tf.compat.v1.saved_model.load_v2(module_path, tags=tags) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 578, in load return load_internal(export_dir, tags) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 602, in load_internal loader = loader_cls(object_graph_proto, File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 123, in __init__ self._load_all() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 134, in _load_all self._load_nodes() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 264, in _load_nodes node, setter = self._recreate(proto, node_id) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 370, in _recreate return factory[kind]() File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 363, in "variable": lambda: self._recreate_variable(proto.variable), File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 426, in _recreate_variable return variables.Variable( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 261, in __call__ return cls._variable_v2_call(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 243, in _variable_v2_call return previous_getter( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 66, in getter return captured_getter(captured_previous, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 418, in uninitialized_variable_creator return resource_variable_ops.UninitializedVariable(**kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/variables.py", line 263, in __call__ return super(VariableMetaclass, cls).__call__(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 1795, in __init__ handle = _variable_handle_from_shape_and_dtype( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py", line 174, in _variable_handle_from_shape_and_dtype gen_logging_ops._assert( # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py", line 55, in _assert _ops.raise_from_not_ok_status(e, name) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 6653, in raise_from_not_ok_status six.raise_from(core._status_to_exception(e.code, message), None) File "", line 3, in raise_from [1171252764, 1171252487, 1171252784] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.01756882667541504 save_final ERROR in last step tfhub_classification2, assertion failed: [0] [Op:Assert] name: EagerVariableNameReuse time spend for datou_step_exec : 38.13166379928589 time spend to save output : 0.026205062866210938 total time spend for step 0 : 38.1578688621521 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171252784': [(1171252784, 'jrm', 0.9677492, 4674, '3609'), 'temp/1687511175_1882837_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853587, 4674, '3609'), 'temp/1687511175_1882837_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262757, 4674, '3609'), 'temp/1687511175_1882837_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} got : None ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.1908278465270996 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Thu May 29 11:23:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 7 wait 20 seconds inside check gpu memory l 3637 free memory gpu now : 3784 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] 2025-05-29 11:25:35.288654: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_2 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_3 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_2[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_3[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 12.479185581207275 time used to load_weights : 0.15398907661437988 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 3.439944267272949 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 time to traite the descriptors : 0.04158663749694824 storage_type for insertDescriptorsMulti : 3 To insert : 1171291875 To insert : 1171275372 To insert : 1171275314 time to insert the descriptors : 0.9868052005767822 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171291875, 1171275372, 1171275314] Looping around the photos to save general results len do output : 3 /1171291875Didn't retrieve data . /1171275372Didn't retrieve data . /1171275314Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.029105186462402344 save_final save missing photos in datou_result : time spend for datou_step_exec : 127.1378436088562 time spend to save output : 0.02974414825439453 total time spend for step 1 : 127.1675877571106 step2:argmax Thu May 29 11:25:40 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 Inside saveOutput : final : True verbose : False photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.97070724, 4723, '3655'), 'temp/1748510612_1921311_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.9674188, 4723, '3655'), 'temp/1748510612_1921311_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.9651765, 4723, '3655'), 'temp/1748510612_1921311_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.02607583999633789 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.019394874572753906 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.02248549461364746 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 7.62939453125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00032401084899902344 time spend to save output : 0.07258772850036621 total time spend for step 2 : 0.07291173934936523 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171291875': [(1171291875, 'tapis_vide', 0.97070724, 4723, '3655'), 'temp/1748510612_1921311_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9674188, 4723, '3655'), 'temp/1748510612_1921311_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.9651765, 4723, '3655'), 'temp/1748510612_1921311_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ERROR tfhub2 FAILED ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 51 thcl : 358 photo_hashtag_type : 337 ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.16633105278015137 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rotate Thu May 29 11:25:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! Needs to change image size ! Needs to change image size ! About to upload 3 photos upload in portfolio : 551782 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748510744_1921311 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.23298978805542 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 1.5119051933288574 time spend to save output : 4.0531158447265625e-05 total time spend for step 1 : 1.5119457244873047 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 3 /1361618912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361618913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361618914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('230', None, None, None, None, None, None, None, None) ('230', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.03746438026428223 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1361618912: ['917849322', 'temp/1748510743_1921311_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1361618913: ['917849322', 'temp/1748510743_1921311_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1361618914: ['917849322', 'temp/1748510743_1921311_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.13071346282958984 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:thcl Thu May 29 11:25:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 500 time to import caffe and check if the image exist : 0.0002808570861816406 time to convert the images to numpy array : 1.5934815406799316 total time to convert the images to numpy array : 1.5941214561462402 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2531 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : orientation_carte_grise_all_2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2531 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 2.4288294315338135 time used to do the prediction : 0.14296174049377441 save descriptor for thcl : 500 time to traite the descriptors : 0.07004928588867188 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.7974686622619629 time spend for datou_step_exec : 11.125033140182495 time spend to save output : 7.295608520507812e-05 total time spend for step 1 : 11.1251060962677 step2:argmax Thu May 29 11:25:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step Argmax ! calculate argmax for thcl : 500 time spend for datou_step_exec : 0.00027298927307128906 time spend to save output : 0.0001442432403564453 total time spend for step 2 : 0.0004172325134277344 step3:rotate Thu May 29 11:25:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_rotate ! We are in a datou with depends ! angle_condi : {'carteGrisesVerticales__port_549774': 0, 'cartegrise_90deg__port_550987': 270, 'portfolio_270deg__port_550988': 90, 'cartesGrisesEnvers__port_549765': 180} rotate photos for hashtag carteGrisesVerticales__port_549774 of 0 degres 1 photos founded : [917849322] batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! About to upload 1 photos upload in portfolio : 551782 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748510757_1921311 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.5923163890838623 Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres 0 photos founded : [] rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres 0 photos founded : [] rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres 0 photos founded : [] time spend for datou_step_exec : 0.7274117469787598 time spend to save output : 3.0994415283203125e-05 total time spend for step 3 : 0.727442741394043 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 1 /1361618922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('233', None, None, None, None, None, None, None, None) ('233', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.023590803146362305 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1361618922: ['917849322', 'temp/1748510745_1921311_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : crop, tile, rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.10610580444335938 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:crop Thu May 29 11:25:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Crop ! param_json : {'hashtag_id_ellipse': 2087736828, 'photo_hashtag_type_from_ellipse': 520, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'crop_detect_varroa', 'photo_hashtag_type': 407, 'feed_id_new_photos_not_used': 549103, 'host': 'www.fotonower.com', 'margin': 8, 'upload_type': 'python'} margin_type : margin margin_value : [8, 8, 8, 8] Loading chi in step crop with photo_hashtag_type : 407 Loading chi in step crop for list_pids : 1 ! batch 1 Loaded 4 chid ids of type : 407 +WARNING : Unexpected points, we should remove this data for chi_id : 8165075, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165076, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165077, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165078, for now we just ignore these empty polygon points WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 4 Here we crop with rles About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=crop_detect_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 23439183 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748510760_1921311 we have uploaded 4 photos in the portfolio 23439183 time of upload the photos Elapsed time : 3.2678592205047607 Now we prepare data that will be used for ellipse search ! About to compute ellipse and record with type : 520 score : 5120 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2311 | arg_min : -30.0 min_score : 1968 | arg_min : 17.8125 min_score : 1614 | arg_min : 31.875 min_score : 1105 | arg_min : 28.5 min_score : 1105 arg_min : 1.9500000000000002 min_score : 1105 arg_min : 25.0 min_score : 1088 arg_min : 24.9375 min_score : 979 arg_min : 31.875 min_score : 979 arg_min : 28.5 min_score : 979 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! score : 5362 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2281 | arg_min : -10.0 min_score : 2127 | arg_min : 25.0 min_score : 2127 | arg_min : 30.9375 min_score : 714 | arg_min : 25.0 min_score : 714 arg_min : 1.9500000000000002 min_score : 714 arg_min : -5.0 min_score : 668 arg_min : 23.4375 min_score : 655 arg_min : 29.53125 min_score : 631 arg_min : 25.0 min_score : 631 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! score : 4603 strategy_opt : 5| | arg_min : 1.85 min_score : 2981 | arg_min : -50.0 min_score : 1356 | arg_min : 30.28125 min_score : 1079 | arg_min : 23.625 min_score : 995 | arg_min : 27.0 min_score : 995 arg_min : 1.6500000000000001 min_score : 961 arg_min : -70.0 min_score : 852 arg_min : 28.6875 min_score : 847 arg_min : 23.625 min_score : 847 arg_min : 27.0 min_score : 847 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! score : 7970 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 1576 | arg_min : 40.0 min_score : 632 | arg_min : 20.15625 min_score : 561 | arg_min : 26.0 min_score : 561 | arg_min : 26.0 min_score : 561 arg_min : 1.8 min_score : 520 arg_min : 40.0 min_score : 520 arg_min : 18.8125 min_score : 494 arg_min : 26.0 min_score : 494 arg_min : 26.0 min_score : 494 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 23439184 Result OK ! uploaded one batch 0 Elapsed time : 20.14493751525879 time spend for datou_step_exec : 27.03702473640442 time spend to save output : 2.3126602172851562e-05 total time spend for step 1 : 27.037047863006592 step2:tile Thu May 29 11:26:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'photo_tile_type': 17, 'whiten': True, 'remove_crop_border': True, 'minimal_size_crop_border': 900, 'stride': 240, 'crop_hashtag_type_tiled': 521, 'ETA': 86400, 'new_width': 480, 'new_height': 480, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_taggage_varroa', 'crop_hashtag_type': 520, 'host': 'www.fotonower.com', 'arg_aux_upload': {'type_upload': 'python'}} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {520: 521} TO DEPRECATE VR 14-6-18 map_filenames : {937852786: 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 Loaded 4 chid ids of type : 520 ++++https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 23439185 with name tile_taggage_varroa feed_id_new_photos : 23439185 filename : temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg photo_id : 937852786 height_image_input : 480 width_image_input : 480 new_width : 480 new_height : 480 stride : 240 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 4 list_bib_to_crops : 1 [(0, 480, 0, 480, 0)] new_crops_tiles : 1 crop_transformed : 4 batch 1 Loaded 1 chid ids of type : 17 treat the image : temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.020886659622192383 on upload les photos avec python init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748510792_1921311 we have uploaded 1 photos in the portfolio 23439185 Importing ! upload mediasElapsed time : 0.6273360252380371 , 0Saving 4 CHIs. batch 1 Loaded 4 chid ids of type : 521 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 0.7058703899383545 time spend for datou_step_exec : 7.974051475524902 time spend to save output : 6.270408630371094e-05 total time spend for step 2 : 7.974114179611206 step3:rotate Thu May 29 11:26:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a datou with depends ! rotate photos of 0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345 degres batch 1 Loaded 4 chid ids of type : 521 ++++++++ map_chi of length : 1 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 23439186 Needs to change image size ! time for calcul the mask position with numpy : 0.0009255409240722656 nb_pixel_total : 1389 time to create 1 rle with old method : 0.005903959274291992 .time for calcul the mask position with numpy : 0.0004525184631347656 nb_pixel_total : 1157 time to create 1 rle with old method : 0.003262042999267578 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00043129920959472656 nb_pixel_total : 694 time to create 1 rle with old method : 0.0016632080078125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00041961669921875 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0034875869750976562 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004227161407470703 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006487369537353516 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00043463706970214844 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0029554367065429688 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00039577484130859375 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004401206970214844 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004143714904785156 nb_pixel_total : 1161 time to create 1 rle with old method : 0.002632617950439453 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00040602684020996094 nb_pixel_total : 414 time to create 1 rle with old method : 0.0010552406311035156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00040841102600097656 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002609729766845703 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00047659873962402344 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0027451515197753906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003974437713623047 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0026769638061523438 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00037479400634765625 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007638931274414062 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004189014434814453 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0031211376190185547 .time for calcul the mask position with numpy : 0.0004010200500488281 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027430057525634766 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00039076805114746094 nb_pixel_total : 694 time to create 1 rle with old method : 0.001646280288696289 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003902912139892578 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002639293670654297 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003924369812011719 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006449222564697266 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039839744567871094 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0030317306518554688 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004172325134277344 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004978179931640625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004246234893798828 nb_pixel_total : 1160 time to create 1 rle with old method : 0.002713441848754883 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004317760467529297 nb_pixel_total : 414 time to create 1 rle with old method : 0.0010609626770019531 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003955364227294922 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0026230812072753906 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00038313865661621094 nb_pixel_total : 1 time to create 1 rle with old method : 3.266334533691406e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.0005257129669189453 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0031447410583496094 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00043654441833496094 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0028839111328125 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003674030303955078 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007278919219970703 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00048279762268066406 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003999233245849609 .time for calcul the mask position with numpy : 0.0004153251647949219 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0025963783264160156 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004324913024902344 nb_pixel_total : 727 time to create 1 rle with old method : 0.0017380714416503906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003943443298339844 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0031392574310302734 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00041222572326660156 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006766319274902344 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004096031188964844 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002649068832397461 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00038933753967285156 nb_pixel_total : 169 time to create 1 rle with old method : 0.00051116943359375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004260540008544922 nb_pixel_total : 1161 time to create 1 rle with old method : 0.002652406692504883 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00040793418884277344 nb_pixel_total : 450 time to create 1 rle with old method : 0.001275777816772461 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004038810729980469 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0027375221252441406 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003628730773925781 nb_pixel_total : 1 time to create 1 rle with old method : 3.6716461181640625e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.0004546642303466797 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0028939247131347656 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004260540008544922 nb_pixel_total : 1158 time to create 1 rle with old method : 0.002646923065185547 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003750324249267578 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006608963012695312 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004363059997558594 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0032231807708740234 .time for calcul the mask position with numpy : 0.00039958953857421875 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027146339416503906 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00040602684020996094 nb_pixel_total : 727 time to create 1 rle with old method : 0.0017566680908203125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00042891502380371094 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0026459693908691406 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00038743019104003906 nb_pixel_total : 250 time to create 1 rle with old method : 0.00070953369140625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003914833068847656 nb_pixel_total : 1155 time to create 1 rle with old method : 0.00261688232421875 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003867149353027344 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005347728729248047 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038933753967285156 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0026044845581054688 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003857612609863281 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011265277862548828 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003886222839355469 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0026290416717529297 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00044989585876464844 nb_pixel_total : 1237 time to create 1 rle with old method : 0.002774477005004883 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003707408905029297 nb_pixel_total : 1157 time to create 1 rle with old method : 0.03533577919006348 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003681182861328125 nb_pixel_total : 234 time to create 1 rle with old method : 0.000659942626953125 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 23439186 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748510795_1921311 we have uploaded 24 photos in the portfolio 23439186 time of upload the photos Elapsed time : 6.6208837032318115 Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 batch 1 Loaded 28 chid ids of type : 529 Number RLEs to save : 1197 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 11.086059331893921 time spend to save output : 0.0001354217529296875 total time spend for step 3 : 11.08619475364685 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1361618952'] Looping around the photos to save general results len do output : 24 /1361619001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619005Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '1361618952', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 time used for this insertion : 0.023441314697265625 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1361619001: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1361619002: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1361619004: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1361619005: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1361619006: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1361619008: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1361619009: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1361619010: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1361619012: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1361619013: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1361619014: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1361619016: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1361619017: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1361619018: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1361619020: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1361619021: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1361619022: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1361619024: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1361619025: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1361619026: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1361619028: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1361619029: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1361619030: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1361619032: ['937852786', 'temp/1748510757_1921311_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} list chi : [[, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], []] ############################### TEST flip ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : flip list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.12002396583557129 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:flip Thu May 29 11:26:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_flip ! We are in a linear step without datou_depend ! batch 1 Loaded 6 chid ids of type : 741 +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points + map_chi_objs of length : 1 photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 Horizontal flip of photo 911785586 About to upload 2 photos upload in portfolio : 1090565 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748510806_1921311 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.8705027103424072 Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 batch 1 Loaded 12 chid ids of type : 741 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 1.112776279449463 time spend to save output : 6.914138793945312e-05 total time spend for step 1 : 1.1128454208374023 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] Looping around the photos to save general results len do output : 2 /1361619063 /1361619064 before output type Managing all output in save final without adding information in the mtr_datou_result ('571', None, None, None, None, None, None, None, None) ('571', None, '911785586', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.014618635177612305 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1361619063': ['911785586', 'temp/1748510805_1921311_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1361619064': ['911785586', 'temp/1748510805_1921311_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', [, , , , , ]]} ############################### TEST crop_rles ################################ # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : TEST CROP RLES Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : crop list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.09660983085632324 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:crop Thu May 29 11:26:46 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Crop ! param_json : {'photo_hashtag_type': 755, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 0, 'host': 'www.fotonower.com', 'crop_type': 'rle', 'margin_relative': 0.1, 'min_score': 0.3, 'upload,type': 'python'} margin_type : margin_relative margin_value : [0.1, 0.1, 0.1, 0.1] Loading chi in step crop with photo_hashtag_type : 755 Loading chi in step crop for list_pids : 1 ! batch 1 Loaded 8 chid ids of type : 755 ++++++++WARNING : margin is only used for type bib ! we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles map_result returned by crop_photo_return_map_crop : length : 8 Here we crop with rles About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 23439187 Result OK ! uploaded one batch 0 Elapsed time : 20.295840978622437 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 20.39071536064148 time spend to save output : 3.814697265625e-05 total time spend for step 1 : 20.390753507614136 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] Looping around the photos to save general results len do output : 8 /1361619094Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619164Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('686', None, None, None, None, None, None, None, None) ('686', None, '950103132', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 time used for this insertion : 0.06305170059204102 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1361619094': ['950103132', 'temp/1748510806_1921311_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1361619107': ['950103132', 'temp/1748510806_1921311_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1361619122': ['950103132', 'temp/1748510806_1921311_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1361619136': ['950103132', 'temp/1748510806_1921311_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1361619150': ['950103132', 'temp/1748510806_1921311_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1361619164': ['950103132', 'temp/1748510806_1921311_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1361619179': ['950103132', 'temp/1748510806_1921311_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1361619194': ['950103132', 'temp/1748510806_1921311_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : angular_coeff list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.4208073616027832 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:angular_coeff Thu May 29 11:27:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter param_json : {'input_type': 846, 'output_type': -1, 'orientation_type': 872, 'ref_crop_type': 846, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'crops_coeffs': {'CAR_EXTERIEUR_angle_avant_droit.*': {'aile-avant': [[15, 0.0], [240, 0.0], [285, 1.0], [345, 1.0]], 'capot': [[45, 1.0], [60, 0.5], [270, 0.0], [315, 1.0], [360, 1.0]]}}} angular_coefficients_to_crops batch 1 Loaded 19 chid ids of type : 846 treating photo 932296368 time spend for datou_step_exec : 0.09462451934814453 time spend to save output : 0.00010061264038085938 total time spend for step 1 : 0.09472513198852539 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {932296368: ([(932296368, 2106233860, 846, 1066, 1277, 93, 340, 0.31964028378983567, 0, []), (932296368, 2106233860, 846, 434, 690, 218, 498, 0.7170410105787726, 0, []), (932296368, 503548896, 846, 902, 1111, 466, 576, 0.31724966, 769189715, []), (932296368, 599722655, 846, 523, 1100, 152, 337, 0.98039776, 0, []), (932296368, 492601069, 846, 143, 1190, 90, 695, 0.9696157, 769189717, []), (932296368, 492601069, 846, 0, 408, 246, 719, 0.9431181, 769189718, []), (932296368, 2096875722, 846, 567, 964, 162, 215, 0.55490255, 769189721, []), (932296368, 2096875709, 846, 437, 939, 24, 198, 0.9983077, 769189723, []), (932296368, 2096875709, 846, 1004, 1263, 28, 144, 0.9485744, 769189724, []), (932296368, 624624117, 846, 595, 1122, 331, 640, 0.99100167, 769189725, []), (932296368, 492624020, 846, 585, 874, 308, 393, 0.78697366, 769189727, []), (932296368, 2096875719, 846, 943, 1100, 428, 547, 0.96733797, 769189729, []), (932296368, 492654799, 846, 253, 467, 35, 441, 0.99621326, 769189730, []), (932296368, 492689227, 846, 1118, 1264, 270, 438, 0.9901647, 769189732, []), (932296368, 492689227, 846, 486, 671, 378, 690, 0.98789483, 769189733, []), (932296368, 492689227, 846, 161, 255, 229, 409, 0.70801014, 769189734, []), (932296368, 492925064, 846, 261, 421, 27, 193, 0.92215157, 769189737, []), (932296368, 492925064, 846, 873, 1045, 46, 156, 0.7535122, 769189738, []), (932296368, 492925064, 846, 1090, 1279, 20, 107, 0.45259848, 769189739, [])],)} test angular coeff is a success ! ############################### TEST detection_filter_by_crop ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_crop list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.12743115425109863 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_crop Thu May 29 11:27:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter param_json : {'input_type': 631, 'output_type': -1, 'condition_type': 445, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'min_surface_ratio': 0.7} conditional_crop_copy batch 1 Loaded 3 chid ids of type : 445 +++batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++batch 1 Loaded 3 chid ids of type : 445 +++ treating photo 946711423 time spend for datou_step_exec : 0.2380220890045166 time spend to save output : 0.00011920928955078125 total time spend for step 1 : 0.23814129829406738 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, ['491,350,489,350,488,349,487,350,483,350,480,348,480,341,482,339,482,337,485,334,487,334,491,330,494,330,495,328,498,326,501,326,503,324,507,325,509,323,514,321,516,319,518,321,520,321,521,319,522,319,524,321,527,321,530,317,530,315,531,314,535,313,540,309,543,310,544,311,542,313,542,314,544,316,541,318,541,322,536,322,535,323,533,323,532,322,528,322,527,321,524,321,522,323,518,322,516,324,517,327,516,328,512,327,510,329,512,332,513,332,515,330,516,331,516,333,514,332,511,333,511,336,514,337,516,336,516,339,515,339,513,338,511,340,512,341,512,342,510,343,507,343,502,347,500,347,497,349,492,349', '514,325,515,324,513,322,512,322,511,325,512,326', '522,327,521,327,521,326,522,325']), (946711423, 599722655, 631, 176, 535, 138, 264, 0.9818268, 1947740373, ['453,253,413,253,412,252,387,252,386,250,386,248,383,246,379,245,376,243,361,243,361,240,362,239,359,238,358,237,356,237,355,236,352,236,351,235,333,235,332,234,329,234,329,233,331,231,331,229,329,228,328,224,330,222,330,221,324,218,308,219,307,218,302,218,298,216,288,217,287,218,285,218,283,220,283,221,287,224,295,225,295,225,294,226,289,226,288,227,283,227,282,228,273,228,272,229,271,228,259,228,258,227,254,227,253,226,247,225,247,225,251,221,248,218,243,216,247,213,248,213,249,212,248,211,246,211,245,210,241,210,240,209,237,209,236,208,231,207,230,206,228,202,224,201,223,200,221,200,220,199,214,198,213,195,211,193,208,193,203,189,203,184,201,181,201,176,198,171,199,170,199,158,203,154,205,153,205,151,206,149,209,149,210,148,225,148,226,147,283,147,284,148,287,148,288,147,305,147,306,148,312,148,313,147,354,147,355,146,428,146,429,147,433,147,434,148,437,148,438,149,451,149,457,156,459,162,462,165,464,166,471,166,472,165,477,165,480,167,480,171,486,175,488,175,489,176,502,176,503,178,503,180,509,185,509,189,512,193,512,199,513,200,513,203,514,204,514,210,513,211,514,217,512,221,513,222,513,225,510,229,510,235,507,237,504,238,502,243,490,243,489,244,485,244,484,245,480,245,479,246,463,246,462,247,460,247,458,249,457,252,454,252', '528,212,528,207,526,206,524,203,526,203,527,202,528,202', '299,215,302,212,299,211,298,210,291,210,290,211,281,212,286,215,290,215,291,216', '375,242,376,240,375,238,363,239,368,242,371,242,372,243']), (946711423, 492844413, 631, 89, 163, 93, 144, 0.9772748, 1947740375, ['159,142,153,141,151,139,148,138,145,135,141,133,139,133,138,132,131,132,130,131,125,131,124,130,121,130,120,129,116,129,115,128,112,128,108,126,106,126,100,123,98,121,94,113,94,104,97,101,103,98,105,98,106,97,110,97,111,96,116,96,117,95,132,95,133,96,139,97,141,99,144,100,149,105,150,107,154,108,155,113,157,115,158,115,160,118,160,120,161,121,161,133,160,134,160,140']), (946711423, 2096875709, 631, 185, 431, 39, 136, 0.97171515, 1947740377, ['331,134,287,134,286,133,284,133,283,134,272,134,271,133,264,133,263,134,258,134,257,133,254,133,253,132,236,132,235,131,225,131,224,132,223,131,213,131,212,130,208,130,207,129,204,129,203,128,199,127,193,121,192,117,189,113,189,110,188,109,187,93,186,92,187,91,187,89,186,88,186,65,185,64,186,63,186,61,185,60,185,48,186,47,186,42,187,40,232,40,233,41,248,41,249,42,281,43,282,44,290,44,291,45,300,45,301,46,308,46,309,47,314,47,315,48,322,49,328,53,334,54,336,56,339,57,344,62,349,64,351,66,353,67,356,67,358,69,359,72,363,76,367,78,369,80,379,91,380,93,383,94,390,100,393,101,395,103,396,106,399,109,402,110,406,115,408,115,410,117,410,120,412,123,411,127,409,129,399,129,398,130,395,130,394,131,378,131,377,132,368,132,367,131,346,131,345,132,342,132,341,133,332,133']), (946711423, 2096875722, 631, 198, 395, 118, 142, 0.9699756, 1947740378, ['328,137,251,137,250,136,249,137,241,137,240,136,219,136,218,135,213,135,212,134,206,133,205,132,201,131,200,130,200,122,201,121,205,121,206,122,222,122,226,124,239,124,240,125,369,125,370,124,371,125,389,125,391,127,391,133,390,134,386,134,385,135,380,135,379,134,375,134,374,135,341,135,340,136,329,136']), (946711423, 499500794, 631, 93, 107, 127, 146, 0.9574813, 1947740379, ['101,143,98,143,95,139,95,131,97,129,100,129,101,133,102,134,102,136,103,137,103,140']), (946711423, 492925064, 631, 71, 125, 36, 95, 0.95296955, 1947740380, ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80,82,75,77,75,75,74,74,74,66,75,65,75,62,77,60,77,58,80,55,80,54,83,51,83,50,88,45,94,44,95,43,99,43,100,42,113,42,117,45,117,47,116,48,116,51,115,52,114,59,113,60,112,65,111,66,111,69,110,70,110,75,109,76,109,83,108,84,108,86,109,87,108,89']), (946711423, 492925064, 631, 101, 167, 38, 127, 0.9508439, 1947740381, 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'230,167,229,166,227,167,228,168']), (946711423, 495920967, 631, 202, 524, 112, 333, 0.45109355, 1947740396, 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'312,179,311,178,308,179,309,180', '268,269,264,269,259,266,259,262,261,258,261,250,265,245,269,250,270,257,274,260,278,265,275,267,269,268', '414,281,401,281,414,281']), (946711423, 2096875722, 631, 433, 558, 248, 286, 0.44133398, 1947740397, ['492,272,474,272,473,271,468,271,465,269,460,269,460,268,465,266,467,266,468,265,470,265,471,264,475,264,476,263,479,263,480,262,486,262,487,261,491,261,492,260,495,260,496,259,502,259,506,257,510,257,514,255,517,255,518,254,530,253,531,252,535,252,536,251,538,251,539,252,543,252,544,253,547,253,549,251,553,251,555,253,555,267,552,270,550,270,550,269,548,267,547,267,547,267,548,266,547,265,545,266,540,266,539,264,530,264,529,263,524,263,519,266,513,266,510,268,507,268,506,269,499,270,498,271,493,271', '438,279,435,279,435,273,436,272,448,271,449,272,448,274,443,274,440,277,440,278']), (946711423, 492654799, 631, 399, 569, 68, 251, 0.41876298, 1947740399, []), (946711423, 492624020, 631, 420, 552, 244, 293, 0.35962066, 1947740400, 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'519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] origin we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.0041179656982421875 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Thu May 29 11:27:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter with classification results param_json : {'input_type': 631, 'output_type': 816, 'condition_type': 872, 'crops_ok': {'CAR_DOCUMENT.*': {}, 'CAR_INTERIEUR.*': {}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'Retroviseur': 2, 'Roue': 2, 'Capot': 1, 'Pare-brise': 1, 'vitre': 10, 'phare': 2, 'Feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'Plaque-immatriculation': 1, 'Essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1}}, 'separation': {'CAR_EXTERIEUR_avant.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-gauche', 'a-droite-de', 'phare-droit']}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-droite', 'a-gauche-de', 'phare-gauche'], 'porte': ['porte-avant', 'a-droite-de', 'porte-arriere']}}} conditional_crop_by_classif_copy batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ treating photo 946711423 batch 1 Loaded 0 chid ids of type : 0 batch 1 Loaded 23 chid ids of type : 816 Number RLEs to save : 1600 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.48679399490356445 time spend to save output : 0.0001285076141357422 total time spend for step 1 : 0.4869225025177002 caffe_path_current : About to save ! 0 After save, about to update current ! test detection filter by classif is a success ! ############################### TEST blur_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : blur_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.12199997901916504 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:blur_detection Thu May 29 11:27:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blur_detection methode: ratio et variance treat image : temp/1748510828_1921311_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.30460214614868164 time spend to save output : 9.012222290039062e-05 total time spend for step 1 : 0.30469226837158203 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {930729675: [(930729675, 12.961859636534896, 492688767)]} {930729675: [(930729675, 12.961859636534896, 492688767)]} ############################### TEST detect_point_224x224 ################################ test_detect_point_224x224 Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 4589 thcl is not linked in the step_by_step architecture ! WARNING : step 4590 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFFFBFBFBFBFFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 1.6157844066619873 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Thu May 29 11:27:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 1528 time to import caffe and check if the image exist : 0.0012688636779785156 time to convert the images to numpy array : 0.019995450973510742 time to import caffe and check if the image exist : 0.005181312561035156 time to convert the images to numpy array : 0.06697654724121094 time to import caffe and check if the image exist : 0.007972478866577148 time to convert the images to numpy array : 0.06822705268859863 time to import caffe and check if the image exist : 0.010199308395385742 time to convert the images to numpy array : 0.06885170936584473 time to import caffe and check if the image exist : 0.015755891799926758 time to convert the images to numpy array : 0.06687116622924805 time to import caffe and check if the image exist : 0.01109457015991211 time to convert the images to numpy array : 0.07477259635925293 time to import caffe and check if the image exist : 0.006508350372314453 time to convert the images to numpy array : 0.07638072967529297 time to import caffe and check if the image exist : 0.013547658920288086 time to convert the images to numpy array : 0.07251667976379395 time to import caffe and check if the image exist : 0.01885390281677246 time to convert the images to numpy array : 0.06499814987182617 time to import caffe and check if the image exist : 0.02137899398803711 time to convert the images to numpy array : 0.06653070449829102 total time to convert the images to numpy array : 0.08948159217834473 list photo_ids error: [] list photo_ids correct : [987515187, 987515224, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515248, 987515249, 987515250, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515188, 987515180, 987515181, 987515182, 987515183, 987515184, 987515185, 987515186, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515222, 987515223, 987515175, 987515176, 987515177, 987515178, 987515179, 987515200, 987515201, 987515202, 987515204, 987515205, 987515239, 987515240] number of photos to traite : 64 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 2137 wait 20 seconds l 3637 free memory gpu now : 2137 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3330 max_wait_temp : 1 max_wait : 0 dict_keys(['res5b', 'prob']) time used to do the prepocess of the images : 0.06570219993591309 time used to do the prediction : 0.2751955986022949 save descriptor for thcl : 1528 time to traite the descriptors : 4.61666464805603 storage_type for insertDescriptorsMulti : 1 To insert : 987515187 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515188 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515222 To insert : 987515223 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515239 To insert : 987515240 time to insert the descriptors : 16.970684051513672 time spend for datou_step_exec : 45.98649477958679 time spend to save output : 6.699562072753906e-05 total time spend for step 1 : 45.98656177520752 step2:argmax Thu May 29 11:27:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 time spend for datou_step_exec : 0.0009694099426269531 time spend to save output : 2.9802322387695312e-05 total time spend for step 2 : 0.0009992122650146484 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515187': [('987515187', 'Carton', 0.98175955, 1927, '1528'), 'temp/1748510829_1921311_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515224': [('987515224', 'Carton', 0.90851194, 1927, '1528'), 'temp/1748510829_1921311_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9870078, 1927, '1528'), 'temp/1748510829_1921311_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9002531, 1927, '1528'), 'temp/1748510829_1921311_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.521938, 1927, '1528'), 'temp/1748510829_1921311_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940634, 1927, '1528'), 'temp/1748510829_1921311_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.99942183, 1927, '1528'), 'temp/1748510829_1921311_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924386, 1927, '1528'), 'temp/1748510829_1921311_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515241': [('987515241', 'Carton', 0.9820955, 1927, '1528'), 'temp/1748510829_1921311_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9357822, 1927, '1528'), 'temp/1748510829_1921311_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.8741555, 1927, '1528'), 'temp/1748510829_1921311_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81740266, 1927, '1528'), 'temp/1748510829_1921311_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8656121, 1927, '1528'), 'temp/1748510829_1921311_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992324, 1927, '1528'), 'temp/1748510829_1921311_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.9996693, 1927, '1528'), 'temp/1748510829_1921311_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.9813611, 1927, '1528'), 'temp/1748510829_1921311_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.98132735, 1927, '1528'), 'temp/1748510829_1921311_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9807864, 1927, '1528'), 'temp/1748510829_1921311_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.87389004, 1927, '1528'), 'temp/1748510829_1921311_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.991715, 1927, '1528'), 'temp/1748510829_1921311_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9678522, 1927, '1528'), 'temp/1748510829_1921311_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.97326547, 1927, '1528'), 'temp/1748510829_1921311_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.98693603, 1927, '1528'), 'temp/1748510829_1921311_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.9869197, 1927, '1528'), 'temp/1748510829_1921311_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99391866, 1927, '1528'), 'temp/1748510829_1921311_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774384, 1927, '1528'), 'temp/1748510829_1921311_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.52846414, 1927, '1528'), 'temp/1748510829_1921311_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936897, 1927, '1528'), 'temp/1748510829_1921311_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.99638003, 1927, '1528'), 'temp/1748510829_1921311_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515233': [('987515233', 'Carton', 0.98344237, 1927, '1528'), 'temp/1748510829_1921311_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94458234, 1927, '1528'), 'temp/1748510829_1921311_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8919999, 1927, '1528'), 'temp/1748510829_1921311_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5363781, 1927, '1528'), 'temp/1748510829_1921311_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7698198, 1927, '1528'), 'temp/1748510829_1921311_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.999574, 1927, '1528'), 'temp/1748510829_1921311_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515188': [('987515188', 'Carton', 0.99565804, 1927, '1528'), 'temp/1748510829_1921311_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515180': [('987515180', 'Carton', 0.9899595, 1927, '1528'), 'temp/1748510829_1921311_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977804, 1927, '1528'), 'temp/1748510829_1921311_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.9924304, 1927, '1528'), 'temp/1748510829_1921311_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999213, 1927, '1528'), 'temp/1748510829_1921311_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997316, 1927, '1528'), 'temp/1748510829_1921311_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.79796827, 1927, '1528'), 'temp/1748510829_1921311_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847018, 1927, '1528'), 'temp/1748510829_1921311_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515189': [('987515189', 'Carton', 0.99778783, 1927, '1528'), 'temp/1748510829_1921311_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9763465, 1927, '1528'), 'temp/1748510829_1921311_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999112, 1927, '1528'), 'temp/1748510829_1921311_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993962, 1927, '1528'), 'temp/1748510829_1921311_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.98464847, 1927, '1528'), 'temp/1748510829_1921311_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.9846405, 1927, '1528'), 'temp/1748510829_1921311_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.96616656, 1927, '1528'), 'temp/1748510829_1921311_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515222': [('987515222', 'Carton', 0.9974728, 1927, '1528'), 'temp/1748510829_1921311_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.992084, 1927, '1528'), 'temp/1748510829_1921311_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998142, 1927, '1528'), 'temp/1748510829_1921311_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.999814, 1927, '1528'), 'temp/1748510829_1921311_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9771508, 1927, '1528'), 'temp/1748510829_1921311_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8576668, 1927, '1528'), 'temp/1748510829_1921311_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9268327, 1927, '1528'), 'temp/1748510829_1921311_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515200': [('987515200', 'Carton', 0.98592895, 1927, '1528'), 'temp/1748510829_1921311_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99546, 1927, '1528'), 'temp/1748510829_1921311_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911186, 1927, '1528'), 'temp/1748510829_1921311_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.99508214, 1927, '1528'), 'temp/1748510829_1921311_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99084866, 1927, '1528'), 'temp/1748510829_1921311_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.9997831, 1927, '1528'), 'temp/1748510829_1921311_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.99952126, 1927, '1528'), 'temp/1748510829_1921311_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg']} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detect_points list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.13594722747802734 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detect_points Thu May 29 11:27:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : 1 To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 1 Inside predict_points step exec : nb paths : 1 treate image : temp/1748510877_1921311_987515173_91fa471b1a04f95b356afdbaf021f623.jpg size of numpy array img : 2408584 scale method : caffe/skimage size of numpy array img_scale : 2408584 (448, 448, 3) nb_h 8 nb_w 8 size of sub images : (224, 224, 3) size of caffe_input : 38535320 (64, 3, 224, 224) time to do the preprocess : 0.044878244400024414 time to do a prediction : 0.39507365226745605 dict_keys(['prob']) shape of output (64, 10, 1, 1) shape of the out_put heatmap (10, 8, 8) number of sub_photos vertical and horizon 8 8 size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) time spend for datou_step_exec : 1.9643335342407227 time spend to save output : 4.482269287109375e-05 total time spend for step 1 : 1.9643783569335938 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.237255937430186e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4499673123568044e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0647096537752532e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.45284598526996e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.92870447790483e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.772281343117356e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.0001227844913955778), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9452843591570854e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.356148520732404e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.211312377653485e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3809916765694652e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.475101385040034e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1302673556201626e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015714035544078797), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004435647279024124), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.531533290399238e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.33409878344537e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.6221443956965231e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5210385956597747e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6135977602971252e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.267760909395292e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.644954505143687e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.0003263938706368208), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003057017456740141), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8532136891735718e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.937279406178277e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7035161110688932e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.8020518837147392e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.34541148529388e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.6989990399451926e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.540152076515369e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.78625360201113e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.104480689828051e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6426173488071072e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.9615397377492627e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4350246146932477e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.856583579268772e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2909436009067576e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.283634426537901e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.170315019611735e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.840578756353352e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.54400788435305e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.959447328976239e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.746564632616355e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.329505827627145e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.19155502540525e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.15350958367344e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.972145350417122e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2039477041980717e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.5699044524808414e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.3268352126469836e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.00015386017912533134), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.0002590872172731906), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.00018799045938067138), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.0002136140683433041), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.00016578043869230896), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.542712758848211e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.7503216440672986e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.934795288136229e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.00012150874681537971), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.0001954126637428999), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.00018771659233607352), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.0001234129158547148), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.00027340833912603557), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.5728419100469182e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.054912096762564e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 7.002739948802628e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.0008732199785299599), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.0026482599787414074), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.003372815204784274), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.03138517960906029), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.05587572231888771), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.0001236581156263128), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020938624220434576), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.0003681157832033932), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.006836811080574989), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.015936901792883873), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.009382501244544983), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.009769863449037075), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.02210138738155365), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.021914605051279068), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.1933259814977646), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.0964028462767601), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.12365290522575378), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.5333306193351746), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.4611850082874298), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.7709454894065857), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8664308786392212), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8500866293907166), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.9844329357147217), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9847314357757568), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919494390487671), (987515173, 1982, 'Carton', 240, -1, 208, -1, 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0.0007343525066971779)]} ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Step 4442 tile have less inputs used (1) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 4441 detect_points is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 4443 count_percent_refus is not consistent : 4 used against 3 in the step definition ! Step 4444 send_mail_dechet have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 1 of step 4440 have datatype=1 whereas input 0 of step 4443 have datatype=2 WARNING : type of output 1 of step 4441 doesn't seem to be define in the database( WARNING : type of input 4 of step 4443 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] origin Catched exception ! Connect or reconnect ! We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.24783611297607422 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 7 step1:init_dechet Thu May 29 11:27:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec debut step init detect dechets input : temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [987321136] Looping around the photos to save general results len do output : 1 /987321136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.018484115600585938 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.0002872943878173828 time spend to save output : 0.018893003463745117 total time spend for step 1 : 0.0191802978515625 step2:tile Thu May 29 11:27:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_correct_upm', 'ETA': 86400, 'new_width': 1500, 'new_height': 20000, 'host': 'www.fotonower.com', 'protocol': 'https', 'photo_tile_type': 1522, 'option_bande': 'True'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {406: 410} map_filenames : {987321136: 'temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 Loaded 0 chid ids of type : 0 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_correct_upm&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 23439220 with name tile_correct_upm feed_id_new_photos : 23439220 filename : temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea.jpg photo_id : 987321136 height_image_input : 439 width_image_input : 562 new_width : 1500 new_height : 20000 stride : 0 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 0 list_bib_to_crops : 1 [(0, 562, 0, 439, 0)] new_crops_tiles : 1 crop_transformed : 0 batch 1 Loaded 1 chid ids of type : 1522 treat the image : temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.010656356811523438 About to upload 1 photos upload in portfolio : 23439220 Result OK ! uploaded one batch 0 Elapsed time : 4.946712017059326 upload mediasElapsed time : 4.957437038421631 , 0Saving 0 CHIs. end of tileElapsed time : 4.969651460647583 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1361619280'] Looping around the photos to save general results len do output : 1 /1361619280Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1361619280', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.012121200561523438 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.990001916885376 time spend to save output : 0.012415885925292969 total time spend for step 2 : 12.002417802810669 step3:detect_points Thu May 29 11:28:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : False To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 2 Inside predict_points step exec : nb paths : 1 treate image : temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea_0.jpg size of numpy array img : 2960752 scale method : caffe/skimage size of numpy array img_scale : 2655880 (416, 532, 3) nb_h 7 nb_w 11 size of sub images : (224, 224, 3) size of caffe_input : 46362776 (77, 3, 224, 224) time to do the preprocess : 0.041504859924316406 time to do a prediction : 16.935800790786743 dict_keys(['prob']) shape of output (77, 10, 1, 1) shape of the out_put heatmap (10, 7, 11) number of sub_photos vertical and horizon 7 11 size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) Inside saveOutput : final : False verbose : False Inside savePoints : final : False verbose : False threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 final : False save missing photos in datou_result : time spend for datou_step_exec : 18.383164167404175 time spend to save output : 0.06328773498535156 total time spend for step 3 : 18.446451902389526 step4:count_percent_refus Thu May 29 11:28:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure debut step count percent refus (987321136, 0.9481481481481482) ('temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) list_photo : [987321136] list_photo_correc : [1361619280] debut step count percent refus Treating photo_id : 987321136 Calcul du count_res count res : ((492774966, 3), (2107752386, 7)) Hashtag_id : 492774966 Hashtag_id : 2107752386 We have 2 classes in this image Inside saveOutput : final : False verbose : False begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.0543365478515625 save missing photos in datou_result : time spend for datou_step_exec : 0.05845808982849121 time spend to save output : 0.05477428436279297 total time spend for step 4 : 0.11323237419128418 step5:brightness Thu May 29 11:28:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea.jpg Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.00873112678527832 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.015803813934326172 save missing photos in datou_result : time spend for datou_step_exec : 0.10703301429748535 time spend to save output : 0.029781103134155273 total time spend for step 5 : 0.13681411743164062 step6:blur_detection Thu May 29 11:28:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1748510879_1921311_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.009571075439453125 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.035965919494628906 save missing photos in datou_result : time spend for datou_step_exec : 0.17693471908569336 time spend to save output : 0.05111575126647949 total time spend for step 6 : 0.22805047035217285 step7:send_mail_dechet Thu May 29 11:28:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail dechet senders@fotonower.com retour de l'envoi du mail : None Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1361619280'] Looping around the photos to save general results len do output : 1 /987321136. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1361619280', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.013508081436157227 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.48337531089782715 time spend to save output : 0.014100790023803711 total time spend for step 7 : 0.49747610092163086 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 30.0, 61.64383561643836, {'carton': 3, 'Papier_Magazine': 7}, {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 0.6164383561643836)} ############################### TEST image_temperature_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_temperature_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14280462265014648 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_temperature_detection Thu May 29 11:28:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanche_jaune_detection treat image : temp/1748510911_1921311_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 time spend for datou_step_exec : 0.162139892578125 time spend to save output : 8.654594421386719e-05 total time spend for step 1 : 0.16222643852233887 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {984484223: [(984484223, 1.004309911525615, 492630606)]} {984484223: [(984484223, 1.004309911525615, 492630606)]} ############################### TEST broca ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.02350473403930664 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:split_time_score Thu May 29 11:28:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec split portfolio by speed calcul order for each photo with time calcul time for a portfolio 2021-12-01 10:11:30 2021-12-01 10:11:32 2021-12-01 10:11:30 2021-12-01 10:11:34 2021-12-01 10:11:32 2021-12-01 10:11:40 2021-12-01 10:11:34 2021-12-01 10:12:17 2021-12-01 10:11:40 2021-12-01 10:12:24 2021-12-01 10:12:17 2021-12-01 10:12:27 2021-12-01 10:12:24 2021-12-01 10:12:29 2021-12-01 10:12:27 2021-12-01 10:12:56 2021-12-01 10:12:29 2021-12-01 10:13:04 2021-12-01 10:12:56 2021-12-01 10:13:13 2021-12-01 10:13:04 2021-12-01 10:13:04 distance 1.4513659170185111 2021-12-01 10:13:13 2021-12-01 10:13:22 2021-12-01 10:13:13 2021-12-01 10:13:30 2021-12-01 10:13:22 2021-12-01 10:16:14 2021-12-01 10:13:30 2021-12-01 10:13:30 distance 8.382409567451603 2021-12-01 10:16:14 2021-12-01 10:16:18 2021-12-01 10:16:14 2021-12-01 10:16:47 2021-12-01 10:16:18 2021-12-01 10:16:53 2021-12-01 10:16:47 2021-12-01 10:16:47 distance 8.03396608896571 2021-12-01 10:16:53 2021-12-01 10:16:57 2021-12-01 10:16:53 dict_time_useful: {0: [1098136690, 1098136784, 48.864288393888884, 2.19199505125, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94]], 1: [1098136974, 1098137007, 48.86291258986111, 2.19361357125, [datetime.datetime(2021, 12, 1, 10, 16, 14), datetime.datetime(2021, 12, 1, 10, 16, 47), 33]]} get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "CS"; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "OM"; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 time spend for datou_step_exec : 0.18303203582763672 time spend to save output : 0.0001659393310546875 total time spend for step 1 : 0.1831979751586914 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(23439221, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]} résultat du premier test BROCA : True True ############################### TEST crop_conditional ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1335 frcnn is not linked in the step_by_step architecture ! WARNING : step 1336 crop_condition is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] origin We have 1 , BBBFBFBFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 time to download the photos : 0.3290219306945801 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:frcnn Thu May 29 11:28:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! Inside try reload ! To loadFromThcl() model_param file didn't exist model_name : learn_piece_voiture_0808_v2 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/learn_piece_voiture_0808_v2 /data/models_weight/learn_piece_voiture_0808_v2/caffemodel size_local : 350215080 size in s3 : 350215080 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 caffemodel already exist and didn't need to update /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt size_local : 7166 size in s3 : 7166 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt caffemodel : /data/models_weight/learn_piece_voiture_0808_v2/caffemodel Loaded network /data/models_weight/learn_piece_voiture_0808_v2/caffemodel About to compute detect_faster_rcnn : len(args) : 6 Inside frcnn step exec : nb paths : 6 image_path : temp/1748510911_1921311_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg image_size (2160, 3840, 3) [[[111 118 91] [113 120 93] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 24 41 38]] [[111 118 91] [112 119 92] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 23 40 37]] [[113 118 91] [114 119 92] [115 120 93] ... [ 22 39 36] [ 23 40 37] [ 23 40 37]] ... [[120 125 94] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 23 37 35]] [[119 124 93] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]] [[118 123 91] [117 122 90] [117 122 91] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]]] Detection took 2.742s for 300 object proposals image_path : temp/1748510911_1921311_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg image_size (480, 640, 3) [[[36 41 44] [36 41 44] [35 40 43] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[37 42 45] [36 41 44] [35 40 43] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[37 42 45] [36 41 44] [35 40 43] ... [ 3 5 5] [ 4 6 6] [ 4 6 6]] ... [[42 47 50] [41 46 49] [40 45 48] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[41 46 49] [41 46 49] [40 45 48] ... [ 0 2 2] [10 12 12] [22 24 24]] [[40 45 48] [40 45 48] [40 45 48] ... [10 12 12] [17 19 19] [26 28 28]]] Detection took 0.062s for 300 object proposals image_path : temp/1748510911_1921311_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg image_size (480, 614, 3) [[[ 44 44 44] [ 49 51 51] [ 42 44 44] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 43 43 43] [ 36 38 38] [ 39 41 41] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[ 70 70 70] [ 40 42 42] [ 41 43 43] ... [ 4 6 6] [ 4 6 6] [ 4 6 6]] ... [[103 101 101] [110 108 108] [ 61 59 59] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 98 96 96] [115 113 113] [ 73 71 71] ... [ 0 2 2] [ 11 13 13] [ 21 23 23]] [[ 92 90 90] [114 112 112] [ 87 82 83] ... [ 10 12 12] [ 18 20 20] [ 25 27 27]]] Detection took 0.036s for 300 object proposals image_path : temp/1748510911_1921311_950003696_11e3a77b72af4b332d366d98984039c7.jpg image_size (2160, 3264, 3) [[[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 48 60 64] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 47 59 63]] ... [[167 164 160] [167 164 160] [167 164 160] ... [ 44 59 61] [ 44 59 61] [ 44 59 61]] [[165 162 158] [165 162 158] [165 162 158] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]] [[164 161 157] [164 161 157] [164 161 157] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]]] Detection took 4.795s for 300 object proposals image_path : temp/1748510911_1921311_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg image_size (294, 285, 3) [[[ 29 29 29] [ 29 29 29] [ 30 30 30] ... [182 172 165] [141 131 124] [103 94 90]] [[ 29 29 29] [ 29 29 29] [ 31 31 31] ... [231 220 212] [202 193 184] [164 154 147]] [[ 30 30 30] [ 27 27 27] [ 26 26 26] ... [223 211 199] [229 219 209] [228 217 209]] ... [[ 22 27 25] [ 16 21 19] [ 11 16 14] ... [166 145 123] [168 147 125] [170 149 127]] [[ 20 25 23] [ 17 22 20] [ 15 20 18] ... [163 142 120] [165 144 122] [166 145 123]] [[ 13 18 16] [ 17 22 20] [ 20 25 23] ... [162 141 119] [163 142 120] [163 142 121]]] Detection took 0.054s for 300 object proposals image_path : temp/1748510911_1921311_950003813_e28be02dfcce79cce594a390a9911a0a.jpg image_size (254, 229, 3) [[[202 190 186] [205 193 189] [205 194 190] ... [ 81 70 56] [ 80 69 55] [ 78 67 53]] [[198 187 183] [200 189 185] [198 189 185] ... [ 50 41 28] [ 44 36 23] [ 45 36 23]] [[192 187 184] [191 186 183] [191 186 183] ... [ 36 30 23] [ 32 29 21] [ 33 27 20]] ... [[187 186 190] [186 185 189] [188 184 189] ... [ 43 38 35] [ 37 33 28] [ 33 28 25]] [[184 185 189] [183 184 188] [184 183 187] ... [ 28 23 22] [ 29 24 21] [ 33 28 27]] [[181 185 186] [180 184 185] [182 184 185] ... [ 23 15 16] [ 22 14 14] [ 24 16 17]]] Detection took 0.067s for 300 object proposals len de result frcnn : 6 time spend for datou_step_exec : 11.319769382476807 time spend to save output : 0.00020956993103027344 total time spend for step 1 : 11.319978952407837 step2:crop_condition Thu May 29 11:28:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 757 Loading chi in step crop for subpids : 6 ! batch 1 Loaded 32 chid ids of type : 757 begin to crop the class : phare param for this class : {'margin_type': 'margin', 'margin_value': 300, 'feed_id_new_photos': 1097966} filtre for class : phare hashtag_id of this class : 492624020 WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : phare begin to crop the class : aile-avant param for this class : {} filtre for class : aile-avant hashtag_id of this class : 2106233860 WARNING : margin is only used for type bib ! now we use margin_relative for the photo_id : 926687666 now we use margin_relative for the photo_id : 950003812 map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : aile-avant time spend for datou_step_exec : 0.5442085266113281 time spend to save output : 9.369850158691406e-05 total time spend for step 2 : 0.544302225112915 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {1071808962: [926687666, 'temp/1748510911_1921311_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1748510911_1921311_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1748510911_1921311_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1748510911_1921311_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1748510911_1921311_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]} ############################### TEST image_blanchir ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_blanchir list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.15586614608764648 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_blanchir Thu May 29 11:28:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanchir_image https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos:23439222 treat image : temp/1748510923_1921311_990111206_7ca22c7e68dd0a10509c7987af0cf549.png blanchir func Result OK ! time spend for datou_step_exec : 6.944372892379761 time spend to save output : 6.4373016357421875e-06 total time spend for step 1 : 6.9443793296813965 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[1818, 0, 990111206, 1, 1, 1, None, 1, None]] time used for this insertion : 0.013696670532226562 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(990111206, '1361619308', 0, 300, 0, 381, 1, 1, 'blanc')] [(990111206, '1361619308', 0, 300, 0, 381, 1, 1, 'blanc')] ############################### TEST darker_image ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : darker_image list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.12625956535339355 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:darker_image Thu May 29 11:28:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec dans la step darker batch 1 Loaded 7 chid ids of type : 2228 +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points treat image : temp/1748510931_1921311_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg Result OK ! batch 1 Loaded 7 chid ids of type : 2228 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! crops sauvegardes time spend for datou_step_exec : 10.508981943130493 time spend to save output : 3.409385681152344e-05 total time spend for step 1 : 10.509016036987305 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[2085, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.012723207473754883 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, '1361619316', 0, 897, 0, 1431, 1, 1, 'darker')] [(989962950, '1361619316', 0, 897, 0, 1431, 1, 1, 'darker')] batch 1 Loaded 7 chid ids of type : 2228 ############################### TEST img_aug ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : data_aug list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14551162719726562 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:data_aug Thu May 29 11:29:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec numpy.version est ancienne, on utilise l'ancien bit generator numpy.version est ancienne, on utilise l'ancien bit generator batch 1 Loaded 7 chid ids of type : 2228 +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points on traite des points Result OK ! batch 1 Loaded 7 chid ids of type : 2260 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1361619320 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1361619320 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1361619320 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1361619320 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1361619320 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1361619320 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1361619320 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! photo_uploade augmentation faite pour la photo : 989962950 time spend for datou_step_exec : 7.249321699142456 time spend to save output : 3.6716461181640625e-05 total time spend for step 1 : 7.249358415603638 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[2041, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.012456178665161133 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, 1361619320, 0, 1431, 0, 897, 1, 1, 'img_aug')] [(989962950, 1361619320, 0, 1431, 0, 897, 1, 1, 'img_aug')] batch 1 Loaded 7 chid ids of type : 2260 ############################### TEST rubbia ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.060161590576171875 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Thu May 29 11:29:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec begin split time score 2022-04-13 10:29:59 0 TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('05', 2), ('07', 25), ('06', 1), ('08', 96), ('09', 44), ('10', 64)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {1: 188, 2: 36, 3: 8} 07092021 4599398 Nombre de photos uploadées : 232 / 23040 (1%) 07092021 4599398 Nombre de photos taguées (types de déchets): 232 / 232 (100%) 07092021 4599398 Nombre de photos taguées (volume) : 232 / 232 (100%) elapsed_time : load_data_split_time_score 4.291534423828125e-06 elapsed_time : order_list_meta_photo_and_scores 0.00025177001953125 elapsed_time : fill_and_build_computed_from_old_data 0.028339624404907227 elapsed_time : insert_dashboard_record_day_entry 0.028581619262695312 Creating list_photo_total elapsed_time : select_descriptors 18.448132514953613 07092021 4599398 Nombre de photos avec descriptors (type 3963) : 232 / 232 (100%) ERROR : Hum hum, what can we do for different size of descriptors (ignore the difference ) : 0 vs 2048 photo_id : 1049293230 photo_id_prec : 0 0:00:00|ON:0:27:28.999934|OFF:1:46:59.999878|ON:0:00:20.000007|OFF:0:01:51.000162|ON:0:12:18.999909|OFF:0:01:01.000055|ON:0:08:50.000116|OFF:0:00:09.999867|ON:0:00:19.999899|OFF:0:00:09.000058|ON:0:00:29.999860|OFF:0:01:40.000249|ON:0:00:30.999931|OFF:0:07:40.000107|ON:0:00:28.999981|OFF:0:00:09.999968|ON:0:00:10.999986|OFF:0:08:09.999919|ON:0:00:40.000176|OFF:0:01:08.999784|ON:0:00:11.000245|OFF:0:00:39.999921|ON:0:00:19.000004|OFF:0:06:31.000039|ON:0:02:09.999929|OFF:0:01:40.000021|ON:0:00:39.000031|OFF:0:07:10.999966|ON:0:12:30.000101|OFF:0:00:18.999765|ON:0:00:39.999946|OFF:0:00:11.000212|ON:0:00:29.999851|OFF:0:00:20.000150|ON:0:00:30.000042|OFF:0:00:18.999771|ON:0:07:31.000243|OFF:0:00:09.999942|ON:0:00:08.999822|OFF:0:00:11.000172|ON:0:00:39.999914|OFF:0:00:20|ON:0:31:10.000147|OFF:0:12:18.999857|ON:0:01:39.999950|OFF:0:00:19.999947|ON:0:00:21.000213|OFF:0:00:28.999911|ON:0:00:21.000117|OFF:0:00:40.000020|ON:0:10:58.999762|OFF:0:00:41.000023|ON:0:00:09.000008|OFF:0:00:21.000234|ON:0:00:29.999765|OFF:0:00:28.999920|ON:0:00:21.000174|OFF:0:00:30.000078|ON:0:00:29.999938|OFF:0:00:29.999871|ON:0:00:08.999965|OFF:0:09:31.000234|ON:0:00:09.999916|OFF:0:00:20.000049|ON:0:04:09.999926|OFF:0:01:09.000014|ON:0:02:00.999957|OFF:0:00:08.999951|ON:0:00:21.000053|OFF:0:00:18.999927|ON:0:00:39.999997|OFF:0:00:30.000158|ON: 07092021 Removing 115 photos because of the 'same image' condition Total on : 7859.999814999999 list_time_on Total off : 10509.0002 list_time_off dist_desc begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 232 time used for this insertion : 0.12407970428466797 photos_removed : len 115 elapsed_time : remove_photo_duplicate 0.22300338745117188 Creating list_photo_total XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX elapsed_time : count_sum_diff_and_build_graph 0.05637526512145996 Total photos : 232 ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 10 hashtag : 2107750911 photo_id =1049308384 : rungis_plastique_fonce ..can't find max_score_info ...can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .....Change port : 25 hashtag : 2107750908 photo_id =1049311795 : rungis_papier ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 4 hashtag : 2107750911 photo_id =1049311961 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 1 hashtag : 2107750908 photo_id =1049312208 : rungis_papier .....Change port : 5 hashtag : 2107750911 photo_id =1049312420 : rungis_plastique_fonce .Change port : 1 hashtag : 2107750908 photo_id =1049312422 : rungis_papier ..can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049312438 : rungis_plastique_fonce ....can't find max_score_info ...can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info ....Change port : 12 hashtag : 2107750908 photo_id =1049312556 : rungis_papier .can't find max_score_info ..can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750911 photo_id =1049312984 : rungis_plastique_fonce ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .........can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 17 hashtag : 2107751280 photo_id =1049317359 : rungis_aluminium .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750913 photo_id =1049317524 : rungis_tetrapak .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049318212 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..........Change port : 12 hashtag : 2107750908 photo_id =1049318287 : rungis_papier ...can't find max_score_info ..Change port : 4 hashtag : 2107750911 photo_id =1049318294 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info . Total photos : 232 Number of lists : 15 counter photos in port : 117 hashtag : rungis_aluminium(2107751280) : 8 photos in 1 portfolios ! hashtag : rungis_carton(2107750907) : 0 photos in 0 portfolios ! hashtag : rungis_papier(2107750908) : 33 photos in 6 portfolios ! hashtag : rungis_plastique_clair(2107750909) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_dur(2107750910) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_fonce(2107750911) : 74 photos in 7 portfolios ! hashtag : rungis_tapis_vide(2107750912) : 0 photos in 0 portfolios ! hashtag : rungis_tetrapak(2107750913) : 2 photos in 1 portfolios ! elapsed_time : group_photo_by_moyenne_exp 0.013817310333251953 elapsed_time : compute_and_correct_tag_with_moyenne_mobile 3.0994415283203125e-06 today str has not a value , we define it as the date of the first image todaystr_first : 07092021 attention , prev_timestamp is 0 , we do nothing *******o** BIG TIME 550.0000100135803 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049307693 2021-09-07 07:45:54.010041 id_data : 12 * BIG TIME 168.99982810020447 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049308235 2021-09-07 07:48:43.009869 id_data : 13 ** BIG TIME 499.9998118877411 (191.00026988983154, 2, 0, 0, 0.93498826, 0, 0, 0, 0, 0, 0.9018999995946884, 0.002, 0.002999984407424927, 2.9, 0.019000051021575926, -0.01666705330212911) on 7 1049309345 2021-09-07 07:58:43.009858 id_data : 20 * BIG TIME 371.0001440048218 (271.00081276893616, 5, 0.24365342, 0, 0, 0, 0, 0.38418204, 0, 0.34177557, 0.9720000085830689, 0.0041, 0.014999971222877502, 2.9, 0.061000473976135255, -0.2516670016447703) on 15 1049310132 2021-09-07 08:09:54.010082 id_data : 51 * BIG TIME 461.0001001358032 (301.0006546974182, 0, 0.5752453, 0, 0, 0, 0, 0, 0, 0, 1.0228999980926514, 0.0056, 0.02589997522830963, 2.9, 0.009999968051910401, -0.0) on 18 1049310905 2021-09-07 08:18:54.009936 id_data : 60 * BIG TIME 370.0001759529114 (359.0003435611725, 5, 0, 0, 0, 0, 0, 0.85983855, 0, 0, 1.0898999773979188, 0.0078, 0.04109999623298645, 2.9, 0.008999762058258056, -0.0) on 24 1049311767 2021-09-07 08:28:24.010105 id_data : 81 *** BIG TIME 411.0001149177551 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312208 2021-09-07 08:40:04.010052 id_data : 97 * BIG TIME 549.9999330043793 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312363 2021-09-07 08:49:14.009985 id_data : 98 ** BIG TIME 168.99987387657166 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312508 2021-09-07 08:58:23.009966 id_data : 123 * BIG TIME 259.99999809265137 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312556 2021-09-07 09:02:43.009964 id_data : 124 * BIG TIME 190.00016593933105 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312803 2021-09-07 09:07:34.010149 id_data : 135 * BIG TIME 180.0000081062317 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312984 2021-09-07 09:10:34.010157 id_data : 136 * BIG TIME 1480.0000269412994 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316209 2021-09-07 09:35:23.009898 id_data : 138 * BIG TIME 668.9998891353607 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316332 2021-09-07 09:47:53.009987 id_data : 147 * BIG TIME 649.9999890327454 (1086.000019311905, 5, 0, 0, 0, 0, 0, 0.69907516, 0, 0.23055789, 1.6300000094890594, 0.0168, 0.08039999685287476, 2.9, 0.01100021505355835, -0.0) on 68 1049317197 2021-09-07 10:02:34.010134 id_data : 168 * BIG TIME 540.0001981258392 (1189.0002081394196, 0, 0.8074409, 0, 0, 0, 0, 0, 0, 0, 1.7199999867916107, 0.0194, 0.09519999706745148, 2.9, 0.009999808073043823, -0.0) on 78 1049318212 2021-09-07 10:16:24.010117 id_data : 198 * BIG TIME 190.00007104873657 (1199.0000162124634, 5, 0, 0.22708784, 0, 0, 0, 0.7244179, 0, 0, 1.781000003194809, 0.0202, 0.10109996955394746, 2.9, 0.011000241041183472, -0.0) on 79 1049318219 2021-09-07 10:20:04.010153 id_data : 202 **Count Time bigger than 30s : 31 #Number Photos for regression : {'07092021': {2107751280: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 80.99965000152588, 2107751017: 0}, 2107750907: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 49.000049114227295, 2107751017: 0}, 2107750908: {2107751013: 0, 2107751014: 11.000201940536499, 2107751015: 0, 2107751016: 534.9997780323029, 2107751017: 0}, 2107750909: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750910: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 31.000057697296143, 2107751017: 0}, 2107750911: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 564.0013737678528, 2107751017: 19.999656200408936}, 2107750912: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750913: {2107751013: 0, 2107751014: 8.999944925308228, 2107751015: 0, 2107751016: 89.99986672401428, 2107751017: 9.999994993209839}}} 07092021|rungis_aluminium, 05102018_papier_non_papier_dense:0 07092021|rungis_aluminium, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_aluminium, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_aluminium, 05102018_papier_non_papier_tres_dense:80.99965000152588 07092021|rungis_aluminium, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_carton, 05102018_papier_non_papier_tres_dense:49.000049114227295 07092021|rungis_carton, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_papier, 05102018_papier_non_papier_dense:0 07092021|rungis_papier, 05102018_papier_non_papier_peu_dense:11.000201940536499 07092021|rungis_papier, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_papier, 05102018_papier_non_papier_tres_dense:534.9997780323029 07092021|rungis_papier, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_dense:31.000057697296143 07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_dense:564.0013737678528 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_peu_dense:19.999656200408936 07092021|rungis_tapis_vide, 05102018_papier_non_papier_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_dense:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_peu_dense:8.999944925308228 07092021|rungis_tetrapak, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_dense:89.99986672401428 07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_peu_dense:9.999994993209839 #Number Photos for regression amount gros magasin papier (time_diff then nb_photo) : We have not displayed the number of photos removed for one material since Rungis_Papier wasn't in the thcl used ! 07092021_time_diff_distrib Number amount portfolio for this type of dechet : aluminium 8 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_aluminium_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439235 with name like 07092021_aluminium_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : carton 6 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_carton_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439236 with name like 07092021_carton_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : papier 29 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_papier_05102018_papier_non_papier_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439237 with name like 07092021_papier_05102018_papier_non_papier_peu_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_papier_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439238 with name like 07092021_papier_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : plastique_clair 0 Number amount portfolio for this type of dechet : plastique_dur 2 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_dur_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439239 with name like 07092021_plastique_dur_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : plastique_fonce 42 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_fonce_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439240 with name like 07092021_plastique_fonce_05102018_papier_non_papier_tres_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_fonce_05102018_papier_non_papier_tres_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439241 with name like 07092021_plastique_fonce_05102018_papier_non_papier_tres_peu_dense Number amount portfolio for this type of dechet : tapis_vide 0 Number amount portfolio for this type of dechet : tetrapak 11 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439242 with name like 07092021_tetrapak_05102018_papier_non_papier_peu_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439243 with name like 07092021_tetrapak_05102018_papier_non_papier_tres_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_tres_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 23439244 with name like 07092021_tetrapak_05102018_papier_non_papier_tres_peu_dense NUMBER BATCH : 15 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['pcnc', 'pcm', 'jrm', 'flux_dev', 'pehd_pp', 'papier', 'carton', 'plastique_dur', 'plastique_clair', 'pet_clair', 'plastique_fonce', 'tetrapak', 'aluminium', 'carton_emr', 'grands_cartons', 'gros_de_magasin', 'tapis_vide'] We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 10, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 191.00026988983154, 3: 0, 4: 0}, 'duration': 9008.999763965607, 'nb_balles_papier': 0.19500026988983155, 'begin_time_port': 'image_07092021_05_20_04_010050m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.19500026988983155 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 25, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 225.99965572357178, 3: 0, 4: 0}, 'duration': 2329.999878883362, 'nb_balles_papier': 0.2299996557235718, 'begin_time_port': 'image_07092021_07_50_23_010046m0.jpg 0.010000231981277466 for time 10.000231981277466, id_amount 3 this amount prod time diff : 0.010000231981277466'} Production hashtag (incorrect ponderation at 20-10-18) : 0.2299996557235718 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 4, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 141.00006794929504, 3: 0, 4: 0}, 'duration': 189.9997718334198, 'nb_balles_papier': 0.14100006794929504, 'begin_time_port': 'image_07092021_08_29_24_010122m0.jpg 0.011000197172164917 for time 11.000197172164917, id_amount 3 this amount prod time diff : 0.011000197172164917'} Production hashtag (incorrect ponderation at 20-10-18) : 0.14100006794929504 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 1, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 9.000005006790161, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.00900000500679016, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.00900000500679016 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 5, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 148.99991703033447, 3: 0, 4: 0}, 'duration': 698.9998500347137, 'nb_balles_papier': 0.15099991703033447, 'begin_time_port': 'image_07092021_08_40_04_010052m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.15099991703033447 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 1, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 11.000216960906982, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.011000216960906983, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.011000216960906983 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 2, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 28.9998140335083, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.0289998140335083, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.0289998140335083 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 1, 1: 0, 2: 11, 3: 0, 4: 0}, 'map_time_amount': {0: 11.000201940536499, 1: 0, 2: 111.00051093101501, 3: 0, 4: 0}, 'duration': 330.0000479221344, 'nb_balles_papier': 0.12300071287155151, 'begin_time_port': 'image_07092021_08_52_53_009918m0.jpg 0.01 for time 10.0, id_amount 3 this amount prod time diff : 0.01'} Production hashtag (incorrect ponderation at 20-10-18) : 0.12300071287155151 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 8, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 60.99999809265137, 3: 0, 4: 0}, 'duration': 291.0001850128174, 'nb_balles_papier': 0.06299999809265137, 'begin_time_port': 'image_07092021_09_02_43_009964m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.06299999809265137 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 1, 1: 0, 2: 16, 3: 0, 4: 0}, 'map_time_amount': {0: 8.999944925308228, 1: 0, 2: 148.99963188171387, 3: 0, 4: 0}, 'duration': 3119.999976873398, 'nb_balles_papier': 0.16199957680702212, 'begin_time_port': 'image_07092021_09_10_34_010157m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.16199957680702212 We filter photos on hashtag condition ! result_one_balle_Type_aluminium:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 7, 3: 1, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 72.00012874603271, 3: 9.999994993209839, 4: 0}, 'duration': 150.00017404556274, 'nb_balles_papier': 0.08200012373924254, 'begin_time_port': 'image_07092021_10_03_24_009930m0.jpg 0.009999775886535644 for time 9.999775886535645, id_amount 3 this amount prod time diff : 0.009999775886535644'} Production hashtag (incorrect ponderation at 20-10-18) : 0.08200012373924254 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_tetrapak:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 2, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 19.99965810775757, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.01999965810775757, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.01999965810775757 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 10, 3: 2, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 81.00049686431885, 3: 19.999656200408936, 4: 0}, 'duration': 428.99973487854004, 'nb_balles_papier': 0.10300015306472779, 'begin_time_port': 'image_07092021_10_16_24_010117m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.10300015306472779 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 4, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 40.00009298324585, 3: 0, 4: 0}, 'duration': 38.99979090690613, 'nb_balles_papier': 0.04000009298324585, 'begin_time_port': 'image_07092021_10_23_44_010139m0.jpg 0.011000287055969239 for time 11.000287055969238, id_amount 3 this amount prod time diff : 0.011000287055969239'} Production hashtag (incorrect ponderation at 20-10-18) : 0.04000009298324585 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 6, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 60.00031113624573, 3: 0, 4: 0}, 'duration': 98.99990916252136, 'nb_balles_papier': 0.060000311136245724, 'begin_time_port': 'image_07092021_10_24_34_010156m0.jpg 0.011000226020812989 for time 11.000226020812988, id_amount 3 this amount prod time diff : 0.011000226020812989'} Production hashtag (incorrect ponderation at 20-10-18) : 0.060000311136245724 We filter photos on hashtag condition ! We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 15 list_same_port_ids : [13545772] find same portfolio which already exist 13545772 , we will use it list_same_port_ids : [13545774] find same portfolio which already exist 13545774 , we will use it list_same_port_ids : [13545777] find same portfolio which already exist 13545777 , we will use it list_same_port_ids : [5570414] find same portfolio which already exist 5570414 , we will use it list_same_port_ids : [13545779] find same portfolio which already exist 13545779 , we will use it list_same_port_ids : [13545780] find same portfolio which already exist 13545780 , we will use it list_same_port_ids : [13545783] find same portfolio which already exist 13545783 , we will use it list_same_port_ids : [13545785] find same portfolio which already exist 13545785 , we will use it list_same_port_ids : [13545787] find same portfolio which already exist 13545787 , we will use it list_same_port_ids : [13545788] find same portfolio which already exist 13545788 , we will use it list_same_port_ids : [13543473] find same portfolio which already exist 13543473 , we will use it list_same_port_ids : [13543474] find same portfolio which already exist 13543474 , we will use it list_same_port_ids : [13543475] find same portfolio which already exist 13543475 , we will use it list_same_port_ids : [13543476] find same portfolio which already exist 13543476 , we will use it list_same_port_ids : [13543477] find same portfolio which already exist 13543477 , we will use it # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545772 To do Qualite : 0.005823220486111111 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545774 AND mptpi.`type`=4199 To do Qualite : 0.1888521086140681 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545777 To do Qualite : 0.007415846836419753 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5570414 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545779 To do Qualite : 0.004572120949074074 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545780 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545783 To do Qualite : 0.00907640496399177 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545785 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545787 To do Qualite : 0.01485129824918373 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545788 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543473 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543474 To do Qualite : 0.003848153410463827 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543475 AND mptpi.`type`=4199 To do Qualite : 0.11478003563407302 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543476 To do Qualite : 0.019897576026366652 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543477 AND mptpi.`type`=4199 To do elapsed_time : count_nb_balles_and_create_portfolio 43.26708793640137 # DISPLAY ALL COLLECTED DATA : {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}} time spend for datou_step_exec : 62.245830059051514 time spend to save output : 3.075599670410156e-05 total time spend for step 1 : 62.24586081504822 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230] Looping around the photos to save general results len do output : 1 /4599398Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318362', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318360', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318358', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318356', 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'4599398', '1049310141', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310139', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310138', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310134', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310132', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309737', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309734', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309732', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309706', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309703', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309701', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309686', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309681', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309677', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309675', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309672', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309670', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309658', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309657', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309656', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309655', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309653', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309651', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309605', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309603', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309599', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309597', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309595', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309592', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309385', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309383', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309382', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309381', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309380', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309379', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309345', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308384', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308381', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308376', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308280', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308276', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308275', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308235', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049307693', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306823', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306804', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306792', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306791', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306635', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306205', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049304810', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049303925', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049296996', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049296121', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049294990', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049293230', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 233 time used for this insertion : 0.11940813064575195 save_final save missing photos in datou_result : After save, about to update current ! Result test rubbia : {'4599398': ([[0, 7, 8, 9, 11, 12, 13, 14, 17, 18], [19, 20, 22, 23, 25, 26, 27, 38, 39, 40, 51, 52, 53, 55, 60, 61, 62, 63, 71, 76, 77, 81, 82, 83, 84], [85, 86, 91, 92], [93], [97, 98, 99, 100, 101], [102], [103, 104], [107, 108, 109, 110, 112, 113, 116, 117, 118, 121, 122, 123], [124, 126, 128, 129, 130, 131, 134, 135], [136, 137, 138, 147, 148, 149, 150, 151, 152, 153, 154, 157, 158, 161, 162, 167, 168], [173, 176, 177, 178, 182, 183, 187, 188], [189, 193], [198, 201, 202, 207, 208, 209, 210, 211, 212, 213, 214, 215], [216, 217, 218, 220], [221, 224, 225, 226, 227, 231]], {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, {13545772: {'list_of_photos': [1049293230, 1049306791, 1049294990, 1049306792, 1049306823, 1049307693, 1049308235, 1049308275, 1049308376, 1049308381], 'hashtag': 'papier'}, 13545774: {'list_of_photos': [1049308384, 1049309345, 1049309380, 1049309381, 1049309383, 1049309385, 1049309592, 1049309658, 1049309670, 1049309672, 1049310132, 1049310134, 1049310138, 1049310141, 1049310905, 1049310907, 1049310909, 1049310911, 1049310994, 1049311199, 1049311252, 1049311767, 1049311771, 1049311791, 1049311793], 'hashtag': 'plastique_fonce'}, 13545777: {'list_of_photos': [1049311795, 1049311932, 1049311943, 1049311960], 'hashtag': 'papier'}, 5570414: {'list_of_photos': [1049311961], 'hashtag': 'plastique_fonce'}, 13545779: {'list_of_photos': [1049312208, 1049312363, 1049312404, 1049312406, 1049312409], 'hashtag': 'papier'}, 13545780: {'list_of_photos': [1049312420], 'hashtag': 'plastique_fonce'}, 13545783: {'list_of_photos': [1049312422, 1049312424], 'hashtag': 'papier'}, 13545785: {'list_of_photos': [1049312438, 1049312440, 1049312442, 1049312444, 1049312449, 1049312460, 1049312463, 1049312464, 1049312484, 1049312488, 1049312489, 1049312508], 'hashtag': 'plastique_fonce'}, 13545787: {'list_of_photos': [1049312556, 1049312566, 1049312571, 1049312573, 1049312574, 1049312579, 1049312588, 1049312803], 'hashtag': 'papier'}, 13545788: {'list_of_photos': [1049312984, 1049313025, 1049316209, 1049316332, 1049316336, 1049316338, 1049316520, 1049316534, 1049316537, 1049316540, 1049316543, 1049316588, 1049316594, 1049316610, 1049316749, 1049316790, 1049317197], 'hashtag': 'plastique_fonce'}, 13543473: {'list_of_photos': [1049317359, 1049317453, 1049317457, 1049317461, 1049317489, 1049317491, 1049317520, 1049317522], 'hashtag': 'aluminium'}, 13543474: {'list_of_photos': [1049317524, 1049317542], 'hashtag': 'tetrapak'}, 13543475: {'list_of_photos': [1049318212, 1049318216, 1049318219, 1049318253, 1049318257, 1049318260, 1049318265, 1049318268, 1049318271, 1049318273, 1049318276, 1049318279], 'hashtag': 'plastique_fonce'}, 13543476: {'list_of_photos': [1049318287, 1049318288, 1049318289, 1049318293], 'hashtag': 'papier'}, 13543477: {'list_of_photos': [1049318294, 1049318311, 1049318337, 1049318339, 1049318342, 1049318362], 'hashtag': 'plastique_fonce'}}, {2107751280: 8, 2107750907: 0, 2107750908: 33, 2107750909: 0, 2107750910: 0, 2107750911: 74, 2107750912: 0, 2107750913: 2}, {'amount_uploaded_and_tagged': {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}}, 'map_all_result_after_group_moy_exp': {'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}, 'map_info_after_moyenne_mobile': {'07092021': {'distrib_time_diff': {'nb': 207, 'mean': 12.512076641626404, 'stddev': 12.296218880583977, 'min': 0.0, 'max': 119.00011491775513, 'quantil_10': {'min': [8.999778032302856], 'max': [11.000291109085083]}, 'quantil_100': {'min': [8.999709129333496], 'max': [61.00024700164795]}, 'quantil_1000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_5000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_10000': {'min': [0.0], 'max': [119.00011491775513]}}, 'time_diff': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 80.99965000152588, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 49.000049114227295, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000201940536499, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 534.9997780323029, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 31.000057697296143, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 564.0013737678528, '05102018_papier_non_papier_tres_peu_dense': 19.999656200408936}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 8.999944925308228, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 89.99986672401428, '05102018_papier_non_papier_tres_peu_dense': 9.999994993209839}}, 'time_diff_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000208854675293, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 158.99993062019348, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 101.00013995170593, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 18.999826192855835, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 168.99926328659058, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 21.00012707710266, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 489.9993727207184, '05102018_papier_non_papier_tres_peu_dense': 11.000169038772583}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 38.99988508224487, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 170.0003685951233, '05102018_papier_non_papier_tres_peu_dense': 0}}, 'nb_photos': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 8, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 6, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 28, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 2, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 40, '05102018_papier_non_papier_tres_peu_dense': 2}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 9, '05102018_papier_non_papier_tres_peu_dense': 1}}, 'nb_photos_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 5, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 2, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 46, '05102018_papier_non_papier_tres_peu_dense': 1}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 4, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 17, '05102018_papier_non_papier_tres_peu_dense': 0}}}}, 'map_amount_per_hashtag': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, 'count': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}})}| Result context_with_local_rubbia.cache_model_config.map_io test rubbia : {'input': {}, 'output': {}}| ############################### TEST rubbia_split_dark ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score_with_photo list_input_json : [] origin We have 1 , BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFBFBFBFFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 4.855128765106201 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score_with_photo Thu May 29 11:30:17 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec ----- Debut du copier-coller des param necessaire pour fonction main de STS ----- TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('18', 4), ('19', 5), ('20', 5), ('24', 8), ('26', 6), ('17', 1), ('27', 9), ('51', 7), ('28', 2), ('21', 4), ('52', 2), ('25', 6), ('50', 3), ('22', 2)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 06102021 4608689 Nombre de photos uploadées : 64 / 23040 (0%) 06102021 4608689 Nombre de photos taguées (types de déchets): 0 / 64 (0%) 06102021 4608689 Nombre de photos taguées (volume) : 0 / 64 (0%) elapsed_time : load_data_split_time_score 1.7642974853515625e-05 elapsed_time : order_list_meta_photo_and_scores 1.7404556274414062e-05 ???????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0054073333740234375 elapsed_time : insert_dashboard_record_day_entry 0.031546592712402344 ***** BEGIN SPLIT BY DARK ***** To DO 08/10/21 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.018659114837646484 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 2 time used for this insertion : 0.008823871612548828 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.008908510208129883 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 4 time used for this insertion : 0.00903010368347168 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 5 time used for this insertion : 0.010904550552368164 elapsed_time : SPLIT_BY_DARK 0.0667879581451416 ***** END SPLIT BY DARK ***** ((1055001085,), (1055008638,), (1055010730,), (1055011086,), (1055012686,)) ***** BEGIN SPLIT TIME ***** [12, 20, 29, 38, 51] ````````````````````````````````````````````````````````````````list printed: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.008597135543823242 ***** END SPLIT TIME ***** NUMBER BATCH : 7 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['refus', 'jrm'] ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 172.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_101733.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 72.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102049.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 52.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102404.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 53.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102525.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 81.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102645.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Empty batch, bug or could have been filtered ! ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 102.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_145039.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 6 list_same_port_ids : [4938484] find same portfolio which already exist 4938484 , we will use it list_same_port_ids : [4938485] find same portfolio which already exist 4938485 , we will use it list_same_port_ids : [4938486] find same portfolio which already exist 4938486 , we will use it list_same_port_ids : [4938487] find same portfolio which already exist 4938487 , we will use it list_same_port_ids : [4938488] find same portfolio which already exist 4938488 , we will use it list_same_port_ids : [4756245] find same portfolio which already exist 4756245 , we will use it # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938484 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938485 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938486 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938487 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938488 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4756245 AND mptpi.`type`=4038 To do elapsed_time : count_nb_balles_and_create_portfolio 1.0715405941009521 # DISPLAY ALL COLLECTED DATA : {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} ------ Fin du Copier-Coller ------ ---------- ONE RESULT --------- ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}) ---------- END de ONE RESULT ---------- Suppression des photos Telecharges time spend for datou_step_exec : 6.942409038543701 time spend to save output : 0.00016880035400390625 total time spend for step 1 : 6.942577838897705 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score_with_photo we use saveGeneral [1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679] Looping around the photos to save general results len do output : 5 /[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]] /{'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]} /{4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}} /{2107757407: 59} /{'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}} before output type Managing all output in save final without adding information in the mtr_datou_result ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003131', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055002045', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001545', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001542', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001092', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001085', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000228', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000070', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000068', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000063', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000059', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000055', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003357', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003348', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003292', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003278', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003266', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003261', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003259', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003249', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003202', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003198', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003197', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003185', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003134', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013727', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013724', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013693', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012727', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012722', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012686', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012684', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011740', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011733', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011726', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011459', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011454', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011441', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011086', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011076', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011074', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011072', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010743', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010739', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010737', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010730', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010725', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010723', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010143', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008638', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008599', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008597', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008184', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008181', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007992', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007953', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007950', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004798', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004627', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004608', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004600', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004278', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004217', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003679', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 64 time used for this insertion : 0.0393068790435791 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}) Result test split dark : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}})| ############################### TEST rubbia_append ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.03072524070739746 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Thu May 29 11:30:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('30', 1), ('36', 1), ('43', 1)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21092021 4599006 Nombre de photos uploadées : 3 / 23040 (0%) 21092021 4599006 Nombre de photos taguées (types de déchets): 0 / 3 (0%) 21092021 4599006 Nombre de photos taguées (volume) : 0 / 3 (0%) elapsed_time : load_data_split_time_score 3.5762786865234375e-06 elapsed_time : order_list_meta_photo_and_scores 1.0251998901367188e-05 ??? elapsed_time : fill_and_build_computed_from_old_data 0.0005056858062744141 elapsed_time : insert_dashboard_record_day_entry 0.03465700149536133 ---------- APPEND TASK BEGIN ---------- ---------- APPEND TASK END ---------- We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21092021': {'nb_upload': 3, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('12', 1), ('-0', 3)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21092021 4505992 Nombre de photos uploadées : 1 / 23040 (0%) 21092021 4505992 Nombre de photos taguées (types de déchets): 0 / 1 (0%) 21092021 4505992 Nombre de photos taguées (volume) : 0 / 1 (0%) elapsed_time : load_data_split_time_score 9.298324584960938e-06 elapsed_time : order_list_meta_photo_and_scores 1.430511474609375e-05 ? elapsed_time : fill_and_build_computed_from_old_data 0.0002658367156982422 elapsed_time : insert_dashboard_record_day_entry 0.03405404090881348 ---------- APPEND TASK BEGIN ---------- ---------- APPEND TASK END ---------- We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4505992_21-09-2021.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4505992 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21092021': {'nb_upload': 1, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} time spend for datou_step_exec : 2.9426372051239014 time spend to save output : 4.4345855712890625e-05 total time spend for step 1 : 2.9426815509796143 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1054572537, 1054572534, 1054572532, 1051605195] Looping around the photos to save general results len do output : 2 /4599006Didn't retrieve data . /4505992Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572537', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572534', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572532', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4505992', '1051605195', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.0402071475982666 save_final save missing photos in datou_result : After save, about to update current ! ############################### TEST rubbia_horaire ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.03192901611328125 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Thu May 29 11:30:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('02', 6), ('05', 8), ('06', 19)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 08032021 3609515 Nombre de photos uploadées : 33 / 23040 (0%) 08032021 3609515 Nombre de photos taguées (types de déchets): 0 / 33 (0%) 08032021 3609515 Nombre de photos taguées (volume) : 0 / 33 (0%) elapsed_time : load_data_split_time_score 4.76837158203125e-06 elapsed_time : order_list_meta_photo_and_scores 1.0251998901367188e-05 ????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0027451515197753906 elapsed_time : insert_dashboard_record_day_entry 0.0285184383392334 Creating list_photo_total elapsed_time : select_descriptors 0.010734081268310547 08032021 3609515 Nombre de photos avec descriptors (type 3963) : 0 / 33 (0%) Missing descriptors for photos 0 and 1014054233 0:00:00|ON:Missing descriptors for photos 1014054233 and 1014054232 Missing descriptors for photos 1014054232 and 1014054231 Missing descriptors for photos 1014054231 and 1014054230 Missing descriptors for photos 1014054230 and 1014054235 Missing descriptors for photos 1014054235 and 1014054234 Missing descriptors for photos 1014054234 and 1014097492 Missing descriptors for photos 1014097492 and 1014097499 Missing descriptors for photos 1014097499 and 1014097497 Missing descriptors for photos 1014097497 and 1014097580 Missing descriptors for photos 1014097580 and 1014097924 Missing descriptors for photos 1014097924 and 1014098236 Missing descriptors for photos 1014098236 and 1014098602 Missing descriptors for photos 1014098602 and 1014099035 Missing descriptors for photos 1014099035 and 1014105778 Missing descriptors for photos 1014105778 and 1014105777 Missing descriptors for photos 1014105777 and 1014105784 Missing descriptors for photos 1014105784 and 1014105783 Missing descriptors for photos 1014105783 and 1014105782 Missing descriptors for photos 1014105782 and 1014105781 Missing descriptors for photos 1014105781 and 1014105786 Missing descriptors for photos 1014105786 and 1014105785 Missing descriptors for photos 1014105785 and 1014105791 Missing descriptors for photos 1014105791 and 1014105790 Missing descriptors for photos 1014105790 and 1014105798 Missing descriptors for photos 1014105798 and 1014105797 Missing descriptors for photos 1014105797 and 1014105796 Missing descriptors for photos 1014105796 and 1014105795 Missing descriptors for photos 1014105795 and 1014105800 Missing descriptors for photos 1014105800 and 1014105799 Missing descriptors for photos 1014105799 and 1014106095 Missing descriptors for photos 1014106095 and 1014106094 Missing descriptors for photos 1014106094 and 1014106093 08032021 Removing 0 photos because of the 'same image' condition Total on : 0 Total off : 0.0 list_time_off Warning in study_and_display_distrib_list : min=max : 0.0 0.0 dist_desc Warning in study_and_display_distrib_list : min=max : -1 -1 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 33 time used for this insertion : 0.026679039001464844 photos_removed : len 0 elapsed_time : remove_photo_duplicate 0.060822248458862305 To do, maybe not at the correct place ! .................................force hashtag to JRM elapsed_time : CREATE_PORT_BATCH_BY_HOUR 0.007997989654541016 NUMBER BATCH : 3 list_ponderation used : [1e-05, 1e-05, 1e-05, 1e-05, 1e-05] , list_hashtag_class_create_as_list : ['jrm'] We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 0 elapsed_time : count_nb_balles_and_create_portfolio 0.027228116989135742 # DISPLAY ALL COLLECTED DATA : {'08032021': {'nb_upload': 33, 'nb_taggue_class': 0, 'nb_taggue_densite': 0, 'nb_descriptors': 0}} time spend for datou_step_exec : 0.19939470291137695 time spend to save output : 3.266334533691406e-05 total time spend for step 1 : 0.19942736625671387 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230] Looping around the photos to save general results len do output : 1 /3609515Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106095', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106094', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106093', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105800', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105799', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105798', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105797', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105796', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105795', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105791', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105790', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105786', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105785', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105784', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105783', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105782', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105781', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105778', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105777', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014099035', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014098602', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014098236', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097924', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097580', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097499', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097497', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097492', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054235', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054234', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054233', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054232', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054231', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054230', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 34 time used for this insertion : 0.11526846885681152 save_final save missing photos in datou_result : After save, about to update current ! got : {'Rungis_JRM': []} expected : {'Rungis_JRM': [(0, 1), (1, 2), (2, 3)]} ERROR rubbia_horaire FAILED ############################### TEST rle_unique_nms_with_priority ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.17293834686279297 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Thu May 29 11:30:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 10 chid ids of type : 2804 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 10 nb_hashtags : 2 time to prepare the origin masks : 0.46249818801879883 time for calcul the mask position with numpy : 0.005857944488525391 nb_pixel_total : 217207 time to create 1 rle with new method : 0.06130671501159668 time for calcul the mask position with numpy : 0.003384828567504883 nb_pixel_total : 1008 time to create 1 rle with old method : 0.002348661422729492 time for calcul the mask position with numpy : 0.0032248497009277344 nb_pixel_total : 751 time to create 1 rle with old method : 0.001734018325805664 time for calcul the mask position with numpy : 0.003403186798095703 nb_pixel_total : 722 time to create 1 rle with old method : 0.001699686050415039 time for calcul the mask position with numpy : 0.0030965805053710938 nb_pixel_total : 2949 time to create 1 rle with old method : 0.006814479827880859 time for calcul the mask position with numpy : 0.003415346145629883 nb_pixel_total : 497 time to create 1 rle with old method : 0.001196146011352539 time for calcul the mask position with numpy : 0.0032193660736083984 nb_pixel_total : 1086 time to create 1 rle with old method : 0.014430761337280273 time for calcul the mask position with numpy : 0.006138324737548828 nb_pixel_total : 1924 time to create 1 rle with old method : 0.004464387893676758 time for calcul the mask position with numpy : 0.003016233444213867 nb_pixel_total : 413 time to create 1 rle with old method : 0.0010077953338623047 time for calcul the mask position with numpy : 0.0029006004333496094 nb_pixel_total : 526 time to create 1 rle with old method : 0.0011837482452392578 create new chi : 0.13439321517944336 time to delete rle : 0.020650386810302734 batch 1 Loaded 10 chid ids of type : 2805 Number RLEs to save : 1674 TO DO : save crop sub photo not yet done ! save time : 1.778156042098999 map_output_result : {998957128: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms time spend for datou_step_exec : 2.5710175037384033 time spend to save output : 0.0001499652862548828 total time spend for step 1 : 2.571167469024658 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {998957128: (0.0, 'Should be the crop_list due to order', 0)} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.27283620834350586 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Thu May 29 11:30:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 10 chid ids of type : 4169 seulement à utiliser dans la step consolidation batch 1 Loaded 10 chid ids of type : 2805 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.10469675064086914 map_output_result : {1066511071: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms time spend for datou_step_exec : 0.5375018119812012 time spend to save output : 9.822845458984375e-05 total time spend for step 1 : 0.537600040435791 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1066511071: (0.0, 'Should be the crop_list due to order', 0)} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.20888400077819824 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Thu May 29 11:30:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 91 chid ids of type : 2596 +++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 43 nb_hashtags : 2 time to prepare the origin masks : 27.614924669265747 time for calcul the mask position with numpy : 0.2604331970214844 nb_pixel_total : 5233657 time to create 1 rle with new method : 0.5370368957519531 time for calcul the mask position with numpy : 0.033193111419677734 nb_pixel_total : 11972 time to create 1 rle with old method : 0.025940656661987305 time for calcul the mask position with numpy : 0.03326749801635742 nb_pixel_total : 15054 time to create 1 rle with old method : 0.0330348014831543 time for calcul the mask position with numpy : 0.032991647720336914 nb_pixel_total : 13954 time to create 1 rle with old method : 0.030039310455322266 time for calcul the mask position with numpy : 0.03347659111022949 nb_pixel_total : 4888 time to create 1 rle with old method : 0.010787248611450195 time for calcul the mask position with numpy : 0.054949045181274414 nb_pixel_total : 1188492 time to create 1 rle with new method : 0.48109984397888184 time for calcul the mask position with numpy : 0.037244319915771484 nb_pixel_total : 184585 time to create 1 rle with new method : 0.46694397926330566 time for calcul the mask position with numpy : 0.03551363945007324 nb_pixel_total : 18620 time to create 1 rle with old method : 0.042474985122680664 time for calcul the mask position with numpy : 0.03488445281982422 nb_pixel_total : 62945 time to create 1 rle with old method : 0.14052414894104004 time for calcul the mask position with numpy : 0.03351712226867676 nb_pixel_total : 9427 time to create 1 rle with old method : 0.02105879783630371 time for calcul the mask position with numpy : 0.033447980880737305 nb_pixel_total : 9081 time to create 1 rle with old method : 0.020514726638793945 time for calcul the mask position with numpy : 0.033946990966796875 nb_pixel_total : 15987 time to create 1 rle with old method : 0.036038875579833984 time for calcul the mask position with numpy : 0.03463149070739746 nb_pixel_total : 33276 time to create 1 rle with old method : 0.07401347160339355 time for calcul the mask position with numpy : 0.0359959602355957 nb_pixel_total : 17533 time to create 1 rle with old method : 0.038805246353149414 time for calcul the mask position with numpy : 0.033243417739868164 nb_pixel_total : 4876 time to create 1 rle with old method : 0.010890960693359375 time for calcul the mask position with numpy : 0.033483266830444336 nb_pixel_total : 25226 time to create 1 rle with old method : 0.056246280670166016 time for calcul the mask position with numpy : 0.03370332717895508 nb_pixel_total : 30773 time to create 1 rle with old method : 0.06780266761779785 time for calcul the mask position with numpy : 0.03914833068847656 nb_pixel_total : 65671 time to create 1 rle with old method : 0.1482985019683838 time for calcul the mask position with numpy : 0.03345942497253418 nb_pixel_total : 12230 time to create 1 rle with old method : 0.02928471565246582 time for calcul the mask position with numpy : 0.03336977958679199 nb_pixel_total : 29560 time to create 1 rle with old method : 0.06611347198486328 time for calcul the mask position with numpy : 0.036745309829711914 nb_pixel_total : 14310 time to create 1 rle with old method : 0.03166985511779785 time for calcul the mask position with numpy : 0.03341388702392578 nb_pixel_total : 15117 time to create 1 rle with old method : 0.03345131874084473 time for calcul the mask position with numpy : 0.036237239837646484 nb_pixel_total : 301487 time to create 1 rle with new method : 0.46410179138183594 time for calcul the mask position with numpy : 0.03472113609313965 nb_pixel_total : 29821 time to create 1 rle with old method : 0.0662086009979248 time for calcul the mask position with numpy : 0.03497934341430664 nb_pixel_total : 40299 time to create 1 rle with old method : 0.09085845947265625 time for calcul the mask position with numpy : 0.03435325622558594 nb_pixel_total : 12680 time to create 1 rle with old method : 0.028206348419189453 time for calcul the mask position with numpy : 0.03409314155578613 nb_pixel_total : 9449 time to create 1 rle with old method : 0.020841121673583984 time for calcul the mask position with numpy : 0.03419232368469238 nb_pixel_total : 15168 time to create 1 rle with old method : 0.03420281410217285 time for calcul the mask position with numpy : 0.03430819511413574 nb_pixel_total : 11140 time to create 1 rle with old method : 0.025028705596923828 time for calcul the mask position with numpy : 0.03747677803039551 nb_pixel_total : 29065 time to create 1 rle with old method : 0.07978272438049316 time for calcul the mask position with numpy : 0.033881425857543945 nb_pixel_total : 22774 time to create 1 rle with old method : 0.05017209053039551 time for calcul the mask position with numpy : 0.03407931327819824 nb_pixel_total : 13880 time to create 1 rle with old method : 0.031116962432861328 time for calcul the mask position with numpy : 0.03501319885253906 nb_pixel_total : 155366 time to create 1 rle with new method : 0.6291069984436035 time for calcul the mask position with numpy : 0.03668665885925293 nb_pixel_total : 63941 time to create 1 rle with old method : 0.14223098754882812 time for calcul the mask position with numpy : 0.03327465057373047 nb_pixel_total : 7836 time to create 1 rle with old method : 0.017342805862426758 time for calcul the mask position with numpy : 0.0403742790222168 nb_pixel_total : 7460 time to create 1 rle with old method : 0.016972780227661133 time for calcul the mask position with numpy : 0.034535884857177734 nb_pixel_total : 44600 time to create 1 rle with old method : 0.0982964038848877 time for calcul the mask position with numpy : 0.03313255310058594 nb_pixel_total : 11879 time to create 1 rle with old method : 0.028004169464111328 time for calcul the mask position with numpy : 0.03314471244812012 nb_pixel_total : 44195 time to create 1 rle with old method : 0.09717750549316406 time for calcul the mask position with numpy : 0.033941030502319336 nb_pixel_total : 23652 time to create 1 rle with old method : 0.05467057228088379 time for calcul the mask position with numpy : 0.03254985809326172 nb_pixel_total : 30006 time to create 1 rle with old method : 0.06550168991088867 time for calcul the mask position with numpy : 0.033049821853637695 nb_pixel_total : 15880 time to create 1 rle with old method : 0.0374298095703125 time for calcul the mask position with numpy : 0.03291201591491699 nb_pixel_total : 29845 time to create 1 rle with old method : 0.0828709602355957 time for calcul the mask position with numpy : 0.03844618797302246 nb_pixel_total : 144263 time to create 1 rle with old method : 0.3189065456390381 create new chi : 6.789700508117676 time to delete rle : 0.534679651260376 batch 1 Loaded 44 chid ids of type : 2805 Number RLEs to save : 27884 TO DO : save crop sub photo not yet done ! save time : 4.016118288040161 map_output_result : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)} End step rle-unique-nms time spend for datou_step_exec : 39.395259618759155 time spend to save output : 0.0002696514129638672 total time spend for step 1 : 39.39552927017212 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)} batch 1 Loaded 54 chid ids of type : 2805 ############################### TEST random_deformation ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : random_deformation list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.13143253326416016 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:random_deformation Thu May 29 11:31:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of datou step random deformation get user info for portfolio 3288640 About to upload 4 photos upload in portfolio : 3287159 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748511077_1921311 we have uploaded 4 photos in the portfolio 3287159 time of upload the photos Elapsed time : 1.319535732269287 time spend for datou_step_exec : 4.003633260726929 time spend to save output : 9.894371032714844e-05 total time spend for step 1 : 4.003732204437256 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : random_deformation we use saveGeneral [1006293201] Looping around the photos to save general results len do output : 4 /1361619419Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619420Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619421Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1361619422Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('2896', None, None, None, None, None, None, None, None) ('2896', '3288640', '1006293201', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.03272414207458496 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1361619419: ['1006293201', 'temp/1006293201_random_deformation_0.png', []], 1361619420: ['1006293201', 'temp/1006293201_random_deformation_1.png', []], 1361619421: ['1006293201', 'temp/1006293201_random_deformation_2.png', []], 1361619422: ['1006293201', 'temp/1006293201_random_deformation_3.png', []]} name 'urllib' is not defined can't unload the photo : 1006293201 t ############################### TEST tile ################################ test tile avec chi rectangles, rles, polygones Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : tile list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.3959379196166992 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:tile Thu May 29 11:31:19 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec verbose : False param_json : {'ETA': 3600, 'new_width': 640, 'new_height': 640, 'token': '5d93a4b2b749464f208c339a1324b78f', 'stride': 0, 'stride_relative': 0, 'portfolio_name': 'results_test_tile', 'crop_hashtag_type_tiled': 3243, 'crop_hashtag_type': 3242, 'arg_aux_upload': {'type_upload': 'python'}, 'host': 'www.fotonower.com'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {3242: 3243} TO DEPRECATE VR 14-6-18 map_filenames : {1008283903: 'temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 Loaded 2 chid ids of type : 3242 ++https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=results_test_tile&access_token=5d93a4b2b749464f208c339a1324b78f created feed_id_new_photos : 23439250 with name results_test_tile feed_id_new_photos : 23439250 filename : temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg photo_id : 1008283903 height_image_input : 2464 width_image_input : 3280 new_width : 640 new_height : 640 stride : 0 stride_relative : 0 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 2 list_bib_to_crops : 24 [(0, 640, 0, 640, 0), (0, 640, 640, 1280, 1), (0, 640, 1280, 1920, 2), (0, 640, 1824, 2464, 3), (640, 1280, 0, 640, 4), (640, 1280, 640, 1280, 5), (640, 1280, 1280, 1920, 6), (640, 1280, 1824, 2464, 7), (1280, 1920, 0, 640, 8), (1280, 1920, 640, 1280, 9), (1280, 1920, 1280, 1920, 10), (1280, 1920, 1824, 2464, 11), (1920, 2560, 0, 640, 12), (1920, 2560, 640, 1280, 13), (1920, 2560, 1280, 1920, 14), (1920, 2560, 1824, 2464, 15), (2560, 3200, 0, 640, 16), (2560, 3200, 640, 1280, 17), (2560, 3200, 1280, 1920, 18), (2560, 3200, 1824, 2464, 19), (2640, 3280, 0, 640, 20), (2640, 3280, 640, 1280, 21), (2640, 3280, 1280, 1920, 22), (2640, 3280, 1824, 2464, 23)] new_crops_tiles : 24 crop_transformed : 7 batch 1 Loaded 24 chid ids of type : 17 treat the image : temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 before upload mediasElapsed time : 0.49954891204833984 on upload les photos avec python init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748511086_1921311 we have uploaded 24 photos in the portfolio 23439250 Importing ! upload mediasElapsed time : 7.279191255569458 , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23Saving 7 CHIs. batch 1 Loaded 7 chid ids of type : 3243 Number RLEs to save : 2937 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 7.643747568130493 time spend for datou_step_exec : 14.291300296783447 time spend to save output : 5.650520324707031e-05 total time spend for step 1 : 14.291356801986694 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1361619433': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg'], '1361619434': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg'], '1361619435': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg'], '1361619436': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg'], '1361619437': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg'], '1361619438': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg'], '1361619439': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg'], '1361619440': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg'], '1361619441': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg'], '1361619442': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg'], '1361619443': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg'], '1361619444': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg'], '1361619445': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg'], '1361619446': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg'], '1361619447': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg'], '1361619448': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg'], '1361619449': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg'], '1361619451': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg'], '1361619452': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg'], '1361619453': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg'], '1361619454': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg'], '1361619455': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg'], '1361619456': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg'], '1361619457': ['temp/1748511078_1921311_1008283903_6d008d31a1477b2e98cbafa96bd48e53_23.jpg']} batch 1 Loaded 7 chid ids of type : 3243 ++++++++++++++fin du test de tile ############################### TEST rotate_chi ################################ test rotate avec chi rectangles, rles, polygones Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.20949125289916992 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rotate Thu May 29 11:31:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a linear step without datou_depend ! rotate photos of 0,90,180,270 degres batch 1 Loaded 16 chid ids of type : 3086 ++++++++++++++++ map_chi of length : 1 https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 23439251 time for calcul the mask position with numpy : 0.012803792953491211 nb_pixel_total : 110633 time to create 1 rle with old method : 0.2648804187774658 .time for calcul the mask position with numpy : 0.009975671768188477 nb_pixel_total : 15826 time to create 1 rle with old method : 0.04947972297668457 .time for calcul the mask position with numpy : 0.010105133056640625 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012115955352783203 .time for calcul the mask position with numpy : 0.012434959411621094 nb_pixel_total : 1633 time to create 1 rle with old method : 0.004880666732788086 .time for calcul the mask position with numpy : 0.013207674026489258 nb_pixel_total : 105533 time to create 1 rle with old method : 0.22178077697753906 .time for calcul the mask position with numpy : 0.011086463928222656 nb_pixel_total : 4393 time to create 1 rle with old method : 0.009329795837402344 .time for calcul the mask position with numpy : 0.013107776641845703 nb_pixel_total : 632 time to create 1 rle with old method : 0.0014462471008300781 .time for calcul the mask position with numpy : 0.012723207473754883 nb_pixel_total : 62627 time to create 1 rle with old method : 0.132307767868042 .time for calcul the mask position with numpy : 0.011223077774047852 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07206940650939941 .time for calcul the mask position with numpy : 0.01124882698059082 nb_pixel_total : 37724 time to create 1 rle with old method : 0.10754227638244629 .time for calcul the mask position with numpy : 0.011943817138671875 nb_pixel_total : 48775 time to create 1 rle with old method : 0.1333768367767334 .time for calcul the mask position with numpy : 0.2615346908569336 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.21431827545166016 .time for calcul the mask position with numpy : 0.009801626205444336 nb_pixel_total : 2310 time to create 1 rle with old method : 0.004752635955810547 .time for calcul the mask position with numpy : 0.01654505729675293 nb_pixel_total : 2256 time to create 1 rle with old method : 0.004762887954711914 .time for calcul the mask position with numpy : 0.00971221923828125 nb_pixel_total : 3112 time to create 1 rle with old method : 0.006602764129638672 .time for calcul the mask position with numpy : 0.00950932502746582 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0035371780395507812 .Needs to change image size ! time for calcul the mask position with numpy : 0.0114288330078125 nb_pixel_total : 110633 time to create 1 rle with old method : 0.24477434158325195 .time for calcul the mask position with numpy : 0.010251998901367188 nb_pixel_total : 15826 time to create 1 rle with old method : 0.034723520278930664 .time for calcul the mask position with numpy : 0.010364055633544922 nb_pixel_total : 5286 time to create 1 rle with old method : 0.011868953704833984 .time for calcul the mask position with numpy : 0.010907173156738281 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0035965442657470703 .time for calcul the mask position with numpy : 0.011331796646118164 nb_pixel_total : 105533 time to create 1 rle with old method : 0.2239370346069336 .time for calcul the mask position with numpy : 0.011168956756591797 nb_pixel_total : 4393 time to create 1 rle with old method : 0.009223222732543945 .time for calcul the mask position with numpy : 0.010589599609375 nb_pixel_total : 632 time to create 1 rle with old method : 0.001547098159790039 .time for calcul the mask position with numpy : 0.01171565055847168 nb_pixel_total : 62627 time to create 1 rle with old method : 0.14229393005371094 .time for calcul the mask position with numpy : 0.011890888214111328 nb_pixel_total : 33681 time to create 1 rle with old method : 0.06894612312316895 .time for calcul the mask position with numpy : 0.012029886245727539 nb_pixel_total : 37724 time to create 1 rle with old method : 0.0803983211517334 .time for calcul the mask position with numpy : 0.011713027954101562 nb_pixel_total : 48775 time to create 1 rle with old method : 0.10322809219360352 .time for calcul the mask position with numpy : 0.07998991012573242 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.20954394340515137 .time for calcul the mask position with numpy : 0.01006007194519043 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005131244659423828 .time for calcul the mask position with numpy : 0.01026606559753418 nb_pixel_total : 2256 time to create 1 rle with old method : 0.00492548942565918 .time for calcul the mask position with numpy : 0.010035514831542969 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007227420806884766 .time for calcul the mask position with numpy : 0.01180410385131836 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0037806034088134766 .time for calcul the mask position with numpy : 0.012192010879516602 nb_pixel_total : 110633 time to create 1 rle with old method : 0.23177814483642578 .time for calcul the mask position with numpy : 0.010418891906738281 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03313136100769043 .time for calcul the mask position with numpy : 0.010228157043457031 nb_pixel_total : 5286 time to create 1 rle with old method : 0.011270761489868164 .time for calcul the mask position with numpy : 0.011016845703125 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0040590763092041016 .time for calcul the mask position with numpy : 0.010574817657470703 nb_pixel_total : 105533 time to create 1 rle with old method : 0.23773574829101562 .time for calcul the mask position with numpy : 0.009991645812988281 nb_pixel_total : 4393 time to create 1 rle with old method : 0.009451150894165039 .time for calcul the mask position with numpy : 0.010773897171020508 nb_pixel_total : 632 time to create 1 rle with old method : 0.001379251480102539 .time for calcul the mask position with numpy : 0.011375188827514648 nb_pixel_total : 62627 time to create 1 rle with old method : 0.13997197151184082 .time for calcul the mask position with numpy : 0.012609004974365234 nb_pixel_total : 33681 time to create 1 rle with old method : 0.06996345520019531 .time for calcul the mask position with numpy : 0.0115203857421875 nb_pixel_total : 37724 time to create 1 rle with old method : 0.07834362983703613 .time for calcul the mask position with numpy : 0.01164102554321289 nb_pixel_total : 48775 time to create 1 rle with old method : 0.10145735740661621 .time for calcul the mask position with numpy : 0.07495236396789551 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.2608928680419922 .time for calcul the mask position with numpy : 0.008950233459472656 nb_pixel_total : 2310 time to create 1 rle with old method : 0.004899740219116211 .time for calcul the mask position with numpy : 0.009111642837524414 nb_pixel_total : 2256 time to create 1 rle with old method : 0.0050432682037353516 .time for calcul the mask position with numpy : 0.00911092758178711 nb_pixel_total : 3112 time to create 1 rle with old method : 0.006636142730712891 .time for calcul the mask position with numpy : 0.009935855865478516 nb_pixel_total : 1662 time to create 1 rle with old method : 0.005263328552246094 .Needs to change image size ! time for calcul the mask position with numpy : 0.01221466064453125 nb_pixel_total : 110633 time to create 1 rle with old method : 0.256939172744751 .time for calcul the mask position with numpy : 0.009004354476928711 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03395366668701172 .time for calcul the mask position with numpy : 0.009552955627441406 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012333393096923828 .time for calcul the mask position with numpy : 0.01053166389465332 nb_pixel_total : 1633 time to create 1 rle with old method : 0.003943920135498047 .time for calcul the mask position with numpy : 0.010712146759033203 nb_pixel_total : 105533 time to create 1 rle with old method : 0.23679685592651367 .time for calcul the mask position with numpy : 0.009283781051635742 nb_pixel_total : 4393 time to create 1 rle with old method : 0.009804010391235352 .time for calcul the mask position with numpy : 0.00947260856628418 nb_pixel_total : 632 time to create 1 rle with old method : 0.001497507095336914 .time for calcul the mask position with numpy : 0.010201215744018555 nb_pixel_total : 62627 time to create 1 rle with old method : 0.13745737075805664 .time for calcul the mask position with numpy : 0.01116323471069336 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07617306709289551 .time for calcul the mask position with numpy : 0.010842561721801758 nb_pixel_total : 37724 time to create 1 rle with old method : 0.0873420238494873 .time for calcul the mask position with numpy : 0.011090517044067383 nb_pixel_total : 48775 time to create 1 rle with old method : 0.10587430000305176 .time for calcul the mask position with numpy : 0.06337738037109375 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.1880950927734375 .time for calcul the mask position with numpy : 0.009141206741333008 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005245208740234375 .time for calcul the mask position with numpy : 0.009720802307128906 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005098581314086914 .time for calcul the mask position with numpy : 0.010570049285888672 nb_pixel_total : 3112 time to create 1 rle with old method : 0.006983280181884766 .time for calcul the mask position with numpy : 0.008951187133789062 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0036804676055908203 . About to upload 4 photos upload in portfolio : 23439251 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748511110_1921311 we have uploaded 4 photos in the portfolio 23439251 time of upload the photos Elapsed time : 1.4111237525939941 Len new_chis : 4 Len list_new_chi_with_photo_id : 64 of type : 3230 batch 1 Loaded 64 chid ids of type : 3230 Number RLEs to save : 24654 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 22.728005170822144 time spend to save output : 0.0001766681671142578 total time spend for step 1 : 22.728181838989258 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1361619473: ['1003369118', 'temp/1748511093_1921311_1003369118_58171420504d0b5f05a1233b6c515509_658263370.jpg', [, , , , , , , , , , , , , , , ]], 1361619474: ['1003369118', 'temp/1748511093_1921311_1003369118_58171420504d0b5f05a1233b6c515509_6582633790.jpg', [, , , , , , , , , , , , , , , ]], 1361619475: ['1003369118', 'temp/1748511093_1921311_1003369118_58171420504d0b5f05a1233b6c515509_65826337180.jpg', [, , , , , , , , , , , , , , , ]], 1361619476: ['1003369118', 'temp/1748511093_1921311_1003369118_58171420504d0b5f05a1233b6c515509_65826337270.jpg', [, , , , , , , , , , , , , , , ]]} batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++fin du test de rotate_chi Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21) name : rubbia_carac_pet_clair_0121 not run because too long ############################### TEST rubbia_carac_pet_clair_0121_no_cnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 6479 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 6480 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7445 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 6509 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 6479 doesn't seem to be define in the database( WARNING : type of input 1 of step 6480 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 6479 doesn't seem to be define in the database( WARNING : type of input 1 of step 7445 doesn't seem to be define in the database( WARNING : type of output 1 of step 7445 doesn't seem to be define in the database( WARNING : type of input 3 of step 6509 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! List Step Type Loaded in datou : merge_mask_thcl_custom, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.24338459968566895 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 4 step1:merge_mask_thcl_custom Thu May 29 11:31:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step merge_mask_thcl_custom batch 1 Loaded 82 chid ids of type : 2800 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++As expected we have just one thcl present begin to find the sub_photo_id : begin to find the sub_photo_id : batch 1 Loaded 76 chid ids of type : 2913 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 76 chid ids of type : 2913 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++End of step merge_mask_thcl_custom time spend for datou_step_exec : 1.8538627624511719 time spend to save output : 8.082389831542969e-05 total time spend for step 1 : 1.8539435863494873 step2:rle_unique_nms_with_priority Thu May 29 11:31:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 43 chid ids of type : 2913 +++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.286198377609253 create new chi : 5.0067901611328125e-05 time to delete rle : 0.0375676155090332 save time : 2.5510787963867188e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.6495721340179443 create new chi : 6.0558319091796875e-05 time to delete rle : 0.015908241271972656 save time : 8.106231689453125e-06 map_output_result : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms time spend for datou_step_exec : 5.179638147354126 time spend to save output : 0.00013780593872070312 total time spend for step 2 : 5.179775953292847 step3:ventilate_hashtags_in_portfolio Thu May 29 11:32:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 3373196 get user id for portfolio 3373196 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3373196 AND mptpi.`type`=2913 AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 2.046607732772827 time spend to save output : 5.1021575927734375e-05 total time spend for step 3 : 2.046658754348755 step4:final Thu May 29 11:32:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! time spend for datou_step_exec : 0.10084223747253418 time spend to save output : 3.62396240234375e-05 total time spend for step 4 : 0.10087847709655762 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} new output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} [1009068683, 1009068724] Looping around the photos to save general results len do output : 2 /1009068683.Didn't retrieve data . /1009068724.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('2719', None, None, None, None, None, None, None, None) ('2719', '3373196', '1009068683', None, None, None, None, None, None) ('2719', None, None, None, None, None, None, None, None) ('2719', '3373196', '1009068724', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.01565098762512207 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 4 output : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} ############################### TEST rubbia_carac_jrm_no_mask_detect ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Step 7557 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 7556 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 7561 merge_mask_and_thcl is not consistent : 3 used against 1 in the step definition ! WARNING : number of inputs for step 7558 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7560 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7559 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7560 doesn't seem to be define in the database( WARNING : type of input 3 of step 7559 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7556 have datatype=6 whereas input 0 of step 7561 have datatype=20 WARNING : type of output 1 of step 7561 doesn't seem to be define in the database( WARNING : type of input 1 of step 7558 doesn't seem to be define in the database( WARNING : type of output 2 of step 7561 doesn't seem to be define in the database( WARNING : type of input 1 of step 7560 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : crop_condition, thcl, argmax, merge_mask_and_thcl, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBBBBFBFBFBFBFBFBFBFBFBFFFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 14 ; length of list_pids : 14 ; length of list_args : 14 time to download the photos : 1.0764944553375244 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 7 step1:crop_condition Thu May 29 11:32:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 3336 Loading chi in step crop for list_pids : 14 ! batch 1 Loaded 121 chid ids of type : 3336 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : teint_dans_la_masse param for this class : {'min_score': 0.7} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : teint_dans_la_masse begin to crop the class : autre_refus param for this class : {'min_score': 0.7} filtre for class : autre_refus hashtag_id of this class : 2107752406 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : autre_refus begin to crop the class : carton_gris param for this class : {'min_score': 0.7} filtre for class : carton_gris hashtag_id of this class : 2107753020 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : carton_gris begin to crop the class : cartonnette param for this class : {'min_score': 0.7} filtre for class : cartonnette hashtag_id of this class : 702398920 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : cartonnette begin to crop the class : carton_brun param for this class : {'min_score': 0.7} filtre for class : carton_brun hashtag_id of this class : 2107753024 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : carton_brun begin to crop the class : plastique param for this class : {'min_score': 0.7} filtre for class : plastique hashtag_id of this class : 492725882 begin to crop the class : kraft param for this class : {'min_score': 0.7} filtre for class : kraft hashtag_id of this class : 493202403 begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 time spend for datou_step_exec : 10.852416515350342 time spend to save output : 0.14258813858032227 total time spend for step 1 : 10.995004653930664 step2:thcl Thu May 29 11:32:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! Beginning of datou step Thcl ! no input time spend for datou_step_exec : 0.001138925552368164 time spend to save output : 4.9114227294921875e-05 total time spend for step 2 : 0.001188039779663086 step3:argmax Thu May 29 11:32:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! Beginning of datou_step Argmax ! no input time spend for datou_step_exec : 6.556510925292969e-05 time spend to save output : 1.1920928955078125e-05 total time spend for step 3 : 7.748603820800781e-05 step4:merge_mask_and_thcl Thu May 29 11:32:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! debut de la step merge mask and classif time spend for datou_step_exec : 0.00014519691467285156 time spend to save output : 1.0967254638671875e-05 total time spend for step 4 : 0.00015616416931152344 step5:rle_unique_nms_with_priority Thu May 29 11:32:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 26 chid ids of type : 3418 ++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.6801624298095703 create new chi : 0.006570577621459961 time to delete rle : 0.6673488616943359 save time : 1.5020370483398438e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 6.108640909194946 create new chi : 0.008654356002807617 time to delete rle : 0.4335613250732422 save time : 5.4836273193359375e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.7389938831329346 create new chi : 3.218650817871094e-05 time to delete rle : 0.632138729095459 save time : 8.344650268554688e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.438035488128662 create new chi : 0.008690357208251953 time to delete rle : 1.2040033340454102 save time : 4.172325134277344e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.7718164920806885 create new chi : 3.981590270996094e-05 time to delete rle : 0.8662688732147217 save time : 2.384185791015625e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.6975278854370117 create new chi : 5.269050598144531e-05 time to delete rle : 1.7169685363769531 save time : 1.430511474609375e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.422329902648926 create new chi : 0.006381511688232422 time to delete rle : 0.6469495296478271 save time : 3.9577484130859375e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.090357542037964 create new chi : 0.008690595626831055 time to delete rle : 0.539750337600708 save time : 2.09808349609375e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.21921706199646 create new chi : 0.007150173187255859 time to delete rle : 0.5892672538757324 save time : 1.0967254638671875e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.854658603668213 create new chi : 4.5299530029296875e-05 time to delete rle : 0.860109806060791 save time : 7.867813110351562e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.451697587966919 create new chi : 4.482269287109375e-05 time to delete rle : 0.5456743240356445 save time : 4.3392181396484375e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.0883820056915283 create new chi : 0.008008480072021484 time to delete rle : 0.853501558303833 save time : 7.62939453125e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.124790906906128 create new chi : 2.6226043701171875e-05 time to delete rle : 0.7699921131134033 save time : 7.62939453125e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.5256247520446777 create new chi : 2.574920654296875e-05 time to delete rle : 0.3454318046569824 save time : 7.152557373046875e-06 map_output_result : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274)} End step rle-unique-nms time spend for datou_step_exec : 44.07845401763916 time spend to save output : 0.003072023391723633 total time spend for step 5 : 44.081526041030884 step6:ventilate_hashtags_in_portfolio Thu May 29 11:33:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! To do loadFromThcl(), then load ParamDescType : thcl2456 thcls : [{'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'}] thcl {'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'} Update svm_hashtag_type_desc : 4999 Iterating over portfolio : 3364276 get user id for portfolio 3364276 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3535038 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('refus','papier')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3364276 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('refus','papier')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 0.2062220573425293 time spend to save output : 0.00010514259338378906 total time spend for step 6 : 0.20632719993591309 step7:final Thu May 29 11:33:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 0.03276419639587402 time spend to save output : 4.839897155761719e-05 total time spend for step 7 : 0.03281259536743164 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',)} new output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',)} [1008921601, 1008921600, 1008922003, 1008922002, 1008921786, 1008922130, 1008922101, 1008922097, 1008922095, 1008922073, 1008922072, 1008921657, 1008921656, 1008921602] Looping around the photos to save general results len do output : 14 /1008921601.Didn't retrieve data . /1008921600.Didn't retrieve data . /1008922003.Didn't retrieve data . /1008922002.Didn't retrieve data . /1008921786.Didn't retrieve data . /1008922130.Didn't retrieve data . /1008922101.Didn't retrieve data . /1008922097.Didn't retrieve data . /1008922095.Didn't retrieve data . /1008922073.Didn't retrieve data . /1008922072.Didn't retrieve data . /1008921657.Didn't retrieve data . /1008921656.Didn't retrieve data . /1008921602.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921601', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921600', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922003', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922002', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921786', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922130', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922101', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922097', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922095', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922073', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922072', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921657', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921656', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921602', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 42 time used for this insertion : 0.019460678100585938 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',)} {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',)} ############################### TEST ventilate_hashtags_in_portfolio ################################ DELETE FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5486631; Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : ventilate_hashtags_in_portfolio list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.019417524337768555 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:ventilate_hashtags_in_portfolio Thu May 29 11:33:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 5363525 get user id for portfolio 5363525 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5363525 AND mptpi.`type`=4268 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','error','environment')) AND mptpi.`min_score`=0.3 To do To do ! Use context local managing function ! time spend for datou_step_exec : 0.24746179580688477 time spend to save output : 0.0002574920654296875 total time spend for step 1 : 0.24771928787231445 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1075306598, 1075306564, 1075306534, 1075306522, 1075304668] Looping around the photos to save general results len do output : 1 /5363525. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306598', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306564', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306534', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306522', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075304668', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.013910531997680664 save_final save missing photos in datou_result : After save, about to update current ! Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21) name : merge_qualipapia_like not run because too long ############################### TEST poly_ro_rle ################################ test creation de rle a partir de polygon Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : poly_to_rle list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.4417588710784912 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:poly_to_rle Thu May 29 11:33:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec can't find the hashtag_type_input ,set the output_type same as the input_type batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++time for calcul the mask position with numpy : 0.009413957595825195 nb_pixel_total : 110633 time to create 1 rle with old method : 0.24549198150634766 time for calcul the mask position with numpy : 0.007576704025268555 nb_pixel_total : 15826 time to create 1 rle with old method : 0.035425662994384766 time for calcul the mask position with numpy : 0.0075016021728515625 nb_pixel_total : 5286 time to create 1 rle with old method : 0.013031005859375 time for calcul the mask position with numpy : 0.007494688034057617 nb_pixel_total : 1633 time to create 1 rle with old method : 0.004071474075317383 time for calcul the mask position with numpy : 0.008081674575805664 nb_pixel_total : 105533 time to create 1 rle with old method : 0.24123263359069824 time for calcul the mask position with numpy : 0.009967803955078125 nb_pixel_total : 4393 time to create 1 rle with old method : 0.014592647552490234 time for calcul the mask position with numpy : 0.007319211959838867 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015990734100341797 time for calcul the mask position with numpy : 0.007906198501586914 nb_pixel_total : 62627 time to create 1 rle with old method : 0.14974617958068848 time for calcul the mask position with numpy : 0.00824427604675293 nb_pixel_total : 33681 time to create 1 rle with old method : 0.09684157371520996 time for calcul the mask position with numpy : 0.008260726928710938 nb_pixel_total : 37724 time to create 1 rle with old method : 0.10075664520263672 time for calcul the mask position with numpy : 0.035215139389038086 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11111664772033691 time for calcul the mask position with numpy : 0.06781601905822754 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.4145638942718506 time for calcul the mask position with numpy : 0.007573366165161133 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005313396453857422 time for calcul the mask position with numpy : 0.007584810256958008 nb_pixel_total : 2256 time to create 1 rle with old method : 0.00555419921875 time for calcul the mask position with numpy : 0.007661581039428711 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007231712341308594 time for calcul the mask position with numpy : 0.007714033126831055 nb_pixel_total : 1662 time to create 1 rle with old method : 0.005611419677734375 batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 2.0774734020233154 time spend to save output : 0.00014328956604003906 total time spend for step 1 : 2.0776166915893555 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1003369118: 'temp/1748511182_1921311_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg'} batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++fin du test de poly_to_rle ############################### TEST cod_sts ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.017708539962768555 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Thu May 29 11:33:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('48', 4), ('42', 3)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 17082021 4453840 Nombre de photos uploadées : 7 / 23040 (0%) 17082021 4453840 Nombre de photos taguées (types de déchets): 0 / 7 (0%) 17082021 4453840 Nombre de photos taguées (volume) : 0 / 7 (0%) elapsed_time : load_data_split_time_score 8.106231689453125e-06 elapsed_time : order_list_meta_photo_and_scores 1.2874603271484375e-05 ??????? elapsed_time : fill_and_build_computed_from_old_data 0.0009353160858154297 elapsed_time : insert_dashboard_record_day_entry 0.028285741806030273 TODO 20-09-21 https://github.com/fotonower/raspi-fotonower-x/issues/253#issuecomment-923099773 TODO 20-9-21 TODO 20-9-21 ***** BEGIN SPLIT TIME ***** ```````list printed: [[0, 1, 2, 3], [4, 5, 6]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.0055081844329833984 ***** END SPLIT TIME ***** NUMBER BATCH : 2 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['jrm'] ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 24.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_094808.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 3.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_104213.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 2 list_same_port_ids : [4453926] find same portfolio which already exist 4453926 , we will use it list_same_port_ids : [4652336] find same portfolio which already exist 4652336 , we will use it # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4453926 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4652336 AND mptpi.`type`=4038 To do elapsed_time : count_nb_balles_and_create_portfolio 1.1495788097381592 # DISPLAY ALL COLLECTED DATA : {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} time spend for datou_step_exec : 1.2345836162567139 time spend to save output : 8.0108642578125e-05 total time spend for step 1 : 1.234663724899292 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106] Looping around the photos to save general results len do output : 1 /4453840Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302186', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302153', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302152', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302146', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302113', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302110', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302106', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.056726694107055664 save_final save missing photos in datou_result : After save, about to update current ! Result test cod : {4453840: ([[0, 1, 2, 3], [4, 5, 6]], {'Rungis_jrm': [(0, 1), (1, 2)]}, {4453926: {'list_of_photos': [1050302106, 1050302146, 1050302110, 1050302152], 'hashtag': 'jrm'}, 4652336: {'list_of_photos': [1050302113, 1050302153, 1050302186], 'hashtag': 'jrm'}}, {2107757407: 7}, {'amount_uploaded_and_tagged': {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2)]}})}| ############################### TEST cod_download ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data [] [] ############################### TEST sendgrid ################################ test sendgrid senders@fotonower.com no problem of authentification, for test if the email can be received, try with a real receiver fin du test de sendgrid ############################### TEST rym_consolidate ################################ test_rym_consolidate Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 9321 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 9357 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 9318 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 9318 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 9410 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 9319 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 9328 blur_detection have less inputs used (0) than in the step definition (1) : maybe we manage optionnal inputs ! Step 9327 brightness have less inputs used (0) than in the step definition (1) : maybe we manage optionnal inputs ! Step 9326 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 9321 have datatype=11 whereas input 0 of step 9318 have datatype=2 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 9357 doesn't seem to be define in the database( WARNING : type of input 1 of step 9318 doesn't seem to be define in the database( WARNING : type of output 1 of step 9357 doesn't seem to be define in the database( WARNING : type of input 3 of step 9319 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 9410 doesn't seem to be define in the database( WARNING : output 1 of step 9318 have datatype=7 whereas input 1 of step 9410 have datatype=None WARNING : type of output 1 of step 9410 doesn't seem to be define in the database( WARNING : type of input 4 of step 9319 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 9410 have datatype=10 whereas input 3 of step 9326 have datatype=6 WARNING : type of output 1 of step 9321 doesn't seem to be define in the database( WARNING : type of input 1 of step 9357 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, send_mail_cod list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.23503375053405762 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 8 step1:copy_chis Thu May 29 11:33:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 0 chid ids of type : 0 time spend for datou_step_exec : 0.00991201400756836 time spend to save output : 3.9577484130859375e-05 total time spend for step 1 : 0.009951591491699219 step2:consolidate_hashtags_from_manual_portfolio Thu May 29 11:33:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 4709558 on est dans le IF portfolio mere 26T SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4683188 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673496 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673497 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673498 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673500 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673501 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673502 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673503 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673504 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673505 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673506 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673507 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673508 AND mpp.hide_status=0 ORDER BY ph.size desc To test ! Use context local managing function ! time spend for datou_step_exec : 11.137911319732666 time spend to save output : 9.751319885253906e-05 total time spend for step 2 : 11.138008832931519 step3:rle_unique_nms_with_priority Thu May 29 11:33:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 65 chid ids of type : 4016 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 3 time to prepare the origin masks : 20.314557790756226 create new chi : 6.341934204101562e-05 time to delete rle : 0.17768359184265137 save time : 8.320808410644531e-05 nb_obj : 0 nb_hashtags : 3 time to prepare the origin masks : 20.87268853187561 create new chi : 4.291534423828125e-05 time to delete rle : 0.471541166305542 save time : 4.315376281738281e-05 map_output_result : {1057289467: (0.0, 'Should be the crop_list due to order', 0.0), 1057289546: (0.0, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms time spend for datou_step_exec : 42.344603061676025 time spend to save output : 0.0001926422119140625 total time spend for step 3 : 42.34479570388794 step4:ventilate_hashtags_in_portfolio Thu May 29 11:34:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 4709558 get user id for portfolio 4709558 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','mal_croppe','pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 0.21568512916564941 time spend to save output : 0.00010156631469726562 total time spend for step 4 : 0.21578669548034668 step5:final Thu May 29 11:34:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 0.03753232955932617 time spend to save output : 3.647804260253906e-05 total time spend for step 5 : 0.03756880760192871 step6:blur_detection Thu May 29 11:34:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection toutes les photos sont déjà traitées, on saute les calculs time spend for datou_step_exec : 0.00932931900024414 time spend to save output : 4.6253204345703125e-05 total time spend for step 6 : 0.009375572204589844 step7:brightness Thu May 29 11:34:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness toutes les photos sont déjà traitées, on saute les calculs time spend for datou_step_exec : 0.004926443099975586 time spend to save output : 2.5987625122070312e-05 total time spend for step 7 : 0.004952430725097656 step8:send_mail_cod Thu May 29 11:34:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/temp in order to get the selector url, please entre the license of selector results_COD_P4709558_29-05-2025_11_34_02.pdf 4673494 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734941748511242 4673496 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734961748511243 4673497 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734971748511245 4673498 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734981748511246 4673500 change filename to text .change filename to text .imagette46735001748511247 4673501 change filename to text .imagette46735011748511248 4673502 imagette46735021748511248 4673503 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735031748511248 4673504 imagette46735041748511249 4673505 imagette46735051748511250 4673506 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735061748511250 4673507 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735071748511250 4673508 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735081748511251 velour_link : ce batch n'est pas dans un dashboard, on n'envoi pas de mail. si vous voulez quand même des mail , veuillez configurer no_mail = 2 args[1057289467] : ((1057289467, -4.424440243329978, 492609224), (1057289467, -0.4062218880770088, 496442774), '0.009511382621534484') We are sending mail with results at marine@fotonower.com args[1057289546] : ((1057289546, -4.333383571220791, 492609224), (1057289546, -0.5998675991292823, 501862349), '0.009511382621534484') We are sending mail with results at marine@fotonower.com refus_total : 0.009511382621534484 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4709558 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 current_id not found list index out of range start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_29-05-2025_11_34_02.pdf results_COD_P4709558_29-05-2025_11_34_02.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_29-05-2025_11_34_02.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3818','4709558','results_COD_P4709558_29-05-2025_11_34_02.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_29-05-2025_11_34_02.pdf','pdf','','0.48','0.009511382621534484') time spend for datou_step_exec : 12.145697355270386 time spend to save output : 5.221366882324219e-05 total time spend for step 8 : 12.145749568939209 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 8 output : fin du test de rym_consolidate ############################### TEST generate_new_image_add_crop ################################ test_generate_new_image_add_crop Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : generate_new_image list_input_json : [] origin We have 1 , BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.12409377098083496 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:generate_new_image Thu May 29 11:34:15 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4789595 ORDER BY ph.size desc We have 1 , we need 3 photos there is already 3 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 3 photo needed from our local_cache list of photo_id_missing : [] batch 1 Loaded 3 chid ids of type : 4021 +++we need 2 photos there is already 2 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 2 photo needed from our local_cache list of photo_id_missing : [] begin to treate photo :1057314774 add chi : 2208326711 , rotate : 97 (713, 713) (241, 709, 45, 280) (235, 468, 3) (235, 468) (713, 713, 3) time for calcul the mask position with numpy : 0.0126800537109375 nb_pixel_total : 65382 time to create 1 rle with old method : 0.14603567123413086 batch 1 Loaded 0 chid ids of type : 0 time for calcul the mask position with numpy : 0.0020208358764648438 nb_pixel_total : 65382 time to create 1 rle with old method : 0.14538121223449707 begin to treate photo :1057314768 add chi : 2208326717 , rotate : 104 (775, 775) (302, 683, 452, 746) (294, 381, 3) (294, 381) (775, 775, 3) time for calcul the mask position with numpy : 0.0017321109771728516 nb_pixel_total : 80911 time to create 1 rle with old method : 0.1737227439880371 batch 1 Loaded 2 chid ids of type : 4021 ++time for calcul the mask position with numpy : 0.00246429443359375 nb_pixel_total : 80840 time to create 1 rle with old method : 0.20381498336791992 time for calcul the mask position with numpy : 0.0018541812896728516 nb_pixel_total : 80911 time to create 1 rle with old method : 0.17457365989685059 time for calcul the mask position with numpy : 0.001310586929321289 nb_pixel_total : 8755 time to create 1 rle with old method : 0.021984577178955078 begin to treate photo :1057314766 add chi : 2208326718 , rotate : 81 (732, 732) (3, 118, 113, 459) (346, 115, 3) (346, 115) (732, 732, 3) time for calcul the mask position with numpy : 0.001984119415283203 nb_pixel_total : 8873 time to create 1 rle with old method : 0.03355550765991211 batch 1 Loaded 3 chid ids of type : 4021 +++time for calcul the mask position with numpy : 0.001829385757446289 nb_pixel_total : 63869 time to create 1 rle with old method : 0.13467907905578613 time for calcul the mask position with numpy : 0.0013213157653808594 nb_pixel_total : 8873 time to create 1 rle with old method : 0.023496627807617188 time for calcul the mask position with numpy : 0.002065420150756836 nb_pixel_total : 91110 time to create 1 rle with old method : 0.1928112506866455 time for calcul the mask position with numpy : 0.001249551773071289 nb_pixel_total : 6807 time to create 1 rle with old method : 0.014746665954589844 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748511258_1921311 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 1 chid ids of type : 4086 Number RLEs to save : 270 TO DO : save crop sub photo not yet done ! we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748511258_1921311 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 3 chid ids of type : 4086 Number RLEs to save : 743 TO DO : save crop sub photo not yet done ! we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1748511259_1921311 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 4 chid ids of type : 4086 Number RLEs to save : 1800 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 4.6937994956970215 time spend to save output : 9.107589721679688e-05 total time spend for step 1 : 4.693890571594238 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : None fin du test de generate_new_image ############################### TEST velours_tree ################################ test velours_tree - Retrieving photos to tag... query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4837217 ORDER BY ph.size desc - Loading descriptors... Size : 512 len(descriptors) : 5 Compute structured hierarchical clustering... ward : AgglomerativeClustering(n_clusters=5) ward.labels_ : [4 3 2 1 0] Elapsed time: 0.018950223922729492 graph_id used : 1145 - Beta version, working pretty good on 11-5-16 ! fin du test de velours_tree ############################### TEST step ACP ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.016941547393798828 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Thu May 29 11:34:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. ATTENTION : le size etait trop grand : 20, on a changé sa valeur à : 9 find save_photo_desc_type : 5705 On sauvegarde les nouveaux descripteurs dans le photo desc type : 5705 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5705, 'ACP_from_type_5619_size_9', 9, 9, 'ACP_from_type_5619_size_9', None, 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2022, 6, 16, 11, 6, 15), None) time to traite the descriptors : 0.00032973289489746094 storage_type for insertDescriptorsMulti : 3 Missing photo l117 : 1069306708 Missing photo l117 : 1069306710 Missing photo l117 : 1069306805 Missing photo l117 : 1069306815 Missing photo l117 : 1069306841 Missing photo l117 : 1069306843 Missing photo l117 : 1069306844 Missing photo l117 : 1069306954 Missing photo l117 : 1069306964 Missing photo l117 : 1069306967 To insert : 1069306708 To insert : 1069306710 To insert : 1069306805 To insert : 1069306815 To insert : 1069306841 To insert : 1069306843 To insert : 1069306844 To insert : 1069306954 To insert : 1069306964 To insert : 1069306967 time to insert the descriptors : 3.7805709838867188 res : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'} time spend for datou_step_exec : 4.790025949478149 time spend to save output : 0.00011181831359863281 total time spend for step 1 : 4.790137767791748 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results len do output : 10 /1069306708Didn't retrieve data . /1069306710Didn't retrieve data . /1069306805Didn't retrieve data . /1069306815Didn't retrieve data . /1069306841Didn't retrieve data . /1069306843Didn't retrieve data . /1069306844Didn't retrieve data . /1069306954Didn't retrieve data . /1069306964Didn't retrieve data . /1069306967Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306967', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306964', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306954', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306844', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306843', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306841', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306815', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306805', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306710', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306708', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.09999322891235352 save_final save missing photos in datou_result : After save, about to update current ! Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.014398336410522461 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Thu May 29 11:34:25 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. ATTENTION : le size etait trop grand : 20, on a changé sa valeur à : 9 Sauvegarde du modèle et envoi sur OVH Attention : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 existe, son contenu risque d'être écrasé model_param file didn't exist model didn't exist , save the new model in s3 local folder to save in s3 : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 update : 1748511268.3628645 done ! 1748511268.5446467 {'files': [{'name': 'pca_model.pkl', 'size': 103314, 'last_modified': '2025-05-29T09:34:28.377190', 'hash': 'd7e2c6aa9a1ef592ffdfc4abe9c66263'}], 'directories': []} Création d'un nouveau thème de classification Le thème de classification 'ACP_from_port_5709050_type_5619_size_9' existe déjà, merci de relancer avec un nouveau nom dans les params-json. time spend for datou_step_exec : 2.7286112308502197 time spend to save output : 3.218650817871094e-05 total time spend for step 1 : 2.7286434173583984 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results object of type 'int' has no len() begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.06219887733459473 save_final save missing photos in datou_result : After save, about to update current ! Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.01485586166381836 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Thu May 29 11:34:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. To do loadFromThcl(), then load ParamDescType : thcl3412 thcls : [{'id': 3412, 'mtr_user_id': 31, 'name': 'ACP_from_port_5709050_type_5619_size_9', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '', 'svm_portfolios_learning': '0', 'photo_hashtag_type': 4398, 'photo_desc_type': 5706, 'type_classification': 'ACP', 'hashtag_id_list': '0'}] thcl {'id': 3412, 'mtr_user_id': 31, 'name': 'ACP_from_port_5709050_type_5619_size_9', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '', 'svm_portfolios_learning': '0', 'photo_hashtag_type': 4398, 'photo_desc_type': 5706, 'type_classification': 'ACP', 'hashtag_id_list': '0'} Update svm_hashtag_type_desc : 5706 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5706, 'ACP_from_port_5709050_type_5619_size_9', 9, 9, 'ACP_from_port_5709050_type_5619_size_9', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2022, 6, 16, 11, 8, 14), datetime.datetime(2022, 6, 16, 11, 8, 14)) model_param file didn't exist model_name : ACP_from_port_5709050_type_5619_size_9 model_type : acp list file need : ['pca_model.pkl'] file exist in s3 : ['pca_model.pkl'] file manque in s3 : [] local folder : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 /data/models_weight/ACP_from_port_5709050_type_5619_size_9/pca_model.pkl size_local : 103314 size in s3 : 103314 create time local : 2025-05-29 11:34:26 create time in s3 : 2025-05-29 09:34:28 pca_model.pkl already exist and didn't need to update model_name : ACP_from_port_5709050_type_5619_size_9 On sauvegarde les nouveaux descripteurs dans le photo desc type : 5706 time to traite the descriptors : 0.0003819465637207031 storage_type for insertDescriptorsMulti : 3 Missing photo l117 : 1069306708 Missing photo l117 : 1069306710 Missing photo l117 : 1069306805 Missing photo l117 : 1069306815 Missing photo l117 : 1069306841 Missing photo l117 : 1069306843 Missing photo l117 : 1069306844 Missing photo l117 : 1069306954 Missing photo l117 : 1069306964 Missing photo l117 : 1069306967 To insert : 1069306708 To insert : 1069306710 To insert : 1069306805 To insert : 1069306815 To insert : 1069306841 To insert : 1069306843 To insert : 1069306844 To insert : 1069306954 To insert : 1069306964 To insert : 1069306967 time to insert the descriptors : 3.551785945892334 res : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'} time spend for datou_step_exec : 5.340717077255249 time spend to save output : 7.557868957519531e-05 total time spend for step 1 : 5.340792655944824 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results len do output : 10 /1069306708Didn't retrieve data . /1069306710Didn't retrieve data . /1069306805Didn't retrieve data . /1069306815Didn't retrieve data . /1069306841Didn't retrieve data . /1069306843Didn't retrieve data . /1069306844Didn't retrieve data . /1069306954Didn't retrieve data . /1069306964Didn't retrieve data . /1069306967Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306967', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306964', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306954', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306844', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306843', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306841', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306815', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306805', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306710', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306708', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.015420913696289062 save_final save missing photos in datou_result : After save, about to update current ! fin du test de la step acp ############################### TEST blur_crop ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : crop_condition list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.2075331211090088 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:crop_condition Thu May 29 11:34:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 4356 Loading chi in step crop for list_pids : 2 ! batch 1 Loaded 3 chid ids of type : 4356 +++++ begin to crop the class : Papier_Magazine param for this class : {} filtre for class : Papier_Magazine hashtag_id of this class : 2107752386 begin to crop the class : carton_brun param for this class : {} filtre for class : carton_brun hashtag_id of this class : 2107753024 begin to crop the class : carton_gris param for this class : {} filtre for class : carton_gris hashtag_id of this class : 2107753020 begin to crop the class : cartonnette param for this class : {} filtre for class : cartonnette hashtag_id of this class : 702398920 begin to crop the class : kraft param for this class : {} filtre for class : kraft hashtag_id of this class : 493202403 begin to crop the class : autre_refus param for this class : {} filtre for class : autre_refus hashtag_id of this class : 2107752406 begin to crop the class : metal param for this class : {} filtre for class : metal hashtag_id of this class : 492628673 begin to crop the class : plastique param for this class : {} filtre for class : plastique hashtag_id of this class : 492725882 begin to crop the class : teint_dans_la_masse param for this class : {} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 begin to crop the class : environnement param for this class : {} filtre for class : environnement hashtag_id of this class : 493012381 begin to crop the class : contaminant param for this class : {} filtre for class : contaminant hashtag_id of this class : 681467679 map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : contaminant time spend for datou_step_exec : 0.24956536293029785 time spend to save output : 0.00016570091247558594 total time spend for step 1 : 0.24973106384277344 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1105701516, 1105701500] Looping around the photos to save general results len do output : 3 /1105703686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1105703688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1105703689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3990', None, None, None, None, None, None, None, None) ('3990', '6135916', '1105701516', None, None, None, None, None, None) ('3990', None, None, None, None, None, None, None, None) ('3990', '6135916', '1105701500', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.029667377471923828 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1105703686: [1105701516, 'temp/1748511274_1921311_1105701516_047b0ce16fe5e308d8512c83125c4058_polygon_blur_2436374092_1.jpg', (25, 175, 137, 235)], 1105703688: [1105701500, 'temp/1748511274_1921311_1105701500_b57a1caec2d74ede6814095fdd28cb27_polygon_blur_2436373819_1.jpg', (108, 300, 16, 138)], 1105703689: [1105701500, 'temp/1748511274_1921311_1105701500_b57a1caec2d74ede6814095fdd28cb27_polygon_blur_2436374262_1.jpg', (47, 300, 91, 247)]} fin du test de la step crop option blur ############################### TEST pma_consolidate ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 12666 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12667 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 12664 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 12664 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12671 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 12666 have datatype=11 whereas input 0 of step 12664 have datatype=2 WARNING : type of output 1 of step 12667 doesn't seem to be define in the database( WARNING : type of input 3 of step 12665 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 12667 doesn't seem to be define in the database( WARNING : type of input 1 of step 12664 doesn't seem to be define in the database( WARNING : type of input 1 of step 12671 doesn't seem to be define in the database( WARNING : output 1 of step 12664 have datatype=7 whereas input 1 of step 12671 have datatype=None WARNING : type of output 1 of step 12671 doesn't seem to be define in the database( WARNING : type of input 4 of step 12665 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 12666 doesn't seem to be define in the database( WARNING : type of input 1 of step 12667 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.4114828109741211 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 5 step1:copy_chis Thu May 29 11:34:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 21 chid ids of type : 4482 batch 1 Loaded 88 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.0673825740814209 time spend to save output : 4.458427429199219e-05 total time spend for step 1 : 0.06742715835571289 step2:consolidate_hashtags_from_manual_portfolio Thu May 29 11:34:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 6549724 on est dans le IF portfolio mere 26T SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=9778120 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755323 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755324 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755325 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755326 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755327 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755328 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755329 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755330 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755331 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755332 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755333 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755334 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755335 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755336 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755337 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755338 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755339 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755340 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755341 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 48 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755342 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : To test ! Use context local managing function ! time spend for datou_step_exec : 2.0740578174591064 time spend to save output : 6.222724914550781e-05 total time spend for step 2 : 2.074120044708252 step3:rle_unique_nms_with_priority Thu May 29 11:34:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 88 chid ids of type : 4490 seulement à utiliser dans la step consolidation batch 1 Loaded 38 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.10789322853088379 seulement à utiliser dans la step consolidation batch 1 Loaded 50 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.040731191635131836 map_output_result : {1114046597: (0.09264291817443555, 'Should be the crop_list due to order', 0.042237513638672064), 1114046377: (0.09264291817443555, 'Should be the crop_list due to order', 0.14304832271019904)} End step rle-unique-nms time spend for datou_step_exec : 0.9079558849334717 time spend to save output : 6.127357482910156e-05 total time spend for step 3 : 0.9080171585083008 step4:ventilate_hashtags_in_portfolio Thu May 29 11:34:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 6549724 get user id for portfolio 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do lien utilise dans velours : https://www.fotonower.com/velours/9755323,9755324,9755325,9755326,9755327,9755328,9755329,9755330,9755331,9755332,9755333,9755334,9755335,9755336,9755337,9755338,9755339,9755340,9755341,9755342,9755344,9755345?tags=papier,carton,metal,pet_clair,pehd,pet_fonce,pet_opaque,barquette_opaque,film_plastique,ela,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement,mal_croppe,flou&datou_id_consolidate=4387&port_consolidate=6549724 time spend for datou_step_exec : 0.4388432502746582 time spend to save output : 6.008148193359375e-05 total time spend for step 4 : 0.4389033317565918 step5:final Thu May 29 11:34:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 0.08439970016479492 time spend to save output : 0.00012540817260742188 total time spend for step 5 : 0.08452510833740234 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1114046597: ('0.3133414848207032',), 1114046377: ('0.3133414848207032',)} new output for save of step final : {1114046597: ('0.3133414848207032',), 1114046377: ('0.3133414848207032',)} [1114046597, 1114046377] Looping around the photos to save general results len do output : 2 /1114046597.Didn't retrieve data . /1114046377.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('4492', None, None, None, None, None, None, None, None) ('4492', '6549724', '1114046597', None, None, None, None, None, None) ('4492', None, None, None, None, None, None, None, None) ('4492', '6549724', '1114046377', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.020552873611450195 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 5 output : {1114046597: ('0.3133414848207032',), 1114046377: ('0.3133414848207032',)} fin du test de portfolio mere absolue dans consolidate ############################### TEST pma_ventilate ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 12795 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12796 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 12793 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 12793 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 0 of step 12795 have datatype=11 whereas input 0 of step 12793 have datatype=2 WARNING : type of output 1 of step 12796 doesn't seem to be define in the database( WARNING : type of input 1 of step 12793 doesn't seem to be define in the database( WARNING : type of input 1 of step 12800 doesn't seem to be define in the database( WARNING : output 1 of step 12793 have datatype=7 whereas input 1 of step 12800 have datatype=None WARNING : type of output 1 of step 12795 doesn't seem to be define in the database( WARNING : type of input 1 of step 12796 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.296649694442749 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 4 step1:copy_chis Thu May 29 11:34:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 21 chid ids of type : 4482 batch 1 Loaded 88 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.05486726760864258 time spend to save output : 4.315376281738281e-05 total time spend for step 1 : 0.05491042137145996 step2:consolidate_hashtags_from_manual_portfolio Thu May 29 11:34:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755323 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755324 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755325 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755326 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755327 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755328 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755329 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755330 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755331 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755332 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755333 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755334 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755335 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755336 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755337 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755338 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755339 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755340 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755341 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 48 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755342 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : To test ! Use context local managing function ! time spend for datou_step_exec : 1.9641168117523193 time spend to save output : 4.5299530029296875e-05 total time spend for step 2 : 1.9641621112823486 step3:rle_unique_nms_with_priority Thu May 29 11:34:40 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 88 chid ids of type : 4490 seulement à utiliser dans la step consolidation batch 1 Loaded 50 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.13919448852539062 seulement à utiliser dans la step consolidation batch 1 Loaded 38 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.035574913024902344 map_output_result : {1114046377: (0.09264291817443555, 'Should be the crop_list due to order', 0.14304832271019904), 1114046597: (0.09264291817443555, 'Should be the crop_list due to order', 0.042237513638672064)} End step rle-unique-nms time spend for datou_step_exec : 1.1159508228302002 time spend to save output : 6.413459777832031e-05 total time spend for step 3 : 1.1160149574279785 step4:ventilate_hashtags_in_portfolio Thu May 29 11:34:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 6549724 get user id for portfolio 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=9974548 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do lien utilise dans velours : https://www.fotonower.com/velours/9755323,9755324,9755325,9755326,9755327,9755328,9755329,9755330,9755331,9755332,9755333,9755334,9755335,9755336,9755337,9755338,9755339,9755340,9755341,9755342,9755344,9755345?tags=papier,carton,metal,pet_clair,pehd,pet_fonce,pet_opaque,barquette_opaque,film_plastique,ela,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement,mal_croppe,flou&datou_id_consolidate=4387&port_consolidate=6549724 time spend for datou_step_exec : 0.9700186252593994 time spend to save output : 6.222724914550781e-05 total time spend for step 4 : 0.9700808525085449 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1114046377, 1114046597] Looping around the photos to save general results len do output : 1 /6549724. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4553', None, None, None, None, None, None, None, None) ('4553', '6549724', '1114046377', None, None, None, None, None, None) ('4553', None, None, None, None, None, None, None, None) ('4553', '6549724', '1114046597', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.014351129531860352 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 4 output : {6549724: [{'papier': 9755346, 'carton': 9755347, 'metal': 9755348, 'pet_clair': 9755349, 'pehd': 9755350, 'pet_fonce': 9755351, 'pet_opaque': 9755352, 'barquette_opaque': 9755353, 'film_plastique': 9755354, 'ela': 9755355, 'sac': 9755356, 'textiles': 9755357, 'verre': 9755358, 'organique': 9755359, 'dasri': 9755360, 'masque': 9755361, 'encombrant': 9755362, 'autre_emballage': 9755363, 'autre_non_emballage': 9755364, 'environnement': 9755365, 'mal_croppe': 9755366, 'flou': 9755367}]} fin du test de portfolio mere absolue dans consolidate #&_#_#&_# TEST sam SUCCEEDED #&_#_#&_# #&_#_#&_# TEST frcnn SUCCEEDED #&_#_#&_# #&_#_#&_# TEST thcl SUCCEEDED #&_#_#&_# #&_#_#&_# TEST tfhub2 FAILED #&_#_#&_# #&_#_#&_# TEST ordonner SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rotate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST data_augmentation_ellipse_varroa_tile_rotate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST flip SUCCEEDED #&_#_#&_# #&_#_#&_# TEST crop_rles SUCCEEDED #&_#_#&_# #&_#_#&_# TEST angular_coeff SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detection_filter_by_crop SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detection_filter_by_classif SUCCEEDED #&_#_#&_# #&_#_#&_# TEST blur_detection SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detect_point_224x224 SUCCEEDED #&_#_#&_# #&_#_#&_# TEST certificat_qualite_papier SUCCEEDED #&_#_#&_# #&_#_#&_# TEST image_temperature_detection SUCCEEDED #&_#_#&_# #&_#_#&_# TEST broca SUCCEEDED #&_#_#&_# #&_#_#&_# TEST crop_conditional SUCCEEDED #&_#_#&_# #&_#_#&_# TEST image_blanchir SUCCEEDED #&_#_#&_# #&_#_#&_# TEST darker_image SUCCEEDED #&_#_#&_# #&_#_#&_# TEST img_aug SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_split_dark SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_append SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_horaire FAILED #&_#_#&_# #&_#_#&_# TEST rle_unique_nms_with_priority SUCCEEDED #&_#_#&_# #&_#_#&_# TEST random_deformation SUCCEEDED #&_#_#&_# #&_#_#&_# TEST tile SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rotate_chi SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_carac_pet_clair_0121_no_cnn SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_carac_jrm_no_mask_detect SUCCEEDED #&_#_#&_# #&_#_#&_# TEST ventilate_hashtags_in_portfolio SUCCEEDED #&_#_#&_# #&_#_#&_# TEST poly_ro_rle SUCCEEDED #&_#_#&_# #&_#_#&_# TEST cod_sts SUCCEEDED #&_#_#&_# #&_#_#&_# TEST cod_download SUCCEEDED #&_#_#&_# #&_#_#&_# TEST sendgrid SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rym_consolidate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST generate_new_image_add_crop SUCCEEDED #&_#_#&_# #&_#_#&_# TEST velours_tree SUCCEEDED #&_#_#&_# #&_#_#&_# TEST step ACP SUCCEEDED #&_#_#&_# #&_#_#&_# TEST blur_crop SUCCEEDED #&_#_#&_# #&_#_#&_# TEST pma_consolidate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST pma_ventilate SUCCEEDED #&_#_#&_# #&_# TEST FAILED #&_# : tests/datou_test #&_# #&_# END OF TEST #&_# : tests/datou_test #&_# #&_# BEGIN OF TEST : mtr/database_queries/CacheModelData_queries #&_# /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelData_queries.py Test Cache Model Data test a faire VR 27-9-17 #&_# TEST SUCCEEDED #&_# : mtr/database_queries/CacheModelData_queries #&_# #&_# END OF TEST #&_# : mtr/database_queries/CacheModelData_queries #&_# #&_# BEGIN OF TEST : tests/cache_photo_data_test #&_# /home/admin/workarea/git/Velours/python/tests/cache_photo_data_test.py Test local_cache_photo ############################### test_download_photos_by_local_cache ################################ test download portfolio 1162416 : 574 photos We have 1 , we need 574 photos there is already 574 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 574 photo needed from our local_cache list of photo_id_missing : [] test download a list if photos : 10 photos (6 exist in ovh and 4 missing in ovh) we need 10 photos there is already 6 photos exist in our local_cache we have to download 4 photos download_photo : 1 to 2000 BBBBHTTP Error 404: Not Found can't download the photo : 1109585120 FHTTP Error 404: Not Found can't download the photo : 1109585436 FHTTP Error 404: Not Found can't download the photo : 1109585121 FHTTP Error 404: Not Found can't download the photo : 1109585109 Fwe have successful downloaded 0 photos there are 4 photos missing finally, we can get 6 photo needed from our local_cache list of photo_id_missing : [1109585120, 1109585436, 1109585121, 1109585109] ############################### test_update_time_created ################################ we need 1 photos there is already 1 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 1 photo needed from our local_cache list of photo_id_missing : [] #&_#_#&_# TEST cache photo data SUCCEEDED #&_#_#&_# #&_# TEST SUCCEEDED #&_# : tests/cache_photo_data_test #&_# #&_# END OF TEST #&_# : tests/cache_photo_data_test #&_# #&_# BEGIN OF TEST : mtr/mask_rcnn/prepare_maskdata #&_# /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/prepare_maskdata.py test prepare mask data 2096875719 599722655 batch 1 Loaded 20 chid ids of type : 840 ++++++++++++++++++++batch 1 Loaded 16 chid ids of type : 840 +++++++++batch 1 Loaded 20 chid ids of type : 840 ++++++++++++++++++++batch 1 Loaded 19 chid ids of type : 840 ++++++++++batch 1 Loaded 20 chid ids of type : 840 ++++++++++++{2096875719: 'Plaque-immatriculation', 599722655: 'capot'} logo-marque 2096875717 907850592 x0 : 38 y1 : 269 width : 32, height : 38, area : 1216, score : 1.0 None pare-choc 624624117 907850592 x0 : 0 y1 : 559 width : 362, height : 235, area : 85070, score : 1.0 None coffre 495920967 907850592 x0 : 0 y1 : 398 width : 302, height : 336, area : 101472, score : 1.0 None coffre 495920967 907850592 x0 : 626 y1 : 206 width : 18, height : 35, area : 630, score : 1.0 None aile-arriere 2106233861 907850592 x0 : 189 y1 : 476 width : 282, height : 235, area : 66270, score : 1.0 None roue 492689227 907850592 x0 : 275 y1 : 620 width : 170, height : 215, area : 36550, score : 1.0 None Plaque-immatriculation 2096875719 907850592 x0 : 10 y1 : 454 width : 108, height : 84, area : 9072, score : 1.0 None feu-arriere 2096875713 907850592 x0 : 192 y1 : 359 width : 147, height : 96, area : 14112, score : 1.0 None poignee 499500794 907850592 x0 : 437 y1 : 315 width : 42, height : 37, area : 1554, score : 1.0 None poignee 499500794 907850592 x0 : 567 y1 : 282 width : 25, height : 31, area : 775, score : 1.0 None Pot-echappement 2096875720 907850592 x0 : 155 y1 : 532 width : 21, height : 20, area : 420, score : 1.0 None toit 492731002 907850592 x0 : 136 y1 : 74 width : 281, height : 21, area : 5901, score : 1.0 None Pare-brise 2096875709 907850592 x0 : 1 y1 : 241 width : 285, height : 165, area : 47025, score : 1.0 None porte 492654799 907850592 x0 : 354 y1 : 442 width : 210, height : 380, area : 79800, score : 1.0 None porte 492654799 907850592 x0 : 501 y1 : 389 width : 135, height : 324, area : 43740, score : 1.0 None Info-modele 2096875721 907850592 x0 : 132 y1 : 295 width : 53, height : 31, area : 1643, score : 1.0 None vitre 492925064 907850592 x0 : 407 y1 : 201 width : 123, height : 122, area : 15006, score : 1.0 None vitre 492925064 907850592 x0 : 377 y1 : 201 width : 64, height : 118, area : 7552, score : 1.0 None vitre 492925064 907850592 x0 : 522 y1 : 203 width : 102, height : 120, area : 12240, score : 1.0 None Essuie-glace 2096875722 907850592 x0 : 7 y1 : 250 width : 72, height : 61, area : 4392, score : 1.0 None /data/data_root/test_preparedata/train/907850592.jpg logo-marque 2096875717 907862724 x0 : 100 y1 : 282 width : 35, height : 41, area : 1435, score : 1.0 None pare-choc 624624117 907862724 x0 : 1 y1 : 483 width : 435, height : 194, area : 84390, score : 1.0 None aile-arriere 2106233861 907862724 x0 : 399 y1 : 411 width : 110, height : 128, area : 14080, score : 1.0 None retroviseur 492844413 907862724 x0 : 600 y1 : 203 width : 41, height : 37, area : 1517, score : 1.0 None coffre 495920967 907862724 x0 : 5 y1 : 351 width : 369, height : 270, area : 99630, score : 1.0 None Cache-reservoir 2096875718 907862724 x0 : 417 y1 : 289 width : 50, height : 61, area : 3050, score : 1.0 None roue 492689227 907862724 x0 : 367 y1 : 541 width : 121, height : 190, area : 22990, score : 1.0 None poignee 499500794 907862724 x0 : 478 y1 : 275 width : 33, height : 25, area : 825, score : 1.0 None toit 492731002 907862724 x0 : 226 y1 : 110 width : 223, height : 36, area : 8028, score : 1.0 None feu-arriere 2096875713 907862724 x0 : 294 y1 : 331 width : 112, height : 146, area : 16352, score : 1.0 None poignee 499500794 907862724 x0 : 566 y1 : 252 width : 20, height : 27, area : 540, score : 1.0 None Plaque-immatriculation 2096875719 907862724 x0 : 55 y1 : 436 width : 142, height : 54, area : 7668, score : 1.0 None Pare-brise 2096875709 907862724 x0 : 6 y1 : 225 width : 366, height : 132, area : 48312, score : 1.0 None porte 492654799 907862724 x0 : 395 y1 : 391 width : 170, height : 311, area : 52870, score : 1.0 None porte 492654799 907862724 x0 : 508 y1 : 354 width : 101, height : 269, area : 27169, score : 1.0 None vitre 492925064 907862724 x0 : 451 y1 : 202 width : 85, height : 99, area : 8415, score : 1.0 None vitre 492925064 907862724 x0 : 524 y1 : 202 width : 70, height : 99, area : 6930, score : 1.0 None vitre 492925064 907862724 x0 : 428 y1 : 208 width : 50, height : 102, area : 5100, score : 1.0 None Essuie-glace 2096875722 907862724 x0 : 114 y1 : 218 width : 186, height : 27, area : 5022, score : 1.0 None Info-modele 2096875721 907862724 x0 : 10 y1 : 306 width : 41, height : 33, area : 1353, score : 1.0 None /data/data_root/test_preparedata/train/907862724.jpg porte 492654799 907863602 x0 : 129 y1 : 380 width : 300, height : 279, area : 83700, score : 1.0 None porte 492654799 907863602 x0 : 193 y1 : 345 width : 228, height : 215, area : 49020, score : 1.0 None porte 492654799 907863602 x0 : 379 y1 : 385 width : 257, height : 286, area : 73502, score : 1.0 None porte 492654799 907863602 x0 : 400 y1 : 365 width : 195, height : 248, area : 48360, score : 1.0 None roue 492689227 907863602 x0 : 0 y1 : 419 width : 124, height : 148, area : 18352, score : 1.0 None roue 492689227 907863602 x0 : 0 y1 : 411 width : 127, height : 129, area : 16383, score : 1.0 None roue 492689227 907863602 x0 : 580 y1 : 449 width : 60, height : 129, area : 7740, score : 1.0 None roue 492689227 907863602 x0 : 584 y1 : 444 width : 50, height : 126, area : 6300, score : 1.0 None retroviseur 492844413 907863602 x0 : 165 y1 : 225 width : 53, height : 51, area : 2703, score : 1.0 None vitre 492925064 907863602 x0 : 211 y1 : 218 width : 197, height : 109, area : 21473, score : 1.0 None vitre 492925064 907863602 x0 : 217 y1 : 214 width : 193, height : 99, area : 19107, score : 1.0 None vitre 492925064 907863602 x0 : 409 y1 : 214 width : 188, height : 98, area : 18424, score : 1.0 None vitre 492925064 907863602 x0 : 414 y1 : 206 width : 187, height : 96, area : 17952, score : 1.0 None poignee 499500794 907863602 x0 : 320 y1 : 271 width : 46, height : 19, area : 874, score : 1.0 None poignee 499500794 907863602 x0 : 321 y1 : 270 width : 44, height : 19, area : 836, score : 1.0 None poignee 499500794 907863602 x0 : 553 y1 : 270 width : 62, height : 25, area : 1550, score : 1.0 None poignee 499500794 907863602 x0 : 565 y1 : 266 width : 42, height : 16, area : 672, score : 1.0 None capot 599722655 907863602 x0 : -1 y1 : 215 width : 135, height : 37, area : 4995, score : 1.0 None capot 599722655 907863602 x0 : 242 y1 : 123 width : 330, height : 52, area : 17160, score : 1.0 None /data/data_root/test_preparedata/train/907863602.jpg porte 492654799 907863940 x0 : 109 y1 : 426 width : 283, height : 265, area : 74995, score : 1.0 None porte 492654799 907863940 x0 : 179 y1 : 399 width : 203, height : 221, area : 44863, score : 1.0 None porte 492654799 907863940 x0 : 354 y1 : 425 width : 277, height : 266, area : 73682, score : 1.0 None porte 492654799 907863940 x0 : 370 y1 : 407 width : 179, height : 233, area : 41707, score : 1.0 None roue 492689227 907863940 x0 : -1 y1 : 463 width : 98, height : 141, area : 13818, score : 1.0 None roue 492689227 907863940 x0 : 0 y1 : 468 width : 88, height : 147, area : 12936, score : 1.0 None roue 492689227 907863940 x0 : 523 y1 : 493 width : 117, height : 146, area : 17082, score : 1.0 None roue 492689227 907863940 x0 : 525 y1 : 493 width : 111, height : 147, area : 16317, score : 1.0 None retroviseur 492844413 907863940 x0 : 138 y1 : 278 width : 46, height : 44, area : 2024, score : 1.0 None vitre 492925064 907863940 x0 : 182 y1 : 266 width : 200, height : 95, area : 19000, score : 1.0 None vitre 492925064 907863940 x0 : 385 y1 : 257 width : 172, height : 90, area : 15480, score : 1.0 None vitre 492925064 907863940 x0 : 394 y1 : 256 width : 144, height : 86, area : 12384, score : 1.0 None vitre 492925064 907863940 x0 : 549 y1 : 253 width : 58, height : 53, area : 3074, score : 1.0 None poignee 499500794 907863940 x0 : 300 y1 : 306 width : 59, height : 17, area : 1003, score : 1.0 None poignee 499500794 907863940 x0 : 307 y1 : 313 width : 51, height : 24, area : 1224, score : 1.0 None poignee 499500794 907863940 x0 : 527 y1 : 308 width : 53, height : 22, area : 1166, score : 1.0 None poignee 499500794 907863940 x0 : 536 y1 : 310 width : 45, height : 24, area : 1080, score : 1.0 None capot 599722655 907863940 x0 : 219 y1 : 181 width : 370, height : 38, area : 14060, score : 1.0 None logo-roue 2106233859 907863940 x0 : 7 y1 : 419 width : 27, height : 43, area : 1161, score : 1.0 None logo-roue 2106233859 907863940 x0 : 594 y1 : 441 width : 26, height : 25, area : 650, score : 1.0 None /data/data_root/test_preparedata/val/907863940.jpg #&_#_#&_# TEST prepare mask data poly SUCCEEDED #&_#_#&_# 2107755846 batch 1 Loaded 15 chid ids of type : 2622 {2107755846: 'pet_clair'} environment 492622729 964453879 x0 : 0 y1 : 479 width : 112, height : 415, area : 46480, score : 1.0 None error 501120777 964453879 x0 : 142 y1 : 479 width : 28, height : 20, area : 560, score : 1.0 None error 501120777 964453879 x0 : 151 y1 : 99 width : 23, height : 42, area : 966, score : 1.0 None error 501120777 964453879 x0 : 175 y1 : 59 width : 117, height : 59, area : 6903, score : 1.0 None error 501120777 964453879 x0 : 282 y1 : 479 width : 35, height : 62, area : 2170, score : 1.0 None error 501120777 964453879 x0 : 315 y1 : 460 width : 19, height : 26, area : 494, score : 1.0 None error 501120777 964453879 x0 : 353 y1 : 361 width : 32, height : 41, area : 1312, score : 1.0 None error 501120777 964453879 x0 : 403 y1 : 215 width : 47, height : 51, area : 2397, score : 1.0 None error 501120777 964453879 x0 : 497 y1 : 253 width : 89, height : 55, area : 4895, score : 1.0 None error 501120777 964453879 x0 : 546 y1 : 195 width : 58, height : 68, area : 3944, score : 1.0 None error 501120777 964453879 x0 : 613 y1 : 323 width : 20, height : 32, area : 640, score : 1.0 None error 501120777 964453879 x0 : 638 y1 : 225 width : 17, height : 27, area : 459, score : 1.0 None pet_clair 2107755846 964453879 x0 : 0 y1 : 479 width : 719, height : 479, area : 344401, score : 1.0 None error 501120777 964453879 x0 : 546 y1 : 195 width : 60, height : 68, area : 4080, score : 1.0 None error 501120777 964453879 x0 : 613 y1 : 323 width : 22, height : 32, area : 704, score : 1.0 None /data/data_root/test_preparedata/val/964453879.jpg #&_#_#&_# TEST prepare mask data rle SUCCEEDED #&_#_#&_# time for calcul the mask position with numpy : 0.00014734268188476562 nb_pixel_total : 6 time to create 1 rle with old method : 6.222724914550781e-05 Sanity check PASSED : sum_rle_size : 12 height * width : 12 7 #&_#_#&_# TEST prepare mask data mat SUCCEEDED #&_#_#&_# #&_# TEST SUCCEEDED #&_# : mtr/mask_rcnn/prepare_maskdata #&_# #&_# END OF TEST #&_# : mtr/mask_rcnn/prepare_maskdata #&_# #&_# BEGIN OF TEST : mtr/database_queries/portfolio_queries #&_# /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py test portfolio queries Catched exception ! Connect or reconnect ! #&_# TEST SUCCEEDED #&_# : mtr/database_queries/portfolio_queries #&_# #&_# END OF TEST #&_# : mtr/database_queries/portfolio_queries #&_# #&_# BEGIN OF TEST : prod/memo/memo #&_# /home/admin/workarea/git/Velours/python/prod/memo/memo.py SLA Mensuel python version used : 3 ############################### TEST memo ################################ Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier nb_day : (0, 31) VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select id from MTRUser.mtr_portfolios where id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(select last_run_id from MTRPhoto.dashboard_entry_day where dashboard_place_id in (select id from MTRPhoto.dashboard_places where name = 'Romainville_Presse_2' and date like '%2022-08%') and created_at like '%2022-08%'))); nb_day : (0, 31) after unwanted_material_data nb_day : (0, 31) after coverage_data after number_of_batch date_start : 2022-08-01 : dt_date_just_month_year : 2022-08-01 00:00:00 : VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! Error parsing crontab ! [Errno 2] No such file or directory: '' You better check your LOGRASPI env !!! after pl.get_datou_sts_from_crontab : verbose : False no sts found, try to find from database SELECT dri.id FROM MTRPhoto.dashboard_run_ids dri, MTRPhoto.dashboard_entry_day ded, MTRPhoto.dashboard_places dp WHERE dp.name= "Romainville_Presse_2" AND ded.dashboard_place_id=dp.id AND dri.dashboard_entry_day=ded.id AND dri.id=ded.last_run_id AND ded.date >= "2022-08-01" AND ded.date <= "2022-08-31" apple3 {'gm': {'mat': 'gm', 'pht': 4209, 'datou_carac_id': 3994, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'emr': {'mat': 'emr', 'pht': 4207, 'datou_carac_id': 3993, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'carton,background'}, 'jrm': {'mat': 'jrm', 'pht': 3726, 'datou_carac_id': 3459, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'ela': {'mat': 'ela', 'pht': 4203, 'datou_carac_id': 3991, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'ela,background'}, 'pehd_pp': {'mat': 'pehd_pp', 'pht': 4211, 'datou_carac_id': 3995, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pehd,background'}, 'pet_fonce': {'mat': 'pet_fonce', 'pht': 4200, 'datou_carac_id': 4153, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_fonce,background'}, 'aluminium': {'mat': 'aluminium', 'pht': 4205, 'datou_carac_id': 3992, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'metal,background'}, 'refus': {'mat': 'refus', 'pht': 3594, 'datou_carac_id': 3318, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'refus,background'}, 'pet_clair': {'mat': 'pet_clair', 'pht': 3327, 'datou_carac_id': 3804, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_clair,bouchon,etiquette,barquette_avec_film,background'}} SELECT h.hashtag as unwanted_material, substr(dr.hashtag,8) as main_material, ptp.type as pht_type, sum(pcr.value*dr.nombre_balle)/sum(dr.nombre_balle) as ratio, count(distinct mpp.mtr_photo_id) as nb_photo, group_concat(distinct ptp.mtr_portfolio_id_2) as list_port_cont, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_port, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", h.hashtag, ":", cast(ptp.type as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_mat FROM MTRPhoto.dashboard_results dr, MTRPhoto.mtr_port_to_port_ids ptp, MTRUser.mtr_portfolio_photos mpp, MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h WHERE dr.dashboard_run_id IN (448643,449568,454450,454465,454459,454472,456239,458795,461498,454567,457375,457649,459844,460382,461634,463066,472528,465865,469297,468129,475039,472453,474892,474538,475316,476524,479832,481665,479903) AND dr.mtr_portfolio_id=ptp.mtr_portfolio_id_1 AND dr.qualite >= 0 AND mpp.mtr_portfolio_id=ptp.mtr_portfolio_id_2 AND pcr.portfolio_id=ptp.mtr_portfolio_id_1 AND h.hashtag_id = pcr.hashtag_id AND ptp.type = pcr.hashtag_type AND mpp.hide_status = 0 AND ptp.hashtag_id=h.hashtag_id AND ptp.type IN (4209,4207,3726,4203,4211,4200,4205,3594,3327) group by h.hashtag, dr.hashtag, ptp.type; VR TODO TO BETTER PARSE ! ({'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0001728785023265608, 'nb_photo': 18, 'list_port_cont': '6861407,6862123,6862547,6867908,6868674,6869389,6869558,6877707,6877972,6881690,6891352,6894202,6907047,6922714,6925593', 'assoc_port': '6790887:6862547,6794193:6862123,6834822:6877972,6840554:6877707,6845438:6869389,6846972:6869558,6851003:6868674,6853215:6861407,6864848:6867908,6881492:6881690,6889190:6891352,6894094:6894202,6906036:6907047,6921897:6922714,6925484:6925593', 'assoc_mat': '6790887:barquette_opaque:4207:6862547,6794193:barquette_opaque:4207:6862123,6834822:barquette_opaque:4207:6877972,6840554:barquette_opaque:4207:6877707,6845438:barquette_opaque:4207:6869389,6846972:barquette_opaque:4207:6869558,6851003:barquette_opaque:4207:6868674,6853215:barquette_opaque:4207:6861407,6864848:barquette_opaque:4207:6867908,6881492:barquette_opaque:4207:6881690,6889190:barquette_opaque:4207:6891352,6894094:barquette_opaque:4207:6894202,6906036:barquette_opaque:4207:6907047,6921897:barquette_opaque:4207:6922714,6925484:barquette_opaque:4207:6925593'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 8.469862336652651e-05, 'nb_photo': 44, 'list_port_cont': '6626450,6627857,6628405,6628829,6644425,6664932,6665236,6666075,6666652,6669243,6669771,6706891,6712605,6719637,6720391,6720629,6722154,6744912,6762879,6775287,6835808,6841133,6842925,6846016,6852775,6853230', 'assoc_port': '6625827:6626450,6627447:6627857,6627801:6628405,6628455:6628829,6630580:6664932,6630581:6665236,6630822:6666075,6631618:6669771,6639123:6644425,6665718:6666652,6668655:6669243,6706262:6706891,6708643:6712605,6719441:6719637,6719951:6720391,6720202:6720629,6721827:6722154,6744086:6744912,6762224:6762879,6774283:6775287,6834821:6835808,6840554:6841133,6842390:6842925,6845438:6846016,6852117:6852775,6852533:6853230', 'assoc_mat': '6625827:barquette_opaque:4209:6626450,6627447:barquette_opaque:4209:6627857,6627801:barquette_opaque:4209:6628405,6628455:barquette_opaque:4209:6628829,6630580:barquette_opaque:4209:6664932,6630581:barquette_opaque:4209:6665236,6630822:barquette_opaque:4209:6666075,6631618:barquette_opaque:4209:6669771,6639123:barquette_opaque:4209:6644425,6665718:barquette_opaque:4209:6666652,6668655:barquette_opaque:4209:6669243,6706262:barquette_opaque:4209:6706891,6708643:barquette_opaque:4209:6712605,6719441:barquette_opaque:4209:6719637,6719951:barquette_opaque:4209:6720391,6720202:barquette_opaque:4209:6720629,6721827:barquette_opaque:4209:6722154,6744086:barquette_opaque:4209:6744912,6762224:barquette_opaque:4209:6762879,6774283:barquette_opaque:4209:6775287,6834821:barquette_opaque:4209:6835808,6840554:barquette_opaque:4209:6841133,6842390:barquette_opaque:4209:6842925,6845438:barquette_opaque:4209:6846016,6852117:barquette_opaque:4209:6852775,6852533:barquette_opaque:4209:6853230'}, {'unwanted_material': 'carton', 'main_material': 'aluminium', 'pht_type': 4205, 'ratio': 0.01022994561299725, 'nb_photo': 16, 'list_port_cont': '6625532,6880743,6880786', 'assoc_port': '6625304:6625532,6880715:6880786,6880716:6880743', 'assoc_mat': '6625304:carton:4205:6625532,6880715:carton:4205:6880786,6880716:carton:4205:6880743'}, {'unwanted_material': 'carton', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.9683717828305286, 'nb_photo': 2565, 'list_port_cont': 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{'unwanted_material': 'papier', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.9902901831097267, 'nb_photo': 2955, 'list_port_cont': '6860598,6860874,6861066,6861173,6861345,6861539,6861959,6862604,6863422,6863593,6864917,6865058,6866086,6866174,6876761,6877571,6877655,6877751,6881620,6886395,6888025,6889756,6894811,6902822,6905423,6907081,6909151,6920947', 'assoc_port': '6860529:6861066,6860533:6861173,6860534:6860598,6860536:6860874,6861163:6861539,6861164:6861345,6861513:6861959,6861515:6862604,6863219:6863593,6863221:6863422,6864847:6866174,6864849:6865058,6864854:6864917,6865737:6866086,6876690:6876761,6877477:6877751,6877481:6877655,6877482:6877571,6881493:6881620,6886257:6886395,6887731:6888025,6889188:6889756,6894658:6894811,6899249:6909151,6900676:6902822,6903711:6907081,6905103:6905423,6920015:6920947', 'assoc_mat': '6860529:papier:4209:6861066,6860533:papier:4209:6861173,6860534:papier:4209:6860598,6860536:papier:4209:6860874,6861163:papier:4209:6861539,6861164:papier:4209:6861345,6861513:papier:4209:6861959,6861515:papier:4209:6862604,6863219:papier:4209:6863593,6863221:papier:4209:6863422,6864847:papier:4209:6866174,6864849:papier:4209:6865058,6864854:papier:4209:6864917,6865737:papier:4209:6866086,6876690:papier:4209:6876761,6877477:papier:4209:6877751,6877481:papier:4209:6877655,6877482:papier:4209:6877571,6881493:papier:4209:6881620,6886257:papier:4209:6886395,6887731:papier:4209:6888025,6889188:papier:4209:6889756,6894658:papier:4209:6894811,6899249:papier:4209:6909151,6900676:papier:4209:6902822,6903711:papier:4209:6907081,6905103:papier:4209:6905423,6920015:papier:4209:6920947'}, {'unwanted_material': 'papier', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.010245453120925422, 'nb_photo': 31, 'list_port_cont': '6647707,6861711', 'assoc_port': '6647200:6647707,6861521:6861711', 'assoc_mat': '6647200:papier:4200:6647707,6861521:papier:4200:6861711'}, {'unwanted_material': 'pehd', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 2.520265649788176e-05, 'nb_photo': 2, 'list_port_cont': '6678458', 'assoc_port': '6678032:6678458', 'assoc_mat': '6678032:pehd:4209:6678458'}, {'unwanted_material': 'pet_clair', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0009691335779369147, 'nb_photo': 50, 'list_port_cont': '6862208,6862361,6862535,6862580,6862650,6867917,6868229,6868248,6868281,6868376,6868672,6869102,6869324,6869466,6869550,6877011,6877046,6877191,6877677,6877777,6877812,6877923,6877993,6878035,6886875,6888290,6891353,6896153,6898498,6904509,6907044,6917257,6922719,6925591', 'assoc_port': '6790887:6862535,6832752:6877923,6833565:6878035,6834821:6877993,6836050:6877777,6838973:6877677,6842390:6877812,6846972:6869550,6846973:6869466,6848417:6869324,6851003:6868672,6852533:6869102,6861512:6868229,6861514:6868281,6861516:6868248,6861517:6868376,6861519:6862580,6861520:6862650,6861524:6862208,6861635:6862361,6864848:6867917,6876683:6877191,6876685:6877011,6876686:6877046,6886258:6886875,6887695:6888290,6889190:6891353,6895749:6896153,6898175:6898498,6904057:6904509,6906036:6907044,6917033:6917257,6921897:6922719,6925484:6925591', 'assoc_mat': '6790887:pet_clair:4207:6862535,6832752:pet_clair:4207:6877923,6833565:pet_clair:4207:6878035,6834821:pet_clair:4207:6877993,6836050:pet_clair:4207:6877777,6838973:pet_clair:4207:6877677,6842390:pet_clair:4207:6877812,6846972:pet_clair:4207:6869550,6846973:pet_clair:4207:6869466,6848417:pet_clair:4207:6869324,6851003:pet_clair:4207:6868672,6852533:pet_clair:4207:6869102,6861512:pet_clair:4207:6868229,6861514:pet_clair:4207:6868281,6861516:pet_clair:4207:6868248,6861517:pet_clair:4207:6868376,6861519:pet_clair:4207:6862580,6861520:pet_clair:4207:6862650,6861524:pet_clair:4207:6862208,6861635:pet_clair:4207:6862361,6864848:pet_clair:4207:6867917,6876683:pet_clair:4207:6877191,6876685:pet_clair:4207:6877011,6876686:pet_clair:4207:6877046,6886258:pet_clair:4207:6886875,6887695:pet_clair:4207:6888290,6889190:pet_clair:4207:6891353,6895749:pet_clair:4207:6896153,6898175:pet_clair:4207:6898498,6904057:pet_clair:4207:6904509,6906036:pet_clair:4207:6907044,6917033:pet_clair:4207:6917257,6921897:pet_clair:4207:6922719,6'}, {'unwanted_material': 'pet_clair', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.00038788845724592604, 'nb_photo': 52, 'list_port_cont': '6613103,6648162,6655833,6657176,6658378,6660889,6666083,6666651,6666947,6668915,6669550,6669705,6669777,6671157,6700228,6704711,6706879,6709548,6712610,6720621,6722887,6723439,6744920,6748621,6748942,6761453,6774102,6776478,6837016,6841130,6846026,6853934', 'assoc_port': '6612572:6613103,6630822:6666083,6631385:6668915,6631618:6669777,6632901:6671157,6635653:6660889,6647202:6648162,6655088:6655833,6656505:6657176,6656506:6658378,6665067:6666947,6665718:6666651,6668654:6669705,6668657:6669550,6699726:6700228,6700555:6704711,6706262:6706879,6707499:6709548,6708643:6712610,6720202:6720621,6722573:6722887,6723094:6723439,6744086:6744920,6747584:6748621,6748313:6748942,6761126:6761453,6773386:6774102,6776077:6776478,6836050:6837016,6840554:6841130,6845438:6846026,6852534:6853934', 'assoc_mat': '6612572:pet_clair:4209:6613103,6630822:pet_clair:4209:6666083,6631385:pet_clair:4209:6668915,6631618:pet_clair:4209:6669777,6632901:pet_clair:4209:6671157,6635653:pet_clair:4209:6660889,6647202:pet_clair:4209:6648162,6655088:pet_clair:4209:6655833,6656505:pet_clair:4209:6657176,6656506:pet_clair:4209:6658378,6665067:pet_clair:4209:6666947,6665718:pet_clair:4209:6666651,6668654:pet_clair:4209:6669705,6668657:pet_clair:4209:6669550,6699726:pet_clair:4209:6700228,6700555:pet_clair:4209:6704711,6706262:pet_clair:4209:6706879,6707499:pet_clair:4209:6709548,6708643:pet_clair:4209:6712610,6720202:pet_clair:4209:6720621,6722573:pet_clair:4209:6722887,6723094:pet_clair:4209:6723439,6744086:pet_clair:4209:6744920,6747584:pet_clair:4209:6748621,6748313:pet_clair:4209:6748942,6761126:pet_clair:4209:6761453,6773386:pet_clair:4209:6774102,6776077:pet_clair:4209:6776478,6836050:pet_clair:4209:6837016,6840554:pet_clair:4209:6841130,6845438:pet_clair:4209:6846026,6852534:pet_clair:4209:6853934'}, {'unwanted_material': 'pet_clair', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.00042088720046359597, 'nb_photo': 1, 'list_port_cont': '6862605', 'assoc_port': '6861515:6862605', 'assoc_mat': '6861515:pet_clair:4209:6862605'}, {'unwanted_material': 'pet_fonce', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0008578698895049885, 'nb_photo': 28, 'list_port_cont': '6860798,6861839,6862207,6868256,6868671,6869549,6877674,6877708,6877816,6877879,6878008,6886869,6888301,6889467,6898496,6904520,6915175,6922716', 'assoc_port': '6832753:6877879,6834821:6878008,6838973:6877674,6840554:6877708,6842390:6877816,6846972:6869549,6851003:6868671,6855642:6860798,6859123:6861839,6861516:6868256,6861524:6862207,6886258:6886869,6887695:6888301,6887698:6889467,6898175:6898496,6904057:6904520,6914259:6915175,6921897:6922716', 'assoc_mat': '6832753:pet_fonce:4207:6877879,6834821:pet_fonce:4207:6878008,6838973:pet_fonce:4207:6877674,6840554:pet_fonce:4207:6877708,6842390:pet_fonce:4207:6877816,6846972:pet_fonce:4207:6869549,6851003:pet_fonce:4207:6868671,6855642:pet_fonce:4207:6860798,6859123:pet_fonce:4207:6861839,6861516:pet_fonce:4207:6868256,6861524:pet_fonce:4207:6862207,6886258:pet_fonce:4207:6886869,6887695:pet_fonce:4207:6888301,6887698:pet_fonce:4207:6889467,6898175:pet_fonce:4207:6898496,6904057:pet_fonce:4207:6904520,6914259:pet_fonce:4207:6915175,6921897:pet_fonce:4207:6922716'}, {'unwanted_material': 'pet_fonce', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.002001176734086916, 'nb_photo': 165, 'list_port_cont': '6613106,6614596,6626457,6628830,6629210,6635370,6641607,6642548,6644426,6646347,6647448,6648893,6654377,6659140,6659225,6660883,6661079,6664546,6664700,6665227,6666079,6666644,6666756,6666950,6668662,6669252,6669485,6669785,6671575,6674270,6678461,6683425,6683978,6702227,6704714,6706892,6708972,6709423,6709577,6711293,6712608,6718881,6719276,6719647,6720634,6722892,6723506,6744910,6748597,6762003,6774109,6776983,6841135,6842934,6847565,6859391', 'assoc_port': '6612572:6613106,6614336:6614596,6625827:6626457,6628455:6628830,6628736:6629210,6630577:6664546,6630578:6664700,6630581:6665227,6630822:6666079,6631384:6668662,6631618:6669785,6633545:6635370,6635651:6661079,6635653:6660883,6639123:6644426,6639695:6642548,6640928:6641607,6645791:6646347,6646682:6647448,6648217:6648893,6652800:6654377,6657819:6659140,6659025:6659225,6665067:6666950,6665718:6666644,6665719:6666756,6668655:6669252,6668656:6669485,6670886:6671575,6673850:6674270,6678032:6678461,6682633:6683425,6683289:6683978,6700555:6704714,6701501:6702227,6706262:6706892,6706760:6709423,6707163:6708972,6707499:6709577,6708643:6712608,6708644:6711293,6718520:6719276,6718521:6718881,6719441:6719647,6720202:6720634,6722573:6722892,6723192:6723506,6744086:6744910,6747584:6748597,6759349:6762003,6773386:6774109,6775574:6776983,6840554:6841135,6842390:6842934,6846972:6847565,6859123:6859391', 'assoc_mat': '6612572:pet_fonce:4209:6613106,6614336:pet_fonce:4209:6614596,6625827:pet_fonce:4209:6626457,6628455:pet_fonce:4209:6628830,6628736:pet_fonce:4209:6629210,6630577:pet_fonce:4209:6664546,6630578:pet_fonce:4209:6664700,6630581:pet_fonce:4209:6665227,6630822:pet_fonce:4209:6666079,6631384:pet_fonce:4209:6668662,6631618:pet_fonce:4209:6669785,6633545:pet_fonce:4209:6635370,6635651:pet_fonce:4209:6661079,6635653:pet_fonce:4209:6660883,6639123:pet_fonce:4209:6644426,6639695:pet_fonce:4209:6642548,6640928:pet_fonce:4209:6641607,6645791:pet_fonce:4209:6646347,6646682:pet_fonce:4209:6647448,6648217:pet_fonce:4209:6648893,6652800:pet_fonce:4209:6654377,6657819:pet_fonce:4209:6659140,6659025:pet_fonce:4209:6659225,6665067:pet_fonce:4209:6666950,6665718:pet_fonce:4209:6666644,6665719:pet_fonce:4209:6666756,6668655:pet_fonce:4209:6669252,6668656:pet_fonce:4209:6669485,6670886:pet_fonce:4209:6671575,6673850:pet_fonce:4209:6674270,6678032:pet_fonce:4209:6678461,6682633:pet_fonce:4209:6683425,6683289:pet_fonce:4209:6683978,6'}, {'unwanted_material': 'pet_fonce', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.0009882492230258019, 'nb_photo': 2, 'list_port_cont': '6894805', 'assoc_port': '6894658:6894805', 'assoc_mat': '6894658:pet_fonce:4209:6894805'}, {'unwanted_material': 'pet_fonce', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.9832720072705387, 'nb_photo': 21, 'list_port_cont': '6647700,6861697', 'assoc_port': '6647200:6647700,6861521:6861697', 'assoc_mat': '6647200:pet_fonce:4200:6647700,6861521:pet_fonce:4200:6861697'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0006428090611349251, 'nb_photo': 9, 'list_port_cont': '6866496,6868369,6869096,6869546,6878036,6904487,6905142,6905991,6922715', 'assoc_port': '6833565:6878036,6846972:6869546,6852533:6869096,6861517:6868369,6864855:6866496,6903710:6905991,6903712:6904487,6905102:6905142,6921897:6922715', 'assoc_mat': '6833565:pet_opaque:4207:6878036,6846972:pet_opaque:4207:6869546,6852533:pet_opaque:4207:6869096,6861517:pet_opaque:4207:6868369,6864855:pet_opaque:4207:6866496,6903710:pet_opaque:4207:6905991,6903712:pet_opaque:4207:6904487,6905102:pet_opaque:4207:6905142,6921897:pet_opaque:4207:6922715'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0005744915736568625, 'nb_photo': 214, 'list_port_cont': '6613105,6614118,6616031,6627858,6628408,6628839,6629216,6629282,6635308,6635367,6639706,6640660,6641595,6644422,6647441,6655823,6657172,6658373,6659149,6660897,6661077,6664543,6664634,6664804,6664940,6665229,6666078,6666656,6666757,6666952,6667994,6668664,6668914,6669254,6669547,6669695,6669784,6669814,6670110,6671566,6671848,6672292,6674152,6676774,6678456,6681180,6683428,6683979,6700223,6704719,6706884,6709575,6711305,6712607,6718352,6718715,6719280,6719890,6720396,6722151,6722589,6722891,6723435,6744915,6748617,6748952,6749035,6760254,6761440,6774110,6776978,6813183,6834135,6834365,6834702,6835809,6839582,6841141,6842938,6853225,6853523,6859402', 'assoc_port': '6612572:6613105,6613636:6614118,6614891:6616031,6627447:6627858,6627801:6628408,6627802:6629282,6628455:6628839,6628736:6629216,6630577:6664543,6630579:6664634,6630580:6664940,6630581:6665229,6630822:6666078,6631154:6667994,6631384:6668664,6631385:6668914,6631618:6669784,6631619:6669814,6631901:6670110,6633543:6635308,6633544:6671848,6633545:6635367,6633546:6672292,6635651:6661077,6635653:6660897,6639123:6644422,6639124:6639706,6640066:6640660,6640928:6641595,6646682:6647441,6655088:6655823,6656505:6657172,6656506:6658373,6657819:6659149,6663042:6664804,6665067:6666952,6665718:6666656,6665719:6666757,6668654:6669695,6668655:6669254,6668657:6669547,6670886:6671566,6671756:6676774,6673385:6674152,6678032:6678456,6680947:6681180,6682633:6683428,6683289:6683979,6699726:6700223,6700555:6704719,6706262:6706884,6707499:6709575,6708643:6712607,6708644:6711305,6715973:6718352,6716500:6718715,6717201:6719890,6718520:6719280,6719951:6720396,6721826:6722589,6721827:6722151,6722573:6722891,6723094:6723435,6744086:6744915,', 'assoc_mat': '6612572:pet_opaque:4209:6613105,6613636:pet_opaque:4209:6614118,6614891:pet_opaque:4209:6616031,6627447:pet_opaque:4209:6627858,6627801:pet_opaque:4209:6628408,6627802:pet_opaque:4209:6629282,6628455:pet_opaque:4209:6628839,6628736:pet_opaque:4209:6629216,6630577:pet_opaque:4209:6664543,6630579:pet_opaque:4209:6664634,6630580:pet_opaque:4209:6664940,6630581:pet_opaque:4209:6665229,6630822:pet_opaque:4209:6666078,6631154:pet_opaque:4209:6667994,6631384:pet_opaque:4209:6668664,6631385:pet_opaque:4209:6668914,6631618:pet_opaque:4209:6669784,6631619:pet_opaque:4209:6669814,6631901:pet_opaque:4209:6670110,6633543:pet_opaque:4209:6635308,6633544:pet_opaque:4209:6671848,6633545:pet_opaque:4209:6635367,6633546:pet_opaque:4209:6672292,6635651:pet_opaque:4209:6661077,6635653:pet_opaque:4209:6660897,6639123:pet_opaque:4209:6644422,6639124:pet_opaque:4209:6639706,6640066:pet_opaque:4209:6640660,6640928:pet_opaque:4209:6641595,6646682:pet_opaque:4209:6647441,6655088:pet_opaque:4209:6655823,6656505:pet_opaque:4209:6657172,'}) select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(448643,449568,454450,454465,454459,454472,456239,458795,461498,454567,457375,457649,459844,460382,461634,463066,472528,465865,469297,468129,475039,472453,474892,474538,475316,476524,479832,481665,479903)); after get_hostname_from_raspi hasthag : emr hasthag that could be used but not yet : _______carton,background hasthag : jrm hasthag that could be used but not yet : _______papier,background hasthag : aluminium hasthag that could be used but not yet : _______metal,background hasthag : pet_fonce hasthag that could be used but not yet : _______pet_fonce,background hasthag : gm hasthag that could be used but not yet : _______papier,background after impurety_average_per_hashtag VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier filepath : /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? {'emr': {'barquette_opaque': ['barquette_opaque', '0.01%', 44], 'carton': ['carton', '0.78%', 1462], 'ela': ['ela', '0.01%', 3], 'etiquette': ['etiquette', '0.08%', 1049], 'film_plastique': ['film_plastique', '0.01%', 9], 'kraft': ['kraft', '0.11%', 1], 'metal': ['metal', '2.6%', 787], 'papier': ['papier', '96.86%', 148871], 'pehd': ['pehd', '0.0%', 2], 'pet_clair': ['pet_clair', '0.04%', 52], 'pet_fonce': ['pet_fonce', '0.2%', 165], 'pet_opaque': ['pet_opaque', '0.06%', 214]}, 'aluminium': {'carton': ['carton', '1.02%', 16], 'metal': ['metal', '98.97%', 54], 'papier': ['papier', '0.13%', 9]}, 'gm': {'carton': ['carton', '0.29%', 10], 'etiquette': ['etiquette', '0.08%', 16], 'metal': ['metal', '3.43%', 14], 'papier': ['papier', '99.03%', 2955], 'pet_clair': ['pet_clair', '0.04%', 1], 'pet_fonce': ['pet_fonce', '0.1%', 2]}, 'pet_fonce': {'carton': ['carton', '0.59%', 4], 'etiquette': ['etiquette', '0.11%', 5], 'papier': ['papier', '1.02%', 31], 'pet_fonce': ['pet_fonce', '98.33%', 21]}} filepath : /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf hash: 9b473a9b7e6f69a2b2147971a7bf4221 for path: /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf voici le hostname : marlene hash: 9b473a9b7e6f69a2b2147971a7bf4221 for path: /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf ############################### TEST one_day ################################ TODO and TOTEST Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_one_day nb_day : (3, 30) nb deleted : 3 VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier Inconsistency in dashboard_places dashboard_places from input : romainville_petite_presse dashboard_name_from_port found from datou STS from crontab from raspi : Romainville_Presse_1 We force the correct camera_place_name ! TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO TODO : Insert select and so on ***** analysis of device with port 20001 for dashboard romainville_petite_presse ***** **** analysis of day 2022/09/01/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-01 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis of day 2022/09/02/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-02 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis of day 2022/09/03/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-03 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis for all days **** Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 17148, not duplicated 17595, two criteria ok 17148, nb forced upload 0 end of day status of photos as found in sqllite Info from dashboard total number of uploaded images : 0 number of useful images : 17148 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) cvs resume : date,nb_photos,% time,nb ok,uploaded,to upload, % uploaded, nb useful, % completed photos, last_update, remark 2022/09/01,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, 2022/09/02,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, 2022/09/03,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, coverage for this period for every 10 second 2022/09/01/ 0.0 2022/09/02/ 0.0 2022/09/03/ 0.0 mean value for this period : 0.0 coverage for this period for every 20 second 2022/09/01/ 0.0 2022/09/02/ 0.0 2022/09/03/ 0.0 mean value for this period : 0.0 ############################### TEST get_data ################################ TODO and TOTEST VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier nb_day : (0, 31) VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select id from MTRUser.mtr_portfolios where id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(select last_run_id from MTRPhoto.dashboard_entry_day where dashboard_place_id in (select id from MTRPhoto.dashboard_places where name = 'Romainville_Presse_1' and date like '%2022-08%') and created_at like '%2022-08%'))); nb_day : (0, 31) after unwanted_material_data nb_day : (0, 31) after coverage_data after number_of_batch date_start : 2022-08-01 : dt_date_just_month_year : 2022-08-01 00:00:00 : VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! after pl.get_datou_sts_from_crontab : verbose : False no sts found, try to find from database SELECT dri.id FROM MTRPhoto.dashboard_run_ids dri, MTRPhoto.dashboard_entry_day ded, MTRPhoto.dashboard_places dp WHERE dp.name= "Romainville_Presse_1" AND ded.dashboard_place_id=dp.id AND dri.dashboard_entry_day=ded.id AND dri.id=ded.last_run_id AND ded.date >= "2022-08-01" AND ded.date <= "2022-08-31" apple3 {'gm': {'mat': 'gm', 'pht': 4209, 'datou_carac_id': 3994, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'emr': {'mat': 'emr', 'pht': 4207, 'datou_carac_id': 3993, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'carton,background'}, 'jrm': {'mat': 'jrm', 'pht': 3726, 'datou_carac_id': 3459, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'ela': {'mat': 'ela', 'pht': 4203, 'datou_carac_id': 3991, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'ela,background'}, 'pet_clair': {'mat': 'pet_clair', 'pht': 3327, 'datou_carac_id': 3804, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_clair,bouchon,etiquette,barquette_avec_film,background'}, 'film_pedb': {'mat': 'film_pedb', 'pht': 4461, 'datou_carac_id': 4322, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'film_plastique'}, 'pehd_pp': {'mat': 'pehd_pp', 'pht': 4211, 'datou_carac_id': 3995, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pehd,background'}, 'pet_fonce': {'mat': 'pet_fonce', 'pht': 4200, 'datou_carac_id': 4153, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_fonce,background'}, 'aluminium': {'mat': 'aluminium', 'pht': 4205, 'datou_carac_id': 3992, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'metal,background'}, 'refus': {'mat': 'refus', 'pht': 3594, 'datou_carac_id': 3318, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'refus,background'}} SELECT h.hashtag as unwanted_material, substr(dr.hashtag,8) as main_material, ptp.type as pht_type, sum(pcr.value*dr.nombre_balle)/sum(dr.nombre_balle) as ratio, count(distinct mpp.mtr_photo_id) as nb_photo, group_concat(distinct ptp.mtr_portfolio_id_2) as list_port_cont, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_port, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", h.hashtag, ":", cast(ptp.type as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_mat FROM MTRPhoto.dashboard_results dr, MTRPhoto.mtr_port_to_port_ids ptp, MTRUser.mtr_portfolio_photos mpp, MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h WHERE dr.dashboard_run_id IN (449271,454376,454388,454411,454399,454424,451519,452689,468274,455185,456478,457640,459828,461074,462392,463490,464885,465870,469411,470781,897152,474755,474338,475306,477478,478927,479886) AND dr.mtr_portfolio_id=ptp.mtr_portfolio_id_1 AND dr.qualite >= 0 AND mpp.mtr_portfolio_id=ptp.mtr_portfolio_id_2 AND pcr.portfolio_id=ptp.mtr_portfolio_id_1 AND h.hashtag_id = pcr.hashtag_id AND ptp.type = pcr.hashtag_type AND mpp.hide_status = 0 AND ptp.hashtag_id=h.hashtag_id AND ptp.type IN (4209,4207,3726,4203,3327,4461,4211,4200,4205,3594) group by h.hashtag, dr.hashtag, ptp.type; VR TODO TO BETTER PARSE ! ({'unwanted_material': 'autre', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.025985557831013643, 'nb_photo': 16651, 'list_port_cont': '6601211,6601249,6601319,6601376,6601558,6601691,6601783,6601837,6602312,6603611,6603837,6603984,6604939,6606283,6606361,6607127,6607396,6607889,6608318,6608592,6608684,6608960,6609911,6610060,6610278,6610907,6611078,6613725,6614148,6615899,6616064,6616199,6616975,6617461,6617846,6618177,6618947,6620505,6620992,6621119,6621146,6621174,6621388,6621447,6621537,6622105,6622162,6622311,6622378,6622426,6622514,6622647,6622758,6626372,6626974,6626997,6627200,6627837,6628024,6628557,6628624,6629354,6629396,6635806,6635864,6635897,6635947,6635974,6636029,6636109,6636139,6636295,6636316,6636423,6636599,6636674,6636727,6636791,6637048,6637689,6637708,6637756,6637854,6637954,6637974,6638095,6638421,6638597,6638696,6638833,6638947,6638968,6639187,6639299,6639353,6639652,6639761,6639844,6639904,6639965,6640123,6640725,6640745,6640776,6640959,6641209,6641306,6641333,6641484,6641521,6641655,6641820,6641954,6642026,6642079,6642285,6643237,6643372,6643479,6643603,6643631,6643664,6643695,6643718,6643895,6644078,6644458,6644663,', 'assoc_port': '6600535:6601249,6600537:6601691,6600543:6601211,6600545:6601376,6600547:6601558,6600550:6601783,6600553:6601319,6601140:6602312,6601199:6601837,6602727:6603837,6602729:6603984,6602732:6603611,6604400:6604939,6604702:6606361,6605500:6606283,6605502:6607127,6606682:6608318,6606685:6607889,6606687:6607396,6607836:6608592,6607838:6608684,6608144:6608960,6609197:6610060,6609198:6609911,6609963:6610278,6610497:6611078,6610499:6610907,6612953:6613725,6613333:6614148,6614966:6616199,6614968:6616064,6615360:6615899,6616171:6616975,6616960:6618177,6616966:6617461,6616967:6617846,6618310:6618947,6620036:6620505,6620039:6621388,6620042:6620992,6620404:6621146,6620441:6621447,6620445:6621174,6620446:6621537,6620449:6621119,6621639:6622647,6621642:6622758,6621645:6622514,6621650:6622378,6621652:6622426,6621655:6622162,6621656:6622105,6621661:6622311,6625901:6626372,6626270:6626974,6626272:6626997,6626275:6627200,6627097:6627837,6627099:6628024,6627946:6628557,6627948:6628624,6628988:6629354,6628991:6629396,6630847:6635947,', 'assoc_mat': '6600535:autre:3327:6601249,6600537:autre:3327:6601691,6600543:autre:3327:6601211,6600545:autre:3327:6601376,6600547:autre:3327:6601558,6600550:autre:3327:6601783,6600553:autre:3327:6601319,6601140:autre:3327:6602312,6601199:autre:3327:6601837,6602727:autre:3327:6603837,6602729:autre:3327:6603984,6602732:autre:3327:6603611,6604400:autre:3327:6604939,6604702:autre:3327:6606361,6605500:autre:3327:6606283,6605502:autre:3327:6607127,6606682:autre:3327:6608318,6606685:autre:3327:6607889,6606687:autre:3327:6607396,6607836:autre:3327:6608592,6607838:autre:3327:6608684,6608144:autre:3327:6608960,6609197:autre:3327:6610060,6609198:autre:3327:6609911,6609963:autre:3327:6610278,6610497:autre:3327:6611078,6610499:autre:3327:6610907,6612953:autre:3327:6613725,6613333:autre:3327:6614148,6614966:autre:3327:6616199,6614968:autre:3327:6616064,6615360:autre:3327:6615899,6616171:autre:3327:6616975,6616960:autre:3327:6618177,6616966:autre:3327:6617461,6616967:autre:3327:6617846,6618310:autre:3327:6618947,6620036:autre:3327:662050'}, {'unwanted_material': 'barquette_avec_film', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.0050634087913797535, 'nb_photo': 1051, 'list_port_cont': '6601216,6601255,6601316,6601366,6601556,6601694,6601795,6601849,6602314,6603610,6603845,6603981,6604938,6606272,6606380,6607136,6607388,6607885,6608324,6608593,6608675,6608967,6609910,6610048,6610273,6611082,6613721,6614147,6615898,6616193,6616972,6617468,6618176,6618950,6620509,6620988,6621144,6621167,6621391,6621449,6622102,6622163,6622316,6622379,6622436,6622519,6622752,6626364,6626973,6627004,6627197,6627840,6628023,6628564,6628623,6629352,6629394,6635811,6635862,6635901,6635945,6635972,6636036,6636112,6636138,6636318,6636424,6636724,6637044,6637685,6637716,6637764,6637852,6637945,6637984,6638094,6638596,6638700,6638835,6638944,6638974,6639298,6639361,6639645,6639764,6639852,6639896,6639968,6640121,6640722,6640783,6640963,6641211,6641294,6641332,6641486,6641520,6641652,6641944,6642022,6642080,6642290,6643373,6643478,6643608,6643683,6643716,6643902,6644079,6644456,6644652,6645162,6645304,6645766,6647408,6648376,6648805,6649738,6651383,6651686,6654487,6654608,6655045,6655877,6656213,6656668,6659085,6660221,', 'assoc_port': '6600535:6601255,6600537:6601694,6600543:6601216,6600545:6601366,6600547:6601556,6600550:6601795,6600553:6601316,6601140:6602314,6601199:6601849,6602727:6603845,6602729:6603981,6602732:6603610,6604400:6604938,6604702:6606380,6605500:6606272,6605502:6607136,6606682:6608324,6606685:6607885,6606687:6607388,6607836:6608593,6607838:6608675,6608144:6608967,6609197:6610048,6609198:6609910,6609963:6610273,6610497:6611082,6612953:6613721,6613333:6614147,6614966:6616193,6615360:6615898,6616171:6616972,6616960:6618176,6616966:6617468,6618310:6618950,6620036:6620509,6620039:6621391,6620042:6620988,6620404:6621144,6620441:6621449,6620445:6621167,6621642:6622752,6621645:6622519,6621650:6622379,6621652:6622436,6621655:6622163,6621656:6622102,6621661:6622316,6625901:6626364,6626270:6626973,6626272:6627004,6626275:6627197,6627097:6627840,6627099:6628023,6627946:6628564,6627948:6628623,6628988:6629352,6628991:6629394,6630847:6635945,6630849:6636036,6630851:6635972,6630854:6637685,6630934:6637764,6630936:6637716,6630938:6638094,', 'assoc_mat': '6600535:barquette_avec_film:3327:6601255,6600537:barquette_avec_film:3327:6601694,6600543:barquette_avec_film:3327:6601216,6600545:barquette_avec_film:3327:6601366,6600547:barquette_avec_film:3327:6601556,6600550:barquette_avec_film:3327:6601795,6600553:barquette_avec_film:3327:6601316,6601140:barquette_avec_film:3327:6602314,6601199:barquette_avec_film:3327:6601849,6602727:barquette_avec_film:3327:6603845,6602729:barquette_avec_film:3327:6603981,6602732:barquette_avec_film:3327:6603610,6604400:barquette_avec_film:3327:6604938,6604702:barquette_avec_film:3327:6606380,6605500:barquette_avec_film:3327:6606272,6605502:barquette_avec_film:3327:6607136,6606682:barquette_avec_film:3327:6608324,6606685:barquette_avec_film:3327:6607885,6606687:barquette_avec_film:3327:6607388,6607836:barquette_avec_film:3327:6608593,6607838:barquette_avec_film:3327:6608675,6608144:barquette_avec_film:3327:6608967,6609197:barquette_avec_film:3327:6610048,6609198:barquette_avec_film:3327:6609910,6609963:barquette_avec_film:3327:6610273'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0006065490165070008, 'nb_photo': 9, 'list_port_cont': '6603426,6627334,6634749,6723417,6780640,6834099,6852168', 'assoc_port': '6602731:6603426,6626721:6627334,6630937:6634749,6671808:6834099,6722813:6723417,6780026:6780640,6851650:6852168', 'assoc_mat': '6602731:barquette_opaque:4203:6603426,6626721:barquette_opaque:4203:6627334,6630937:barquette_opaque:4203:6634749,6671808:barquette_opaque:4203:6834099,6722813:barquette_opaque:4203:6723417,6780026:barquette_opaque:4203:6780640,6851650:barquette_opaque:4203:6852168'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0005293780694215035, 'nb_photo': 1, 'list_port_cont': '6692906', 'assoc_port': '6691778:6692906', 'assoc_mat': '6691778:barquette_opaque:4209:6692906'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.008662761320534096, 'nb_photo': 659, 'list_port_cont': '6600860,6600903,6601023,6601433,6602273,6602593,6603403,6606082,6607689,6608069,6608289,6609462,6610653,6614532,6615427,6615792,6616525,6618779,6618922,6620328,6621053,6621664,6621960,6622002,6622135,6622199,6624057,6626607,6627268,6628481,6629183,6629252,6634382,6635139,6638289,6638441,6640989,6642058,6642706,6650155,6650276,6650959,6658495,6664041,6668175,6669610,6672035,6674014,6674549,6674630,6674772,6675081,6675105,6675139,6675482,6676524,6677132,6677217,6678042,6678216,6678577,6678660,6678688,6678769,6678960,6678988,6681252,6687138,6692292,6693194,6693681,6693891,6694045,6694217,6694420,6698421,6698805,6699377,6699427,6700669,6707231,6711232,6711409,6711495,6712142,6713437,6713607,6713753,6714397,6716562,6717498,6717555,6718025,6719023,6719736,6720660,6720723,6721143,6721925,6722677,6723045,6723102,6724740,6724912,6724973,6725830,6729866,6730263,6730718,6731855,6732324,6732440,6735375,6738136,6740197,6740602,6741090,6741939,6743256,6743597,6743876,6745278,6747827,6751581,6751678,6751683,6752336,6754140,', 'assoc_port': '10844008:10846065,10844009:10845784,10844011:10845552,10844012:10845981,10844014:10845176,10844016:10845161,10844017:10845129,10844018:10844946,10844019:10845048,10844020:10844679,12107427:12108055,12107429:12107821,6600536:6601433,6600544:6600860,6600546:6601023,6600551:6600903,6601138:6602593,6601649:6602273,6602733:6603403,6604399:6608069,6605499:6610653,6605688:6606082,6606683:6607689,6607839:6608289,6609199:6609462,6614199:6614532,6614969:6615427,6615362:6615792,6616173:6616525,6618307:6618779,6618311:6618922,6620040:6620328,6620422:6621664,6620444:6621053,6621638:6624057,6621647:6622199,6621651:6622135,6621654:6621960,6621659:6622002,6626268:6626607,6627098:6627268,6627945:6628481,6628987:6629183,6628989:6629252,6630852:6674014,6630932:6674630,6630935:6674772,6630939:6675105,6631065:6675081,6631069:6675482,6631193:6635139,6631477:6676524,6631489:6677132,6631492:6677217,6631967:6634382,6632585:6678216,6632660:6678577,6632663:6678660,6632714:6678769,6632787:6678688,6632877:6678960,6632881:6678988,6635669:', 'assoc_mat': '10844008:barquette_opaque:4211:10846065,10844009:barquette_opaque:4211:10845784,10844011:barquette_opaque:4211:10845552,10844012:barquette_opaque:4211:10845981,10844014:barquette_opaque:4211:10845176,10844016:barquette_opaque:4211:10845161,10844017:barquette_opaque:4211:10845129,10844018:barquette_opaque:4211:10844946,10844019:barquette_opaque:4211:10845048,10844020:barquette_opaque:4211:10844679,12107427:barquette_opaque:4211:12108055,12107429:barquette_opaque:4211:12107821,6600536:barquette_opaque:4211:6601433,6600544:barquette_opaque:4211:6600860,6600546:barquette_opaque:4211:6601023,6600551:barquette_opaque:4211:6600903,6601138:barquette_opaque:4211:6602593,6601649:barquette_opaque:4211:6602273,6602733:barquette_opaque:4211:6603403,6604399:barquette_opaque:4211:6608069,6605499:barquette_opaque:4211:6610653,6605688:barquette_opaque:4211:6606082,6606683:barquette_opaque:4211:6607689,6607839:barquette_opaque:4211:6608289,6609199:barquette_opaque:4211:6609462,6614199:barquette_opaque:4211:6614532,6614969:barq'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.0009915902324084817, 'nb_photo': 5, 'list_port_cont': '6649286,6687459,6784020,6813915,6920756', 'assoc_port': '6631070:6649286,6686295:6687459,6782249:6784020,6813389:6813915,6920100:6920756', 'assoc_mat': '6631070:barquette_opaque:4200:6649286,6686295:barquette_opaque:4200:6687459,6782249:barquette_opaque:4200:6784020,6813389:barquette_opaque:4200:6813915,6920100:barquette_opaque:4200:6920756'}, {'unwanted_material': 'bouchon', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.002148587132747826, 'nb_photo': 2481, 'list_port_cont': 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'assoc_port': 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{'unwanted_material': 'pet_opaque', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0009264502462378221, 'nb_photo': 9, 'list_port_cont': '6608814,6660097,6712722,6735145,6799275,6808109,6869931', 'assoc_port': '6608146:6608814,6632879:6660097,6685476:6799275,6690887:6735145,6712332:6712722,6786466:6808109,6869544:6869931', 'assoc_mat': '6608146:pet_opaque:4203:6608814,6632879:pet_opaque:4203:6660097,6685476:pet_opaque:4203:6799275,6690887:pet_opaque:4203:6735145,6712332:pet_opaque:4203:6712722,6786466:pet_opaque:4203:6808109,6869544:pet_opaque:4203:6869931'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.007255625347838711, 'nb_photo': 19, 'list_port_cont': '6616740,6617521,6620419,6621938,6627043,6638500,6668139,6668209,6670564,6670896,6678093,6740485,6791207', 'assoc_port': 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'6600901,6602283,6603013,6606069,6608294,6610401,6610657,6611146,6614528,6615400,6621062,6621675,6621998,6622129,6622205,6626613,6629184,6629244,6634651,6635837,6638286,6639164,6650157,6668179,6669616,6672038,6674023,6674634,6674773,6675099,6675136,6677119,6677811,6678215,6678578,6678957,6679224,6681250,6691510,6691659,6693900,6694213,6694417,6698434,6698768,6698799,6699430,6711411,6711493,6712146,6712497,6712754,6713439,6716561,6717501,6717546,6718028,6719452,6720657,6720722,6724914,6725690,6725833,6728841,6729870,6730329,6730727,6731863,6732437,6735376,6738126,6740205,6740607,6741938,6743382,6745286,6751579,6751673,6754142,6754990,6755968,6756060,6756975,6762171,6763524,6767274,6768410,6773018,6774679,6775056,6778932,6780264,6780486,6783061,6791499,6793831,6794248,6794400,6795572,6796823,6799103,6799441,6799779,6800492,6800530,6801272,6801402,6806174,6808133,6808682,6808713,6808832,6808873,6809086,6810005,6815419,6822330,6839899,6842610,6853846,6855228,6855448,6863478,6866679,6869888,6870654,6875922,6890225,', 'assoc_port': '10844011:10845551,10844021:10844440,6600551:6600901,6601649:6602283,6602728:6603013,6605499:6610657,6605688:6606069,6607835:6610401,6607839:6608294,6610992:6611146,6614199:6614528,6614967:6615400,6620422:6621675,6620444:6621062,6621647:6622205,6621651:6622129,6621659:6621998,6626268:6626613,6628987:6629184,6628989:6629244,6630852:6674023,6630856:6634651,6630932:6674634,6630935:6674773,6630939:6675099,6631489:6677119,6631960:6677811,6632585:6678215,6632660:6678578,6632877:6678957,6632963:6679224,6633862:6635837,6635671:6693900,6635682:6694213,6637236:6698768,6637845:6638286,6638776:6639164,6640595:6806174,6647215:6717546,6648944:6650157,6653523:6725690,6654038:6724914,6654837:6725833,6655426:6728841,6656895:6729870,6657891:6731863,6666361:6668179,6668952:6751579,6668953:6669616,6671805:6756060,6671810:6672038,6672701:6755968,6674723:6675136,6681005:6681250,6683620:6691510,6685431:6799103,6685494:6799441,6685525:6799779,6686006:6855228,6686021:6855448,6686094:6800530,6686269:6800492,6686302:6801272,6686308:6801', 'assoc_mat': '10844011:pet_opaque:4211:10845551,10844021:pet_opaque:4211:10844440,6600551:pet_opaque:4211:6600901,6601649:pet_opaque:4211:6602283,6602728:pet_opaque:4211:6603013,6605499:pet_opaque:4211:6610657,6605688:pet_opaque:4211:6606069,6607835:pet_opaque:4211:6610401,6607839:pet_opaque:4211:6608294,6610992:pet_opaque:4211:6611146,6614199:pet_opaque:4211:6614528,6614967:pet_opaque:4211:6615400,6620422:pet_opaque:4211:6621675,6620444:pet_opaque:4211:6621062,6621647:pet_opaque:4211:6622205,6621651:pet_opaque:4211:6622129,6621659:pet_opaque:4211:6621998,6626268:pet_opaque:4211:6626613,6628987:pet_opaque:4211:6629184,6628989:pet_opaque:4211:6629244,6630852:pet_opaque:4211:6674023,6630856:pet_opaque:4211:6634651,6630932:pet_opaque:4211:6674634,6630935:pet_opaque:4211:6674773,6630939:pet_opaque:4211:6675099,6631489:pet_opaque:4211:6677119,6631960:pet_opaque:4211:6677811,6632585:pet_opaque:4211:6678215,6632660:pet_opaque:4211:6678578,6632877:pet_opaque:4211:6678957,6632963:pet_opaque:4211:6679224,6633862:pet_opaque:4211:6635'}, {'unwanted_material': 'pet_opaque', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.006625372408584668, 'nb_photo': 447, 'list_port_cont': '6600927,6602060,6604867,6606114,6609165,6613073,6615618,6618507,6620186,6620865,6621897,6626785,6628362,6634254,6635389,6637069,6637443,6638357,6639233,6641563,6646016,6647330,6649289,6649475,6649908,6653440,6654682,6655700,6658472,6662384,6665186,6669577,6670052,6672606,6674518,6683671,6684478,6686802,6687225,6687464,6696442,6696817,6698458,6711562,6712277,6712786,6714508,6717607,6719819,6720158,6720206,6720247,6720358,6721243,6722532,6724146,6739282,6740800,6745521,6745789,6750245,6754191,6754564,6759415,6765265,6766702,6767358,6780406,6780528,6780714,6784016,6786843,6788645,6791330,6793704,6795548,6797668,6817665,6821371,6824096,6835190,6840200,6842443,6849228,6851781,6852374,6854530,6859022,6862735,6865768,6866326,6867982,6869259,6874403,6875763,6882730,6886804,6888096,6889498,6895283,6900338,6902503,6904834,6914452,6920753,6921628,6924247', 'assoc_port': '6600538:6600927,6601136:6602060,6604397:6604867,6605689:6606114,6608145:6609165,6612632:6613073,6615365:6615618,6618309:6618507,6620043:6620186,6620439:6620865,6621653:6621897,6626271:6626785,6627944:6628362,6630848:6646016,6631070:6649289,6631186:6634254,6631194:6649908,6631961:6655700,6632583:6662384,6632786:6635389,6632966:6670052,6635673:6637443,6635681:6637069,6637843:6638357,6638774:6639233,6641286:6641563,6646447:6647330,6648940:6649475,6650951:6653440,6654041:6654682,6657889:6658472,6664429:6665186,6668950:6669577,6671809:6672606,6673895:6674518,6681680:6683671,6683618:6684478,6685440:6720358,6685511:6720206,6686027:6719819,6686041:6720158,6686295:6687464,6686312:6687225,6686325:6686802,6688871:6714508,6690894:6698458,6693641:6696442,6696476:6696817,6710860:6711562,6711680:6712277,6712324:6712786,6716688:6717607,6718843:6720247,6720803:6721243,6722354:6722532,6723892:6724146,6738748:6739282,6740018:6740800,6740709:6745521,6744268:6745789,6748062:6750245,6753406:6754564,6753419:6754191,6759057:6759415,', 'assoc_mat': '6600538:pet_opaque:4200:6600927,6601136:pet_opaque:4200:6602060,6604397:pet_opaque:4200:6604867,6605689:pet_opaque:4200:6606114,6608145:pet_opaque:4200:6609165,6612632:pet_opaque:4200:6613073,6615365:pet_opaque:4200:6615618,6618309:pet_opaque:4200:6618507,6620043:pet_opaque:4200:6620186,6620439:pet_opaque:4200:6620865,6621653:pet_opaque:4200:6621897,6626271:pet_opaque:4200:6626785,6627944:pet_opaque:4200:6628362,6630848:pet_opaque:4200:6646016,6631070:pet_opaque:4200:6649289,6631186:pet_opaque:4200:6634254,6631194:pet_opaque:4200:6649908,6631961:pet_opaque:4200:6655700,6632583:pet_opaque:4200:6662384,6632786:pet_opaque:4200:6635389,6632966:pet_opaque:4200:6670052,6635673:pet_opaque:4200:6637443,6635681:pet_opaque:4200:6637069,6637843:pet_opaque:4200:6638357,6638774:pet_opaque:4200:6639233,6641286:pet_opaque:4200:6641563,6646447:pet_opaque:4200:6647330,6648940:pet_opaque:4200:6649475,6650951:pet_opaque:4200:6653440,6654041:pet_opaque:4200:6654682,6657889:pet_opaque:4200:6658472,6664429:pet_opaque:4200:6665186,'}, {'unwanted_material': 'textiles_sanitaires', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 5.317677982697793e-05, 'nb_photo': 1, 'list_port_cont': '6754069', 'assoc_port': '6753416:6754069', 'assoc_mat': '6753416:textiles_sanitaires:4203:6754069'}, {'unwanted_material': 'textiles_sanitaires', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.00014015295179752374, 'nb_photo': 14, 'list_port_cont': '6608063,6675111,6678967,6709734,6720656,6724749,6725824,6735649,6787860,6791503,6813248,6828520,6855445,10845973', 'assoc_port': '10844012:10845973,6604399:6608063,6630939:6675111,6632877:6678967,6654837:6725824,6663625:6735649,6686021:6855445,6709374:6709734,6720298:6720656,6724591:6724749,6787210:6787860,6790919:6791503,6812962:6813248,6814154:6828520', 'assoc_mat': '10844012:textiles_sanitaires:4211:10845973,6604399:textiles_sanitaires:4211:6608063,6630939:textiles_sanitaires:4211:6675111,6632877:textiles_sanitaires:4211:6678967,6654837:textiles_sanitaires:4211:6725824,6663625:textiles_sanitaires:4211:6735649,6686021:textiles_sanitaires:4211:6855445,6709374:textiles_sanitaires:4211:6709734,6720298:textiles_sanitaires:4211:6720656,6724591:textiles_sanitaires:4211:6724749,6787210:textiles_sanitaires:4211:6787860,6790919:textiles_sanitaires:4211:6791503,6812962:textiles_sanitaires:4211:6813248,6814154:textiles_sanitaires:4211:6828520'}) select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(449271,454376,454388,454411,454399,454424,451519,452689,468274,455185,456478,457640,459828,461074,462392,463490,464885,465870,469411,470781,897152,474755,474338,475306,477478,478927,479886)); after get_hostname_from_raspi hasthag : emr hasthag that could be used but not yet : _______carton,background hasthag : jrm hasthag that could be used but not yet : _______papier,background hasthag : aluminium hasthag that could be used but not yet : _______metal,background hasthag : pet_fonce hasthag that could be used but not yet : _______pet_fonce,background hasthag : gm hasthag that could be used but not yet : _______papier,background after impurety_average_per_hashtag ############################### TEST csv ################################ Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_csv VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier create_report_from_API() got an unexpected keyword argument 'outfolder' ERROR csv FAILED #&_#_#&_# TEST memo SUCCEEDED #&_#_#&_# #&_#_#&_# TEST one_day SUCCEEDED #&_#_#&_# #&_#_#&_# TEST get_data SUCCEEDED #&_#_#&_# #&_#_#&_# TEST csv FAILED #&_#_#&_# #&_# TEST FAILED #&_# : prod/memo/memo #&_# #&_# END OF TEST #&_# : prod/memo/memo #&_# /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) /usr/lib/python3/dist-packages/paramiko/transport.py:220: CryptographyDeprecationWarning: Blowfish has been deprecated "class": algorithms.Blowfish, #######all_test_python_finish###### insert result in bdd : Test are not running on charlot, the path should be tested ! /data_2/data_log/job/2025/May/29052025/python_test3/output_tests_python-1135.html new path : /proc/1921311/ /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:720: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_origin_portfolio_ids = [int(item) for item in options.list_origin_portfolio_ids.split(",")] if options.list_origin_portfolio_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:721: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_photo_ids = [int(item) for item in options.list_photo_ids.split(",")] if options.list_photo_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:722: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval = [int(item) for item in options.interval_rotation.split(",")] if options.interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:723: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval = [float(item) for item in options.interval_resize.split(",")] if options.interval_resize is not "" else None /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:750: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi = [float(item) for item in options.mother_crop_portfolio_multi.split(",")] if options.mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1952: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1958: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2143: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2149: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None Name Stmts Miss Cover Missing ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ /home/admin/.local/lib/python3.8/site-packages/Crypto/Hash/SHA256.py 46 29 37% 72-80, 89-93, 104-112, 122, 135-140, 145, 158, 171-185 /home/admin/.local/lib/python3.8/site-packages/Crypto/Hash/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/Numbers.py 11 7 36% 36-42 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/Primality.py 154 141 8% 65-116, 134-213, 244-277, 314-335, 354-369 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/_IntegerBase.py 226 121 46% 43, 47, 51, 55, 60, 65, 69, 73, 77, 81, 85, 89, 94, 99, 103, 107, 111, 115, 119, 123, 127, 131, 135, 139, 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426-475, 518-618, 635-638, 646-647, 652-655, 660-661, 665-670, 676-688, 693-712, 756-791 /home/admin/.local/lib/python3.8/site-packages/Crypto/PublicKey/__init__.py 33 29 12% 46-60, 65-73, 79-94 /home/admin/.local/lib/python3.8/site-packages/Crypto/Random/__init__.py 16 6 62% 31, 35, 39, 43, 48, 52 /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/PKCS1_v1_5.py 14 9 36% 42-46, 49-52 /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/pkcs1_15.py 49 39 20% 49, 53, 73-84, 106-138, 191-207, 221 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/_file_system.py 8 1 88% 46 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/_raw_api.py 164 105 36% 42-46, 61, 66, 77, 99, 113, 118-126, 130, 134, 137-143, 149, 152, 155, 158, 162-261, 268-269, 272, 275-276, 279-284, 304-305, 307-309, 314, 318 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158, 169, 175-176, 182-183, 189, 199, 209-210, 214, 218-220, 224-228, 232-239, 243-254, 259-273, 287-290, 293, 296-297, 301-306, 309-320, 323-324, 340-348, 357-358, 361, 387-397, 400-401, 405-413, 421-422, 426, 434-435, 439, 447-448, 452, 455-456, 478-490, 494, 500-523, 528-556, 560, 563-582, 618-619, 628-636, 639-641, 650-656, 659, 663-664, 667, 670, 678-687, 695-703, 706, 710-712, 718-732, 737-739, 746-761, 799-804, 811-813, 817-831, 835-836, 876-878, 885-890, 894-898, 914, 933, 936-943, 951-954, 957-969, 979-1002, 1009-1013, 1021, 1024-1041, 1065-1068, 1072-1090, 1097-1118, 1121-1157, 1169-1174, 1180-1191, 1206-1212, 1215-1227, 1231-1233, 1257-1261, 1268-1283, 1286-1288, 1301-1306, 1312-1329, 1384-1401, 1404-1408, 1411, 1421-1458, 1466-1512, 1515-1522, 1528, 1535-1566, 1572-1573, 1576-1584, 1587-1614, 1617-1624, 1629-1639, 1660-1668, 1671, 1674-1678, 1681-1686, 1689-1692, 1695-1725, 1733, 1746, 1757-1760, 1772-1798, 1806-1810, 1814-1816, 1827-1832, 1837-1838, 1841-1848, 1858-1861, 1866-1882, 1885, 1895, 1911-1915, 1931-1932, 1941-1947, 1959, 1962-1965, 1973-1985, 1994-2000, 2009-2021, 2025-2027, 2031-2057, 2075-2083, 2086-2111, 2114-2128 /home/admin/.local/lib/python3.8/site-packages/botocore/discovery.py 183 139 24% 40-42, 47, 51-52, 56-60, 63-67, 70-75, 78, 83-90, 95-107, 110-115, 118-121, 124-125, 128, 131-133, 138-142, 145-148, 151-160, 163-168, 171, 174-214, 219, 222-228, 231-236, 239-251, 254-274 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/__init__.py 11 8 27% 28-38 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/docstringparser.py 119 83 30% 24-26, 29-30, 34-36, 39-42, 45, 48, 51, 61-64, 67-79, 82-87, 90, 93, 98, 101, 106-107, 110-111, 114, 117-118, 126-128, 131-133, 136-138, 141-143, 148, 151-152, 160-170, 178-181, 184, 187-200 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/restdoc.py 112 74 34% 25-33, 36-37, 43, 49, 56, 62, 68, 74-78, 81, 84-85, 88-97, 101-103, 120-128, 133, 141, 145, 149, 153, 156-157, 175-183, 187, 191, 201-209, 212, 215, 218 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/style.py 288 206 28% 22-25, 29, 33, 36, 39, 42-43, 46, 49, 52, 55, 58, 61, 64, 70-73, 76, 79, 82, 91-101, 104, 107, 110-111, 114-115, 118-121, 124-126, 129-132, 135, 138, 141, 144, 147, 150-153, 156-157, 160-161, 164-165, 168-169, 172-175, 178-181, 184-185, 188-191, 194-195, 198-201, 204-205, 208-219, 222, 225-230, 233-254, 257-258, 261-262, 265-267, 270-271, 274-277, 280-283, 286-289, 293-296, 299-302, 305, 308, 311, 314, 317-319, 322-323, 332-334, 337-342, 345-351, 354-357, 360-361, 364-368, 371-374, 377-378, 381-387, 390-391, 394-397, 400-401, 404-406, 409-412, 415-418 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/client.py 170 130 24% 28-32, 39-43, 46, 49-67, 71, 75-81, 84-86, 90-94, 97, 100, 103-110, 113-132, 167-168, 171-174, 177, 180-192, 195-196, 199-210, 213-214, 217-223, 226-229, 232-246, 249-254, 257-264, 267-276, 281-290 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/docstring.py 38 19 50% 33-36, 40, 43, 58-60, 63, 70-72, 75-81, 86, 91, 96 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/example.py 127 107 16% 36-41, 46, 50-56, 60-66, 70-81, 85-110, 114-125, 128-132, 135-139, 142-146, 152-158, 166-169, 177-208 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/method.py 97 86 11% 33-39, 60-78, 99-105, 117-123, 169-281 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/paginator.py 49 39 20% 22-24, 31-43, 46-65, 94-167 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/params.py 132 108 18% 33-34, 39, 43, 47-56, 60-77, 82-95, 98, 101-105, 108-117, 120-121, 124-125, 134-145, 149, 159-177, 182-212, 215-216, 219-220 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/service.py 53 36 32% 24-32, 46-55, 58-59, 66, 69-75, 78, 81-88, 91-96, 99-102 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/shape.py 39 31 21% 27-32, 61-89, 92-98, 101-107, 110-117 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/sharedexample.py 143 123 14% 33-38, 41-55, 58-72, 88-97, 101-125, 128-147, 150-164, 169-170, 173, 176-180, 183-187, 190-192, 195-198, 213-218 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/utils.py 75 33 56% 27, 50, 69-77, 117-128, 160-166, 176-180, 197 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/waiter.py 35 25 29% 22-24, 31-38, 41-56, 83-119 /home/admin/.local/lib/python3.8/site-packages/botocore/endpoint.py 153 117 24% 54-70, 85-94, 97, 100-102, 105-118, 122-124, 127-128, 131-158, 166-183, 186-230, 236-245, 249-266, 269, 274, 285-303, 314, 323-327 /home/admin/.local/lib/python3.8/site-packages/botocore/errorfactory.py 32 21 34% 28, 44, 47-51, 58, 70-74, 77-88 /home/admin/.local/lib/python3.8/site-packages/botocore/eventstream.py 246 149 39% 33-34, 40-43, 49-52, 58-61, 71-72, 110, 123, 135-136, 148-149, 161-162, 175-176, 189-190, 203-204, 225-229, 251-253, 265, 279, 285-287, 293-295, 306, 318, 328, 334-337, 340-343, 384, 396-397, 400-406, 409-411, 414-416, 419-421, 424-428, 431, 442-444, 452, 455-459, 462-468, 471-472, 475-477, 480-483, 487-488, 491-494, 497-502, 506-507, 515-524, 527, 530, 577-581, 584-587, 590-594, 597-602, 605-612, 616 /home/admin/.local/lib/python3.8/site-packages/botocore/exceptions.py 200 32 84% 24-28, 40-42, 45, 82-84, 87, 369-370, 410-420, 423-430, 436, 620-622 /home/admin/.local/lib/python3.8/site-packages/botocore/handlers.py 434 313 28% 80, 90-106, 125-134, 138-152, 163-185, 189-198, 202-205, 210-214, 218-224, 228-236, 246, 256, 260-272, 276, 285, 289-297, 305-312, 316-330, 365-374, 378-392, 396-401, 414-420, 427-429, 434-443, 450-465, 475-477, 483-496, 505-514, 518-522, 544-562, 573-584, 588-593, 597-598, 603-605, 620-637, 658-668, 672, 676-690, 696-699, 703-707, 719, 732, 745, 763-773, 777-781, 822-835, 838-855, 858-862, 875, 885, 894-900, 903-905, 911-923, 927-929, 934-935 /home/admin/.local/lib/python3.8/site-packages/botocore/history.py 27 10 63% 22, 31, 34, 37, 40-47 /home/admin/.local/lib/python3.8/site-packages/botocore/hooks.py 249 196 21% 31-35, 60-63, 82, 99, 112, 124, 130-132, 143, 146-147, 158-163, 171-175, 195-215, 228, 243-247, 251, 256, 261, 266-302, 306-336, 339-344, 349-352, 355-356, 359-360, 364-365, 371-372, 378-379, 385-386, 391-420, 423-427, 430, 464, 472-483, 493-497, 500-528, 538-540, 543-564, 572-575, 581-589 /home/admin/.local/lib/python3.8/site-packages/botocore/httpsession.py 210 158 25% 39-41, 45-51, 61-88, 99-105, 109-113, 117-122, 126, 129-134, 137-139, 142-146, 169-195, 199-206, 209-219, 222, 225-234, 237-243, 246-251, 254-274, 277-281, 284-301, 304, 307-359 /home/admin/.local/lib/python3.8/site-packages/botocore/loaders.py 139 98 29% 127-134, 154, 166-175, 192-199, 222-238, 242, 246, 267-287, 310, 327-340, 374-389, 393-400, 419-424, 430-441, 455-456, 460-461 /home/admin/.local/lib/python3.8/site-packages/botocore/model.py 398 242 39% 42, 81-93, 118-126, 145-150, 161, 164, 167, 172, 178-188, 192-195, 199-206, 212, 218, 222, 228, 259-266, 269, 273, 277-281, 284, 288, 292-297, 301-305, 309, 313, 327-330, 334-337, 348-351, 355, 359, 363, 367-370, 374-379, 382-385, 394-397, 401, 404, 437-444, 448-451, 465, 469, 473, 477, 483, 487, 491-495, 500-505, 510-514, 520, 524-525, 529, 533, 537, 541, 544, 547, 551-556, 560, 564, 567, 570, 574-581, 584, 599-600, 603-616, 624-636, 643, 647, 688-691, 702-703, 712-719, 724-734, 737-745, 748-752, 755-762, 765-773, 776, 779-782, 793, 821-823 /home/admin/.local/lib/python3.8/site-packages/botocore/monitoring.py 221 149 33% 42-43, 47-48, 56-61, 73, 89, 92, 97, 104-110, 114-124, 127-129, 136-138, 141, 145-147, 150, 153, 171-173, 176, 179-181, 214-220, 229-235, 287-296, 342-343, 346-347, 362-370, 374, 380, 383, 386, 389-391, 394-408, 412-415, 419, 422, 426-433, 438, 442-444, 448-451, 456-460, 464-467, 470-472, 475-481, 484, 490, 493, 496-500, 503-508, 531-533, 542-550 /home/admin/.local/lib/python3.8/site-packages/botocore/paginate.py 369 304 18% 52-68, 72-79, 83-90, 94-101, 105, 127-136, 140-144, 158-161, 172-173, 178, 181-186, 193-207, 211, 216, 220-232, 236, 239-303, 321-329, 332, 335, 338-344, 349-357, 360-364, 370-397, 401-424, 427-440, 443-444, 448-490, 493-506, 513-534, 541-550, 557-566, 570, 573-576, 579-585, 588-591, 594-596, 599-604, 607, 617-618, 629-646, 668-669, 672-677 /home/admin/.local/lib/python3.8/site-packages/botocore/parsers.py 528 407 23% 135, 147, 150-151, 155, 165-174, 199-207, 214, 233-262, 265, 279-284, 289-291, 299, 302, 306, 310-312, 317-321, 324, 327-329, 334-336, 339-357, 360, 368-370, 373-399, 402-406, 413-421, 427-444, 447-458, 461-467, 471-474, 478, 482, 486, 490, 494, 504-517, 520, 523, 526-538, 541-542, 545-551, 557-560, 575-580, 583-588, 594-609, 612-619, 622, 625, 628-652, 655-656, 660-669, 675-685, 688-706, 709-726, 730-740, 747, 753, 759-761, 770-778, 781, 784-793, 799-800, 806-810, 813-818, 821-823, 826-836, 839-860, 864-879, 885-893, 900, 903-907, 915, 918-920, 925-933, 942-944, 960-970, 973, 986-1006, 1010-1011 /home/admin/.local/lib/python3.8/site-packages/botocore/regions.py 90 69 23% 58, 65, 85, 94-96, 99-102, 106-116, 119-136, 141-165, 169-173, 176-190, 193-195, 199 /home/admin/.local/lib/python3.8/site-packages/botocore/response.py 67 45 33% 44-46, 60-69, 76-87, 92, 97-100, 110-117, 124-127, 133-135, 141, 145-162 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/retries/adaptive.py 70 50 29% 14-35, 44-50, 53-54, 58-77, 90-96, 99-113, 117 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/base.py 6 2 67% 9, 27 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/bucket.py 70 48 31% 10, 13, 16, 24-32, 36, 40-56, 60, 64, 76-77, 80-100, 103, 106-114 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/quota.py 24 16 33% 12-16, 28-32, 46-53, 57 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/special.py 28 17 39% 23-27, 36-48 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/standard.py 200 132 34% 40-61, 71-73, 77-93, 108-129, 136-138, 172-189, 199-204, 215, 218, 223-224, 227, 230, 239-241, 257, 265, 268-272, 290-298, 301-310, 334-336, 341, 348, 351-354, 376-391, 396-398, 402-406, 421-422, 433, 439, 442, 458-460, 463-475, 478, 490-498 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/throttling.py 25 15 40% 13-17, 20-21, 24-28, 35-38, 50 /home/admin/.local/lib/python3.8/site-packages/botocore/retryhandler.py 158 118 25% 52-58, 68, 73-77, 85-87, 93-118, 124-128, 132-144, 148-155, 173-174, 183-187, 219-225, 228, 231, 245-247, 250-263, 266-277, 282, 285-291, 296-297, 300-307, 312, 315-320, 326, 329-340, 359 /home/admin/.local/lib/python3.8/site-packages/botocore/serialize.py 357 275 23% 65-69, 117, 122-130, 135-139, 142, 145-147, 150-157, 162, 168-170, 174-186, 190-192, 200-222, 232-234, 237-243, 246-262, 265-278, 282, 285, 289-292, 295, 298, 314-322, 325-328, 335-357, 360-362, 365-379, 382-385, 388-397, 400, 403, 407, 428-476, 485-493, 502-519, 523-525, 533-569, 572-574, 577, 580-591, 597-599, 606-610, 613-615, 618-641, 644-652, 663-671, 677-682, 685-686, 689-690, 694-695 /home/admin/.local/lib/python3.8/site-packages/botocore/session.py 384 276 28% 102-131, 134-141, 144, 147, 151, 156, 161-165, 169, 173, 177-186, 189-192, 196, 200-215, 218-226, 230, 236-238, 242-245, 248-251, 261-282, 308-313, 316, 340-353, 365-388, 398, 408, 427, 440-443, 468-480, 489, 505-506, 509-512, 515-518, 524-534, 540, 548, 573-588, 602-616, 652, 685, 690, 693-694, 697-707, 713, 719, 722, 725, 800-855, 860-877, 880-884, 892-893, 915-925, 931-932, 935-945, 948-952, 955-959, 964-965, 968, 971-972, 975, 978, 981, 992-995, 1017-1018, 1032-1038, 1051 /home/admin/.local/lib/python3.8/site-packages/botocore/signers.py 216 174 19% 64-71, 75, 79, 83, 90, 120-162, 174-199, 221-242, 271-276, 315-316, 333-351, 354-355, 385-396, 401, 406, 427-457, 462, 506-533, 537, 561-598, 604, 671-719, 725-734 /home/admin/.local/lib/python3.8/site-packages/botocore/translate.py 21 16 24% 21-38, 42-55, 70-76 /home/admin/.local/lib/python3.8/site-packages/botocore/utils.py 1126 888 21% 181-184, 196, 207-214, 218-220, 226-229, 236-256, 262-267, 276-300, 310, 322-331, 337, 340-364, 367-395, 413-432, 435-436, 439-441, 444, 450-453, 456-459, 462-472, 482-513, 516, 523-528, 531-536, 539, 545, 552-558, 569-587, 592-595, 599-604, 611-619, 642-655, 671-676, 681-695, 710-716, 747-762, 775-783, 804-810, 827-843, 855-860, 875-880, 910, 922-923, 926-949, 952-958, 963-966, 971-974, 980-981, 993-1004, 1007, 1011-1016, 1030-1039, 1053-1060, 1077-1124, 1128, 1150-1159, 1170-1179, 1184-1187, 1191-1193, 1197-1212, 1222-1230, 1238, 1243-1250, 1253-1256, 1266-1343, 1358-1376, 1379-1381, 1388-1395, 1398, 1407-1413, 1435-1437, 1440, 1443-1451, 1454-1462, 1465-1470, 1480-1481, 1490-1499, 1515-1523, 1526-1527, 1533-1553, 1556-1567, 1570, 1573-1600, 1608-1615, 1618-1619, 1622-1633, 1636-1662, 1669-1671, 1674-1679, 1682-1688, 1691-1697, 1705-1732, 1738-1744, 1750-1766, 1777-1785, 1788, 1791-1802, 1805, 1808, 1811-1835, 1838-1839, 1847-1853, 1856-1858, 1861-1862, 1865-1876, 1879-1883, 1886, 1889-1891, 1894-1906, 1909-1918, 1921, 1924-1925, 1928-1933, 1936-1942, 1945-1951, 1954-1957, 1960, 1967-1969, 1972, 1978-1985, 1988-1997, 2000, 2003-2013, 2016-2018, 2022-2031, 2037-2043, 2046-2051, 2054-2063, 2069-2075, 2078-2083, 2095-2100, 2111-2112, 2115-2119, 2125-2127, 2138-2139, 2142-2156, 2159-2180, 2183, 2187-2190, 2205-2211, 2221-2232, 2236-2240, 2244-2245, 2249-2254, 2259-2263, 2268-2269, 2272-2273, 2278-2280, 2283, 2286-2296 /home/admin/.local/lib/python3.8/site-packages/botocore/validate.py 164 114 30% 46-49, 55-56, 59-64, 71-84, 89, 92-94, 97-100, 103-128, 132-137, 140, 160-162, 165-166, 169-173, 179-182, 187-202, 215, 219-222, 226-231, 235, 238-244, 250, 254, 260, 266-269, 273-279, 284-285, 288-294 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/exceptions.py 26 6 77% 21-27 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/exceptions.py 67 15 78% 17-18, 22, 28-29, 33, 73-78, 85-87, 135-138 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/six.py 444 211 52% 49-72, 98-99, 112, 118-121, 131-133, 145, 154-157, 192-193, 203, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /home/admin/.local/lib/python3.8/site-packages/botocore/waiter.py 175 140 20% 44-73, 79-82, 87, 90-93, 113-121, 124-125, 132-136, 147-154, 158-162, 167-171, 175-186, 197-208, 212-219, 222-239, 242-259, 262-271, 274-284, 303-307, 310-367 /home/admin/.local/lib/python3.8/site-packages/cached_property.py 93 61 34% 14-15, 30-37, 40-47, 57-59, 62-74, 85-91, 94-95, 98-115, 118, 121, 124-128, 143-144, 147-148 /home/admin/.local/lib/python3.8/site-packages/cffi/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/cffi/api.py 544 341 37% 8-11, 52-59, 82, 97-98, 115-117, 121-123, 129-130, 133-135, 147, 160, 166, 169, 174, 190, 200, 203-211, 217-221, 227-229, 238-240, 284-291, 299, 318, 335, 361-365, 382, 392-403, 411-419, 431, 454-473, 476, 478, 483, 486-487, 495-508, 511-515, 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350-388 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/constant.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/legacy.py 19 14 26% 22-50 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/models.py 174 110 37% 20-34, 37-43, 49-62, 66, 70-72, 75, 78-86, 90, 97-103, 107, 111, 119, 127-147, 151, 155-157, 161, 165, 172, 176, 180, 184-192, 201, 208-212, 219, 229, 232, 239-246, 249, 252, 259-272, 278-280, 286, 308-318, 322, 337 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/utils.py 214 158 26% 24-28, 40-46, 54-60, 65-69, 74-78, 83-93, 98-108, 113-118, 123-128, 133, 137-139, 144-149, 154-159, 164-169, 174-179, 184-189, 194, 199, 212-237, 245, 266-276, 280, 284-296, 300-310, 314-334, 342, 353-358, 372-414 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/colorama/__init__.py 4 0 100% 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82-94, 98, 102-104, 107, 110-113, 116, 121-133, 137, 143-145, 148, 151-154, 157, 162-165, 169, 172, 175-178, 181, 186-189, 193, 196, 199-202, 205, 210-225, 229, 232-237, 242, 245-248, 251, 256-262, 266, 270, 273, 278-281, 284 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/name.py 232 142 39% 64-87, 91-106, 118-164, 168, 172, 180, 191-197, 200-203, 206, 209, 214-225, 230, 241, 247-250, 253, 256, 259, 262, 268, 274, 282-293, 304, 319, 327, 331, 334, 337-340, 345, 348-350, 353, 356-357, 388-391, 394, 397-399, 402-404, 407-412, 423-429, 432-437, 440-460 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/oid.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/__init__.py 16 2 88% 18-19 /home/admin/.local/lib/python3.8/site-packages/cv2/data/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/__init__.py 13 4 69% 6-7, 17, 24 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222-225, 230-235, 240-244, 249-253, 258-281 /home/admin/.local/lib/python3.8/site-packages/dill/logger.py 124 79 36% 123, 126, 129-135, 137-168, 182-184, 187-188, 192-209, 257, 263-266, 268-278, 280-285 /home/admin/.local/lib/python3.8/site-packages/dill/objtypes.py 9 1 89% 18 /home/admin/.local/lib/python3.8/site-packages/dill/pointers.py 60 50 17% 29-34, 44-51, 67-74, 84-115 /home/admin/.local/lib/python3.8/site-packages/dill/session.py 266 226 15% 40-55, 66-75, 78-117, 120-128, 221-262, 266-267, 273, 275, 277, 279, 281, 283-292, 296-304, 308-327, 431-507, 511-512, 571-603 /home/admin/.local/lib/python3.8/site-packages/dill/settings.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dill/source.py 613 574 6% 37-40, 45-49, 54-102, 115-258, 271-329, 345-346, 368-439, 444, 448-463, 468-473, 478, 483-508, 513-521, 525-529, 537-546, 552-560, 571-599, 604-624, 640-666, 679-713, 727-766, 791-827, 833-881, 891-921, 941-1001, 1006, 1009, 1011 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/home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/base.py 8 2 75% 8, 12 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/binary.py 15 5 67% 15, 26, 36, 48-49 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/compatibility.py 24 13 46% 13, 19, 22-39 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/der.py 149 110 26% 27-28, 32-43, 47-53, 57, 61, 65, 69-74, 78-89, 93-111, 115-121, 125-131, 135-144, 148-152, 156-164, 168-180, 184-194, 198-207, 211-227, 231-232, 239 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/integer.py 5 1 80% 16 /home/admin/.local/lib/python3.8/site-packages/filetype/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/filetype/filetype.py 21 12 43% 28, 45-46, 63-64, 79-82, 95-98 /home/admin/.local/lib/python3.8/site-packages/filetype/helpers.py 23 12 48% 23-26, 41-44, 76, 92, 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76, 91-143 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/table_builder.py 118 99 16% 49, 53, 57-72, 84-86, 89-119, 122-181, 186-188, 191-223 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/unbuilder.py 41 32 22% 6-10, 17-21, 26-34, 37-38, 41-58, 62-81 /home/admin/.local/lib/python3.8/site-packages/fontTools/config/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/designspaceLib/__init__.py 1422 1195 16% 49-56, 64, 68-70, 77-78, 81, 86-95, 105-109, 118-120, 185-296, 311, 315, 322, 329, 339-345, 379-390, 402, 411-421, 431-446, 525-650, 661, 665, 669, 672, 675, 678, 681, 684, 687, 690, 720-732, 744-752, 774-785, 794, 799-812, 830-869, 925-945, 953, 968-972, 976-982, 1031-1052, 1064, 1074-1076, 1116-1137, 1152-1156, 1161, 1187-1208, 1215, 1223, 1249-1271, 1286-1299, 1315-1320, 1338, 1342, 1346, 1350, 1353-1356, 1359-1409, 1421-1439, 1443-1460, 1463-1465, 1469-1497, 1500-1527, 1532-1548, 1551-1555, 1560-1568, 1577-1596, 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35-37, 40-43, 64-74, 78-88, 92-107, 111-147 /home/admin/.local/lib/python3.8/site-packages/fontTools/encodings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/encodings/codecs.py 62 50 19% 10-18, 21-36, 39, 42, 45-56, 109-132 /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/ast.py 1046 766 27% 81-84, 146-153, 161-163, 166, 172, 175, 190-192, 195, 202, 207, 210, 217-219, 223, 226, 233-237, 241, 244-250, 254, 258, 265-269, 275-279, 284-288, 296-298, 302, 305, 313-315, 319, 322, 329-331, 334-337, 344-345, 351-352, 355-356, 368-369, 372, 379-380, 386-395, 398-404, 412-414, 417-419, 422-425, 432-433, 437-439, 442-448, 455-456, 459-462, 469-471, 475, 478, 489-492, 496-500, 503, 527-529, 533-545, 549, 552-553, 580-583, 587, 590, 602-604, 608-614, 617-629, 649-652, 655-666, 673-674, 677-681, 688-690, 694-695, 698, 718-726, 730-733, 738-758, 776-784, 788-791, 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7 4 43% 69-72 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/etree.py 263 257 2% 47-478 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/filenames.py 76 64 16% 99-134, 168-191, 221-239, 243-246 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/fixedTools.py 35 20 43% 81, 109-110, 137-138, 157-158, 188-190, 212-213, 231-239, 251-253 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/intTools.py 9 2 78% 9, 25 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/loggingTools.py 240 173 28% 57-76, 79-86, 134-185, 192-226, 294-296, 306, 312, 316-319, 327-336, 340-342, 348-357, 370, 374-375, 384, 387, 390, 421-423, 426-434, 439-444, 447-456, 459-464, 467, 470-478, 511-514, 519, 528-533, 541-543 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/plistlib/__init__.py 259 182 30% 66-78, 82, 103-105, 109, 112, 117-122, 125, 131-142, 188-201, 204-207, 210-212, 215, 218-220, 225-238, 241-243, 250-252, 256-258, 262-264, 268-270, 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149, 153-155 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/timeTools.py 32 19 41% 48-56, 60, 64-69, 74-77, 81, 85-88 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/transform.py 126 78 38% 69-75, 175-177, 189-190, 202-204, 215-216, 227, 241-243, 255-259, 271-273, 285-287, 309-311, 332-338, 350, 354, 376, 379, 393, 405-407, 430-460, 480-488, 492-495 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/treeTools.py 26 24 8% 12-45 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/vector.py 80 42 48% 22-30, 33, 36-41, 44-46, 49, 52, 57, 60, 63, 68, 71, 74, 77, 80, 83-86, 89, 92, 97, 101, 105, 110-111, 116-120, 124-129, 133, 139, 143 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/visitor.py 88 42 52% 22, 54, 58-70, 82-94, 98, 102-103, 107-108, 113, 131-143 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/xmlWriter.py 146 118 19% 20-55, 58, 61, 64-65, 69, 73, 80, 84, 88-94, 97-102, 105-111, 114-116, 119-123, 126-130, 133-144, 147, 150-151, 154-167, 171-176, 180-182, 188-195, 199-204 /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/builder.py 1114 926 17% 55-59, 99-123, 130-137, 140, 149, 153, 156, 165-170, 173-177, 180-184, 187-191, 194-198, 201-208, 218, 249-250, 253, 262-265, 268, 271, 279, 284, 287, 293-296, 302-306, 309-319, 322-329, 334, 339-346, 349-359, 368-429, 432-475, 478-547, 550-559, 562-580, 583, 593-599, 612-621, 624-633, 636-640, 643-647, 652-673, 676, 712-714, 718-726, 757-759, 762-774, 778-787, 814-815, 818, 827-830, 833, 860-861, 864, 867-868, 871, 892-893, 896, 913-914, 923-924, 956-958, 961, 968-970, 979-997, 1032-1034, 1037, 1044-1046, 1055-1065, 1096-1098, 1101, 1108-1110, 1119-1137, 1167-1168, 1171, 1180-1190, 1194, 1220-1221, 1224, 1233-1234, 1237, 1240, 1255-1259, 1272-1286, 1290, 1294-1295, 1298-1304, 1328-1331, 1344, 1357-1372, 1375, 1385, 1398-1423, 1444-1446, 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140-149, 158-172, 202-206, 210-212, 216-221, 226, 230-260, 264-292, 301-302, 309, 317-452 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/basePen.py 178 122 31% 67, 71, 92, 109, 115, 121, 133, 147, 155, 158, 161, 164, 167, 170, 173, 176, 209-210, 214-224, 228, 240-241, 246, 249, 252, 257, 260, 267-274, 282, 285-286, 289-290, 293-294, 297-298, 301-327, 330-355, 368-390, 403-412, 419, 422, 425, 431, 435-444 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/boundsPen.py 56 41 27% 25-27, 30-31, 34-36, 39-47, 50-51, 54-59, 62-66, 83-90, 93-100 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/filterPen.py 55 31 44% 8, 58-59, 62-63, 66-67, 70-71, 74-75, 78-79, 82-83, 95-96, 99-100, 103-104, 107-111, 126, 155, 158, 161, 164 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/pointPen.py 292 246 16% 36, 40, 52, 62, 78, 91, 94-96, 124, 127-172, 177-179, 192-194, 197-259, 262-264, 274-278, 281-285, 288-289, 292-294, 297-303, 306-316, 319-331, 334-337, 340-342, 352-354, 357-394, 397-402, 405-407, 412-416, 419-423, 428-432, 446-448, 451-498, 501-505, 508-511, 516-520, 523-525 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/recordingPen.py 61 35 43% 22-23, 50, 53, 56, 59, 62, 65, 68, 71, 74, 77, 135, 138-140, 143, 148-150, 153-155, 160-162, 167-168, 172-179 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/transformPen.py 52 35 33% 18-25, 28, 31, 34, 37-41, 44-45, 48, 51, 54-55, 86-92, 95, 100-101, 105-111 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/__init__.py 2014 1674 17% 451-454, 466, 470-471, 477, 483, 489-491, 497, 503, 512-514, 520-534, 540, 545, 550-553, 558-560, 565-570, 575, 580-585, 590, 601-611, 616-625, 630-646, 651, 656-659, 664-674, 679-706, 711-752, 757-773, 778-786, 791-799, 804-810, 815-837, 842-850, 855-880, 885-894, 899-921, 926-934, 951, 968, 983, 990, 1000, 1010-1160, 1165-1272, 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2916-2964, 2970, 3084-3131, 3134-3137, 3140-3214, 3228-3234, 3237-3245, 3248-3273, 3276-3433, 3436-3459, 3462-3492, 3495-3498, 3501-3504, 3509-3536, 3541-3547, 3551-3563, 3567-3574, 3578, 3582-3583, 3589-3724, 3739 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/cff.py 345 294 15% 11-12, 15-25, 30-48, 52-68, 73-89, 94-132, 137-144, 151-185, 190-195, 198-199, 202-203, 213-229, 236-240, 243-264, 267-269, 272-274, 277-278, 281-282, 285-286, 289-290, 293-295, 298-300, 303-317, 320-324, 327-353, 358-381, 385-388, 396, 401-464, 469-536 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/svg.py 141 114 19% 10-13, 36, 43, 49-57, 65-87, 95-105, 112-124, 132-156, 160-164, 169-174, 179-190, 195-251 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/util.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/__init__.py 50 36 28% 21-23, 54-119, 123 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/sfnt.py 378 307 19% 33-44, 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/home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/base.py 32 10 69% 44, 53, 67, 87, 104-109, 124-127 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/es256.py 67 34 49% 48, 53-73, 90-101, 117-118, 123, 127-132, 156-160, 164-170, 174-175 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/rsa.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/environment_vars.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/exceptions.py 31 6 81% 22-24, 28, 58, 70 /home/admin/.local/lib/python3.8/site-packages/google/auth/iam.py 42 21 50% 84-86, 90-116, 126, 130-131 /home/admin/.local/lib/python3.8/site-packages/google/auth/jwt.py 237 160 32% 89-115, 120-127, 143-168, 184-185, 201-228, 257-316, 395-406, 424-426, 443-444, 458-461, 491-493, 514-517, 528, 543-560, 570, 574, 579, 584, 589, 637-648, 666-668, 685-686, 700-703, 729-731, 748-751, 763, 779, 790-806, 821-827, 841, 858-864, 868, 873, 878 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439-450, 494-508 /home/admin/.local/lib/python3.8/site-packages/google/oauth2/service_account.py 249 165 34% 174-197, 215, 243-246, 260-263, 268, 273, 282, 285-299, 303-306, 321-330, 334-338, 350-352, 366-370, 374-376, 380-382, 393-415, 420, 423-425, 429-452, 461-490, 496, 501, 506, 510-516, 599-621, 639-643, 661-664, 678-681, 684-695, 709-711, 730-739, 743-745, 749-751, 762-782, 803-809, 820-828, 833, 837, 842, 847 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/any_pb2.py 16 4 75% 31-34 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor.py 404 261 35% 66-73, 80-82, 85, 88, 95-97, 138-143, 151-155, 163-181, 209-220, 232-238, 327-369, 376-379, 397, 406, 558, 575-607, 616-618, 635-638, 684-697, 706, 742-750, 781-790, 827-828, 833-845, 856, 865, 892-893, 903-913, 925-931, 977-980, 984, 991-1011, 1019, 1028-1029, 1034-1050, 1055-1058, 1063-1075, 1096-1179 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor_database.py 57 42 26% 50-51, 63-85, 104, 128-141, 145, 149, 152-158, 171-177 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor_pool.py 465 393 15% 75-80, 99, 104-107, 111, 140-155, 165-193, 202, 216-221, 227, 240-246, 252, 264-287, 293, 303-308, 314, 329-356, 361, 374-379, 392-394, 409-423, 439-450, 465-499, 514-517, 532-535, 549-552, 566-569, 583-599, 618-622, 637-646, 655-674, 688-691, 705-708, 722-731, 745-826, 843-920, 939-972, 995-1005, 1037-1056, 1067-1132, 1145, 1169-1188, 1204-1209, 1229-1234, 1246-1250, 1263-1272, 1276, 1285 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/api_implementation.py 31 9 71% 44-45, 48, 65-67, 86, 102, 107, 112 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/containers.py 328 229 30% 62-63, 67, 71, 76, 79, 82, 88-90, 93, 114-115, 119-121, 125-127, 132-146, 152-153, 157-158, 162-164, 168-174, 178, 182-186, 190-191, 195-196, 200-206, 231-232, 238-243, 247-252, 256-261, 268-276, 282, 286-287, 291-293, 297, 301-302, 306-307, 311-316, 340-344, 347-353, 358-359, 365-368, 371-374, 377-378, 381, 384, 387, 390-391, 396-398, 402-403, 406, 430-434, 437-446, 460, 466-469, 472-473, 476, 479-481, 484, 487, 490, 494-499, 506-508, 512-513, 516, 527-530, 534, 537-540, 548-550, 553-557, 562-564, 568-570, 574-576, 587, 590-599, 602, 605-608, 611-613, 616-617, 620-623, 626-636, 639-643 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/decoder.py 499 420 16% 108-120, 131-144, 173-179, 196-243, 257-258, 281-283, 309-328, 353-368, 375-508, 547-593, 600-634, 640-681, 687-731, 752-832, 839-876, 887-892, 897-900, 905-906, 912-916, 923-927, 933-944, 950-967, 973, 979-982, 988-989, 995, 1024-1025 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/encoder.py 475 391 18% 82-91, 96-106, 113, 129-148, 158-177, 187-203, 231-247, 253-269, 275-287, 293-308, 326-334, 346-364, 379-381, 394-402, 442-466, 476-500, 516-538, 559-566, 569-578, 582-615, 650-681, 687-705, 711-727, 733-748, 754-769, 787-801, 820-829 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/enum_type_wrapper.py 33 19 42% 53-64, 69-73, 83, 103, 108-114 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/type_checkers.py 121 59 51% 61, 71-76, 80, 92-101, 123-132, 142, 153-155, 157, 164, 172, 175-181, 184, 195-217, 220, 261-272, 275 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/well_known_types.py 447 337 25% 68-72, 76-80, 85, 89, 108-126, 139-190, 194, 198, 202, 207, 212, 216-217, 221-222, 226-227, 231-232, 236, 249-250, 267-288, 301-321, 326, 330-331, 335-336, 340, 344, 349, 355, 361-362, 366, 372, 378-382, 386-395, 406-411, 421-424, 428-433, 437-440, 444-446, 458-459, 463-467, 471-477, 492-493, 499-508, 513-516, 522-546, 551-561, 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/home/admin/.local/lib/python3.8/site-packages/googleapiclient/http.py 672 543 19% 43-44, 90-147, 170-229, 243-244, 253-256, 269-270, 279-282, 318, 326, 334, 342, 355, 367, 376, 389-396, 405, 419-425, 469-478, 486, 494, 502, 510, 523-524, 536, 545, 549, 592-601, 606-607, 616, 620-621, 658-659, 695-713, 734-780, 801-804, 816-820, 852-874, 896-939, 951, 992-1093, 1109-1126, 1133-1141, 1146-1149, 1162, 1210-1247, 1259-1277, 1290-1296, 1313-1319, 1331-1367, 1379-1395, 1405-1408, 1440-1453, 1468-1525, 1544-1606, 1624-1630, 1638, 1681-1682, 1700-1720, 1732-1744, 1755-1759, 1762, 1794-1796, 1808-1826, 1848-1877, 1898-1930, 1944-1962 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/mimeparse.py 56 41 27% 45-56, 73-83, 95-120, 133, 147-149, 166-177, 181-183 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/model.py 170 103 39% 38, 51, 79, 94, 118-130, 152-182, 193-205, 209-215, 230-241, 252, 264, 284, 287-293, 296-307, 311, 327, 331, 347, 351, 375, 378, 381, 385, 409-429 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/sample_tools.py 31 22 29% 58-108 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/schema.py 110 86 22% 81-84, 99-114, 127, 142-145, 158, 167, 184-200, 208, 216, 225-232, 236, 240, 251-302, 316-317 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/httplib2/__init__.py 914 737 19% 59-61, 151-188, 192-194, 202-217, 228-229, 233-245, 258-274, 281, 290-292, 296-304, 342-386, 390-412, 416-429, 433-471, 475-478, 482, 498-502, 505-506, 510-511, 516, 526, 529, 532, 535, 538, 541, 544, 547, 552, 557, 567-579, 583-615, 618-630, 639-678, 682-689, 705-708, 721, 726-730, 740-765, 770, 791-794, 797-805, 808-811, 814-816, 824, 827, 830-832, 840, 843-845, 877-881, 900, 911, 914, 918-929, 932, 942-949, 955-979, 994-998, 1002-1066, 1093-1114, 1118-1202, 1272, 1313-1317, 1320-1327, 1330-1331, 1337-1341, 1346, 1351, 1356-1357, 1360-1431, 1439-1510, 1513, 1542-1751, 1775-1793, 1796-1799 /home/admin/.local/lib/python3.8/site-packages/httplib2/auth.py 40 17 58% 40-49, 54-69 /home/admin/.local/lib/python3.8/site-packages/httplib2/certs.py 29 9 69% 10-11, 17, 30-33, 35, 38, 42 /home/admin/.local/lib/python3.8/site-packages/httplib2/error.py 25 3 88% 10-12 /home/admin/.local/lib/python3.8/site-packages/httplib2/iri2uri.py 41 30 27% 49-57, 64-72, 76-124 /home/admin/.local/lib/python3.8/site-packages/httplib2/socks.py 244 201 18% 44, 127, 139-142, 155-162, 169-175, 181-183, 190-206, 209-210, 241, 256-352, 358, 364, 371, 378-422, 429-467, 477-518 /home/admin/.local/lib/python3.8/site-packages/imgaug/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/base.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/batches.py 266 116 56% 24, 168, 285-311, 374, 380, 386, 392, 398, 415, 431, 583-610, 624, 630, 717, 751, 771-785, 803-816, 853-888, 943-954, 979-981, 997, 1018-1029, 1051-1068, 1081-1088 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/bbs.py 484 365 25% 49-58, 73-76, 91, 106, 121, 136, 148, 160, 172, 184, 196, 214-218, 249-253, 282, 318-322, 355, 381-387, 406, 433-437, 459-464, 493-501, 521-523, 548-552, 581-585, 592, 614-628, 647, 673-677, 721-723, 784-795, 843-895, 946-957, 1003-1085, 1101-1103, 1122-1124, 1158-1173, 1201-1203, 1228-1244, 1277, 1319, 1332, 1345, 1348, 1351, 1382-1383, 1397, 1411, 1425, 1439, 1451, 1473-1481, 1500, 1529-1544, 1574-1580, 1601-1606, 1620, 1652-1670, 1694-1695, 1735-1748, 1773-1778, 1800, 1825, 1847, 1854, 1870-1876, 1887, 1913-1915, 1958-1960, 1974-1989, 2015-2028, 2042-2045, 2066-2072, 2094-2100, 2113, 2128, 2141, 2144, 2147, 2158-2165, 2169-2187, 2190, 2194-2195, 2203-2227, 2230-2246, 2251-2258, 2261-2271 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/heatmaps.py 143 116 19% 45-96, 113-125, 156-187, 221-254, 280-286, 323-333, 381-396, 413-414, 435-436, 446, 467-475, 494-496, 536-537, 580-584, 614-659, 671, 682 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/kps.py 364 260 29% 37-67, 100-102, 114, 126, 140, 154, 184-186, 213, 235-239, 265, 287-289, 308, 347-408, 456-487, 512-526, 554, 575, 601, 604, 647, 661, 673, 685, 718-726, 744, 785-790, 819, 845, 863, 879, 901-903, 922, 936, 975, 996-1006, 1077-1097, 1139-1184, 1214-1228, 1280-1334, 1351, 1374-1384, 1405-1411, 1452, 1467, 1480, 1483, 1486 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/lines.py 517 418 19% 48-67, 80-82, 94, 106, 122, 138, 153-155, 170-172, 192-198, 211-213, 240-262, 285-288, 313, 343-344, 371, 400-409, 431-433, 456-463, 495-502, 520-614, 634-671, 697-699, 742-744, 772-778, 812-823, 854-864, 907-923, 967-1052, 1097-1104, 1176-1210, 1266-1305, 1325-1327, 1356-1360, 1372-1373, 1385-1390, 1405-1406, 1443-1444, 1483-1484, 1520-1539, 1563-1565, 1587, 1609, 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993-1008, 1031-1041, 1062-1069, 1089, 1101-1103, 1125, 1138-1142, 1157-1159, 1178-1181, 1208-1223, 1250, 1297-1306, 1340-1342, 1364, 1385, 1400, 1413, 1416, 1419-1423, 1455-1456, 1470, 1484, 1496, 1517-1525, 1543, 1637-1653, 1678-1682, 1702, 1725, 1747, 1777-1781, 1806, 1831-1833, 1876-1878, 1896-1898, 1916, 1930-1932, 1964-1988, 2003-2009, 2032-2047, 2072-2078, 2104-2110, 2123, 2138, 2151, 2154, 2157, 2164-2182, 2188-2230, 2233-2281, 2284-2299, 2303-2317, 2321-2328, 2331-2392, 2397-2407, 2413-2526, 2531-2573, 2579-2589, 2592-2760, 2777-2782, 2806-2829 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/segmaps.py 124 101 19% 21, 103-167, 195-205, 211, 236-255, 312-381, 418-421, 466-476, 482, 505-507, 536-540, 568-572 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/utils.py 114 88 23% 12, 25-37, 57-59, 90-111, 139-142, 167-173, 204-218, 252-270, 290-296, 323-334, 339-342, 348-361 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/__init__.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/arithmetic.py 791 668 16% 101-118, 130-161, 165-204, 247-261, 274-283, 298-325, 383-400, 411-442, 448-492, 548-565, 571-583, 587-628, 686, 754-781, 816-847, 877-893, 943-1004, 1038, 1132-1186, 1190-1193, 1199-1202, 1207-1211, 1236-1252, 1259-1261, 1268-1281, 1287-1309, 1335, 1369, 1407-1470, 1552-1559, 1564-1607, 1611, 1698-1705, 1710-1729, 1733, 1836-1845, 1959-1968, 2075-2081, 2167-2174, 2179-2221, 2225, 2311-2318, 2323-2353, 2357, 2365-2373, 2554-2570, 2576-2587, 2591-2627, 2631-2663, 2680-2698, 2703, 2792-2794, 2803-2834, 2993-3010, 3113-3128, 3132-3165, 3171-3209, 3214, 3301-3315, 3319-3353, 3357-3359, 3364-3366, 3371, 3483-3491, 3496-3537, 3541, 3612, 3675, 3816-3831, 3905-3913, 4035-4055, 4132-4139, 4259-4279, 4414-4431, 4436-4460, 4464-4485, 4496, 4505-4510, 4579, 4664-4665, 4742-4748, 4754-4765, 4769 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/artistic.py 105 84 20% 99-157, 164-170, 175-189, 194-198, 203-206, 211-221, 226-239, 244-246, 365-381, 385-396, 400-401, 413 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/base.py 14 7 50% 21-27, 43, 49 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/blend.py 584 447 23% 38-43, 111-211, 216-242, 247-258, 403-422, 426-472, 476, 481, 486, 491-498, 616-648, 652-695, 704-718, 724-785, 789, 794, 799, 804-810, 958-962, 970, 1160-1190, 1411-1442, 1581, 1704, 1833, 1962, 2064, 2196, 2325, 2407-2409, 2418-2423, 2429-2451, 2562-2578, 2586-2592, 2598, 2607-2626, 2667-2684, 2690-2694, 2701-2703, 2708-2735, 2740-2744, 2752-2762, 2773-2777, 2783, 2792-2801, 2842-2881, 2942, 2982, 3041, 3081, 3134-3142, 3153-3158, 3164-3175, 3205-3242, 3275, 3291, 3305, 3315-3320, 3348-3359, 3434-3450, 3462-3469, 3475-3490, 3518-3528, 3598-3614, 3626-3637, 3643-3658, 3686-3699, 3724-3725, 3734-3740, 3758, 3782, 3809, 3842 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/blur.py 259 182 30% 174-181, 186-215, 219, 221, 223, 225, 242, 263, 265, 329-367, 372, 376, 461, 473, 570-616, 622-686, 690, 768-781, 787-815, 819, 932-942, 949-975, 979, 1075-1088, 1098-1101, 1106-1134, 1213-1220, 1227-1236, 1240-1241, 1251 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/collections.py 50 33 34% 192-218, 233, 254-293, 340-341 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/color.py 642 462 28% 69-72, 240-310, 374-395, 405-407, 414, 436-454, 459-853, 888-950, 983, 994, 1063-1069, 1073-1093, 1096-1100, 1104, 1108, 1111, 1226-1234, 1238-1266, 1270-1277, 1282-1285, 1289-1293, 1298, 1303, 1307, 1396-1402, 1414, 1497, 1571, 1666-1675, 1679-1688, 1692-1712, 1716-1744, 1747-1751, 1755, 1759, 1762, 1891-1974, 2048, 2117, 2196-2204, 2380-2398, 2401-2438, 2442-2483, 2501-2511, 2516-2521, 2525, 2530-2543, 2547-2554, 2558-2564, 2572-2582, 2653, 2726, 2858-2905, 2908-2913, 2917-2939, 2943, 3021, 3076-3083, 3087-3095, 3100, 3115-3126, 3129-3139, 3143-3153, 3157-3210, 3218, 3228-3238, 3380, 3396, 3399, 3409, 3484-3526, 3663, 3679, 3682, 3824, 3836, 3858, 3889, 3957-3974, 3993-3995, 4015-4017, 4023, 4031, 4039-4053, 4081, 4133-4136, 4165 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/contrast.py 303 239 21% 37-45, 49-90, 94, 149-172, 233-258, 318-340, 388-421, 489-493, 589-599, 676-681, 754-761, 788-803, 806-898, 902, 1001-1012, 1017-1082, 1086, 1265-1275, 1280-1309, 1313-1315, 1388, 1394-1420, 1424, 1541-1549, 1554-1583, 1587-1588 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/convolutional.py 149 115 23% 126-143, 150-234, 238, 313-322, 330-331, 334-353, 424-433, 441-442, 445-464, 521-527, 535, 538-556, 654-664, 672-673, 676-717 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/debug.py 470 319 32% 42-77, 138-140, 145, 150, 154-159, 171, 176, 181-184, 188-191, 203-205, 210, 215, 219-250, 255-256, 271-272, 277, 282, 286-294, 305, 314, 323, 332, 340-341, 358-359, 367-373, 470-496, 502, 509, 515-527, 533-558, 564-593, 599-611, 617-671, 677-690, 696-708, 714-756, 762-825, 832-843, 849-850, 863, 868, 873, 878-882, 887, 892-894, 899-901, 906-908, 913-915, 920, 925-926, 931-932, 937, 942-943, 948-949, 953-957, 961-965, 970, 975, 980, 985-987, 992, 997, 1002, 1007-1010, 1051, 1055-1056, 1060-1061, 1071-1075, 1079-1080, 1086, 1123-1124, 1128-1130, 1170-1174, 1178-1193, 1256-1266, 1273-1274 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/edges.py 94 68 28% 93-101, 110-114, 117-159, 162, 327-375, 382-415, 419-468, 472, 476 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/flip.py 114 92 19% 722-724, 728, 734-761, 805, 811, 817, 872-875, 879-925, 929, 984-987, 991-1039, 1043 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/geometric.py 1751 1369 22% 92-96, 102, 106-120, 129, 139, 143-150, 158, 162, 169, 185-188, 215-244, 267, 269, 298-312, 315, 317, 323-351, 399-443, 475-508, 543-582, 607, 611, 639, 672, 681, 1178-1181, 1224-1229, 1253-1268, 1279-1284, 1309-1314, 1340, 1345, 1366-1371, 1392, 1414, 1417, 1422-1424, 1427, 1434-1473, 1479, 1489, 1503, 1513-1515, 1536, 1607, 1684, 1771-1774, 1862-1865, 1941, 2016, 2091, 2405-2571, 2576-2583, 2591-2676, 2679-2691, 2694-2707, 2711-2774, 2778, 2783, 2788, 2793, 2797-2841, 2849-2854, 2857-2860, 3009-3043, 3047-3081, 3085-3128, 3133-3169, 3174-3242, 3245-3271, 3287-3354, 3358, 3365-3369, 3551-3586, 3592-3621, 3632-3681, 3685-3753, 3758-3817, 3821-3847, 3852-3962, 3972-3988, 3993-4011, 4015, 4021-4026, 4239-4269, 4273-4275, 4281-4293, 4298-4304, 4310-4358, 4363-4379, 4385-4433, 4438-4496, 4501-4503, 4509-4511, 4516-4518, 4522, 4527-4554, 4672-4793, 4901-4911, 4914, 4919-4943, 4948-4966, 4971-4991, 4996-5029, 5033, 5165-5168, 5172-5204, 5209-5238, 5243-5278, 5283, 5288, 5293, 5298, 5304, 5310, 5316, 5321, 5326-5328, 5333-5335, 5340, 5345, 5350, 5355, 5360-5413, 5419-5476, 5481-5505, 5511-5532, 5537-5560, 5566-5589, 5594-5608, 5613-5629, 5635-5654, 5667-5707, 5712, 5717, 5721-5725, 5729-5733, 5849-5862, 5866-5935, 5939-5955, 5961-5970, 5976-5986, 5991-5996, 6001-6007, 6011, 6018-6021 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/imgcorruptlike.py 197 121 39% 96-112, 144-212, 243-257, 297, 329, 361, 393, 425, 457, 470-515, 550, 582, 614, 646, 678, 710, 742, 774, 806, 838, 870, 902, 934, 1009-1014, 1020-1029, 1033-1037, 1042, 1098, 1157, 1216, 1275, 1334, 1393, 1452, 1511, 1570, 1629, 1688, 1747, 1806, 1865, 1924, 1983, 2042, 2101, 2164, 2171-2177 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/meta.py 1062 774 27% 47, 53, 59-61, 68-72, 77-100, 108-117, 122-125, 130-142, 147-149, 153-157, 166-173, 207-210, 215, 219, 296, 299-304, 313-314, 320, 370-528, 550, 598-602, 610-616, 622-624, 626-632, 649, 654-659, 668-670, 713-737, 762-771, 821-822, 874, 901, 940, 966, 1010, 1071, 1116, 1180, 1246, 1315, 1364, 1406, 1449, 1479, 1524-1529, 1558, 1592-1596, 1631-1656, 1682-1687, 1933-1934, 1945-1957, 1984, 1995-2000, 2003, 2081-2082, 2139-2186, 2213-2214, 2252-2256, 2272-2287, 2296, 2357-2373, 2393-2397, 2443-2449, 2479-2487, 2552-2613, 2627, 2664, 2686-2696, 2740-2757, 2780, 2805-2813, 2871-2888, 2897, 2935-2947, 2958, 2975, 2978, 2981-2983, 3094, 3100, 3107, 3119, 3132-3137, 3141, 3152, 3156, 3159-3164, 3287-3315, 3319-3343, 3346-3355, 3358-3362, 3366-3374, 3378-3411, 3414-3419, 3423, 3434, 3438, 3441-3446, 3512, 3599-3607, 3612-3641, 3644-3653, 3657, 3661-3666, 3669-3673, 3746-3766, 3770-3811, 3816, 3824-3831, 3841-3849, 3859-3861, 3865-3869, 3873-3878, 3882-3898, 3901-3905, 3909, 3913, 3916-3920, 3980, 3986, 3991, 4035, 4222-4231, 4234-4236, 4239-4252, 4255-4269, 4273-4287, 4291-4311, 4316-4333, 4338-4363, 4367, 4497-4504, 4521-4522, 4526-4529, 4669-4677, 4697-4718, 4725-4733, 4741, 4746-4748, 4754, 4757-4759, 4765, 4768-4770, 4776, 4779-4781, 4787, 4791-4794, 4800, 4803-4805, 4811, 4815-4818, 4892-4906, 4910-4922, 4926, 4972-4992, 5088-5092, 5096-5103, 5108, 5176, 5182-5188, 5193 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/pillike.py 343 243 29% 80-90, 122, 151, 182, 211, 244-249, 294-313, 320-347, 352-362, 415-426, 432, 446-531, 536-554, 597, 641, 684, 727, 732-750, 787, 824, 861, 898, 936, 973, 1010, 1047, 1084, 1121, 1132-1189, 1278-1316, 1373, 1444, 1451-1454, 1459, 1533-1540, 1558-1562, 1568-1574, 1578, 1583, 1643, 1709, 1775, 1841, 1855-1858, 1862-1865, 1870, 1917, 1967, 2018, 2070, 2122, 2173, 2224, 2274, 2324, 2375, 2489-2503, 2507-2513, 2520-2544, 2549-2567, 2572 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/pooling.py 113 76 33% 26-36, 49-59, 66-76, 83-92, 96-111, 115, 120, 126-150, 154-168, 172-174, 179-181, 186-188, 193, 312, 318, 433, 441, 556, 564, 679, 687 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/segmentation.py 353 269 24% 56-66, 212-222, 226-283, 287-319, 323, 357-378, 383-388, 392-393, 398-416, 421-441, 598-611, 615-634, 637-654, 658, 776, 940, 1122, 1174-1187, 1245-1248, 1253-1257, 1260-1263, 1267-1279, 1283-1286, 1290-1313, 1316, 1319, 1377-1381, 1387-1391, 1396-1410, 1413, 1417, 1463-1468, 1474-1495, 1498-1505, 1508-1512, 1515-1518, 1522-1538, 1541, 1545, 1580, 1585-1596, 1600-1603, 1608-1620, 1623, 1626, 1664-1671, 1676-1682, 1688-1692, 1695, 1699 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/size.py 1205 1047 13% 48-66, 70-72, 77-90, 95, 102, 110-147, 154-166, 170-174, 179-219, 225-253, 257, 262-279, 285-330, 348-350, 425-553, 607-621, 680-695, 734-766, 807-838, 874-902, 937-965, 1007-1037, 1084-1115, 1123, 1267-1272, 1276-1359, 1363-1378, 1382-1410, 1414-1434, 1438-1461, 1465-1475, 1478-1490, 1494-1529, 1533, 1539-1548, 1553, 1558, 1804-1828, 1833-1847, 1852-1902, 1906-1964, 1968-1997, 2001-2019, 2024-2043, 2047-2056, 2059-2169, 2173, 2377-2399, 2563-2585, 2731-2756, 2764-2790, 2794-2812, 2816-2831, 2836-2860, 2863-2884, 2889-2904, 2908, 2972, 3109-3121, 3129-3153, 3157-3171, 3175-3189, 3193-3204, 3210-3223, 3226-3245, 3249, 3310, 3381-3386, 3390-3412, 3417, 3477, 3551-3557, 3561-3583, 3588, 3657, 3739-3744, 3748-3770, 3775, 3835, 3914-3920, 3924-3946, 3951, 4019, 4085-4089, 4093-4118, 4123, 4180, 4248-4253, 4257-4279, 4284, 4340, 4399, 4461, 4523, 4579, 4701-4737, 4742-4781, 4787-4807, 4813-4825, 4830-4838, 4843-4855, 4858-4901, 4904-4908, 4912, 4916, 4919-4927 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/weather.py 226 166 27% 140-150, 153-159, 163-186, 190, 353-366, 371-379, 383, 394-414, 422-451, 457-466, 471-478, 484-495, 563-594, 666, 855-876, 880-888, 892, 903-953, 957-958, 965-969, 975, 979-988, 997-1004, 1012-1017, 1022-1023, 1028-1029, 1204-1217, 1301, 1311, 1317-1319, 1328-1339, 1428-1441 /home/admin/.local/lib/python3.8/site-packages/imgaug/dtypes.py 148 109 26% 20, 35-49, 54-97, 104, 108-112, 116-135, 150-172, 177-183, 187-189, 196-197, 201-207, 217-253, 258-282, 319-345 /home/admin/.local/lib/python3.8/site-packages/imgaug/external/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/external/opensimplex.py 1412 1385 2% 14-15, 85, 100-113, 116-120, 123-129, 132-140, 148-244, 252-740, 748-1934 /home/admin/.local/lib/python3.8/site-packages/imgaug/imgaug.py 599 461 23% 14-15, 86-87, 106, 147, 165-187, 192, 365, 382, 400-402, 419, 440-448, 462, 491-506, 525-526, 541-542, 567-572, 586-587, 610-613, 632-633, 656-657, 670-671, 703-726, 761-802, 839-846, 866, 889-906, 928-957, 983-1003, 1029-1056, 1083-1106, 1142-1146, 1200-1216, 1273-1306, 1403-1572, 1576-1578, 1608-1619, 1691-1744, 1791, 1838, 1882, 1926, 1976-2034, 2061-2062, 2095-2123, 2142-2143, 2150-2151, 2153, 2180, 2227-2273, 2352-2355, 2369-2371, 2389-2391, 2402-2404, 2415-2417, 2455-2461 /home/admin/.local/lib/python3.8/site-packages/imgaug/parameters.py 1056 827 22% 37, 40-43, 51-62, 74-99, 112-153, 161-187, 197-247, 254-293, 299-301, 306-314, 320-336, 342, 387, 420, 423-425, 431-433, 439-441, 447-453, 459-461, 467-469, 475-477, 483-485, 491-493, 499-501, 507-513, 519-521, 527-529, 535-537, 551, 562, 594-628, 659, 663, 675, 678-682, 706-727, 731-739, 743, 747-750, 787-799, 802-856, 859, 862, 899-900, 903-907, 910, 913, 955-958, 962-968, 971, 974, 1014-1016, 1019-1022, 1025, 1028, 1068-1071, 1075-1080, 1083, 1086, 1143-1149, 1153-1170, 1173, 1176, 1220-1223, 1227-1232, 1235, 1238, 1273-1275, 1279-1281, 1284, 1287, 1323-1325, 1329-1331, 1334, 1337, 1379-1382, 1386-1392, 1395, 1398, 1438-1445, 1448-1452, 1455, 1458, 1538-1589, 1592-1653, 1656, 1659-1670, 1703-1715, 1718-1723, 1726, 1729-1737, 1766-1769, 1772-1791, 1794, 1797-1798, 1846-1850, 1853-1867, 1870, 1873, 1926-1930, 1934-1958, 1961, 1964, 2018-2022, 2025-2038, 2041, 2044, 2093-2097, 2100-2113, 2116, 2119, 2169-2173, 2176-2202, 2205, 2208, 2229-2233, 2236-2237, 2240, 2243-2244, 2269-2280, 2283-2300, 2303, 2306-2307, 2347-2364, 2367-2404, 2407, 2410-2411, 2449, 2491, 2570-2622, 2628-2659, 2662, 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/home/admin/.local/lib/python3.8/site-packages/importlib_resources/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_common.py 101 56 45% 35-46, 56, 68-72, 77, 82, 87, 95-104, 112-114, 129-141, 145, 156-158, 167, 176, 184-185, 194-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_compat.py 58 36 38% 13, 20-23, 28-29, 42, 46, 49-75, 101-103, 107, 117-126 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/abc.py 65 23 65% 26, 39, 47, 52, 79-80, 86-87, 109-124, 130, 161, 164, 167, 170 /home/admin/.local/lib/python3.8/site-packages/jmespath/__init__.py 12 4 67% 10-12, 19, 23 /home/admin/.local/lib/python3.8/site-packages/jmespath/ast.py 44 22 50% 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46, 50, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90 /home/admin/.local/lib/python3.8/site-packages/jmespath/compat.py 40 21 48% 16-48 /home/admin/.local/lib/python3.8/site-packages/jmespath/exceptions.py 68 36 47% 13-19, 23-24, 34-37, 41-42, 50-57, 60-61, 68-71, 74, 82-85, 91, 103-106, 109, 117 /home/admin/.local/lib/python3.8/site-packages/jmespath/functions.py 228 141 38% 73-81, 84-91, 94-97, 104-120, 124-134, 137-161, 166, 170-173, 177-179, 183-186, 190-193, 198-211, 215, 219, 223, 227, 231-234, 238, 242, 246, 250-253, 257-260, 264-267, 271-274, 278, 282, 288, 292, 296-307, 311-327, 331-337, 341-347, 350-359, 362 /home/admin/.local/lib/python3.8/site-packages/jmespath/lexer.py 139 120 14% 27-110, 114-118, 121-127, 130-135, 140-156, 159-176, 180-188, 193-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/jmespath/parser.py 313 244 22% 79-82, 85-92, 95-105, 108-116, 119-135, 138, 141, 144-152, 155-160, 163, 166, 169-171, 174-177, 180-181, 184-198, 205-213, 219-237, 240, 243-244, 247-259, 262-263, 266-267, 270-271, 274-291, 295-301, 304, 307, 310, 313, 316, 319, 322-325, 328-344, 347-353, 356-357, 360-369, 372-389, 393-406, 417-434, 437-440, 443, 447-451, 455-458, 461, 464, 467, 470, 473-476, 480-488, 492-493, 498, 504-505, 508-510, 522-524, 527 /home/admin/.local/lib/python3.8/site-packages/jmespath/visitor.py 212 161 24% 9-12, 32-35, 43, 54-56, 70-71, 76-77, 80, 85, 88-94, 97, 113-123, 126, 129-132, 135-138, 142-158, 161, 164, 167-171, 174-184, 187-197, 200, 205-210, 213-216, 219-222, 225, 228, 231-236, 239-244, 247-250, 253-256, 259-264, 267-270, 273-281, 284-294, 300, 304, 309-311, 314-319, 322-328 /home/admin/.local/lib/python3.8/site-packages/lxml/__init__.py 11 9 18% 12-22 /home/admin/.local/lib/python3.8/site-packages/matplotlib/__init__.py 517 265 49% 165-178, 190-191, 223, 240-243, 276-277, 356-460, 465-480, 505, 511, 514-515, 521-537, 608-609, 617, 702-706, 708-709, 711-714, 717-718, 721-722, 724-725, 731-734, 737-740, 745-748, 758-764, 767, 775, 788-789, 803, 808-814, 821-828, 863-865, 872, 875-878, 889-902, 927-945, 977, 1033-1052, 1074-1077, 1090-1092, 1115-1119, 1168-1177, 1233-1240, 1252, 1263, 1270, 1288-1293, 1308-1316, 1320-1325, 1348, 1366, 1368, 1448-1472 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_afm.py 242 190 21% 54, 61-65, 69, 73-74, 78, 82-85, 105-168, 206-237, 252-269, 306-323, 339-355, 362-364, 367-369, 376-394, 398-424, 428, 432-434, 440-442, 446, 450-452, 458-459, 466, 470, 474, 478-481, 485-493, 498, 502, 506, 510, 514, 518, 525, 532 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/__init__.py 126 30 76% 47, 58, 83, 89-93, 124, 128-131, 158-168, 187, 191-192, 256, 270, 281, 336, 341, 357-359, 382 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/deprecation.py 173 33 81% 28-29, 142-143, 156-159, 162-164, 167-169, 292-296, 310, 370-373, 387, 392, 400-403, 449, 486-503 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_blocking_input.py 8 7 12% 21-30 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm.py 141 12 91% 59-64, 145-152 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm_listed.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_color_data.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_constrained_layout.py 373 352 6% 102-149, 162-194, 202-240, 247-260, 264-297, 303-335, 347-440, 447-479, 507-576, 583-596, 615-624, 632-665, 689-751, 761-768, 772-783 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_docstring.py 39 4 90% 35, 53, 59-60 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_enums.py 57 36 37% 24, 89-111, 161-177 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_fontconfig_pattern.py 46 7 85% 89-91, 97, 101-105, 114-118 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_layoutgrid.py 208 174 16% 40-103, 106-118, 126-128, 132-137, 144-162, 166, 173-206, 213-245, 266-267, 287-288, 303-304, 322-323, 339-347, 352, 359-367, 374-391, 398-411, 418-429, 436-448, 455-466, 473-484, 490, 497, 502-547 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext.py 1244 988 21% 57-66, 100-102, 105-111, 116-147, 170-171, 180, 218-219, 227-228, 234, 240, 247, 255, 264, 274-282, 285-295, 298-300, 304-323, 334-343, 349, 353-358, 380-387, 392-405, 464, 489-519, 524, 527-586, 589-592, 599-617, 621-632, 699-704, 709-753, 757-773, 912-919, 926, 929, 932, 939, 949-952, 955-959, 962, 969, 976, 993-1002, 1005, 1008-1015, 1018, 1027-1034, 1037, 1042-1047, 1057-1061, 1064-1065, 1068, 1077-1083, 1086, 1093-1104, 1108-1113, 1120-1123, 1133-1148, 1187-1226, 1233-1234, 1258-1305, 1320-1321, 1324, 1331-1334, 1341-1342, 1368-1375, 1378-1381, 1391, 1401, 1419-1420, 1423, 1426-1428, 1441-1467, 1480-1495, 1508-1637, 1646-1649, 1664-1668, 1671, 1675, 1679-1681, 1685, 1701-1711, 1800-1955, 1964-1977, 1981, 1985, 1989, 1992, 1995, 1998-2000, 2003-2009, 2019-2028, 2046-2048, 2051, 2054-2096, 2099, 2127-2141, 2148-2150, 2153-2185, 2188-2192, 2195-2196, 2199, 2204-2205, 2208-2209, 2212-2216, 2219-2221, 2224-2226, 2229, 2232-2391, 2394-2429, 2432, 2435, 2441, 2446, 2451, 2456-2483, 2488-2525, 2528-2544, 2547-2566, 2569 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext_data.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_pylab_helpers.py 67 27 60% 41, 55-67, 72-75, 80-83, 88, 93, 98, 115, 130-132 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_text_helpers.py 23 1 96% 34 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_bbox.py 47 44 6% 18-70, 80-84 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_layout.py 133 125 6% 48-157, 170-191, 226-301 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_type1font.py 396 320 19% 56-58, 61, 65, 69, 73, 77, 81, 84, 91, 94, 101, 108, 115, 118, 137-141, 145-151, 158, 165, 168-171, 190-270, 294-315, 364-373, 377-402, 415-441, 457-463, 482-489, 498-592, 595-626, 630-653, 660-680, 684-692, 714-770 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_version.py 11 2 82% 5-6 /home/admin/.local/lib/python3.8/site-packages/matplotlib/artist.py 664 260 61% 36-37, 58-61, 66-67, 70, 75, 77, 81-82, 97-98, 113, 145, 217-221, 241-259, 304, 317, 327, 350, 367-376, 405, 415, 457, 462, 483-485, 504-508, 518, 531-555, 590, 602, 616, 630, 641, 713-717, 727-728, 754, 813-814, 816, 822-823, 825-826, 828-829, 832, 864, 879-881, 892, 896, 908-910, 959-964, 985-986, 1003-1005, 1017, 1020, 1036, 1040-1046, 1076-1078, 1127, 1178, 1193, 1197, 1271-1286, 1317, 1337-1373, 1397-1403, 1414-1417, 1435-1437, 1441, 1485, 1493, 1515-1519, 1596-1600, 1614, 1616-1617, 1635-1666, 1683-1700, 1704-1715, 1746-1751, 1816-1838 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/__init__.py 9 1 89% 10 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py 2254 1885 16% 98-102, 158, 172, 179, 193-195, 317, 323, 381-398, 461-511, 547-550, 585-591, 617-623, 704, 761-776, 829-844, 849-851, 904-926, 966-974, 1022-1031, 1072-1111, 1152-1191, 1304-1437, 1772-1776, 1821-1829, 1872-1876, 1919-1923, 1996, 2073-2105, 2174-2176, 2190-2228, 2340-2525, 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78-84, 91-97, 102-121, 126-129, 136-137, 144-145, 153-154, 162-168, 185-189, 205-225 /home/admin/.local/lib/python3.8/site-packages/matplotlib/image.py 760 617 19% 83-110, 134-157, 171-213, 221-227, 259-274, 277-281, 285, 289-292, 302-306, 318, 325-326, 358-587, 607, 615, 620-646, 650-677, 681-683, 695-731, 743, 754, 771-776, 788-792, 796-797, 811-814, 818, 830-831, 835, 846-850, 854, 920-922, 936-938, 942-949, 954, 977-1002, 1006-1014, 1025-1041, 1058-1059, 1063, 1067-1133, 1148-1165, 1168, 1177-1180, 1183-1185, 1188, 1191, 1194-1196, 1199-1201, 1245-1248, 1252-1281, 1284, 1304-1338, 1341, 1345-1354, 1379-1389, 1393-1394, 1399-1410, 1416-1417, 1440-1451, 1454-1462, 1466-1476, 1480-1486, 1538, 1541-1553, 1558, 1621, 1627-1633, 1641, 1648, 1657, 1665-1666, 1679-1686, 1711-1724, 1734-1754, 1796-1818 /home/admin/.local/lib/python3.8/site-packages/matplotlib/layout_engine.py 69 39 43% 63-64, 70, 78-80, 88-90, 96, 103, 122-124, 130, 158-162, 181-189, 207-209, 249-259, 269-274, 303-305 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67 41% 55-57, 61-63, 70, 76, 83, 90, 100-105, 108, 114-116, 119-125, 130-139, 142-144, 147-148, 161-163, 166-167, 170, 173, 225-226, 230-252, 278-287 /home/admin/.local/lib/python3.8/site-packages/matplotlib/mlab.py 275 235 15% 69, 80, 108-127, 152-157, 179, 198-213, 246-250, 255-288, 298-446, 455-472, 584-587, 638-651, 772-790, 829-840, 888-925, 929, 932, 959-985 /home/admin/.local/lib/python3.8/site-packages/matplotlib/offsetbox.py 659 353 46% 66-67, 73, 131-154, 196-197, 199-200, 271-278, 325-326, 336-337, 362, 387-388, 393-394, 399, 403-404, 481-483, 514, 552-564, 568-569, 573-583, 586-589, 593-594, 631, 635-636, 665, 678, 680, 683, 702, 748-749, 753, 764-765, 771, 794, 807-809, 841-846, 850-852, 859, 877-880, 884, 888-898, 902-905, 971-990, 1001-1004, 1008, 1012, 1016-1018, 1022-1029, 1040-1054, 1059-1065, 1068-1070, 1074-1086, 1131-1140, 1161-1178, 1181-1183, 1186, 1189-1190, 1193, 1197, 1200, 1203-1208, 1212-1214, 1229, 1311-1341, 1345, 1349-1350, 1354, 1358-1359, 1362-1367, 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397, 400-403, 406, 421-423, 427-442, 446, 454-456, 460-473, 477, 483-488, 492-493, 496, 502 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/polar.py 719 577 20% 50-54, 63, 68-77, 81-131, 135, 165-169, 175-184, 207-210, 219-231, 235, 246-253, 259, 262, 265, 268, 271, 274, 277, 290-291, 294-295, 298-302, 305-306, 324-332, 337-344, 347-352, 355-396, 413-416, 420-422, 425-433, 437-445, 458-459, 462, 466-471, 478-479, 483-487, 490-494, 514-518, 523-544, 559-561, 566-615, 618-695, 710-711, 714-716, 720-722, 725-726, 735, 744, 761-765, 771-800, 815-821, 825-844, 848-849, 857-951, 955-956, 959, 962, 965-970, 973-982, 985-991, 994-1037, 1040, 1043-1053, 1057, 1061, 1065, 1069, 1087-1098, 1104-1106, 1112, 1129-1138, 1150-1159, 1171, 1181, 1190, 1200, 1209, 1219, 1227, 1230, 1244-1256, 1266, 1277, 1280-1281, 1285, 1288, 1340-1349, 1402-1415, 1419-1441, 1453, 1463, 1473, 1476-1486, 1496, 1499-1523 /home/admin/.local/lib/python3.8/site-packages/matplotlib/pyplot.py 860 424 51% 119-120, 136, 145-157, 163-167, 175, 180-182, 188-191, 208, 234-267, 276-279, 304-322, 337, 352, 362-373, 375, 445-446, 476, 512-516, 552-556, 576-584, 589, 594, 599-601, 609, 614, 619, 658-686, 804-807, 814-825, 831, 846, 849-853, 867, 889-890, 906, 911, 921-923, 940, 945, 950, 969-991, 1017, 1022-1025, 1032, 1118-1126, 1133-1135, 1142-1143, 1149, 1290-1352, 1613-1621, 1664-1683, 1696-1699, 1712-1715, 1726-1734, 1753-1756, 1791-1795, 1830, 1881-1883, 1893, 1944-1946, 1954-1956, 2020-2029, 2088-2097, 2105-2108, 2116-2118, 2130-2138, 2155-2159, 2165, 2184-2190, 2195, 2242-2252, 2267-2274, 2284, 2295, 2303, 2315, 2324, 2335, 2343, 2349, 2355, 2361, 2370, 2380, 2389, 2395, 2401, 2407, 2419, 2425, 2431, 2439, 2447, 2457, 2467, 2483, 2500, 2508, 2517, 2527-2531, 2537-2541, 2550, 2564, 2578, 2588, 2598, 2608, 2627-2635, 2645, 2658, 2669-2674, 2682, 2695-2704, 2716, 2722, 2730, 2739, 2745, 2751, 2759-2764, 2773-2778, 2786, 2822, 2833, 2843-2847, 2853, 2874, 2880, 2890-2897, 2905-2909, 2917, 2929, 2940, 2953-2963, 2991, 2999, 3008-3010, 3016-3018, 3026-3030, 3036, 3045, 3058, 3069, 3107, 3113, 3124, 3135, 3146, 3157, 3168, 3179, 3190, 3201, 3212, 3223, 3234, 3245, 3256, 3267, 3278, 3289, 3300, 3311, 3322 /home/admin/.local/lib/python3.8/site-packages/matplotlib/quiver.py 390 338 13% 291-314, 318, 322-345, 348, 357-362, 365, 373-374, 377-385, 407-437, 441-443, 477-506, 516-527, 530-536, 540-544, 549-571, 575-577, 592-596, 599-605, 608-663, 670-723, 897-941, 967-973, 1024-1117, 1122-1162, 1173-1180 /home/admin/.local/lib/python3.8/site-packages/matplotlib/rcsetup.py 414 127 69% 68-69, 75-82, 99, 110, 127, 135-136, 159, 169-170, 185, 188-189, 218, 230-234, 238, 260, 282, 288, 290, 292, 294, 296-300, 304, 344-347, 354, 366-367, 381-384, 395-398, 411, 415, 427-428, 438, 457-483, 506-524, 534, 537-541, 549-552, 560, 568, 583-589, 683, 686, 689-692, 695-698, 705, 716-718, 738-739, 746, 750, 758, 761, 783, 786-792 /home/admin/.local/lib/python3.8/site-packages/matplotlib/scale.py 274 146 47% 69, 76, 111, 145-150, 153, 156, 180-182, 186, 190-198, 205-209, 213, 218-236, 239, 246-247, 250, 253, 256, 281-282, 288-291, 297, 301-304, 333-335, 339, 343, 350-361, 364-371, 374, 382-388, 391-398, 401, 440-441, 449-453, 457, 465-469, 472, 475, 483-484, 487, 490, 551-557, 562, 565-574, 581-584, 588-593, 596, 599, 606-607, 611, 614, 617, 646-648, 652, 657-665, 677-679, 726 /home/admin/.local/lib/python3.8/site-packages/matplotlib/spines.py 315 142 55% 33, 90-99, 103-109, 113-114, 126-131, 137-138, 156, 171-176, 189-194, 231, 234, 240, 243-270, 282, 314, 317, 319, 329-330, 338-341, 351, 357-386, 408-419, 423, 438, 448-451, 456-460, 476-477, 491, 494-505, 508-512, 546, 549, 554-555, 560-563, 566, 568-571, 578, 582 /home/admin/.local/lib/python3.8/site-packages/matplotlib/stackplot.py 42 37 12% 71-127 /home/admin/.local/lib/python3.8/site-packages/matplotlib/streamplot.py 370 328 11% 91-241, 247-248, 274-284, 288, 291, 294, 297, 300-301, 304-305, 308-311, 314, 321-362, 366, 372, 386-396, 399, 403-404, 408-409, 417-426, 443-502, 535-602, 607-624, 633-667, 678-707 /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/core.py 92 45 51% 22, 127-180, 220-224, 242, 256, 262-266 /home/admin/.local/lib/python3.8/site-packages/matplotlib/table.py 335 92 73% 101, 118, 143, 161, 188-189, 202, 207, 211-217, 302, 354-355, 365, 402, 404, 407, 431-444, 448, 452-457, 500-508, 512-516, 520-521, 526, 532, 543-545, 568-570, 585, 598-605, 614, 616, 618, 620, 627, 629, 631, 633, 650, 738, 744-746, 752, 757, 760, 774-777, 780-781, 787-790, 796, 821-827 /home/admin/.local/lib/python3.8/site-packages/matplotlib/texmanager.py 151 103 32% 48-49, 105-106, 110-115, 120-130, 134-171, 178-187, 194-195, 200, 205-207, 246-249, 253-275, 284-305, 314-329, 334-344, 357-361, 366-373 /home/admin/.local/lib/python3.8/site-packages/matplotlib/text.py 812 357 56% 41-49, 67-90, 130, 228, 233, 236-239, 246-268, 279, 292-313, 318, 390, 479, 486-489, 492, 494, 496, 498, 531-552, 559, 571-582, 589, 633-652, 659-675, 681-685, 697-736, 761-762, 768-769, 785, 789-790, 796, 814, 824, 834, 844, 864, 874, 884, 916, 941, 952, 954, 977-983, 1026-1028, 1065-1066, 1080-1081, 1095-1096, 1126, 1148, 1181-1182, 1234, 1246-1247, 1297-1299, 1301, 1303, 1332, 1371, 1395-1397, 1407-1408, 1412, 1415-1419, 1436-1454, 1470-1478, 1483-1488, 1490-1496, 1498-1499, 1501, 1503, 1505, 1508, 1510-1513, 1517, 1524, 1526, 1537-1538, 1545-1546, 1548, 1550-1552, 1557, 1562, 1569, 1606-1607, 1612, 1616-1617, 1639-1654, 1673, 1856, 1872-1880, 1888-1895, 1908, 1918, 1922, 1938, 1946, 1960-2016, 2024, 2030-2032, 2041-2058, 2062-2064 /home/admin/.local/lib/python3.8/site-packages/matplotlib/textpath.py 192 152 21% 34-37, 40, 46, 49-70, 112-134, 142-164, 173-215, 221-223, 230-280, 287-298, 354-369, 373-374, 378, 385-386, 393, 402-408 /home/admin/.local/lib/python3.8/site-packages/matplotlib/ticker.py 1228 803 35% 165-167, 170, 173, 176, 179, 182, 186, 196-197, 213, 225, 233, 269, 283-284, 294-297, 300, 303, 316-317, 325, 328, 331, 346, 354, 365, 374, 452, 485-486, 498, 512, 531, 555-556, 559-564, 573, 588, 620-622, 626-650, 654-666, 676-692, 712-713, 736, 749-750, 753-754, 761-762, 764, 768, 772, 774, 782, 789, 792, 795, 805, 809, 874-883, 893, 902, 914, 925, 933-984, 987-993, 997-1016, 1019-1020, 1024, 1028-1046, 1054-1062, 1072, 1076-1110, 1120-1125, 1167-1172, 1184, 1193, 1205, 1218, 1221-1260, 1263-1286, 1289-1292, 1295-1313, 1318-1322, 1388-1392, 1395, 1398-1401, 1406, 1409-1412, 1417-1421, 1438-1473, 1503-1506, 1510-1512, 1536-1556, 1559, 1570-1579, 1583, 1616, 1623, 1631, 1645, 1685-1686, 1690-1693, 1697-1698, 1701, 1723-1724, 1741-1747, 1791-1795, 1800, 1804, 1808-1811, 1815-1816, 1819-1830, 1835-1850, 1860, 1864-1865, 1869-1870, 1873-1879, 1886-1896, 1905, 1928, 1935-1937, 1946, 1953, 2013, 2017, 2020, 2022, 2053, 2065, 2072, 2086-2088, 2097-2098, 2104-2109, 2117, 2140-2141, 2148, 2150, 2152, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trifinder.py 26 15 42% 20-21, 38-42, 55-63, 79, 86, 93 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triinterpolate.py 535 450 16% 34-56, 157-207, 228, 258-261, 265, 270, 275-283, 381-418, 421, 426, 431-446, 466-476, 497-515, 539-543, 561-571, 689-706, 727-762, 783-787, 803-828, 846-879, 896-909, 935-978, 996-1004, 1007, 1013-1017, 1043-1058, 1065-1068, 1084-1105, 1112-1127, 1135-1153, 1163-1164, 1172-1210, 1224-1227, 1234-1235, 1243-1248, 1254-1259, 1265-1269, 1272, 1277-1280, 1312-1350, 1406-1426, 1440-1472, 1479, 1486, 1494-1514, 1531-1544, 1556-1574 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tripcolor.py 62 56 10% 61-154 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triplot.py 28 23 18% 38-86 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trirefine.py 93 81 13% 43-44, 62, 94-131, 157-169, 191-307 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/home/admin/.local/lib/python3.8/site-packages/numpy/lib/_datasource.py 177 73 59% 59-66, 110-111, 116-117, 123-126, 252-254, 259-261, 274-278, 289, 325-342, 364-366, 371, 373, 411, 468-485, 522, 529, 533, 578-579, 582, 586-591, 595, 618, 652, 683, 700-704 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_iotools.py 352 295 16% 31-33, 44-45, 53-57, 81-84, 120-131, 168, 172-197, 201-206, 209-216, 219-224, 227, 288-310, 339-380, 383, 413-419, 505, 524, 529, 539-541, 568-582, 587-596, 601-669, 672-675, 678-700, 703, 707-723, 746-751, 754-763, 796-820, 861-898 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_version.py 75 61 19% 56-76, 80-97, 101-112, 115-134, 137, 140, 143, 146, 149, 152, 155 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraypad.py 218 200 8% 29-30, 55, 81-83, 109-126, 146-151, 175-183, 208-227, 257-293, 321-378, 401-451, 482-518, 522, 736-876 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py 197 117 41% 34, 82-122, 130, 276-317, 330-331, 338-344, 350, 352-355, 357-358, 435-436, 441-442, 446-447, 455-461, 467, 500-510, 589, 600-602, 610-631, 738, 775, 779, 819-824 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arrayterator.py 71 58 18% 85-90, 93, 101-125, 132-134, 161-162, 172, 177-219 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/format.py 266 178 33% 193-194, 212-216, 232-233, 238-245, 270-281, 307-336, 352-364, 371-389, 396-413, 430-440, 452, 468, 499, 532, 563, 579, 599-601, 603-604, 607-609, 614-615, 617-618, 621-623, 663-696, 735, 742-751, 770-780, 784-785, 842-890, 912-913, 915-916 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/function_base.py 1153 803 30% 116, 121, 125, 131, 133, 143, 232, 234, 383-412, 415-417, 488, 494-497, 588-618, 622-623, 665-719, 810-811, 989-1157, 1250, 1252, 1258, 1263-1268, 1273-1278, 1281, 1298, 1406-1439, 1443, 1485-1496, 1500, 1569-1596, 1600, 1627-1637, 1686, 1689-1693, 1698, 1750, 1754, 1794-1798, 1833-1840, 1866-1869, 1887-1905, 1926-1934, 1939, 1945-1948, 2113, 2118, 2122, 2131, 2148-2161, 2169, 2186, 2197, 2208-2214, 2219, 2237, 2239, 2251, 2257-2316, 2321, 2448-2543, 2548, 2679-2701, 2796-2801, 2905-2910, 3009-3014, 3109-3114, 3182-3190, 3194, 3198, 3202, 3256-3262, 3388-3392, 3396, 3475-3477, 3481, 3508-3510, 3542-3560, 3570, 3655-3660, 3666-3716, 3866, 3873, 3976-3979, 3990, 3998-4000, 4003, 4030-4033, 4038, 4047, 4053, 4055, 4057, 4059, 4063, 4075-4084, 4095-4098, 4116-4120, 4126, 4215-4240, 4356, 4379, 4447-4560, 4564, 4656-4755, 4814, 4821, 4915-4932 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/histograms.py 287 254 11% 29, 49-50, 72-73, 96-97, 118-119, 146-161, 182-196, 224-226, 263-270, 285-301, 309-331, 342-357, 382-451, 460, 467, 668-670, 675, 791-929, 934-940, 1014-1129 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/index_tricks.py 258 182 29% 32, 93-107, 149-207, 325-420, 423, 593, 607, 610, 657-661, 665, 678-681, 695-696, 775, 891-909, 977-978, 982, 1006-1013 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/mixins.py 59 12 80% 10-13, 19-21, 29-31, 39, 54 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/nanfunctions.py 279 219 22% 61-66, 96-110, 135-139, 164-180, 209-221, 225, 313-336, 340, 428-451, 455, 494-500, 504, 544-550, 554, 647-648, 652, 717-718, 722, 787-788, 792, 854-855, 859, 937-957, 965-974, 984-1000, 1010-1020, 1025, 1113-1124, 1129, 1245-1249, 1255, 1358-1362, 1371-1381, 1391-1405, 1413-1418, 1424, 1518-1567, 1572, 1670-1676 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/npyio.py 854 664 22% 34-37, 83, 86-89, 97, 108-112, 189-202, 205, 208, 215-221, 224, 228, 231, 242-260, 269-273, 277-281, 408, 414-415, 431-434, 438, 444-450, 455, 519-529, 534-535, 618, 622-623, 689, 695-726, 733-736, 740, 742, 744, 747-758, 768, 904, 927-944, 950, 954, 990, 992, 994-995, 1005-1006, 1016, 1028, 1039-1054, 1065, 1072-1075, 1081, 1085-1086, 1091, 1100-1104, 1113, 1122-1138, 1152-1156, 1162, 1167, 1176-1179, 1182-1186, 1199, 1326-1447, 1510-1541, 1557, 1751-2284, 2311-2317, 2339-2345, 2369-2377, 2403-2415 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/polynomial.py 438 319 27% 41, 138-165, 231, 238, 248, 257, 265, 342-366, 370, 432-446, 450, 620-689, 693, 764-772, 776, 830-844, 884-898, 956-961, 965, 1020-1038, 1042-1065, 1180, 1185-1186, 1191, 1197, 1202, 1208, 1220-1231, 1233, 1236, 1239, 1246-1249, 1252-1254, 1257, 1260-1314, 1317, 1320, 1323, 1326-1330, 1333-1337, 1340-1341, 1344-1345, 1348-1353, 1356-1357, 1360-1361, 1364-1368, 1373-1377, 1382-1386, 1389-1391, 1395-1400, 1403-1411, 1414, 1427, 1440 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/scimath.py 70 38 46% 106-110, 135-138, 162-165, 189-192, 196, 239-240, 287-288, 337-338, 342, 376-378, 425-426, 430, 473-475, 519-520, 565-566, 616-617 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/shape_base.py 263 148 44% 31-49, 53, 161-170, 174, 251-260, 264, 359-414, 418, 487-505, 509, 588-602, 648, 718, 722, 727-732, 778, 868-872, 878, 937-942, 989-991, 1034-1036, 1043-1048, 1055-1060, 1064, 1136-1164, 1252 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py 91 46 49% 21-22, 29-34, 97-114, 119, 301-335, 343, 345, 363, 411, 426-427, 470-471 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/twodim_base.py 177 100 44% 34-40, 95-98, 153, 158, 211, 216, 220, 295, 300-303, 346-363, 367, 412-424, 433, 469-472, 498-501, 505, 582-597, 602-615, 741-752, 821-823, 904-906, 911, 938-940, 1023-1025, 1057-1059 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/type_check.py 138 78 43% 70-77, 81, 112-114, 159-160, 202-203, 240-244, 339-340, 390, 395-397, 401, 498-521, 526, 574-583, 588-590, 618, 696, 713, 753-769 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/ufunclike.py 57 30 47% 24-36, 50-53, 65, 70, 117-124, 188-196, 260-268 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/utils.py 454 363 20% 38-46, 83-84, 96-97, 116, 123-124, 221, 260-277, 329-379, 391-407, 415-431, 452-482, 537-628, 672-677, 737-812, 835-947, 950-956, 1028-1043, 1056-1070 /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py 678 507 25% 88, 91, 94, 97, 100, 129, 133, 142, 146-149, 153-154, 165-174, 177-185, 188-190, 196, 203, 208, 212, 230, 235, 286-306, 310, 378-395, 399, 456-467, 550, 618-666, 756-764, 770, 890-982, 1075, 1081, 1159-1176, 1179-1181, 1314-1333, 1453-1473, 1624-1639, 1652-1657, 1666-1674, 1678, 1761-1797, 1801, 1898-1906, 1912, 1995-2011, 2092-2101, 2153-2160, 2166, 2266-2328, 2354-2356, 2360, 2514-2611, 2617-2618, 2707-2739, 2750-2760, 2780-2801, 2806-2812 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ma/core.py 2405 1672 30% 102-113, 122, 124, 204-212, 217-222, 270-277, 282-291, 342, 393, 414-425, 441, 443-451, 456-457, 463-468, 532-534, 543-547, 575-579, 628, 633-636, 646-663, 713, 768-776, 804, 812-813, 831-832, 848-853, 868-869, 884-885, 895, 928-969, 1011-1050, 1057-1080, 1087-1105, 1112-1116, 1155-1192, 1284-1291, 1295-1297, 1305, 1534-1537, 1547, 1621-1636, 1683, 1726-1753, 1788-1809, 1814-1817, 1924-1943, 1969, 1995, 2021, 2047, 2073, 2101-2103, 2139-2143, 2179-2183, 2244-2251, 2323-2331, 2370, 2400, 2407, 2414, 2421, 2424, 2439-2447, 2489-2495, 2525-2550, 2581-2590, 2647-2653, 2656, 2659-2670, 2674-2676, 2696-2703, 2834, 2840, 2845-2847, 2857-2861, 2869-2871, 2874-2875, 2879-2882, 2887, 2889, 2895-2896, 2900-2909, 2915-2932, 2938, 2943, 2956, 3012, 3033, 3037-3040, 3047, 3054-3058, 3062, 3065, 3074-3121, 3180-3183, 3190-3194, 3204-3210, 3236-3251, 3261-3267, 3276, 3283-3286, 3291, 3301-3330, 3347-3402, 3411-3419, 3427-3431, 3438-3500, 3516, 3532-3535, 3539, 3554-3555, 3570-3571, 3576, 3591-3594, 3599, 3635, 3660, 3664-3665, 3697-3706, 3710-3725, 3778, 3781, 3786, 3789-3790, 3794-3809, 3833-3836, 3898-3908, 3915-3939, 3942, 3949-4026, 4031-4040, 4053-4104, 4117, 4130, 4137-4139, 4148, 4155-4157, 4164, 4168-4170, 4179, 4186-4188, 4195-4197, 4204, 4211-4213, 4220, 4227-4229, 4236, 4243-4253, 4260-4269, 4276-4285, 4292-4303, 4310-4321, 4328-4339, 4346-4360, 4367-4373, 4380-4385, 4407-4409, 4434-4436, 4497-4538, 4588, 4652-4658, 4673-4676, 4739-4761, 4785-4787, 4815, 4841-4855, 4871-4885, 4983, 4990-4997, 5037, 5078-5099, 5133-5140, 5160-5181, 5206-5213, 5241-5260, 5293-5300, 5317-5363, 5380-5388, 5401-5413, 5479-5493, 5535-5538, 5572-5575, 5648-5658, 5692-5724, 5788-5792, 5826-5859, 5936-5947, 5950-5953, 5956-5959, 5964-5985, 6024-6046, 6057-6061, 6101, 6116, 6163-6175, 6184-6186, 6200-6203, 6209, 6214-6221, 6229-6231, 6241-6257, 6262, 6269-6284, 6287-6291, 6294-6302, 6308-6316, 6319, 6343, 6356-6366, 6462-6469, 6472, 6475, 6478, 6481-6485, 6491-6500, 6505, 6510, 6524, 6527, 6530, 6536-6542, 6559, 6614-6616, 6640-6647, 6651-6679, 6683-6695, 6698-6705, 6710-6717, 6723-6729, 6767-6768, 6775, 6812-6813, 6833-6865, 6872-6882, 6898-6908, 6923, 6967-6983, 6998-7001, 7016-7022, 7037-7043, 7059-7062, 7083-7101, 7136-7139, 7156-7158, 7216-7222, 7230, 7237, 7243, 7308-7340, 7388-7415, 7442-7448, 7527-7540, 7605-7625, 7636-7642, 7650-7660, 7670-7682, 7710, 7738, 7783-7796, 7871-7904, 8001, 8011, 8017, 8082, 8116-8127, 8186 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/extras.py 560 464 17% 48, 101-102, 152-154, 207-209, 259, 262, 272-280, 306-318, 333-341, 364-369, 376-451, 459-477, 587-631, 700-714, 719-799, 823-842, 894-896, 911-914, 928-931, 975-981, 1024-1030, 1049-1063, 1078-1087, 1113-1119, 1133-1146, 1168-1188, 1209-1210, 1225, 1248-1253, 1267-1301, 1358-1374, 1425-1461, 1486-1487, 1491-1494, 1569-1576, 1621-1626, 1674-1684, 1744-1760, 1769-1789, 1825-1828, 1864-1867, 1880-1884, 1894-1921 /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py 238 158 34% 15-33, 69, 123-128, 131-139, 142, 149, 151, 153, 157, 160, 173-187, 190-213, 216-221, 224, 227-228, 231, 234-235, 238, 244-251, 257-260, 284, 319, 372, 411, 445, 479, 513, 546, 569, 609, 644, 683, 718, 757, 790, 830-835, 865, 894, 933, 966, 998-1001, 1011-1032, 1089-1111 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/__init__.py 18 7 61% 171-180 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/_polybase.py 419 296 29% 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 193-198, 216, 234, 252, 280-288, 291-304, 307-311, 314-324, 327-329, 337-367, 375-380, 389-394, 398-403, 409, 413-462, 469-473, 476, 481-483, 486, 489, 494, 497, 500-505, 508-513, 516-521, 527-532, 535-538, 541-544, 547-556, 559-561, 564-568, 571-575, 578-582, 586, 591, 594-597, 600-603, 606-614, 617-622, 625, 640, 653, 678, 700-701, 723-730, 761-767, 796, 823-829, 849-851, 865-866, 894-898, 971-986, 1014-1027, 1054-1060, 1091-1099, 1137-1141 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/chebyshev.py 357 294 18% 152-155, 177-180, 207, 243-274, 302-306, 333-340, 389-394, 441-455, 508-511, 566, 608, 652, 686-698, 742-747, 797-814, 855-872, 935-964, 1052-1091, 1153-1175, 1224, 1277, 1328, 1384, 1422-1437, 1490, 1544, 1670, 1700-1715, 1766-1776, 1827-1843, 1881-1888, 1915-1916, 1946-1953, 1979-1986, 2065-2069 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite.py 267 214 20% 134-139, 180-197, 251-254, 310, 350, 390, 432-443, 485-509, 557, 594, 652-677, 763-799, 871-895, 944, 997, 1048, 1104, 1151-1165, 1218, 1272, 1403, 1433-1448, 1502-1512, 1544-1555, 1594-1622, 1649-1650 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite_e.py 264 211 20% 135-140, 181-197, 250-253, 309, 349, 389, 427-438, 480-504, 550, 587, 645-670, 756-792, 864-887, 936, 989, 1040, 1096, 1143-1156, 1209, 1263, 1395, 1426-1441, 1495-1505, 1537-1548, 1587-1615, 1641-1642 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/laguerre.py 252 200 21% 134-138, 179-193, 245-248, 304, 345, 385, 427-439, 481-505, 551, 588, 646-674, 761-798, 870-893, 942, 995, 1046, 1102, 1149-1162, 1215, 1269, 1400, 1429-1444, 1498-1508, 1547-1572, 1598-1599 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/legendre.py 261 209 20% 140-145, 193-207, 261-264, 319, 361, 405, 447-461, 505-529, 578, 609, 672-701, 789-829, 891-914, 963, 1016, 1067, 1123, 1161-1176, 1229, 1283, 1411, 1441-1455, 1506-1516, 1555-1584, 1611-1612 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polynomial.py 221 166 25% 145-148, 212, 248, 285, 317-325, 361-363, 400-421, 460, 515-542, 623-661, 745-757, 835-845, 895, 948, 999, 1055, 1096-1109, 1157, 1211, 1361, 1390-1401, 1454-1464, 1514, 1518, 1522-1529 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polyutils.py 229 204 11% 71-77, 130-153, 200-208, 248-254, 297-301, 366-368, 372-374, 422-443, 452-453, 469-483, 497-513, 527-529, 547-565, 571-578, 584-592, 606-680, 697-713, 732-750 /home/admin/.local/lib/python3.8/site-packages/numpy/random/__init__.py 17 1 94% 210 /home/admin/.local/lib/python3.8/site-packages/numpy/random/_pickle.py 22 12 45% 31-37, 54-60, 77-83 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/decorators.py 74 61 18% 61-65, 100-105, 143-186, 226-251, 282-304, 323-329 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/nosetester.py 174 157 10% 36-58, 96-109, 164-193, 212-230, 233-250, 259-260, 276-324, 397-463, 523-536, 540-544 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/utils.py 873 732 16% 59-75, 89-95, 109-113, 127-132, 146-151, 156-186, 196-208, 220-244, 249-272, 325-432, 463-473, 545-599, 660-698, 716, 741-745, 749, 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/home/admin/.local/lib/python3.8/site-packages/oauth2client/_pkce.py 14 8 43% 41-49, 66-67 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pure_python_crypt.py 63 39 38% 55-62, 73, 88-92, 113-125, 136, 147-148, 166-184 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pycrypto_crypt.py 38 22 42% 34, 48-49, 64-75, 87, 98-99, 116-124 /home/admin/.local/lib/python3.8/site-packages/oauth2client/client.py 708 520 27% 147-148, 184-187, 213, 222, 231, 239, 255-274, 283, 298-314, 328, 351, 358-359, 367-368, 378, 388, 395, 405-409, 419-423, 434-438, 489-506, 535-536, 545, 554, 562, 580-581, 595-596, 610-633, 641-652, 660-664, 677, 689-697, 701, 705-707, 711-712, 716-722, 726-733, 748-763, 774-819, 827, 841-863, 871, 885-902, 944, 956-960, 971, 980, 996-1005, 1014-1030, 1039-1045, 1101, 1111, 1118, 1124-1147, 1152, 1171-1174, 1187-1190, 1207-1230, 1249-1261, 1271, 1287-1298, 1310-1315, 1331-1340, 1344-1351, 1362-1379, 1385-1415, 1420, 1427, 1433-1435, 1439-1441, 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/home/admin/.local/lib/python3.8/site-packages/pandas/_libs/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/tslibs/__init__.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/window/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/__init__.py 396 256 35% 121-125, 253-255, 265, 276, 292-317, 325-330, 338, 343, 350-351, 358-359, 363-367, 371-385, 389-392, 396-399, 403, 407-410, 416-418, 424, 428-429, 433-437, 441-450, 462-468, 473-477, 481, 485, 489-492, 496-497, 501-503, 509-511, 515, 519, 524-525, 529-530, 534-543, 547, 551-552, 585-664, 739-771, 775-798, 802-805, 818, 822, 830, 834, 840, 859-872, 891-892, 909-911, 934-942, 959-967, 975, 979, 983, 987, 991, 995, 1005-1047 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_io.py 130 98 25% 30, 76-79, 102-111, 209-248, 267-278, 303-308, 329-337, 358-366, 388-418, 426-435 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_random.py 9 3 67% 14-19, 29 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_warnings.py 61 48 21% 86-102, 112-115, 126-150, 163-188, 196-199, 205-216 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/asserters.py 402 358 11% 92-142, 163-170, 176-177, 234-343, 352-377, 394-416, 420-435, 440-443, 471-504, 525-535, 539-542, 548-554, 560-564, 570-599, 633-679, 732-782, 877-1032, 1152-1224, 1253-1280, 1292-1313, 1317-1318, 1330-1336, 1350, 1358-1364, 1373-1378 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/compat.py 10 6 40% 10-14, 22-24 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/contexts.py 86 61 29% 46-47, 72-89, 114-134, 145-150, 173-184, 189-202, 206-213 /home/admin/.local/lib/python3.8/site-packages/pandas/_typing.py 149 33 78% 34-84, 204, 209, 213, 217, 223, 229, 233, 238, 243, 249, 253, 257, 262, 314 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11% 49-90, 121-157, 167-207 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/_arrow_utils.py 24 16 33% 17-20, 43-61 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/array.py 1003 805 20% 105-109, 117-120, 142-147, 154-159, 168-181, 234-245, 252-285, 294-348, 374-417, 423-429, 433, 437, 440, 443, 446, 449, 454-456, 459-460, 463-487, 490-536, 539, 542, 545-549, 556, 563, 573, 577-585, 589, 597, 655, 713, 723-732, 735-748, 751, 754, 766, 776, 785-834, 838-844, 860-861, 868-895, 898, 927, 936-944, 1006-1042, 1051-1081, 1091-1104, 1123-1151, 1168-1175, 1207-1225, 1251-1345, 1369-1424, 1438-1491, 1508-1530, 1548-1571, 1575-1588, 1613-1632, 1658-1685, 1689, 1698-1700, 1708-1718, 1723-1733, 1736-1739, 1742-1745, 1756-1764, 1767-1774, 1779-1781, 1786-1788, 1791-1804, 1807-1824, 1827, 1830-1832, 1835-1837, 1842-1846, 1853-1857, 1860, 1863, 1866, 1869, 1872, 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1487, 1501-1505, 1519-1523, 1549-1560, 1564-1570, 1573-1580, 1637-1638, 1799-1866, 1870, 1877, 1881-1888, 1903-1928, 1937, 1943, 1950, 1953-1967, 1974-1980, 1985-1993, 1997-2017, 2026, 2035, 2044, 2051, 2056, 2062, 2077-2091, 2100-2109, 2117-2147, 2152, 2157, 2178-2184, 2210-2221, 2241-2249, 2265-2267 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/datetimes.py 617 485 21% 90-91, 109-112, 117-148, 198, 263-265, 275-287, 291, 309-370, 389-502, 508-514, 517, 520-522, 529-531, 556, 569, 574, 584, 591, 595, 601-605, 615-634, 641-701, 709-713, 722-729, 733-748, 756-782, 794-797, 863-873, 1021-1058, 1071, 1111-1118, 1164-1190, 1244-1250, 1301-1307, 1319-1321, 1330, 1343-1345, 1381-1390, 1911-1917, 1971-1979, 2022-2134, 2172-2202, 2229-2256, 2281-2290, 2315-2344, 2370-2397, 2422-2440, 2446-2453, 2478-2486, 2521-2595 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/floating.py 34 3 91% 36, 40, 51 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/integer.py 70 9 87% 36, 40, 50-57 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/interval.py 638 486 24% 104, 220, 238-275, 288-293, 305-379, 389, 395-400, 458-460, 536-548, 608-631, 645-661, 674-677, 684, 688, 693, 699, 702, 706, 710, 715-731, 734-738, 742-817, 821, 825, 829, 833, 837, 841, 851-859, 864-878, 881-895, 926-935, 956-996, 999-1002, 1023-1033, 1043-1046, 1049, 1052-1077, 1133-1146, 1150-1168, 1171-1182, 1185-1205, 1225, 1233-1260, 1266-1270, 1273-1274, 1284-1286, 1293-1295, 1302, 1309-1313, 1372-1385, 1396, 1438-1444, 1466-1474, 1487-1498, 1504-1550, 1573-1577, 1582-1591, 1608-1613, 1616-1624, 1632-1635, 1683-1686, 1691-1720, 1724-1730, 1736-1748, 1753-1757, 1776-1796 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/masked.py 572 451 21% 88-90, 121-134, 140-141, 145, 149, 153, 158-167, 173-198, 204, 214-231, 234-248, 251-264, 267, 271, 275, 278-280, 283-285, 288-290, 294-296, 300, 326-330, 336, 339, 342, 345, 415-439, 443-446, 450, 454, 458, 461-504, 513, 521-592, 598-600, 609, 614-625, 628-730, 735-773, 782-821, 824, 828, 832, 840-842, 854-876, 881-898, 901-904, 914-915, 924-932, 939-971, 975, 994-1021, 1025-1037, 1050-1078, 1084-1097, 1100-1109, 1119-1135, 1147-1155, 1160-1167, 1174-1182, 1189-1197, 1202-1203, 1211-1212, 1281-1298, 1362-1380, 1385-1391 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/numeric.py 152 112 26% 41, 52, 56, 60, 64, 72-114, 118, 125-136, 145, 149-235, 248-263, 267-268, 274-280, 286-289 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/numpy_.py 186 130 30% 80-95, 101-120, 123, 130, 136, 143-188, 194-202, 205, 208-211, 214-218, 231-233, 243-245, 250-254, 259-263, 273-277, 287-291, 302-304, 315-319, 331-335, 347-351, 363-367, 378-382, 393-397, 408-420, 426, 429, 432, 435, 438-459, 466-472 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/period.py 420 311 26% 83-92, 105-107, 179, 215-236, 247-249, 259-274, 280, 297-298, 302-319, 331-338, 341, 344-346, 353, 361, 364-370, 376-397, 479, 497-536, 541, 589-609, 615-617, 626-643, 650-664, 672-677, 680-688, 708-712, 715-718, 731-740, 754-781, 803-818, 837-847, 912-943, 948, 953, 977-993, 1018-1033, 1037-1081, 1094-1128, 1132-1143 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/accessor.py 110 78 29% 20, 30-31, 34, 46-47, 50, 53-58, 102-108, 185-190, 216-218, 232-234, 265-287, 309-312, 334-355, 362-363, 367-386 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/array.py 786 648 18% 107-123, 155-158, 180-266, 275-287, 378-497, 506-510, 538-554, 557-579, 585-586, 590, 594, 604, 618, 622, 631, 635, 642-645, 649-651, 654, 658, 661-664, 668, 682, 696, 701-706, 746-772, 775-799, 809-820, 823-834, 838, 849-853, 868-893, 900, 907, 913-1001, 1004-1012, 1017-1030, 1035-1097, 1100-1120, 1128-1133, 1136-1137, 1143-1195, 1254-1274, 1316-1326, 1336, 1341-1344, 1351-1361, 1364-1367, 1374-1384, 1398-1405, 1419-1426, 1454-1470, 1490-1498, 1512-1521, 1538-1539, 1556-1557, 1572-1592, 1595-1618, 1621-1624, 1627-1630, 1639-1702, 1709-1739, 1742-1765, 1774-1782, 1785, 1788, 1791, 1794, 1800-1803, 1808, 1832-1870, 1875, 1880, 1885-1892 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/dtype.py 143 94 34% 39, 85-99, 104, 109-140, 156, 159-160, 178, 182, 186, 193, 197, 201, 205, 208, 219-221, 250-273, 297-307, 311-317, 357-370, 396-398, 403-426 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/string_.py 257 193 25% 58-65, 105, 110-120, 124, 154-165, 180-185, 193-215, 225-227, 315-320, 324-336, 340-365, 371, 375-377, 383-390, 393-396, 399-429, 435, 438-468, 473-476, 479-483, 486-490, 493-497, 500-503, 512-517, 520-547, 560-608 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/string_arrow.py 200 142 29% 53-55, 112-116, 128, 132-152, 158, 165, 168-170, 174-185, 188-201, 204-216, 231-283, 288-306, 309-310, 313-314, 325-331, 336-338, 343-345, 348-349, 352-353, 356-357, 360-361, 364-365, 368-369, 372-373, 376-377, 380-381, 384-385, 388, 391, 394-398, 401-405, 408-412 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/timedeltas.py 440 333 24% 78, 83-96, 142, 164-167, 186, 197-199, 207-214, 218-227, 242-271, 279-319, 325-331, 334, 338, 348-368, 371-384, 400-407, 419-426, 432-441, 447-449, 454-460, 466-467, 473-500, 509-550, 553-559, 567-581, 586-610, 615-631, 635-660, 664-678, 683-685, 690-692, 697-702, 707-712, 715-718, 721, 725, 784-785, 795, 826-853, 896-966, 985-1006, 1039-1042, 1046-1062 /home/admin/.local/lib/python3.8/site-packages/pandas/core/base.py 322 197 39% 75-82, 113, 120, 126-131, 138-144, 163, 172-178, 196-200, 204-207, 212, 217-230, 233-246, 261, 264, 283, 288, 299-300, 320, 324, 331, 348-350, 357, 364, 429, 526-561, 566, 608-610, 666-678, 722-724, 730-742, 763, 780-784, 799, 802, 818-823, 849-926, 1015, 1025-1031, 1068-1071, 1082, 1093-1095, 1106-1108, 1135-1144, 1164-1177, 1293, 1302, 1311-1323, 1331-1333, 1337, 1340-1350, 1357 /home/admin/.local/lib/python3.8/site-packages/pandas/core/common.py 195 126 35% 57, 77-81, 85-92, 126-138, 141-145, 164, 176, 183, 190, 197, 204, 211, 221, 226, 230-258, 276-287, 291-293, 301, 310, 322, 334, 342, 352-364, 379, 405-413, 418, 425, 453-478, 510-518, 527-533, 543-548, 563-568, 576, 626, 634, 653 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/align.py 99 76 23% 32-35, 42-51, 57, 64, 71-80, 87-142, 149-165, 188-213 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/api.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/check.py 8 1 88% 8 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/common.py 29 23 21% 14-16, 24-48 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/engines.py 50 23 54% 23, 37-42, 53-55, 63, 77-82, 88, 113-121, 134, 137 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/eval.py 111 90 19% 27, 51-72, 88-89, 95-99, 119-120, 147-149, 153-167, 299-413 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/expr.py 361 213 41% 65-66, 84-91, 114-117, 165-166, 262, 314, 397-401, 404-415, 418-421, 424, 428-451, 454-459, 462-482, 491, 504-532, 535-537, 540, 543-545, 548, 551, 554, 557, 560-561, 564-565, 571, 574-591, 595-605, 617-635, 638-655, 658-706, 709, 712-730, 733-735, 738-746, 768, 776, 804-809, 813, 816, 819, 822, 828, 835-837 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py 107 70 35% 24, 46, 58-61, 68-70, 75-89, 93-130, 171, 176-188, 196-199, 211-220, 235-240, 255-256, 267-268, 272-273, 281-283 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/ops.py 293 182 38% 72-75, 81-87, 91, 94, 97, 100, 103-117, 129-135, 139, 143-152, 158, 162-167, 171, 175, 179, 183, 188, 191, 195, 200, 214-216, 219, 226-227, 232-234, 238-240, 244, 248, 252-257, 265-273, 281-289, 346-355, 359, 374-387, 405-408, 427-457, 464-490, 493-511, 515, 529-539, 565-571, 577-579, 582, 586-593, 598-599, 603-605, 608-609, 614-617, 620 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/parsing.py 45 33 27% 35-67, 90-93, 125-130, 159-164, 181-195 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/pytables.py 352 257 27% 49-50, 57-61, 64, 68-78, 83, 88-89, 92, 103-106, 109, 112-151, 155-159, 164, 172, 177, 182, 187, 191-192, 200-256, 259, 266-268, 272-278, 282, 285-310, 313-316, 321, 324, 329, 336, 342, 345-368, 373-374, 379-393, 401-404, 411-416, 419, 422-425, 430-444, 449-470, 473, 476, 497-503, 545-585, 588-590, 594-609, 616-619, 623-631, 636-641 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/scope.py 125 84 33% 36-40, 49-53, 60, 76-82, 88-89, 118-120, 153-188, 191-193, 207, 226-246, 261-271, 285-294, 304-314, 330-338, 343, 356-357 /home/admin/.local/lib/python3.8/site-packages/pandas/core/config_init.py 197 20 90% 40-42, 54-56, 68-70, 290-292, 307-310, 345, 421-423, 641-643, 664-672 /home/admin/.local/lib/python3.8/site-packages/pandas/core/construction.py 218 142 35% 67-71, 290-379, 386, 393, 442-450, 453, 464-466, 469-471, 480-491, 520, 524, 530-532, 535-536, 539-542, 548-553, 557-559, 564, 572-576, 580-581, 596, 599, 622-633, 641, 656, 662-679, 696-699, 710, 734-767 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/api.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/astype.py 114 92 19% 40, 50, 57, 83-140, 149-159, 177-193, 220-251, 266-306 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/base.py 140 59 58% 33-36, 104, 124-133, 138, 141, 152, 164, 180, 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/home/admin/.local/lib/python3.8/site-packages/pandas/core/generic.py 2254 1609 29% 193-200, 279, 292-311, 331-333, 352, 357, 448-451, 457-467, 478, 488, 503-507, 514-515, 520-521, 538, 543-569, 573-579, 590-595, 601, 605, 612, 621, 644, 670, 708, 714-721, 729-731, 744-780, 848-850, 853-856, 962-963, 987-1042, 1055, 1068, 1081, 1216-1256, 1313-1324, 1331, 1414-1417, 1424-1438, 1442-1453, 1457-1462, 1466, 1507-1518, 1588-1589, 1593, 1597, 1629-1631, 1689, 1723-1735, 1775-1778, 1782-1792, 1825-1872, 1891, 1905, 1917-1918, 1922, 1927, 1987, 1997-2010, 2016, 2023-2024, 2035-2067, 2075-2076, 2084-2087, 2095-2100, 2237-2252, 2522-2532, 2678-2682, 2876-2878, 2953-2955, 3032-3034, 3113-3118, 3145, 3172, 3356-3459, 3515-3533, 3560, 3587, 3761-3772, 3798, 3817-3824, 3827, 3907-3909, 3922-3938, 3948-3952, 4064, 4067-4078, 4083-4131, 4134, 4142-4151, 4158-4159, 4172-4174, 4202-4249, 4255-4286, 4293-4294, 4353-4356, 4362, 4466-4474, 4488, 4502, 4516, 4529-4555, 4582-4642, 4657-4660, 4721-4734, 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3036-3093, 3144-3310, 3376-3394, 3451-3453, 3527-3546, 3564-3569, 3582-3587, 3602-3610, 3627-3635, 3686-3774, 3804-3820, 3844-3861, 3884-3902, 3939-3940, 3978-3983, 4000-4006, 4040-4104, 4202-4235, 4247-4258, 4282-4292 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/grouper.py 405 322 20% 54, 250-254, 265-278, 294-309, 332-395, 400-409, 414-420, 425-431, 436-442, 447-454, 458-465, 520-611, 614, 617, 621, 625-639, 646-654, 658, 663-667, 671, 679-686, 692-698, 702-720, 725-783, 787, 822-1019, 1023, 1027-1044 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/indexing.py 105 78 26% 24-28, 114-120, 126-149, 152-155, 158-169, 172-184, 187-226, 230-235, 239-244, 250, 283-284, 293, 300, 303 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/numba_.py 52 39 25% 45-56, 93-119, 153-179 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/ops.py 533 383 28% 99, 121-123, 156-179, 197-220, 234-265, 268-284, 287-296, 312-322, 337-381, 386-397, 408-419, 434-461, 475-497, 519-615, 631-657, 692-697, 701, 705, 708, 712, 725-727, 736-737, 742-748, 754-782, 787-792, 802-819, 824, 828, 832, 839-845, 850-855, 861, 869, 873-878, 883-884, 891-897, 902, 906-908, 912-917, 925-935, 953-959, 987-1001, 1007-1025, 1068-1074, 1081-1086, 1091, 1096-1100, 1111-1125, 1129-1137, 1141-1151, 1160, 1164-1167, 1171, 1175, 1179-1185, 1189-1196, 1211-1216, 1221, 1226, 1229-1239, 1243, 1246, 1252-1254, 1264-1266, 1272-1278 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/objects.py 130 95 27% 55-59, 70, 85-100, 119, 140-142, 153-213, 228, 268-282, 315-318, 336-375, 390 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/utils.py 148 122 18% 29-30, 54-57, 94-99, 114-118, 152-186, 226-234, 270-274, 278-279, 282-284, 300-331, 342-343, 358-370, 390-396, 403-414, 513-555 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/accessors.py 140 92 34% 43, 56-64, 67-80, 85-114, 117, 123-144, 163-170, 173-191, 194-210, 213, 216-229, 332, 336, 368, 438, 467, 475, 554-580 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/api.py 126 97 23% 94-95, 103-109, 138-157, 174-191, 210-308, 332-346, 362-364 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py 2276 1712 25% 187-193, 259-267, 279-297, 366-372, 379-386, 393-400, 407-414, 433, 482, 488, 492-495, 499, 502, 505-513, 516, 518, 522, 528, 533-535, 541, 544, 548, 552-557, 572, 575, 580, 589-606, 609-611, 614-616, 622-625, 658, 671-681, 685, 701-706, 725-738, 756-758, 764-767, 774-776, 800-807, 818, 827-834, 840, 842, 844, 851, 865, 885, 888-919, 925-929, 951, 956-981, 1007-1038, 1080, 1082, 1095, 1109-1117, 1165-1170, 1202-1208, 1212, 1222, 1232-1243, 1252, 1259, 1266-1275, 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4818-4863, 4872-4885, 4892-4900, 4925, 4930-4935, 4971, 4984, 4992-4999, 5006-5010, 5014-5018, 5053-5058, 5066, 5081-5091, 5099-5101, 5107, 5147-5148, 5157, 5177-5203, 5209-5210, 5222-5228, 5242-5258, 5264-5268, 5283-5312, 5373-5399, 5412, 5479-5496, 5531-5541, 5606-5624, 5631, 5689, 5731, 5734-5737, 5744, 5803-5837, 5858-5861, 5867-5894, 5917-5941, 5947, 5953, 5959, 5988-5999, 6009, 6020-6057, 6065-6081, 6089-6099, 6105-6111, 6130-6138, 6159-6187, 6198-6208, 6291-6293, 6298, 6344-6352, 6365, 6373-6376, 6401-6408, 6411-6422, 6442-6484, 6517-6572, 6603-6612, 6629-6664, 6690-6701, 6712-6735, 6744-6785, 6789-6795, 6799-6804, 6807-6817, 6821-6822, 6825, 6828, 6831, 6835, 6876-6882, 6923-6929, 6936-6944, 6948-6956, 6960-6968, 6972-6993, 6997-7018, 7029, 7062-7069, 7105, 7109-7110, 7113, 7119, 7122-7124, 7128, 7135-7140, 7155-7159, 7163-7164, 7174, 7178, 7196-7199, 7215-7228, 7232-7243 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/category.py 123 74 40% 178, 182, 194, 213-223, 246-273, 285-296, 303, 311-319, 322-326, 332, 337-340, 356-368, 376-381, 384-396, 401, 470-471, 475-486 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/datetimelike.py 384 272 29% 73, 91, 95, 100, 104, 109, 114, 119, 125, 131-161, 165-170, 173-174, 189-200, 206, 212, 218-226, 230-234, 243, 247, 251-269, 273-277, 295-321, 340-354, 386, 392-401, 423, 437-438, 441-442, 447, 451-473, 478, 487-490, 493, 496-512, 516-519, 523-526, 532-548, 552-568, 572-586, 592-616, 622-651, 655-667, 676-679, 684-687, 691, 695-696, 705-721, 727-748, 752-754, 758-762, 776-787 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/datetimes.py 285 213 25% 64, 75-100, 254, 264-265, 269-270, 279-280, 284-287, 291-292, 296-297, 301, 320-354, 367-372, 375-376, 382-386, 393-396, 404-416, 428-445, 456-471, 491-510, 513-523, 530-534, 544-586, 591-598, 617-659, 667, 690-705, 731-755, 942-956, 1031-1049, 1063-1064 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/extension.py 83 37 55% 28-29, 62, 71-78, 81, 90, 96-105, 154, 160-171, 177, 188, 191-192 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/frozen.py 44 21 52% 45-47, 63-65, 74-76, 79-81, 84-86, 91, 96, 100, 106, 109, 112 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/interval.py 376 267 29% 113-123, 127-137, 145, 221-232, 263-267, 299-303, 334-336, 344-348, 363-373, 377, 380-386, 391, 398, 408, 415-432, 482, 502-506, 525-571, 574-597, 639-667, 676-693, 699-724, 733-736, 744-768, 772, 779-790, 798, 801, 804-807, 813, 817, 821, 825, 833, 839, 844, 854-867, 882-889, 907-918, 926, 932, 941, 955-957, 1059-1137 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/multi.py 1380 1162 16% 105, 143-153, 184-194, 206-211, 326-359, 377-383, 407-445, 489-510, 561-597, 645-658, 717-722, 729-750, 754, 766, 776-779, 782, 790, 800-804, 818-846, 931-940, 956, 972, 979, 990-1016, 1074-1078, 1089-1113, 1119, 1123-1125, 1128-1137, 1180-1204, 1208, 1212-1214, 1218-1223, 1227, 1232-1235, 1243, 1248, 1261-1270, 1279-1280, 1285-1316, 1328-1390, 1396, 1425-1454, 1479, 1482-1506, 1513-1537, 1545, 1550, 1554-1557, 1567, 1571-1580, 1599-1605, 1655-1657, 1661-1665, 1727-1755, 1787, 1823, 1835-1837, 1873-1898, 1941-1989, 1996-2002, 2007-2032, 2044-2050, 2067-2086, 2113-2137, 2140-2144, 2148-2152, 2184-2221, 2226-2242, 2287-2298, 2334-2344, 2357-2363, 2425-2480, 2483-2496, 2499-2506, 2512-2516, 2524, 2529-2539, 2542-2559, 2565-2569, 2619-2621, 2677, 2680-2727, 2748-2752, 2795-2872, 2915-2926, 2934-3075, 3085-3188, 3226-3319, 3343-3407, 3435-3447, 3463-3514, 3521-3527, 3533-3564, 3567, 3575-3579, 3587-3596, 3599-3600, 3603-3608, 3611-3627, 3633-3644, 3647-3658, 3674-3696, 3714-3731, 3743-3744, 3753-3768, 3804-3808, 3812-3836, 3840-3845, 3862-3878, 3897-3901, 3908-3918 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/period.py 191 122 36% 61-67, 157, 162, 175-176, 180-181, 186, 191, 196, 211-265, 272, 291-308, 314-322, 338-343, 351-356, 362, 372-378, 400-440, 443-455, 458-463, 467-470, 473-474, 478-482, 537-547 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/range.py 508 388 24% 103, 117-142, 155-161, 183-188, 213-214, 217-219, 228-231, 235, 239-245, 277-278, 306, 315, 319, 323, 326-331, 335, 342-351, 360-381, 388, 393, 397, 401-413, 416-418, 422-424, 427-433, 437-439, 443-445, 460-471, 478-483, 489-491, 500-526, 534-575, 579-580, 589-597, 602-608, 630-683, 687-770, 773-782, 791-808, 811-829, 840-890, 900, 906-924, 930-931, 935-947, 953, 956, 961-964, 975-1037 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/timedeltas.py 69 38 45% 110, 121, 136-176, 184, 197-204, 208-209, 213-215, 221, 308-315 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexing.py 917 749 18% 83, 137, 548, 613, 661, 676-683, 689-716, 728-775, 789-829, 833-849, 871, 879-893, 900-910, 919-921, 926-928, 932-940, 950-965, 970-1026, 1033-1084, 1087, 1093-1097, 1106, 1109, 1112, 1115, 1120-1123, 1144-1158, 1161, 1173-1192, 1215-1219, 1238-1242, 1269-1273, 1278-1289, 1293, 1297-1307, 1310-1343, 1350-1362, 1379-1434, 1459-1464, 1479-1518, 1529-1554, 1566-1569, 1589, 1594-1598, 1617-1621, 1625, 1627, 1633, 1636, 1639, 1642-1643, 1647, 1653, 1662-1669, 1675, 1679-1685, 1701-1837, 1844-1928, 1933-1950, 1953-1999, 2011-2036, 2042-2078, 2084-2181, 2188-2198, 2217-2299, 2302-2348, 2360, 2363-2371, 2374-2386, 2401-2404, 2409-2410, 2413-2419, 2422-2430, 2441-2444, 2462-2464, 2471-2473, 2502-2525, 2533-2541, 2549-2552, 2559-2562, 2572-2579, 2589, 2602, 2618, 2627 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/dataframe_protocol.py 101 1 99% 158 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/from_dataframe.py 171 151 12% 48-54, 73-91, 108-137, 156-164, 181-216, 233-307, 313-340, 357-376, 411-436, 469-499 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/utils.py 44 11 75% 75-90 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/api.py 36 26 28% 50-81, 88-97 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/array_manager.py 583 443 24% 130, 134-138, 142, 150, 155, 160-161, 165-167, 170, 176, 179, 182-183, 186-193, 216-255, 261-310, 313-319, 327, 330, 333-339, 347-348, 351, 354-361, 366-370, 375-378, 381-401, 404, 407-411, 423-425, 434, 438, 442, 447, 453, 457, 460-468, 479, 490, 509-529, 544-545, 578-632, 644-660, 665-679, 686-689, 698, 708-715, 718-736, 756-768, 771-781, 787-788, 794, 802, 822-861, 872-881, 894-916, 922-927, 944-969, 983-1001, 1008-1013, 1023-1034, 1052-1089, 1115-1144, 1158, 1166-1177, 1180-1185, 1192, 1196-1199, 1203, 1207, 1211, 1215, 1219, 1223, 1227-1230, 1234-1238, 1242, 1245, 1248-1253, 1256-1258, 1261-1265, 1276-1278, 1284-1289, 1293-1296, 1305, 1311-1314, 1331, 1335, 1351-1361 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/base.py 88 40 55% 44, 48, 52, 61-70, 85, 98-100, 114, 121-130, 138, 142, 148, 151, 154, 160, 181-193, 196-201, 205, 222 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/blocks.py 1041 770 26% 120-121, 135-139, 167, 175-178, 186, 190, 201-203, 211, 221-228, 241, 249-256, 260, 273-277, 288-293, 298-299, 315-318, 329-331, 337-348, 353-369, 376-385, 402-408, 422-424, 430-447, 457-459, 472-474, 509-526, 531-532, 537-544, 567-647, 678-701, 715-806, 839-862, 878, 884, 890, 906, 922-924, 939-940, 954, 961, 991-1007, 1034-1068, 1087-1141, 1160-1260, 1275-1311, 1333-1393, 1398-1399, 1412-1433, 1459-1467, 1484-1500, 1512-1551, 1556, 1563, 1570, 1573, 1608-1639, 1645-1709, 1715-1778, 1782-1789, 1793, 1799-1803, 1806, 1819-1825, 1854-1870, 1875-1877, 1888-1905, 1910-1912, 1919-1934, 1944-1976, 1981, 1985, 2006-2025, 2034-2035, 2040-2041, 2052-2053, 2070-2094, 2103, 2107, 2115, 2134, 2140, 2163-2166, 2171-2173, 2181-2186, 2197, 2211-2238, 2271-2298, 2325, 2329, 2354, 2356, 2358, 2361, 2364, 2379-2382, 2421, 2429, 2434-2440, 2450-2459, 2467-2478, 2486-2494, 2507-2581, 2593-2607 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/concat.py 350 303 13% 68-69, 90-117, 139-173, 194-252, 262-290, 303-319, 336-395, 402-406, 409, 413-418, 422-428, 435-458, 462-487, 492-569, 576-615, 622-638, 651-668, 678-681, 707, 720-738, 749-791 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py 428 290 32% 128-140, 147, 156-159, 174-194, 209-230, 244-388, 400-408, 429-464, 479, 496, 503, 529-560, 567-571, 584-591, 596-605, 622, 633-634, 636-637, 641-642, 645, 648, 650, 655, 658, 663-668, 682-703, 707-721, 730-739, 766-768, 794-804, 807-821, 825-827, 831-838, 848, 861-883, 909-920, 935-937, 972, 987-994, 1042, 1044-1063 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py 951 659 31% 155, 161, 165, 190, 196-209, 212, 219-221, 225-226, 235, 243-244, 252, 259-266, 273-274, 277-278, 295-304, 327-356, 359-365, 374, 386-394, 397-403, 413-414, 417, 422-426, 429-433, 443-451, 460-468, 471-475, 484, 494-505, 512, 516, 521, 526-536, 539-540, 553-565, 574-580, 586-614, 618, 634-665, 675-681, 715, 721-727, 734, 737, 740, 795-901, 908-927, 992-1002, 1005-1011, 1052-1082, 1106-1108, 1121-1136, 1148-1279, 1299-1326, 1340-1353, 1364-1385, 1398-1427, 1441-1444, 1453-1466, 1472-1479, 1496-1515, 1530-1539, 1545, 1552, 1580-1592, 1610-1655, 1670-1675, 1703-1704, 1717, 1724, 1727, 1729-1730, 1737, 1761-1762, 1764, 1766, 1781-1800, 1829-1832, 1840, 1869-1871, 1880-1881, 1887-1892, 1900, 1903-1919, 1922-1939, 1942, 1951, 1956, 1960-1979, 1984-1994, 1998, 2002, 2005, 2009, 2017, 2020-2022, 2026, 2038-2042, 2050-2054, 2061, 2073-2074, 2082-2086, 2106-2116, 2139-2140, 2153-2169, 2184, 2195-2196, 2213, 2217, 2221-2235, 2241, 2267-2276, 2282-2311, 2316-2320, 2326-2343 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/ops.py 62 46 26% 12-15, 33-52, 61-86, 93-95, 107-136, 143-147 /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/describe.py 148 115 22% 46, 80-95, 108, 127-130, 153-159, 162-176, 180-196, 201-207, 220-247, 263-279, 295-327, 341-351, 364-373, 387-408 /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/selectn.py 120 97 19% 34, 42-47, 50, 54, 58, 67-69, 88-156, 176-182, 185-262 /home/admin/.local/lib/python3.8/site-packages/pandas/core/missing.py 314 268 15% 46, 53-61, 81-118, 123-140, 168-183, 204-224, 247-267, 302-359, 366-381, 408-489, 507-566, 605-611, 650-654, 734-740, 765-787, 818-858, 866-870, 880-888, 899-901, 910-912, 917-924, 929-936, 943-946, 950, 984-1013, 1028-1030 /home/admin/.local/lib/python3.8/site-packages/pandas/core/nanops.py 626 505 19% 71, 83, 88-104, 130-160, 167-182, 186-195, 202-217, 253-261, 310-350, 354-356, 361-397, 415-429, 451-462, 474-494, 533-551, 588-606, 643-655, 664-674, 711-740, 768-821, 844-848, 880-895, 934-941, 981-1017, 1058-1070, 1083-1097, 1145-1149, 1191-1195, 1237-1285, 1327-1384, 1419-1427, 1439-1456, 1483-1497, 1513-1545, 1568-1576, 1581-1585, 1599-1614, 1620-1643, 1657-1671, 1675-1700, 1708-1720, 1747-1767 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/__init__.py 184 134 27% 78, 133-150, 163-173, 183-201, 233-334, 343-365, 384-409, 417-433, 446-473, 489-494 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/array_ops.py 191 155 19% 68-83, 99-139, 164-188, 217-234, 256-298, 302-339, 358-405, 424, 430, 433, 435, 448, 470-519, 537-542 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/common.py 56 34 39% 64, 69-81, 101-105, 128-151 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/dispatch.py 6 1 83% 26 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/docstrings.py 57 2 96% 50, 60 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/invalid.py 17 9 47% 30-37, 54-55 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/mask_ops.py 59 52 12% 42-73, 106-126, 156-184, 188-189 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/methods.py 34 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/missing.py 56 48 14% 49-73, 105-134, 158-180 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/api.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/concat.py 284 246 13% 54-58, 78, 95, 112, 129, 146, 364-385, 406-563, 570-623, 626-629, 632-633, 639-640, 653-699, 702-705, 709, 713-821 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/encoding.py 154 138 10% 147-224, 236-334, 447-533 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/melt.py 138 117 15% 33-35, 50-158, 214-248, 489-540 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/merge.py 919 816 11% 104-106, 128, 148-162, 176-217, 324-358, 616-633, 680-747, 760-803, 806-825, 830-831, 835-840, 847-869, 872-884, 906-927, 935-1034, 1038, 1046-1101, 1125-1135, 1155-1276, 1285-1440, 1461-1463, 1471-1558, 1562-1605, 1642-1690, 1737-1779, 1799-1800, 1816-1842, 1846-1847, 1859-1864, 1891-1898, 1914-1990, 1996-2063, 2068-2189, 2204-2234, 2240-2242, 2247, 2275-2279, 2285-2309, 2369-2468, 2475-2505, 2511-2517, 2527-2554, 2558-2560, 2564, 2568-2575, 2591-2645 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/pivot.py 366 333 9% 51, 71-110, 129-255, 269-339, 345-362, 368-436, 448-480, 484-494, 506-562, 671-734, 740-814, 818-831, 864-885 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/tile.py 181 158 13% 241-305, 369-389, 403-472, 481-505, 522-539, 557-561, 568-591, 602-608, 617-625, 632-640, 647-651 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/util.py 28 22 21% 33-60, 77-82 /home/admin/.local/lib/python3.8/site-packages/pandas/core/roperator.py 29 15 48% 11, 15, 19, 23, 27, 31, 38-42, 46, 50, 54, 58, 62 /home/admin/.local/lib/python3.8/site-packages/pandas/core/sample.py 58 48 17% 19, 31-76, 90-113, 144-151 /home/admin/.local/lib/python3.8/site-packages/pandas/core/series.py 1122 756 33% 179-187, 221-230, 384, 394-519, 546-570, 576, 584-586, 591, 605, 619, 716, 754-756, 762, 778-781, 787, 857-863, 916-921, 938, 945-960, 971, 985, 990, 997, 1001, 1004, 1009-1033, 1037-1073, 1077-1093, 1096-1097, 1113, 1121-1138, 1141-1219, 1222-1225, 1230-1248, 1251-1256, 1259-1263, 1280-1290, 1298, 1302-1305, 1311-1312, 1320, 1325, 1331-1336, 1346-1367, 1376, 1429-1432, 1446, 1458, 1470, 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/home/admin/.local/lib/python3.8/site-packages/pandas/core/shared_docs.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/sorting.py 252 201 20% 47-49, 81-106, 144-200, 223-224, 228-232, 239-254, 276-287, 293-301, 329-366, 399-400, 410, 417, 436-439, 458-473, 480-484, 516-533, 549-577, 587-594, 606-623, 656-670, 681-692, 710-725 /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/accessor.py 581 386 34% 49, 123-129, 140-141, 179-195, 220-236, 239-240, 251-393, 411-448, 593-683, 888-895, 912-913, 1008-1009, 1022-1023, 1085-1086, 1151-1152, 1281-1290, 1321-1322, 1354-1355, 1485-1508, 1551-1552, 1613-1625, 1650, 1655, 1660, 1723-1728, 1802-1803, 1878-1879, 1898-1907, 1925-1926, 2014-2015, 2023-2024, 2032-2033, 2088-2089, 2096-2097, 2155-2156, 2197-2198, 2224-2225, 2292-2293, 2359-2363, 2429-2433, 2525-2526, 2612-2654, 2732, 2771-2776, 2788-2793, 2812-2813, 2855-2860, 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/home/admin/.local/lib/python3.8/site-packages/pandas/io/_util.py 6 2 67% 9-10 /home/admin/.local/lib/python3.8/site-packages/pandas/io/api.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/clipboards.py 11 1 91% 20 /home/admin/.local/lib/python3.8/site-packages/pandas/io/common.py 444 337 24% 124-132, 135, 138, 154-156, 161, 166, 183-185, 189-214, 219, 226, 252-260, 268-270, 278, 319-458, 480-482, 522-531, 560-586, 598-600, 615, 630, 645, 702-913, 936, 939-951, 963-969, 977-984, 991-999, 1003-1006, 1017-1025, 1032-1037, 1041-1042, 1053, 1056, 1059-1061, 1064-1066, 1069-1071, 1078-1084, 1087, 1090-1100, 1107-1134, 1139-1148, 1154-1167, 1175-1187, 1209-1212, 1233-1253 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_base.py 413 301 27% 395, 434, 473-515, 523-545, 552, 556, 559-569, 574, 578, 582, 586, 589-591, 596-597, 620-626, 655-690, 716-897, 1103-1121, 1129, 1134, 1192-1233, 1240, 1247, 1254, 1257, 1260-1264, 1280-1298, 1306-1310, 1314, 1322, 1326-1327, 1368-1401, 1470-1513, 1516, 1551, 1575, 1579, 1583, 1586, 1594 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py 141 113 20% 25, 44-45, 49-51, 54-56, 61, 66-69, 72-76, 79-89, 97-158, 166-168, 171-173, 179-183, 186-222, 229-249 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_odswriter.py 138 115 17% 27, 46-62, 71, 76-82, 88-90, 103-149, 164-171, 186-219, 241-278, 293-335 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_openpyxl.py 242 189 22% 35-36, 56-88, 97, 102-103, 109-112, 138-148, 172-177, 207-225, 243, 270-306, 325-339, 366-379, 400-402, 420, 437-439, 450-529, 548-549, 553-555, 558-560, 566, 569-570, 573-574, 577-592, 597-626 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_pyxlsb.py 56 38 32% 34-37, 41-43, 46-52, 56, 59-60, 63-66, 71-80, 87-112 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_util.py 102 71 30% 24-27, 43, 65-86, 90-93, 115-125, 149-158, 163, 168, 173, 178, 197-209, 214, 219, 223-236, 260-271, 295-305, 327-332 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlrd.py 62 43 31% 33-35, 39-41, 44-50, 54, 57-58, 61-62, 67-126 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlsxwriter.py 83 63 24% 101-172, 192-210, 219, 223-224, 230, 241-275 /home/admin/.local/lib/python3.8/site-packages/pandas/io/feather_format.py 43 28 35% 54-96, 139-162 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/__init__.py 4 2 50% 5-8 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/console.py 33 28 15% 15-47, 63-76, 87-94 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/format.py 908 752 17% 112, 209-214, 217-231, 234, 243-261, 279-295, 298-322, 325-363, 366-374, 377, 386-421, 426, 429, 432, 435, 442-451, 457-460, 468-476, 480-484, 504-510, 537-549, 585-609, 615-621, 625, 631, 635, 639, 643, 647, 651, 655, 659, 663, 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820, 830-835, 838, 859, 863-864, 871-872, 880-883, 894-900, 903-911, 914-921, 925-926, 930-931, 945-948, 952-960, 965-967, 970, 974, 982, 991-992, 996-997, 1011, 1014-1016, 1021, 1025, 1039-1043, 1057-1060, 1064-1072, 1075, 1080-1082, 1086, 1090, 1101 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/printing.py 203 183 10% 43-59, 66-71, 107-130, 140-161, 195-234, 240-241, 245-275, 279, 320-456, 481-497, 504 /home/admin/.local/lib/python3.8/site-packages/pandas/io/gbq.py 21 14 33% 12, 18-23, 173-186, 214-215 /home/admin/.local/lib/python3.8/site-packages/pandas/io/html.py 367 291 21% 54, 67-80, 105, 127-134, 150-161, 242-247, 257-258, 278, 294, 310, 326, 341, 360, 375, 401, 420, 431, 458-476, 501-558, 576-579, 603-606, 609-630, 633-634, 637, 640, 643, 646, 649-652, 655, 658-661, 664-679, 698-702, 728-729, 732, 737, 740-765, 768, 786-824, 827-842, 845-848, 851, 855-862, 866-886, 918-935, 939-940, 944-968, 972-1021, 1192-1212 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_json.py 501 386 23% 84, 107, 127, 146-204, 222-237, 240, 243-244, 266-269, 272-273, 281-286, 292-301, 327-379, 383, 408, 433, 458, 483, 743-784, 817-862, 872-878, 894-919, 925, 931, 935, 939, 946-981, 987-1008, 1017-1018, 1021, 1025, 1029, 1033, 1036-1061, 1064, 1072, 1099-1122, 1128-1131, 1134-1141, 1144, 1150-1160, 1163, 1176-1241, 1251-1280, 1283, 1291-1298, 1301-1307, 1315-1346, 1354-1374, 1377-1382, 1387-1415 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_normalize.py 142 126 11% 35-39, 86-120, 146-166, 184-191, 237-244, 388-536 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_table_schema.py 134 114 15% 42-43, 79-96, 101-120, 124-151, 195-224, 283-316, 355-382 /home/admin/.local/lib/python3.8/site-packages/pandas/io/orc.py 52 38 27% 80-97, 162-205 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parquet.py 168 131 22% 46-75, 88-115, 121-122, 125, 128, 133-141, 153-194, 205-237, 244-247, 259-287, 299-342, 405-425, 493-509 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/arrow_parser_wrapper.py 67 53 21% 21-25, 31-42, 48-79, 101-136, 149-164 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/base_parser.py 578 491 15% 96, 109-176, 202-234, 240, 245, 253-267, 294-340, 349-352, 359-383, 387-406, 410-434, 438-451, 455-503, 515-593, 617-662, 683-766, 784-837, 845, 853, 862-874, 891-898, 911, 917, 931-933, 957-964, 991-1013, 1016-1050, 1055-1096, 1105-1165, 1221-1302, 1308-1327, 1353-1362, 1371-1384, 1388 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/c_parser_wrapper.py 177 149 16% 50, 61-192, 196-199, 208-220, 232-337, 341-346, 349-357, 360-365, 375-402, 412-423 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/python_parser.py 630 576 9% 50, 67-176, 179-243, 250-300, 307-323, 333-336, 343-364, 376-573, 587-621, 627-630, 642-683, 698, 701-754, 773-777, 792-825, 828-846, 863-872, 875-878, 885-899, 902-905, 910, 928-989, 992-1075, 1078-1156, 1159-1163, 1180-1204, 1234-1245, 1251-1267, 1272-1281, 1292-1294, 1297, 1313, 1321, 1345-1351 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/readers.py 486 399 18% 473, 478, 483, 502-513, 531-537, 546-583, 640, 697, 754, 811, 884-912, 969, 1026, 1083, 1140, 1213-1242, 1312-1352, 1368-1407, 1410-1412, 1415-1456, 1460-1465, 1472-1620, 1623-1627, 1634-1683, 1686, 1689-1724, 1727-1733, 1736, 1744, 1802-1803, 1808-1840, 1845-1853, 1858-1878, 1936-2008, 2019-2028, 2050-2052, 2074-2106, 2123-2127 /home/admin/.local/lib/python3.8/site-packages/pandas/io/pickle.py 28 15 46% 92-103, 178-204 /home/admin/.local/lib/python3.8/site-packages/pandas/io/pytables.py 2255 1832 19% 118-124, 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5124-5150, 5157-5172, 5194-5230, 5234-5253, 5259-5265, 5271-5289 /home/admin/.local/lib/python3.8/site-packages/pandas/io/sas/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/sas/sasreader.py 51 29 43% 29, 40, 44, 47, 55, 69, 83, 134-180 /home/admin/.local/lib/python3.8/site-packages/pandas/io/spss.py 22 12 45% 18-20, 54-67 /home/admin/.local/lib/python3.8/site-packages/pandas/io/sql.py 746 610 18% 71-72, 85-90, 96-118, 126-139, 148-165, 178-188, 208-219, 238, 253, 332-353, 368, 383, 464-469, 494, 510, 629-663, 758-769, 804-805, 821-841, 868-886, 889, 892-894, 898-900, 903-914, 928-930, 941-946, 949-986, 992-1030, 1043-1066, 1077-1113, 1117-1145, 1148-1159, 1162-1189, 1207-1244, 1247-1314, 1317-1343, 1352, 1355, 1368, 1382, 1399, 1403, 1407, 1418, 1437, 1442, 1458-1470, 1475-1502, 1526-1544, 1547-1548, 1552-1556, 1560-1563, 1627-1631, 1653-1671, 1738-1765, 1782-1815, 1826-1840, 1908-1933, 1937, 1940-1943, 1946-1956, 1959-1964, 1974-1983, 2001-2005, 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2486-2494, 2509-2567, 2570-2574, 2580-2640, 2650-2677, 2683-2730, 2740-2743, 2759, 2762-2763, 2770-2818, 2821-2822, 2827-2830, 2834-2835, 2839-2840, 2844-2852, 2856-2876, 2880, 2883-2912, 2915, 2919-2920, 2923, 2944-2965, 2972-2974, 3013-3037, 3040-3041, 3072-3092, 3123-3155, 3266-3284, 3289-3291, 3295-3296, 3304-3354, 3362-3384, 3387-3391, 3394-3402, 3405-3407, 3410-3415, 3418-3430, 3434-3462, 3465-3466, 3469-3472, 3475-3476, 3482-3488, 3491-3493, 3501-3504, 3511-3522, 3525-3536, 3659-3684, 3707-3721 /home/admin/.local/lib/python3.8/site-packages/pandas/io/xml.py 241 201 17% 50-54, 162-176, 186, 210-288, 313-390, 407, 421, 432, 442-461, 471-502, 507-520, 527-543, 561-580, 583-607, 612-626, 633-661, 671-676, 696-721, 733-739, 751-758, 801-846, 1116-1118 /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/_core.py 191 134 30% 30-32, 98-99, 222-223, 474-475, 506-507, 596-597, 791, 802-892, 895-975, 1044, 1136, 1222, 1289, 1351, 1459, 1534, 1583-1589, 1674, 1760-1765, 1786-1831, 1857-1864 /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/_misc.py 73 43 41% 12-16, 40-41, 64-65, 84-85, 159-160, 252-253, 321-322, 387-388, 455-456, 514-515, 549-550, 571-574, 577-578, 581-584, 587-588, 599, 602, 610-615 /home/admin/.local/lib/python3.8/site-packages/pandas/testing.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/__init__.py 4 2 50% 5-11 /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/api.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/frequencies.py 307 236 23% 98, 132-175, 184-210, 216, 222, 226, 230, 241-280, 284-285, 289-290, 294, 298, 301, 305-306, 310, 313-342, 345-353, 356-367, 370-381, 384-391, 395-405, 413-427, 432-433, 437, 441-446, 470-506, 525-564, 580-583, 587-589, 593-594, 598-599, 603-604, 608-609 /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/offsets.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/util/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/util/_decorators.py 135 79 41% 56-94, 164-214, 243-252, 257-260, 291-337, 368, 436, 448-449, 491 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_exceptions.py 48 36 25% 16-27, 36-51, 75-89 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_print_versions.py 48 34 29% 24-27, 34-36, 56-90, 108-134 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_str_methods.py 12 1 92% 23 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_tester.py 18 11 39% 25-35 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_validators.py 122 87 29% 33-41, 55-79, 117-123, 132-136, 161-163, 206-221, 259, 285-302, 326-336, 341, 346, 357, 377-390, 409-424, 435-442, 446-448 /home/admin/.local/lib/python3.8/site-packages/pandas/util/version/__init__.py 270 129 52% 28, 31, 34, 37, 40, 43, 46, 49, 52, 60, 63, 66, 69, 72, 75, 78, 81, 84, 123-126, 139, 146, 151-154, 157-160, 164, 169-172, 175-178, 183-186, 193, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 255-268, 276-294, 338, 363, 366-391, 395-396, 400-401, 405-406, 410, 418-421, 425, 429-438, 442, 446, 450, 454, 458, 462, 471-489, 493-495, 508, 537, 543, 550, 557, 570 /home/admin/.local/lib/python3.8/site-packages/psutil/__init__.py 950 691 27% 37-38, 127-128, 131-132, 135-136, 139-140, 143-180, 248-259, 271-281, 290-292, 298-304, 346, 349-393, 396-411, 421-423, 426, 429-431, 436, 467-505, 518-549, 556-568, 574-579, 586-601, 617-618, 628-647, 654-686, 690, 694-697, 703-715, 722-724, 728, 732-737, 746, 752, 758, 764, 776, 793-798, 813-816, 829-837, 850, 858, 862-866, 872, 876, 886, 915-956, 993-1048, 1059, 1070, 1074, 1090, 1102-1119, 1133-1147, 1154, 1178, 1184-1202, 1211-1212, 1222-1223, 1233-1234, 1244-1245, 1254-1255, 1273-1275, 1322-1323, 1326, 1329-1331, 1334-1347, 1350-1356, 1360-1364, 1383-1385, 1393-1403, 1431-1482, 1521-1571, 1593-1599, 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873-908, 913-946, 949-977, 985, 992-1022, 1027-1043, 1058-1146, 1151-1182, 1203-1290, 1303-1322, 1332-1405, 1415-1428, 1434-1441, 1452, 1459-1484, 1491-1506, 1515-1526, 1536-1539, 1545, 1558-1580, 1589-1590, 1595-1597, 1600-1602, 1605-1607, 1611-1615, 1618-1632, 1636-1657, 1661-1663, 1667-1672, 1678-1704, 1709-1715, 1720, 1724, 1728-1735, 1750-1753, 1766-1788, 1801-1858, 1862-1869, 1874-1883, 1890-1891, 1895-1919, 1928, 1932, 1939, 1944-1949, 1953-1968, 1975-1978, 1982-1988, 1997-2017, 2021-2025, 2029-2069, 2073-2075, 2079, 2083, 2087-2089, 2093-2095 /home/admin/.local/lib/python3.8/site-packages/psutil/_psposix.py 89 67 25% 30-47, 61-110, 120-158, 167-175 /home/admin/.local/lib/python3.8/site-packages/pyarrow/__init__.py 170 121 29% 41-59, 80-93, 97-102, 106-116, 123-157, 312-315, 343, 347, 351-356, 360-367, 375, 393-416, 426-466 /home/admin/.local/lib/python3.8/site-packages/pyarrow/_compute_docstrings.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/_generated_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/compute.py 203 92 55% 114, 137-138, 175-179, 208-211, 215-232, 238-245, 248-262, 391-403, 425-441, 484-485, 539-544, 584-591, 631-638, 663-664, 700-713, 730 /home/admin/.local/lib/python3.8/site-packages/pyarrow/filesystem.py 225 139 38% 54-55, 66, 79, 94-105, 108, 118, 124, 130, 143, 156, 167, 178, 189, 196, 225-228, 235, 239, 260-261, 265-266, 270-274, 278-279, 283-284, 288, 292-293, 300-301, 305, 311-312, 321-325, 329, 333, 341, 345-346, 350-351, 355-359, 366-367, 370-371, 377-378, 385-393, 397-402, 411-433, 437-440, 444-459, 467-511 /home/admin/.local/lib/python3.8/site-packages/pyarrow/hdfs.py 82 52 37% 42-49, 52, 59, 63, 67, 71, 86, 90, 94, 111, 126-131, 135-149, 153-165, 169-172, 176-185, 223-227, 235-240 /home/admin/.local/lib/python3.8/site-packages/pyarrow/ipc.py 61 36 41% 51-52, 84-85, 109-110, 121-122, 126-141, 146-150, 154, 190, 195, 234, 259-264, 282-285 /home/admin/.local/lib/python3.8/site-packages/pyarrow/types.py 155 47 70% 56, 61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116, 121, 126, 131, 136, 141, 146, 151, 156, 161, 166, 171, 176, 181, 186, 191, 196, 201, 206, 211, 223, 228, 233, 238, 243, 248, 253, 258, 263, 268, 273, 278, 283, 288, 293 /home/admin/.local/lib/python3.8/site-packages/pyarrow/util.py 96 60 38% 64, 101-106, 114-123, 127-131, 135, 142-151, 158, 177-194, 198-202, 206-207, 213-230 /home/admin/.local/lib/python3.8/site-packages/pyarrow/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/vendored/docscrape.py 473 282 40% 24, 46, 62, 82, 101, 104, 109-112, 155-157, 164, 169, 172, 183, 187-188, 222, 224, 238-241, 297-341, 349-360, 365, 373-376, 395-396, 398-399, 406, 412-420, 424-428, 431-442, 447, 450, 453-455, 458-460, 463-465, 468-481, 484-489, 492-521, 524-538, 541-555, 565-574, 577-582, 585-599, 604-607, 616-666, 670-672, 680-682, 689-693, 697-716 /home/admin/.local/lib/python3.8/site-packages/pycparser/__init__.py 25 18 28% 32-48, 82-90 /home/admin/.local/lib/python3.8/site-packages/pycparser/ast_transforms.py 21 18 14% 64-96, 103-105 /home/admin/.local/lib/python3.8/site-packages/pycparser/c_ast.py 782 446 43% 25-28, 37-51, 56, 80-100, 149-158, 164-165, 176-179, 182-185, 192-194, 197-200, 203-206, 213-216, 219-222, 225-228, 235-238, 241-244, 247-250, 257, 260, 263-264, 271-273, 276-280, 283-286, 293-295, 298-301, 304-307, 314-315, 318-321, 324-325, 332-334, 337-340, 343-346, 358-359, 362-363, 370, 373, 376-377, 394-398, 401-406, 413-414, 417-420, 423-424, 431-432, 435-438, 441-442, 449-451, 454-457, 460-463, 470, 473, 476-477, 484, 487, 490-491, 498-500, 503-505, 508-509, 516-518, 521-523, 526-527, 534-535, 538-541, 544-545, 552-553, 556-559, 562-563, 574-577, 580-581, 588-592, 595-600, 603-610, 617-619, 622-625, 628-631, 643-646, 649-652, 659-662, 665-670, 673-678, 685-686, 689-690, 693-694, 701-702, 705-706, 709-710, 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339-340, 360-361, 368-396, 399-407, 411, 416, 419-422, 440, 450-454, 463-469, 494-523, 546, 578-581, 587-589, 592-594, 597-599, 605-612, 622-630, 637-638, 642-644, 647-649, 651-653, 655-657, 679-681, 693-694, 696-699, 708, 711-712, 717-718, 730-822, 833-856, 867, 881-882, 896, 902-903, 909-910, 920-1047, 1056-1080, 1092 /home/admin/.local/lib/python3.8/site-packages/pycparser/ply/yacc.py 1916 1562 18% 97, 114, 119, 122, 129, 132, 140-146, 150-156, 176-177, 180-181, 184-185, 190-198, 219, 222, 242, 246, 252, 264-266, 272-274, 277, 295, 298-303, 321, 325-327, 329, 350-683, 697-989, 1015-1016, 1030, 1063, 1087-1088, 1123-1135, 1157-1168, 1176-1271, 1312-1339, 1342, 1345, 1348, 1351, 1354, 1358-1370, 1374-1375, 1392, 1395, 1429-1437, 1440-1444, 1447, 1455-1460, 1475-1507, 1511, 1514, 1525-1530, 1551-1624, 1634-1640, 1652-1662, 1673-1727, 1737-1745, 1754-1759, 1769-1774, 1786-1791, 1804-1829, 1837-1864, 1875-1914, 1933-1958, 1980, 1986, 1999-2023, 2056-2064, 2067-2087, 2101-2133, 2138-2154, 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383-410, 416-423, 430-434, 441-457, 467-477, 484-486, 495-496, 505-511, 518-523, 531-533 /home/admin/.local/lib/python3.8/site-packages/pydrive/drive.py 15 5 67% 16-17, 28, 39, 47 /home/admin/.local/lib/python3.8/site-packages/pydrive/files.py 267 199 25% 41-43, 55, 63-72, 95-120, 133-142, 155-157, 169-173, 190-194, 207-213, 228-244, 252-270, 279-285, 292, 300, 309, 322-331, 341-342, 352, 362-375, 384-395, 405-416, 427-435, 446-460, 471-481, 490-492, 503-506, 518-531, 543-571, 583-609 /home/admin/.local/lib/python3.8/site-packages/pydrive/settings.py 46 34 26% 5-6, 141-146, 156, 169-171, 186-213 /home/admin/.local/lib/python3.8/site-packages/pygit2/__init__.py 92 43 53% 123-162, 203-225 /home/admin/.local/lib/python3.8/site-packages/pygit2/_build.py 18 12 33% 46-53, 58-67 /home/admin/.local/lib/python3.8/site-packages/pygit2/blame.py 69 36 48% 33-36, 44-47, 52, 58, 63, 68, 72, 77, 82, 86, 91-95, 102-105, 108, 111, 114-118, 130-137, 140 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/home/admin/.local/lib/python3.8/site-packages/python_http_client/client.py 106 27 75% 11-15, 52, 59-62, 126, 129-130, 135, 145, 177-184, 208-216, 243, 246, 253, 293, 296 /home/admin/.local/lib/python3.8/site-packages/python_http_client/exceptions.py 46 16 65% 8-17, 20, 30, 93-97 /home/admin/.local/lib/python3.8/site-packages/pytz/__init__.py 198 125 37% 56-75, 87-108, 113-124, 167-190, 195, 204-206, 226-228, 231, 234, 237, 240, 244-246, 250-254, 257, 260, 295, 307, 347, 350-366, 379-390, 403-406, 409, 412, 415, 418, 421, 425-427, 431-435, 491-502, 509-512, 516 /home/admin/.local/lib/python3.8/site-packages/pytz/exceptions.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pytz/lazy.py 100 59 41% 4-8, 21-28, 31-38, 41-48, 51-58, 61-68, 87, 98-106, 142, 151-160 /home/admin/.local/lib/python3.8/site-packages/pytz/tzfile.py 76 66 13% 21, 25-123, 126-133 /home/admin/.local/lib/python3.8/site-packages/pytz/tzinfo.py 178 126 29% 7-8, 34-41, 49-58, 66, 76, 87-89, 97, 105, 113, 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/home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_macroman.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_pdfdoc.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_standard.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_symbol.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_winansi.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_zapfdingbats.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courier.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courierbold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courierboldoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courieroblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helvetica.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticabold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticaboldoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticaoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_symbol.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesbold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesbolditalic.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesitalic.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesroman.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_zapfdingbats.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/pdfdoc.py 1657 716 57% 32-35, 78, 89, 140, 165, 188-189, 194, 203, 206-213, 219, 223, 229-230, 236, 251-254, 273, 276-277, 283, 301, 304, 307-308, 311-314, 317-335, 342, 350, 359, 366, 373, 380, 385, 388-395, 410, 432-436, 444, 462, 468-469, 475-487, 516, 519, 526-529, 550, 554-555, 558, 565-572, 579-580, 593-595, 604-621, 627-633, 637-638, 642-643, 645, 647, 650, 653, 662, 676, 678, 680, 685-687, 701, 705-708, 714-717, 720, 725-726, 729-734, 766-769, 780, 783, 805, 832-839, 856, 861-864, 893-894, 913, 948, 950, 972, 983, 1041, 1044, 1047-1048, 1051-1052, 1070, 1116, 1127, 1134-1135, 1139, 1154, 1159, 1179, 1195, 1198-1209, 1274-1278, 1281-1283, 1287-1296, 1312, 1318-1327, 1362-1395, 1398, 1402-1408, 1411-1412, 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/home/admin/.local/lib/python3.8/site-packages/requests/__version__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/_internal_utils.py 21 3 86% 33, 49-50 /home/admin/.local/lib/python3.8/site-packages/requests/adapters.py 194 82 58% 61, 93, 97, 145, 158, 163-169, 211-235, 255, 261, 271-275, 278-290, 318, 343-351, 368, 390-391, 395, 426-432, 455-456, 472-476, 481, 500-536 /home/admin/.local/lib/python3.8/site-packages/requests/api.py 19 6 68% 85, 99-100, 130, 145, 157 /home/admin/.local/lib/python3.8/site-packages/requests/auth.py 173 141 18% 35-66, 73, 80-81, 84, 92, 95-96, 103-104, 111-114, 118-124, 131-234, 238-239, 250-284, 288-304, 307, 315 /home/admin/.local/lib/python3.8/site-packages/requests/certs.py 4 1 75% 17 /home/admin/.local/lib/python3.8/site-packages/requests/compat.py 30 5 83% 12-13, 36-37, 42 /home/admin/.local/lib/python3.8/site-packages/requests/cookies.py 239 149 38% 19-20, 41, 44, 47, 52-58, 70, 73, 76, 80, 85, 92, 96, 100, 121, 132, 156-167, 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4 76% 5-10 /home/admin/.local/lib/python3.8/site-packages/requests/sessions.py 268 118 56% 56, 86, 100-103, 116-124, 129-157, 178-281, 288-301, 315-332, 338-354, 601-602, 612-613, 623-624, 637, 649, 661, 671, 684, 689, 716-717, 732-735, 763, 794, 810, 813-814, 817-818, 833 /home/admin/.local/lib/python3.8/site-packages/requests/status_codes.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/structures.py 39 11 72% 55, 65, 68-73, 77, 80, 91, 96, 99 /home/admin/.local/lib/python3.8/site-packages/requests/utils.py 485 315 35% 76-121, 127-130, 141, 144-157, 170-191, 204, 216-220, 223-224, 230-253, 274-297, 303-310, 331-337, 358, 361, 393-398, 424-433, 445-459, 469-474, 485, 493-506, 548, 556, 566-577, 582-587, 602-626, 643-655, 674-678, 689-693, 703-704, 711-715, 724-739, 752-753, 758-761, 784, 789-809, 815-816, 819, 831, 845, 856-857, 873-886, 920-946, 962-984, 993-1013, 1038-1040, 1044-1056, 1068-1076, 1083-1094 /home/admin/.local/lib/python3.8/site-packages/seaborn/__init__.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/_core.py 638 564 12% 48, 52-54, 58, 62-65, 90-138, 142-159, 163-174, 180-211, 215-254, 273-314, 318-325, 329-338, 342-399, 403-478, 498-546, 550-554, 558-577, 604-615, 621-627, 632, 647-653, 657-681, 706-821, 850-935, 963-1016, 1021-1058, 1062-1073, 1091-1161, 1165-1178, 1186-1191, 1218-1273, 1304-1348, 1369-1399, 1418-1445, 1467-1484 /home/admin/.local/lib/python3.8/site-packages/seaborn/_decorators.py 31 7 77% 32-46 /home/admin/.local/lib/python3.8/site-packages/seaborn/_docstrings.py 36 1 97% 30 /home/admin/.local/lib/python3.8/site-packages/seaborn/_statistics.py 196 168 14% 69-79, 83-87, 91-96, 100-114, 118-126, 130-140, 144-158, 162-182, 186-189, 232-241, 245-259, 263-312, 316-342, 346-366, 370-373, 389-391, 395, 399-413, 417-426 /home/admin/.local/lib/python3.8/site-packages/seaborn/algorithms.py 66 60 9% 36-86, 91-105, 114-129 /home/admin/.local/lib/python3.8/site-packages/seaborn/axisgrid.py 1011 915 9% 38-43, 47-50, 54-56, 60-64, 95-176, 180-183, 187-196, 200-226, 231-234, 309-474, 581-606, 632-688, 720-755, 759-763, 768-779, 783-785, 791-799, 803-804, 808-815, 819-826, 830-837, 841-853, 857-865, 890-957, 964, 969, 974-980, 991, 998-1011, 1016-1023, 1028-1035, 1040-1052, 1057-1069, 1145-1250, 1263-1267, 1280-1282, 1295-1297, 1310-1321, 1335-1406, 1411-1447, 1454-1469, 1473-1508, 1512-1553, 1557-1562, 1566-1570, 1591-1669, 1673-1676, 1700-1702, 1724-1738, 1761-1799, 1820-1822, 1830-1831, 1962-2049, 2064-2214 /home/admin/.local/lib/python3.8/site-packages/seaborn/categorical.py 1249 1116 11% 44-240, 245-265, 269-324, 329-337, 342-346, 350-388, 392-397, 406-415, 419-476, 481-505, 509-512, 522-546, 551-671, 675-686, 690-692, 702-714, 718-724, 728-747, 752-757, 761-935, 939-946, 954-979, 987-995, 1001-1010, 1015-1016, 1021-1039, 1043-1046, 1057-1082, 1086-1088, 1099-1112, 1116-1156, 1160-1164, 1172-1177, 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2395-2403, 2543-2661 /home/admin/.local/lib/python3.8/site-packages/seaborn/external/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/external/docscrape.py 455 272 40% 43, 64, 80, 100, 119, 122, 127-130, 173-175, 182, 187, 190, 201, 236, 238, 250-253, 308-350, 358-369, 374, 382-385, 391, 404-405, 407-408, 415, 421-429, 432-443, 448, 451-454, 457-460, 463-466, 469-472, 475-488, 491-496, 499-528, 531-546, 549-563, 567-571, 580, 585-609, 612-617, 620-634, 643-691, 695-697, 705-707, 714-718 /home/admin/.local/lib/python3.8/site-packages/seaborn/external/husl.py 194 151 22% 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72-91, 95-114, 118-119, 123, 127-130, 134-137, 141-144, 148-153, 157-175, 179-184, 188-189, 193-194, 198-199, 203-219, 223-235, 239-247, 251-257, 261-271, 275-285, 289-299, 303-313 /home/admin/.local/lib/python3.8/site-packages/seaborn/matrix.py 548 491 10% 25-28, 33-36, 41-49, 61-86, 98-182, 189-239, 243-255, 259-268, 272-284, 289-343, 535-549, 563-616, 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/home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/__init__.py 14 6 57% 19, 30, 46-50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/eventwebhook_header.py 5 1 80% 10 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/__init__.py 63 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/amp_html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/asm.py 33 20 39% 16-23, 31, 40-43, 52, 62-65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/attachment.py 75 47 37% 43-62, 70, 79-82, 90, 99-102, 110, 119-122, 137, 162-165, 176, 191-194, 203-218 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/batch_id.py 15 7 53% 14-17, 25, 34, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings.py 27 16 41% 16-23, 31, 40, 48, 57, 66-72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings_email.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_bounce_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_list_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_spam_management.py 16 9 44% 15-18, 26, 35, 44-47 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_unsubscribe_management.py 16 9 44% 17-20, 28, 37, 46-49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/category.py 13 6 54% 10-13, 21, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/cc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/click_tracking.py 27 16 41% 12-19, 27, 36, 45, 56, 65-71 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content.py 30 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content_id.py 13 6 54% 13-16, 27, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/custom_arg.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/disposition.py 13 6 54% 21-24, 39, 63, 72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/dynamic_template_data.py 24 12 50% 16-22, 30, 39, 47, 57, 64, 73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/email.py 79 13 84% 50-54, 78, 137, 154, 171, 189, 202-203, 209, 224 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/exceptions.py 22 10 55% 24-31, 39, 48, 56, 65 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_content.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_name.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_type.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_html.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_settings.py 38 23 39% 14-25, 33, 42, 50, 59, 67, 76, 85-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_text.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/from_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ganalytics.py 63 35 44% 26-38, 48-49, 57, 66, 75, 86, 94, 103, 111, 120, 128, 137, 145, 154, 163-176 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/group_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/groups_to_display.py 15 8 47% 13-16, 25, 37-39, 48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/header.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ip_pool_name.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail.py 470 241 49% 72, 78, 88, 151-167, 171, 177-178, 183-187, 213, 229-241, 262-265, 273, 280, 296-308, 324-330, 335, 356-368, 388-394, 416-431, 445, 454-458, 466-490, 494, 503-507, 515-535, 547, 557-561, 569-592, 612-629, 633, 642-653, 674, 692-696, 708, 717-721, 737-748, 752, 768, 777-781, 789, 806-809, 821, 829-833, 841, 853, 861-865, 872, 889, 906, 923, 940, 957, 997-1013 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail_settings.py 93 58 38% 38-69, 77, 86, 94, 103, 111, 120, 128, 137, 145, 154, 162, 171, 179, 188, 196, 206, 215-243 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mime_type.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking.py 27 16 41% 16-23, 31, 40, 50, 65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking_substitution_tag.py 13 6 54% 12-15, 26, 39, 49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/personalization.py 130 38 71% 23-29, 55, 63-67, 70, 73-76, 90, 98, 110, 118, 133, 145, 152, 164, 171-174, 186, 193, 207, 220-223, 242, 247-250 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/plain_text_content.py 25 14 44% 15-19, 27, 35, 44-45, 54-60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/reply_to.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/sandbox_mode.py 16 9 44% 12-15, 23, 32, 41-44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/section.py 25 14 44% 12-18, 26, 35, 43, 52, 61-64 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/send_at.py 24 12 50% 22-28, 36, 45, 53, 63, 70, 79 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_check.py 44 27 39% 18-27, 35, 44, 54, 68-71, 80, 91-94, 103-112 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_threshold.py 13 6 54% 15-18, 29, 44, 53 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_url.py 13 6 54% 12-15, 24, 35, 44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subject.py 23 4 83% 18, 43, 53, 60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_html.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_substitution_tag.py 13 6 54% 18-21, 32, 48, 58 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_text.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_tracking.py 49 30 39% 21-33, 41, 50, 59, 71, 80, 92, 103, 120, 129-142 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/substitution.py 34 19 44% 17-26, 34, 43, 51, 60, 68, 78, 87-90 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/template_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/to_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/tracking_settings.py 49 30 39% 30-45, 53, 63, 71, 81, 89, 98, 106, 115, 124-134 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_campaign.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_content.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_medium.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_source.py 13 6 54% 11-14, 23, 34, 43 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_term.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/validators.py 27 10 63% 23-24, 46-55, 69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/stats.py 166 108 35% 12-22, 29, 38-53, 61, 70, 78, 87, 95, 104, 112, 121, 129, 138, 146, 155, 163, 172, 187-194, 202-220, 228, 236-238, 253-260, 268-286, 294, 302-304, 317-319, 327, 336, 344, 357-359, 367, 376, 384 /home/admin/.local/lib/python3.8/site-packages/sendgrid/sendgrid.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/twilio_email.py 9 4 56% 63-73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/__check_build/__init__.py 18 12 33% 19-31, 45-46 /home/admin/.local/lib/python3.8/site-packages/sklearn/__init__.py 29 9 69% 69, 103-112 /home/admin/.local/lib/python3.8/site-packages/sklearn/_config.py 21 13 38% 75-82, 144-150 /home/admin/.local/lib/python3.8/site-packages/sklearn/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/glm_distribution.py 86 54 37% 59-66, 132, 156, 175, 204, 208, 215-235, 246, 272-323, 329, 335, 341, 347 /home/admin/.local/lib/python3.8/site-packages/sklearn/base.py 259 142 45% 54-88, 108-138, 159, 169, 197-198, 221-244, 265-291, 304, 310, 322, 325-333, 362, 365, 413-419, 424-434, 449-453, 460, 464-467, 499-500, 503, 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303-315, 320-321, 437-441, 460-461, 464-518, 529-534, 555-561, 564-568, 588-595, 614-617, 623-659 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_dbscan.py 52 37 29% 141-145, 276-283, 310-361, 389-390 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_feature_agglomeration.py 19 12 37% 38-51, 70-73 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py 526 454 14% 85-144, 152-158, 289-298, 354-428, 484-541, 579-601, 771-781, 785-844, 848-853, 858-861, 869-887, 924-951, 979-1052, 1077, 1105, 1124-1127, 1131, 1154-1160, 1183-1189, 1193, 1264-1337, 1346-1403, 1571-1579, 1585, 1591, 1597, 1600-1633, 1660-1781, 1805-1813, 1835-1891, 1914-1917, 1920, 1988-2019 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_mean_shift.py 116 94 19% 68-86, 92-109, 186-191, 223-240, 358-364, 377-450, 465-468 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_optics.py 218 193 11% 210-222, 246-287, 291-298, 327-338, 448-503, 509-538, 571-578, 631-646, 690-709, 716-722, 736-744, 790-898, 920-927 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_spectral.py 120 96 20% 76-158, 260-284, 462-476, 499-544, 568, 571, 580 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/__init__.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_base.py 47 28 40% 37-44, 57-77, 131, 154-158 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_dict_learning.py 359 309 14% 27-28, 113-191, 297-354, 394-435, 547-632, 766-891, 899-905, 910-926, 945-946, 1066-1071, 1089, 1096, 1115, 1118, 1122, 1126, 1304-1318, 1336-1358, 1546-1560, 1578-1599, 1623-1650 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_factor_analysis.py 139 119 14% 155-167, 183-264, 282-294, 306-310, 320-339, 354-362, 379, 384-390, 396-414 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py 181 154 15% 49-50, 57-60, 69-95, 104-122, 128-136, 140-143, 147, 272-301, 399-411, 431-540, 557, 574-575, 593-600, 617-624 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_incremental_pca.py 88 76 14% 170-173, 191-216, 237-319, 350-358 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_kernel_pca.py 106 80 25% 151-168, 176, 179-185, 192-256, 259-267, 283-295, 310-318, 331-344, 361-369, 372 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_lda.py 238 208 13% 75-132, 303-318, 322-335, 341-362, 396-426, 456-476, 479, 491-494, 510-537, 556-609, 624-640, 658-664, 692-740, 757-763, 787-813, 837 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_nmf.py 422 383 9% 38, 51, 55-61, 91-167, 172-187, 192-203, 207-233, 238-248, 311-401, 413-432, 508-538, 544-633, 638-715, 789-850, 1022-1093, 1268-1279, 1282, 1307-1326, 1342-1343, 1358-1374, 1391-1392 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_pca.py 164 92 44% 59-97, 105-109, 376-386, 394, 402-405, 414, 416, 424-427, 435-436, 439, 445, 468, 476-477, 484, 500-564, 583-592, 613, 616 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_sparse_pca.py 68 52 24% 116-126, 144-175, 198-206, 309-316, 334-362 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_truncated_svd.py 58 39 33% 125-129, 146-147, 164-208, 223-225, 242-243, 246 /home/admin/.local/lib/python3.8/site-packages/sklearn/exceptions.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/isotonic.py 109 84 23% 56-76, 117-131, 222-225, 228-231, 237-247, 252-295, 325-342, 361-385, 400, 404-407, 414-416, 419 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py 207 160 23% 81-101, 124-179, 197-207, 218-221, 238, 245-249, 252, 282-293, 309-314, 323-330, 353-357, 385-388, 485-489, 514-575, 588-642 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_bayes.py 203 180 11% 162-174, 197-300, 324-332, 343-356, 361-386, 515-526, 546-633, 641-650, 656-661, 685-694 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py 492 409 17% 58-75, 124-168, 311, 440-551, 706-717, 751-874, 879, 893-898, 1031, 1085-1146, 1157-1171, 1200-1356, 1518, 1526, 1529, 1532, 1725-1740, 1743, 1746, 1749, 1880-1889, 1913-1958, 1961, 2078-2087, 2263-2276, 2279, 2282, 2285, 2444, 2452, 2455, 2458 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/glm.py 158 122 23% 32-35, 40-48, 133-141, 161-298, 313-317, 333-335, 371-376, 380-388, 458, 465, 469-470, 540, 547, 551-552, 654, 664-666, 670-673 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/link.py 40 13 68% 68, 71, 74, 77, 84, 87, 90, 93, 100, 103, 106, 109-110 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_huber.py 88 74 16% 52-122, 229-234, 255-307 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_least_angle.py 435 384 12% 166-171, 301, 442-798, 917-926, 930-936, 940-994, 1017-1035, 1177-1188, 1195-1197, 1283-1310, 1438-1442, 1449, 1467-1529, 1685-1695, 1823-1832, 1835, 1858-1904 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py 546 500 8% 75-82, 114-133, 162-169, 202-246, 286-302, 343-355, 396-428, 432-458, 462-475, 632-819, 957-1009, 1261-1275, 1306-1435, 1463-1478, 1499, 1751-1767, 1789-2062, 2085-2088, 2091 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_omp.py 273 245 10% 72-138, 194-264, 349-408, 490-544, 632-636, 655-687, 735-764, 870-876, 894-919 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_passive_aggressive.py 34 20 41% 173-191, 216-228, 254-256, 401-418, 435-437, 464-466 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_perceptron.py 6 1 83% 164 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ransac.py 153 133 13% 47-54, 215-226, 256-464, 480-482, 502-504, 507 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py 614 520 15% 41-114, 118-132, 137-156, 161-217, 221-228, 232-235, 366, 385-518, 527-534, 539-600, 737, 762, 895-899, 924-946, 950, 954-966, 982-984, 995-999, 1002-1003, 1008, 1014, 1025-1029, 1032-1040, 1043-1050, 1057, 1060, 1070, 1073, 1122-1130, 1135, 1140-1143, 1176-1192, 1223-1236, 1261-1276, 1281-1290, 1297-1313, 1319-1338, 1347-1355, 1370-1385, 1394-1397, 1402-1412, 1421-1433, 1454-1581, 1590-1597, 1627-1665, 1917-1920, 1943-1959, 1963, 1966 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_sag.py 75 63 16% 67-85, 234-344 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py 446 345 23% 56-62, 65-68, 80-102, 117-119, 127-156, 160-168, 171-174, 178-182, 187-241, 258-281, 285-288, 298, 307, 315, 323, 331-355, 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89-95, 120-141, 221-360, 463-469, 472, 481, 503-536, 560-579, 601-602 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py 258 224 13% 58-64, 94-121, 163-193, 255-272, 345-401, 453-477, 664-679, 684-841, 854-910, 932-934, 950-951 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/__init__.py 78 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_base.py 78 71 9% 67-131, 175-202, 234-251 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py 511 452 12% 48-52, 83-128, 132-137, 202-210, 296-355, 456-557, 618-639, 758-785, 852-875, 935-946, 1068, 1192-1200, 1214-1247, 1251-1261, 1269-1299, 1458-1540, 1653-1660, 1771-1778, 1846-1858, 1966-2060, 2135-2152, 2225-2281, 2365-2403, 2477-2506 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/base.py 37 33 11% 26-45, 81-114 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/confusion_matrix.py 61 47 23% 71-72, 107-162, 255-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/det_curve.py 44 36 18% 65-68, 88-129, 210-229 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/precision_recall_curve.py 47 35 26% 77-81, 107-140, 203-225 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/roc_curve.py 47 35 26% 73-77, 100-132, 210-230 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_ranking.py 336 292 13% 83-106, 199-224, 294-317, 326-349, 522-547, 595-648, 688-730, 811-823, 913-956, 1005-1046, 1090-1106, 1149-1191, 1238-1250, 1290-1299, 1303-1307, 1407-1411, 1458-1466, 1564-1569, 1646-1717 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_regression.py 168 137 18% 88-122, 182-194, 257-271, 335-351, 408-416, 477-492, 552-584, 676-723, 753-756, 808-821, 857, 896 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py 226 132 42% 52-60, 77, 81-92, 107-122, 133-134, 155-166, 169-171, 199, 204, 236-242, 276-288, 291, 326-362, 365, 383-392, 397, 426-459, 485-530, 614 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/__init__.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_bicluster.py 32 22 31% 12-17, 22-28, 38-45, 80-86 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_supervised.py 170 139 18% 43-69, 74-83, 127-149, 214-229, 289-299, 383-389, 453-473, 542, 611, 710, 768-798, 889-919, 998-1020, 1091-1100, 1115-1123 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_unsupervised.py 93 76 18% 33-34, 109-117, 135-149, 214-248, 281-298, 339-363 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/pairwise.py 412 339 18% 45-61, 135-164, 194-198, 272-323, 399-439, 451-508, 512-514, 587-601, 670-673, 722-723, 782-804, 833-841, 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195-221, 239-275, 414-421, 441-487, 504-529, 548-616 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_graph.py 60 35 42% 16-21, 29-36, 106-113, 187-194, 307-311, 327, 345-347, 371, 374, 490-494, 510, 528-529, 553, 556 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_kde.py 86 66 23% 101-119, 124-139, 164-179, 197-211, 233, 256-283, 286 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_lof.py 80 54 32% 184-190, 218-223, 246, 265-299, 320-327, 346-356, 384-393, 421, 450-458, 486-498, 521-526 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nca.py 157 129 18% 169-176, 197-244, 265-268, 304-377, 400-443, 453-456, 483-524, 527 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nearest_centroid.py 70 54 23% 91-92, 107-181, 202-205 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_regression.py 62 40 35% 152-157, 161, 170, 190, 206-229, 352-358, 378, 395-426 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_unsupervised.py 10 2 80% 118, 142 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/__init__.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_data.py 810 681 16% 71-80, 161-217, 323-325, 335-341, 362-363, 386-417, 432-441, 456-463, 466, 545-561, 683-685, 695-699, 726-727, 762-860, 877-897, 914-937, 940, 1007, 1017-1020, 1040-1041, 1065-1085, 1100-1110, 1125-1134, 1137, 1198-1215, 1319-1323, 1344-1387, 1402-1416, 1431-1444, 1447, 1536-1555, 1632-1635, 1639-1641, 1646-1651, 1668-1681, 1701-1708, 1739-1837, 1891-1937, 2001-2002, 2023-2024, 2043-2045, 2048, 2083-2098, 2157-2158, 2179-2180, 2200-2205, 2208, 2253, 2272-2282, 2299-2310, 2313, 2321, 2350-2378, 2484-2489, 2499-2519, 2531-2568, 2589-2625, 2630-2694, 2699-2717, 2737-2750, 2768-2771, 2789-2793, 2796, 2920-2931, 3020-3022, 3043-3044, 3047, 3050-3077, 3092-3106, 3139-3152, 3158-3163, 3169-3185, 3192-3207, 3217-3219, 3228-3243, 3267-3290, 3293, 3394-3395 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_discretization.py 117 102 13% 131-134, 153-237, 242-271, 288-318, 337-353 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py 272 241 11% 42-67, 70-74, 77-110, 113-156, 159, 318-322, 325-333, 339-397, 416-420, 442-443, 459-505, 524-600, 617-635, 721-724, 743-771, 787-793, 809-844 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_function_transformer.py 41 26 37% 11, 91-97, 100-102, 106-109, 128-132, 147, 162, 166-171, 174 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_label.py 274 229 16% 100-102, 116-118, 132-138, 152-163, 166, 262-274, 289-298, 321, 343-350, 387-403, 406, 471-569, 577-613, 619-657, 725-726, 742-754, 773-798, 816-824, 827-831, 847-864, 881-898, 902 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_base.py 362 291 20% 39-60, 81-104, 108, 117, 152-240, 249-250, 253-255, 262-287, 291-323, 342-344, 347-361, 370-378, 393-400, 417-430, 433-440, 449-457, 471-496, 500-514, 517, 521-532, 542-544, 552-564, 592-595, 614-625, 632-636, 666-667, 670-676, 706-707, 710, 713-727, 730-738, 752-764, 768, 772, 791-830, 930-995 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_bounds.py 21 14 33% 54-74 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_classes.py 124 65 48% 187-198, 224-246, 249, 382-391, 417-432, 435, 657, 667, 877, 887, 1042, 1054, 1062, 1065, 1211, 1218, 1346, 1376-1379, 1396-1397, 1412, 1431-1432, 1440, 1448, 1451 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_classes.py 291 224 23% 103-115, 128-129, 139-140, 145-397, 401-411, 436-463, 489-491, 515-516, 520-539, 576-578, 598-600, 846, 898-903, 929-951, 970-979, 1197, 1247-1252, 1271-1277, 1511, 1732 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_export.py 377 339 10% 44-70, 75, 181-194, 203-214, 218-237, 241-262, 266-366, 376-406, 412-427, 431-436, 439-463, 466-524, 534-560, 565-574, 577-625, 628-662, 769-795, 802-815, 876-972 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_reingold_tilford.py 131 110 16% 9-22, 25, 28, 31-38, 41-44, 48, 51, 54-56, 60-64, 68-70, 74-95, 99-132, 136-144, 148-154, 162-165, 169-178, 183-188 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/__init__.py 366 299 18% 84, 87, 90, 93-96, 107, 125-132, 165-167, 172-179, 184-193, 198-205, 224-268, 312-346, 355-409, 502-563, 631, 651-661, 668-672, 708-722, 755-768, 778-783, 817-819, 845-851, 868-878, 898-903, 933-944, 1019-1045, 1059-1062, 1080-1084, 1113-1182 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_arpack.py 5 3 40% 28-30 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_encode.py 115 99 14% 30-50, 60-65, 84-102, 108-112, 115-117, 122-123, 128-144, 176-187, 215-269 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_estimator_html_repr.py 76 62 18% 39-50, 53, 61-76, 82-102, 111-143, 303-311 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_joblib.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_mask.py 20 14 30% 9-21, 41-54 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_pprint.py 243 172 29% 79, 99, 101, 105, 185-199, 203, 207, 222-268, 276-317, 323-332, 354-379, 383-413, 418, 420, 427, 441, 446-447 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_show_versions.py 33 26 21% 24-32, 44-73, 82-93 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_tags.py 16 13 19% 50-67 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/class_weight.py 61 55 10% 41-72, 115-181 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/deprecation.py 56 11 80% 67-68, 86-87, 101-102, 117-123 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/home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/graph_pb2.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/meta_graph_pb2.py 129 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/node_def_pb2.py 25 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/op_def_pb2.py 38 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/resource_handle_pb2.py 22 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/rewriter_config_pb2.py 62 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/saved_object_graph_pb2.py 100 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/saver_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/step_stats_pb2.py 56 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/struct_pb2.py 100 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/summary_pb2.py 70 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_description_pb2.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_pb2.py 36 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_shape_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/trackable_object_graph_pb2.py 30 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/types_pb2.py 62 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/variable_pb2.py 38 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/verifier_config_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/versions_pb2.py 14 0 100% 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298-307 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tf_utils.py 89 60 33% 32-34, 68-70, 98-115, 132-133, 150-165, 182-195, 227, 232-236, 241-249, 253-258 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tools/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/uncompressed_module_resolver.py 37 24 35% 32-34, 37, 41-47, 65-77, 80-87 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/_VF.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/__config__.py 7 3 57% 9, 16, 20 /home/admin/.local/lib/python3.8/site-packages/torch/__future__.py 5 1 80% 16 /home/admin/.local/lib/python3.8/site-packages/torch/__init__.py 362 162 55% 20, 27, 59-142, 148, 175-188, 207, 214-231, 262-278, 298, 307, 329-331, 377, 502, 508, 515, 533-555, 565-571, 577, 622, 634, 640, 664, 669, 674, 679, 684, 689, 694, 699, 704, 709, 714, 719, 724, 729, 734, 739, 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121, 125-127, 148-149, 187, 189, 195, 200-201, 204-211, 225-228, 232-234, 243-245, 263, 320-322, 326, 329, 332, 336, 340, 344, 349, 357-359, 365, 371, 377, 391-412, 446-449, 453-458, 462, 466, 469-477, 485-488, 491, 494, 497, 500-502, 510-513, 522-527, 530-543, 546, 587-590, 593-599, 602-603, 611-615, 618-620, 623, 626, 629, 636, 639, 651-653, 658-659, 667-669, 675-680, 690, 699, 704, 709, 714, 729-751, 754-766, 769, 772-774, 777-779, 782-783 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/conv.py 331 168 49% 51, 84, 86, 89-94, 98, 115-122, 152-166, 169-171, 287-294, 299-303, 307, 450, 580-585, 590-602, 607, 616, 632-665, 774-780, 785-795, 940, 1080-1086, 1091-1102, 1124-1127, 1146-1150, 1155-1169, 1173-1180, 1185, 1233-1251, 1254, 1302-1320, 1323, 1371-1389, 1392, 1438-1457, 1460, 1506-1525, 1528, 1574-1593, 1596 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/distance.py 25 9 64% 37-40, 43, 72-74, 77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/dropout.py 33 12 64% 13-18, 21, 58, 100, 149, 191, 233, 282 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/flatten.py 48 27 44% 40-42, 45, 48, 106-116, 119-125, 129-135, 138, 141 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/fold.py 38 15 61% 136-141, 144, 148, 287-291, 294, 298 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/instancenorm.py 72 40 44% 18-19, 23, 26, 29, 32, 38-62, 67-72, 143, 146-147, 182, 185-186, 259, 262-263, 298, 301-302, 375, 378-379, 414, 417-418 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/lazy.py 79 49 38% 15, 18, 23, 26, 29, 33, 37, 41, 45, 49, 175-178, 185-194, 208-217, 224, 231-236, 248-256, 260 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/linear.py 91 36 60% 42, 45, 100, 117, 130, 180-191, 194-197, 200, 203, 243-250, 253-254, 257-263 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py 201 83 59% 16-20, 25-27, 93, 96, 207-208, 211, 217-220, 286-289, 292, 367-369, 372, 461-462, 465, 527, 530, 610, 613, 707-711, 714, 773-774, 777, 838, 841, 918-919, 922, 978-979, 982, 1020, 1023, 1159-1161, 1164, 1210, 1213, 1262-1263, 1266, 1319-1320, 1323, 1392-1397, 1400, 1477-1481, 1484, 1591-1595, 1598, 1739-1741, 1744 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py 660 395 40% 22-24, 30-38, 79-81, 111-113, 131-139, 176-184, 201, 304, 307, 310, 313, 315, 317, 319, 343, 347, 350, 352, 354, 359, 363, 382, 385, 388, 390, 392, 397, 447-465, 489-503, 527-540, 557, 572, 593, 608-611, 614-621, 667-670, 689, 708, 727, 738, 752, 763, 774, 785, 796, 808, 813, 817, 821, 911-915, 923, 943-951, 986-994, 1001-1013, 1016-1053, 1077-1079, 1100-1102, 1105-1122, 1132-1174, 1179-1192, 1217, 1222, 1227-1231, 1236, 1241-1245, 1249-1253, 1258-1266, 1275-1277, 1292-1300, 1308, 1312, 1357-1386, 1403-1407, 1435-1437, 1474, 1485-1489, 1497, 1501-1504, 1508-1515, 1519-1522, 1524, 1532, 1559, 1570, 1583-1584, 1595, 1599, 1604, 1610-1619, 1642-1643, 1665-1669, 1690-1691, 1713-1717, 1772-1773, 1807-1818, 1836, 1880-1882, 1892-1908, 1912, 1915, 1924, 1928-1949, 1952-1962, 1965-1975 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/normalization.py 97 36 63% 48-52, 55, 59, 69-73, 76, 80, 178-179, 193, 248-264, 267-269, 272, 276 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/padding.py 83 28 66% 19-20, 23, 26, 75-76, 125-126, 165-166, 174, 177, 216-217, 267-268, 319-320, 328, 331, 370-371, 421-422, 461-462, 512, 515 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/pixelshuffle.py 23 8 65% 49-50, 53, 56, 99-100, 103, 106 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/pooling.py 241 104 57% 21-27, 30, 88, 162, 240, 248, 312-315, 318, 395-398, 401, 461-464, 467, 475, 534-539, 542, 613-619, 622, 699-705, 708, 712-715, 765-778, 782, 834-847, 851, 865-869, 872, 914, 969, 978-980, 983, 1016, 1058, 1101, 1108-1109, 1112, 1140, 1179, 1218 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/rnn.py 473 382 19% 21, 43-124, 127-131, 140-175, 182-191, 194-196, 199-205, 210-221, 225-226, 229-232, 235-237, 241-254, 257-292, 296, 299-304, 411-420, 425, 430, 433-499, 673, 676-683, 692-695, 703-705, 712, 719, 722-784, 898-900, 905, 910, 913-967, 983-997, 1000-1005, 1008-1010, 1072-1074, 1077-1108, 1173-1174, 1177-1197, 1264-1265, 1268-1288 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/sparse.py 130 71 45% 129-133, 142-144, 154-155, 158, 163-174, 205-218, 318-341, 344-345, 348-350, 383, 390-400, 432-447 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/transformer.py 187 144 23% 57-83, 136-149, 156, 161-163, 186-190, 203-246, 267-270, 288-299, 361-388, 391-393, 410-466, 471-475, 479-480, 523-546, 549-551, 570-580, 585-589, 594-598, 602-603, 607, 611-616 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/upsampling.py 37 17 54% 141-150, 153, 157-162, 207, 253 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/utils.py 35 17 51% 32-38, 57-75 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/__init__.py 10 3 70% 11-14 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_functions.py 88 60 32% 14-30, 34, 41-45, 49, 56-75, 79-82, 89-104, 108, 118-124 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_replicated_tensor_ddp_utils.py 13 7 46% 18-23, 27, 31 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/comm.py 81 70 14% 29-38, 56-58, 76-104, 126-149, 186-199, 228-241 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py 94 76 19% 17-37, 122-145, 148-169, 172, 175, 178, 181, 199-232 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py 440 339 23% 46-53, 57-60, 67-79, 83-137, 159-168, 172-178, 186-193, 200-230, 237, 538-664, 667-672, 675-677, 691-766, 769-776, 780-794, 798-839, 850-860, 865-898, 905-916, 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/home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/modules/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/modules/linear.py 7 3 57% 20-22 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/conv.py 84 50 40% 29-35, 38, 48-65, 71-95, 126-130, 148, 181-185, 202, 206, 239-243, 260, 264 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/embedding_ops.py 56 38 32% 27-36, 39, 50-64, 67-72, 95-104, 107, 120-134, 137-142 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/linear.py 38 26 32% 30-34, 37, 45-69, 72-77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/activation.py 242 222 8% 64-83, 86, 90-150, 160-219, 224-247, 305, 321-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/rnn.py 230 196 15% 34-48, 51-73, 76-80, 83, 93-104, 108-115, 126-128, 131-136, 140-143, 151-157, 160-209, 218-241, 277-312, 315-361, 364, 368-379, 383 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/conv.py 108 58 46% 19-35, 51-54, 67-71, 74, 78, 87-90, 103-107, 110, 114, 123-126, 139-143, 146, 150, 160-177, 195-198, 211-221, 224, 228, 239-242, 254-266, 269, 273, 283-286, 299-309, 312, 316 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/linear.py 23 11 52% 25-26, 29, 42-44, 48-55 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/rnn.py 262 210 20% 11, 14-22, 25-29, 32-36, 41-54, 57-79, 83, 86, 89, 92, 95, 105-107, 110, 115-146, 150-162, 172-173, 176, 179-199, 203-214, 224-225, 228, 231-251, 255-266, 274-289, 292-312, 322, 329-331, 334-341, 350-353, 365-375, 378-388, 391-453, 456, 460-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/sparse.py 29 12 59% 19-21, 24, 27-28, 34, 60-63, 66, 69-70, 78 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/utils.py 73 61 16% 6-41, 52-55, 64-67, 76-77, 83-87, 98-112, 124-133, 136-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/conv.py 111 64 42% 56-67, 72, 77-84, 120-131, 136, 141-147, 184-195, 200, 205-211, 253-259, 264, 269-271, 313-319, 324, 329-331, 373-379, 384, 389-391 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/linear.py 58 43 26% 34-39, 43-55, 58, 61-66, 70-72, 83-112, 122-126 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/rnn.py 506 403 20% 12, 16-33, 37-38, 41-42, 46-47, 59-123, 126, 129-140, 147-170, 173-179, 184-191, 197-198, 202-204, 208-210, 214-216, 221-254, 258-341, 346-364, 367, 370, 391, 394, 401-426, 432-440, 446-455, 461-463, 469-474, 479-482, 486, 490-504, 616, 619, 622-625, 633-670, 677-685, 691-699, 704-706, 710-713, 717, 725-763, 766, 769-774, 777-778, 783-789, 795-841, 845-873, 877-886, 889, 892, 897-901, 907-909, 913-915, 937-938, 941, 944-962, 966, 989, 992, 995-1001, 1008, 1029, 1032, 1035-1039, 1047 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/functional.py 143 114 20% 40-42, 70-72, 89-91, 106-109, 154-168, 213-227, 273-287, 325-327, 358-363, 374-378, 390-394, 409-411, 431-441, 446-450, 460-462, 475-481, 492-494, 499-503, 516-518, 570-571, 592-593, 614-615 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/__init__.py 38 13 66% 44-50, 53, 58-60, 63, 82, 85, 89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/activation.py 94 49 48% 28-29, 32, 35, 39, 49-51, 54, 58, 62-63, 67, 78-80, 83, 87, 91-92, 96, 108-111, 114, 118, 122-123, 127, 138-140, 143, 147-148, 159-162, 165-172, 176, 180-181, 185 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/batchnorm.py 64 38 41% 8-11, 15-26, 30-43, 52-53, 56, 61-62, 67, 73, 82-83, 86, 91-92, 97, 103 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/conv.py 381 265 30% 25-30, 37, 46-80, 83, 86, 89, 92-104, 119-124, 128-129, 152-160, 166-179, 182-186, 189, 195-212, 216-238, 249-265, 316-324, 329, 332-336, 341-342, 345, 348, 353-360, 370, 416-423, 428, 431-435, 439, 442, 445, 450-456, 467, 513-521, 526, 529-533, 537, 540, 543, 548-554, 565, 577-582, 588-592, 602-623, 634-651, 699-706, 711, 714, 719-720, 723-724, 727-728, 733-735, 740, 787-794, 799, 802, 807-808, 811-812, 815-816, 821-823, 828, 876-883, 888, 891, 896-897, 900-901, 904-905, 910-912, 917 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/dropout.py 13 4 69% 15, 18, 22, 26 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/embedding_ops.py 134 95 29% 12-22, 26-29, 34-37, 40, 48-50, 54-61, 65, 93-110, 113-116, 119, 122, 125-129, 132, 135, 145-175, 179-190, 220-225, 229-234, 239, 249-276, 280-292 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/functional_modules.py 105 69 34% 34-35, 38, 43-45, 49-52, 56-58, 62-65, 69-71, 75-78, 93, 98-99, 103-104, 108-109, 113-114, 118-119, 123-125, 154-157, 160-162, 167-169, 173, 176, 181, 186-188, 192-195, 199-201, 205-208, 212-214, 218-220, 224-230 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/linear.py 141 103 27% 16-22, 26-31, 36-41, 44, 63-65, 69-86, 91, 125-147, 150, 153, 158, 161, 194-196, 203-218, 225, 228, 231, 234, 244-276, 288-296 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/normalization.py 100 59 41% 15-22, 25, 30, 34-38, 42, 58-64, 67, 72, 76-80, 93-99, 102, 107, 111-115, 119, 134-140, 143, 148, 152-156, 160, 175-181, 184, 189, 193-197, 201 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/utils.py 50 35 30% 12, 15-31, 37-41, 48-71 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/clip_grad.py 34 26 24% 30-56, 68-70, 85-89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/convert_parameters.py 29 24 17% 16-24, 36-54, 73-84 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/fusion.py 33 27 18% 7-14, 17-33, 36-43, 46-53 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/init.py 10 7 30% 44-51 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/memory_format.py 8 6 25% 64-69 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/parametrizations.py 152 124 18% 13-17, 24-28, 45-65, 68-105, 109-168, 258-281, 292-316, 320-326, 362-370, 374-388, 393, 472-486 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/parametrize.py 223 198 11% 48-54, 58-61, 97-189, 209-254, 258-269, 281-299, 315-346, 471-552, 566-573, 601-656, 665-668, 686-728 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/rnn.py 126 90 29% 22-24, 64, 76, 82-85, 89-92, 95, 98, 101, 104, 107, 110, 113, 116, 134-142, 147, 151, 166-184, 193-195, 199-204, 244-261, 321-335, 379-396, 433-446, 481-482, 511-513 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/spectral_norm.py 138 111 20% 26-32, 35-41, 73-93, 96-102, 105, 111-112, 116-152, 160, 172-198, 206, 209-214, 273-281, 295-314 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/stateless.py 53 41 23% 12-36, 39-48, 52-55, 63-69, 81-84, 125-142 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py 52 36 31% 15-18, 22-24, 28-54, 57-61, 64, 108-109, 123-129 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/__init__.py 63 28 56% 42-47, 51-54, 348-350, 386-388, 392-394, 408-410, 414-416, 420-422, 430-432, 464-466, 473, 480, 487, 498, 509 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_constants.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_globals.py 20 6 70% 33, 37-41 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_patch_torch.py 112 90 20% 49-72, 77-78, 84-99, 103-116, 123, 132-133, 137, 145-167, 184-222, 230-231 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_caffe2.py 151 113 25% 11-35, 39-46, 50-51, 55-56, 63-65, 70-76, 83-85, 92-105, 112-125, 130-136, 141-149, 154-160, 165, 172, 178-191, 196-212, 226-249, 253-262, 267-285, 289-301, 306-318 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_helper.py 624 503 19% 67-112, 121-123, 127-130, 134-140, 144-146, 150-157, 163, 199-232, 284-315, 324-325, 335-343, 347, 351, 355, 362, 366, 370, 379-380, 388, 395-397, 401-409, 413-418, 422-430, 435-440, 444, 453, 462, 471-479, 483-488, 492-507, 511-518, 522-537, 552-555, 559-572, 578-591, 597-604, 608-611, 625-635, 639-659, 663-684, 688-710, 714-726, 730-737, 741-752, 756-785, 790-838, 855-933, 946-952, 956-961, 965-972, 976-1000, 1004-1008, 1012-1015, 1024-1043, 1052-1062, 1066-1113, 1117-1122, 1126-1135, 1149-1165, 1169-1176, 1180-1182, 1186-1193, 1207-1229, 1248-1277, 1286-1297, 1301-1304, 1309, 1313, 1317, 1323, 1432 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset10.py 268 194 28% 24-27, 32-35, 39-55, 60, 65, 74-118, 155-179, 191-202, 218-221, 225-248, 252-287, 300, 311, 327-393, 405-430, 439, 443-447, 451-456, 460, 467-506, 526-535, 539-544, 548-553, 557-561, 576-588, 603-614 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset11.py 585 467 20% 19-38, 44-71, 76-84, 89-97, 101-121, 127, 131-237, 242-245, 264, 271-275, 280-295, 302-311, 315-316, 320-333, 337-343, 347-353, 357-358, 362, 366-378, 382, 386, 390, 394-398, 402-406, 411-414, 429-450, 462-465, 470, 477, 481, 485-487, 492-524, 529, 534-543, 553-600, 604-608, 612-614, 618-620, 632-642, 646, 650, 654-709, 714-720, 724-726, 730-777, 781-784, 788, 792-808, 812-829, 833-839, 845-866, 872-893, 905-936, 943-944, 948-954, 968-1012, 1016-1017, 1025-1045, 1050-1066, 1082-1153, 1158-1185, 1195-1206, 1215-1216, 1224-1240 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset9.py 2398 1890 21% 58-60, 64, 68-70, 74, 78-79, 83-91, 96-98, 102, 106, 110-113, 118-119, 124-131, 137-165, 169-189, 194, 198, 212-225, 230-232, 237-238, 243-247, 251, 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/home/admin/.local/lib/python3.8/site-packages/torch/optim/adamw.py 165 152 8% 78-91, 94-104, 114-177, 203-218, 250-313, 331-415 /home/admin/.local/lib/python3.8/site-packages/torch/optim/asgd.py 108 95 12% 32-39, 42-57, 67-113, 136-148, 174-203, 219-247 /home/admin/.local/lib/python3.8/site-packages/torch/optim/lbfgs.py 255 240 6% 9-31, 46-180, 223-240, 243-245, 248-257, 260-266, 269, 272-273, 276-280, 290-475 /home/admin/.local/lib/python3.8/site-packages/torch/optim/lr_scheduler.py 596 506 15% 27-77, 85, 94, 99, 103, 108-115, 122-168, 196-205, 218-225, 237-245, 248-252, 279-288, 298-305, 314-322, 325-333, 365-367, 370-376, 380, 412-414, 417-423, 427-428, 462-467, 470-482, 485, 524-533, 536-546, 551, 569-570, 573-579, 583, 616-638, 641-647, 656-662, 671-678, 722-724, 727-743, 749, 779-787, 790-792, 801-807, 816-823, 879-911, 915-917, 921-943, 946-954, 959, 962-974, 977-989, 992, 995-996, 1110-1161, 1165-1171, 1174, 1177, 1180, 1190-1222, 1255-1264, 1267-1271, 1300-1343, 1464-1563, 1567-1573, 1577-1578, 1582, 1585-1616 /home/admin/.local/lib/python3.8/site-packages/torch/optim/nadam.py 123 110 11% 59-74, 77-88, 98-148, 172-190, 218-247, 264-299 /home/admin/.local/lib/python3.8/site-packages/torch/optim/optimizer.py 160 139 13% 13, 34-59, 62, 69-70, 73-81, 85-97, 100-115, 128-143, 156-210, 227-251, 264, 276-314 /home/admin/.local/lib/python3.8/site-packages/torch/optim/radam.py 114 101 11% 64-76, 79-86, 96-141, 163-178, 202-236, 251-286 /home/admin/.local/lib/python3.8/site-packages/torch/optim/rmsprop.py 105 93 11% 69-82, 85-89, 99-154, 176-188, 214-235, 251-275 /home/admin/.local/lib/python3.8/site-packages/torch/optim/rprop.py 90 78 13% 58-64, 67-69, 79-125, 145-157, 177-198, 211-237 /home/admin/.local/lib/python3.8/site-packages/torch/optim/sgd.py 106 94 11% 93-105, 108-112, 122-163, 185-197, 220-241, 256-301 /home/admin/.local/lib/python3.8/site-packages/torch/optim/sparse_adam.py 58 51 12% 26-51, 61-111 /home/admin/.local/lib/python3.8/site-packages/torch/optim/swa_utils.py 100 76 24% 99-110, 113, 116-132, 161-187, 233-246, 250-257, 261, 265, 269-271, 274-286 /home/admin/.local/lib/python3.8/site-packages/torch/overrides.py 212 145 32% 71-72, 303-304, 337-1330, 1355-1366, 1400-1431, 1472-1510, 1562-1634, 1645, 1662-1664, 1669-1671, 1695, 1737-1738, 1817, 1820, 1824, 1829-1831, 1839-1844, 1849, 1853, 1856, 1889, 1908, 1912, 1915 /home/admin/.local/lib/python3.8/site-packages/torch/package/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/package/_digraph.py 97 78 20% 14-24, 33-40, 48-53, 57-60, 64-67, 72-74, 79, 83, 87-90, 95-103, 108-116, 121-140, 144-158, 166-167 /home/admin/.local/lib/python3.8/site-packages/torch/package/_directory_reader.py 31 17 45% 11, 14, 28, 31-33, 36-39, 42-43, 48-52 /home/admin/.local/lib/python3.8/site-packages/torch/package/_importlib.py 44 36 18% 22-24, 29-33, 38-50, 60-82, 90-94 /home/admin/.local/lib/python3.8/site-packages/torch/package/_mangling.py 25 14 44% 16-22, 25-26, 34-38, 41, 53-58, 62 /home/admin/.local/lib/python3.8/site-packages/torch/package/_package_pickler.py 57 48 16% 18-28, 34-98, 102-107 /home/admin/.local/lib/python3.8/site-packages/torch/package/_package_unpickler.py 15 9 40% 15-16, 20-26 /home/admin/.local/lib/python3.8/site-packages/torch/package/_stdlib.py 19 12 37% 14-15, 19-29 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/find_first_use_of_broken_modules.py 10 7 30% 21-29 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/is_from_package.py 7 3 57% 13-16 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/trace_dependencies.py 24 21 12% 17-60 /home/admin/.local/lib/python3.8/site-packages/torch/package/file_structure_representation.py 60 50 17% 13-15, 27-32, 41-43, 53-61, 64-66, 72-101, 126-132 /home/admin/.local/lib/python3.8/site-packages/torch/package/find_file_dependencies.py 70 55 21% 15-18, 21-23, 26-28, 31-32, 35-43, 46-49, 52-55, 59-99 /home/admin/.local/lib/python3.8/site-packages/torch/package/glob_group.py 31 19 39% 42-45, 48, 51, 54-55, 61-64, 70-82 /home/admin/.local/lib/python3.8/site-packages/torch/package/importer.py 101 75 26% 52, 73-133, 143-162, 169, 172, 185, 197-203, 206-224, 227-232 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_exporter.py 441 342 22% 74, 107-109, 128-152, 200-241, 256-290, 299-301, 320-350, 368-381, 393-396, 399-408, 411-415, 418-428, 434-485, 498-503, 513-559, 586-686, 696, 706-707, 724-726, 743-745, 762-764, 788, 829, 860, 874, 879-930, 933, 939-944, 947-961, 965-972, 977-979, 985-1035, 1039, 1048-1052, 1056-1058, 1061-1063, 1066-1067, 1078, 1083-1089, 1098, 1107, 1116, 1125, 1133-1136, 1146, 1161-1163 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_importer.py 346 274 21% 70-111, 132-134, 147-148, 168-169, 183-264, 273, 290, 303-304, 311, 320-356, 359-371, 374-376, 382-383, 388-394, 397-402, 406-425, 429-446, 457-461, 472-502, 505-527, 535-545, 548-555, 560-574, 582-595, 598-602, 616-617, 624, 641-642, 657-658, 661-663, 668-676, 679-680, 683-704 /home/admin/.local/lib/python3.8/site-packages/torch/profiler/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/profiler/profiler.py 171 127 26% 27, 70-77, 80-81, 84, 87-98, 101-107, 110-111, 117-128, 144-145, 155-156, 163-164, 171-172, 179, 182-186, 211-232, 240, 249-266, 393-424, 464-465, 468, 471-474, 477-479, 485-497, 500-501, 504-507 /home/admin/.local/lib/python3.8/site-packages/torch/quantization/__init__.py 14 2 86% 18-19 /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fake_quantize.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuse_modules.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuser_method_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/observer.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/qconfig.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quant_type.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantization_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize_jit.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/stubs.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quasirandom.py 66 54 18% 48-68, 85-104, 120-129, 135-137, 148-153, 156-171, 174-179 /home/admin/.local/lib/python3.8/site-packages/torch/random.py 46 33 28% 18, 23, 36-42, 49-55, 62, 85-129 /home/admin/.local/lib/python3.8/site-packages/torch/return_types.py 18 2 89% 11, 14 /home/admin/.local/lib/python3.8/site-packages/torch/serialization.py 527 356 32% 38-40, 93-114, 118-119, 123-124, 133-147, 151-157, 165-169, 178, 184, 188-190, 211, 214, 225, 230, 233, 237, 247, 250, 255-256, 259-260, 265-269, 273-277, 286-293, 299-305, 310-312, 323-325, 374-381, 385-525, 529-604, 707-711, 713, 721-726, 734-946, 957, 964-979, 987, 1013, 1039-1040 /home/admin/.local/lib/python3.8/site-packages/torch/sparse/__init__.py 24 9 62% 11-12, 209-218 /home/admin/.local/lib/python3.8/site-packages/torch/special/__init__.py 37 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/storage.py 475 260 45% 13-14, 31, 70-71, 76, 79, 82, 87, 93-95, 98, 106, 110-113, 116-121, 125, 129, 133, 137, 141, 145, 149, 153, 157, 161, 165, 169, 173-177, 188-195, 200-207, 210, 218, 259-278, 281-284, 292-293, 297, 303-357, 373, 379, 384, 389, 396, 403-424, 431, 442, 445, 448-470, 473-490, 497-516, 519-520, 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84-164 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_deprecated.py 59 30 49% 32-36, 55-57, 72-80, 116-141 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_legacy.py 80 38 52% 45-47, 57, 61, 65, 68, 72, 75, 79, 83, 86, 90, 93, 97, 100, 104, 107, 111, 123-130, 133, 137, 141, 145-150, 154, 158 /home/admin/.local/lib/python3.8/site-packages/torch/torch_version.py 36 20 44% 21-27, 30, 33, 58-71, 74-81 /home/admin/.local/lib/python3.8/site-packages/torch/types.py 44 11 75% 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75 /home/admin/.local/lib/python3.8/site-packages/torch/utils/__init__.py 11 2 82% 10, 15 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_crash_handler.py 17 8 53% 9, 12-17, 21, 25 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_mode_utils.py 60 38 37% 27-33, 48, 52, 59, 66-102, 110-132 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_pytree.py 118 79 33% 43, 46, 49, 52, 55, 58, 61, 64, 74-81, 84-86, 90, 100-103, 106, 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33-47, 50 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/callable.py 21 7 67% 15, 49-52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combinatorics.py 24 11 54% 44-49, 52-54, 59-60, 63 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combining.py 54 35 35% 35-42, 45-51, 54-56, 80-87, 90-96, 99-101 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/grouping.py 39 25 36% 40-46, 49-59, 62-70 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/utils.py 17 9 47% 32-42, 45, 48 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/common.py 116 91 22% 21-22, 30-39, 50-76, 80-90, 94-101, 138-173, 183, 186-187, 190-192, 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75-109, 114-133, 139-168, 179-196, 199, 210-219, 222, 225-241 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kinetics.py 100 72 28% 20, 116-156, 160-167, 176-197, 211-229, 236, 239, 242-248, 312-323 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kitti.py 58 39 33% 61-84, 105-109, 112-128, 131, 135, 139-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lfw.py 125 96 23% 42-58, 61-63, 66-72, 75-82, 85, 88, 91, 123-126, 129-144, 147-151, 161-170, 173, 205-207, 210-235, 245-255 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lsun.py 94 75 20% 18-31, 34-50, 53, 79-93, 96-136, 146-161, 164, 167 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py 243 152 37% 65-66, 70-71, 75-76, 80-81, 91-104, 107-111, 118-119, 122-128, 138-150, 153, 157, 161, 165, 168, 176-195, 198-199, 294-298, 302, 306, 310, 314, 318, 321, 324, 329-339, 421-428, 432-433, 437-438, 441, 444-461, 467-474, 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61-96, 99-100, 103-104, 110-116, 119, 122-123 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sbu.py 59 45 24% 36-58, 68-77, 81, 85-89, 93-114 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/semeion.py 43 28 35% 37-51, 61-73, 76, 79-83, 86-91 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/stanford_cars.py 49 38 22% 41-74, 77, 81-88, 91-111, 118-121 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/stl10.py 104 79 24% 55-88, 91-100, 111-126, 129, 132-145, 148-154, 157-161, 164, 168-176 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sun397.py 36 23 36% 36-51, 56, 59-68, 71, 74-76 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/svhn.py 44 30 32% 61-91, 101-113, 116, 119-122, 125-126, 129 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/ucf101.py 45 32 29% 71-101, 105, 108-118, 121, 124-130 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/usps.py 34 23 32% 52-70, 80-92, 95 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/utils.py 235 185 21% 38-45, 49-50, 54-63, 70-74, 78, 82-86, 90-100, 106-115, 130-167, 178-182, 195-199, 203-215, 229-280, 288-289, 299-302, 333-361, 377-393, 411-429, 440-450, 454, 466-485 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/video_utils.py 199 164 18% 28-29, 41-49, 62, 65, 68, 75, 122-140, 143-166, 169-173, 177-182, 185-193, 212-232, 245-255, 258, 261, 267, 274-279, 283-291, 306-379, 382-407, 411-423 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/vision.py 60 42 30% 38-54, 64, 67, 70-78, 81-82, 85, 90-91, 94-98, 101-102, 105-111 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/voc.py 94 61 35% 9-10, 79-127, 130, 158, 168-174, 203, 213-219, 223-237 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/widerface.py 95 76 20% 65-81, 94-103, 106, 109-110, 113-158, 161-169, 173-180, 183-194 /home/admin/.local/lib/python3.8/site-packages/torchvision/extension.py 48 12 75% 15, 27-28, 33, 49, 56-57, 66, 84-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/__init__.py 12 2 83% 9-10 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/_load_gpu_decoder.py 6 1 83% 6 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/_video_opt.py 154 124 19% 13, 31-32, 58-65, 70-71, 87-105, 111-120, 188-217, 226-252, 259-262, 335-369, 380-408, 418-423, 432-484, 490-505 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/image.py 72 48 33% 12-13, 45-48, 60-62, 82-85, 103-106, 121-124, 157-164, 182-188, 202-205, 227-230, 249-252, 256-257 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/video.py 207 181 13% 21-31, 41-42, 46, 81-139, 151-219, 225-234, 263-347, 351-356, 360-364, 384-415 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/video_reader.py 48 33 31% 9-10, 17-18, 24, 94-111, 125-133, 136, 151-152, 160, 179-181 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/__init__.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_api.py 66 38 42% 53-60, 63, 66, 86-107, 121-142 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_meta.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_utils.py 104 67 36% 51-64, 67-73, 83-89, 125-126, 132-142, 174-228, 236-240, 244-247, 252-256 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/alexnet.py 37 17 54% 19-37, 48-52, 106-116 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/convnext.py 131 84 36% 32-35, 46-60, 63-66, 77-79, 82-87, 101-167, 170-173, 176, 186-194, 299-308, 331-340, 361-370, 393-402 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/densenet.py 158 98 38% 36-46, 49-51, 55-58, 62-65, 69, 73, 78-94, 109-118, 121-125, 130-134, 164-211, 214-219, 227-238, 249-257, 360-362, 385-387, 410-412, 435-437 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/_utils.py 231 193 16% 22-23, 41-71, 87-119, 136-137, 140-144, 155-160, 163-181, 193-224, 238, 253-271, 286-301, 341-346, 359-386, 397-415, 420, 423-431, 447-449, 464-479, 484, 506-510, 521-538 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/anchor_utils.py 125 105 16% 40-50, 65-74, 77, 80, 85-113, 116-133, 163-182, 187-202, 206, 212-236, 239-247, 250-268 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/backbone_utils.py 82 64 22% 42-54, 57-59, 112-113, 125-146, 158-174, 193-194, 205-242 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/faster_rcnn.py 188 134 29% 38-40, 201-280, 293-296, 299-304, 322-341, 355-357, 360-369, 547-569, 613-644, 656-685, 739-751, 812-820 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585-673, 794-819, 862-891 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/roi_heads.py 481 441 8% 28-49, 69-79, 91-94, 109-126, 131-162, 168-213, 220-231, 244-295, 300-326, 331-342, 347-360, 368-383, 389, 394-400, 405-425, 429-461, 466-471, 476-489, 522-548, 551-557, 560-566, 570-601, 605-610, 614-616, 620-628, 636-666, 676-725, 742-876 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/rpn.py 175 149 15% 29-41, 53-62, 73-79, 83-86, 90-111, 161-181, 184-186, 189-191, 197-229, 232-240, 250-297, 314-337, 363-391 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssd.py 273 223 18% 49-53, 58-60, 63, 71-73, 80-87, 90-103, 108-112, 117-121, 201-242, 248-251, 260-319, 327-410, 415-461, 466-535, 539-548, 552-568, 643-679 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssdlite.py 119 79 34% 33, 49-51, 74-78, 85-87, 90, 100-104, 109-113, 125-148, 152-161, 169-184, 262-328 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/transform.py 185 159 14% 14-16, 22, 32-71, 96-105, 110-146, 149-157, 165-166, 173-194, 200-218, 221-225, 228-245, 253-267, 270-275, 279-293, 297-309 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/efficientnet.py 265 159 40% 59, 76-81, 85, 100-102, 113-162, 165-169, 179-223, 226-230, 255-342, 345-352, 355, 366-374, 382-429, 754-757, 782-785, 810-813, 838-841, 866-869, 894-897, 930-933, 966-969, 1003-1006, 1040-1043, 1077-1080 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/googlenet.py 185 143 23% 42-100, 103-108, 112-163, 167-170, 173-181, 196-212, 218-224, 227-228, 239-246, 250-263, 268-270, 273-275, 319-342 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/inception.py 299 243 19% 38-93, 96-101, 105-155, 159-162, 165-173, 180-192, 195-208, 211-212, 217-224, 227-236, 239-240, 247-263, 266-282, 285-286, 291-300, 303-313, 316-317, 322-336, 339-360, 363-364, 371-378, 382-395, 400-402, 405-407, 457-475 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mnasnet.py 119 73 39% 38-45, 60-63, 70-77, 84-87, 93-94, 113-156, 159-162, 174-209, 306-314, 339-341, 366-368, 393-395, 420-422 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenet.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv2.py 105 72 31% 23-32, 43-78, 81-84, 112-181, 186-191, 194, 265-275 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv3.py 138 97 30% 29-33, 54-61, 65, 76-124, 127-130, 155-223, 226-233, 236, 242-285, 295-303, 398-401, 428-431 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/_utils.py 25 18 28% 10-17, 21-23, 33-45 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/raft.py 288 219 24% 31-59, 62-68, 75-92, 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/home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv3.py 88 53 40% 34-36, 39, 42, 54-72, 86-87, 90-93, 104-106, 109-112, 115-122, 133-158, 231-234 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/resnet.py 120 70 42% 39-40, 43-57, 60-62, 67-70, 73-88, 91-95, 100-103, 106-112, 121-124, 135-149, 315-317, 364-366, 413-417, 456-460 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/shufflenetv2.py 86 43 50% 37-38, 41-49, 55-57, 60-63, 75-82, 99-113, 253-254, 306-307, 351-352, 396-397 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/utils.py 30 24 20% 8-18, 22-42, 48-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/regnet.py 273 142 48% 62, 81-105, 122-141, 144-148, 168-182, 195-200, 234-266, 276, 287-293, 307-374, 377-384, 393-402, 1123-1126, 1148-1151, 1173-1178, 1200-1205, 1227-1232, 1254-1259, 1281-1286, 1308-1313, 1335-1338, 1360-1363, 1389-1392, 1418-1421, 1447-1450, 1476-1479, 1505-1508 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/resnet.py 236 150 36% 42, 56, 73-87, 90-105, 128-141, 144-163, 178-223, 233-264, 268-282, 285, 295-303, 668-670, 693-695, 724-726, 755-757, 786-788, 813-817, 842-846, 870-874, 904-907, 937-940 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/_utils.py 27 18 33% 14-18, 21-37 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/deeplabv3.py 127 81 36% 50, 61-66, 71, 79-82, 87-101, 109-113, 121-128, 203-218, 257-273, 312-328, 365-381 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/fcn.py 64 36 44% 38-47, 103-110, 152-168, 210-226 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/lraspp.py 70 44 37% 38-41, 44-51, 56-68, 71-79, 83-93, 157-175 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/home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ciou_loss.py 25 20 20% 45-71 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/deform_conv.py 65 50 23% 63-92, 126-151, 154-159, 170, 182-195 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/diou_loss.py 32 26 19% 45-57, 66-87 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/drop_block.py 68 52 24% 28-52, 74-102, 114-119, 129, 132-133, 145, 155 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/feature_pyramid_network.py 100 80 20% 33, 84-110, 122-134, 149-156, 163-170, 184-204, 218-220, 229-235, 243-249 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/focal_loss.py 18 14 22% 35-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/giou_loss.py 24 20 17% 43-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/misc.py 93 67 28% 27-33, 45-49, 56-62, 65, 85-114, 153, 202, 237-243, 246-250, 253-254, 282-298, 309-310, 313 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/poolers.py 123 92 25% 20-34, 45, 68-72, 80-85, 89-96, 101-107, 114-135, 140-144, 169-228, 278-288, 291-292, 295-296, 303-304, 325-331, 341 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_align.py 29 18 38% 46-57, 71-75, 78, 81-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_pool.py 28 17 39% 40-49, 58-61, 64, 67-68 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_align.py 31 18 42% 53-61, 78-83, 86, 89-97 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_pool.py 30 17 43% 42-51, 60-63, 66, 69-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/stochastic_depth.py 34 24 29% 26-44, 56-59, 62, 65-66 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/_pil_constants.py 18 8 56% 17-25 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1436-1441, 1458-1463, 1481-1486 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional_pil.py 261 192 26% 19, 26-33, 38-40, 45-50, 55-58, 63-66, 71-76, 81-86, 91-96, 101-120, 130-142, 153-222, 234-237, 249, 251, 254, 256-275, 278, 293-311, 322-327, 340-344, 355-360, 365-378, 383-385, 390-392, 397-399, 404-409, 414-416, 421-423 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py 555 501 10% 10, 14-15, 19-21, 25-28, 33-34, 38-44, 48-59, 63-65, 69-117, 121-123, 127-129, 133-142, 146-162, 166-173, 177-190, 194-219, 223-233, 237-253, 257-259, 263-298, 302-319, 326-350, 354-370, 374-426, 436-503, 515-542, 546-558, 562-571, 576-603, 619-629, 635-642, 653-675, 685-693, 704-722, 728-745, 749-755, 761-764, 768-790, 795-803, 808-817, 822-832, 836-855, 859-869, 874-891, 899-912, 916, 921-933, 937-960, 964-970 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/transforms.py 745 571 23% 88-90, 93-95, 98-103, 124, 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/home/admin/.local/lib/python3.8/site-packages/torchvision/utils.py 276 245 11% 57-132, 154-160, 201-266, 295-346, 378-418, 436-453, 468-488, 501-535, 539-540, 562 /home/admin/.local/lib/python3.8/site-packages/torchvision/version.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_dist_ver.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_monitor.py 45 31 31% 31-39, 42-45, 49, 54-92, 95 /home/admin/.local/lib/python3.8/site-packages/tqdm/_tqdm_pandas.py 10 6 40% 12-24 /home/admin/.local/lib/python3.8/site-packages/tqdm/asyncio.py 53 36 32% 24-34, 37, 40-52, 55, 62-67, 75-81, 88 /home/admin/.local/lib/python3.8/site-packages/tqdm/auto.py 14 3 79% 27-28, 37 /home/admin/.local/lib/python3.8/site-packages/tqdm/autonotebook.py 12 5 58% 16-20 /home/admin/.local/lib/python3.8/site-packages/tqdm/cli.py 188 173 8% 17-40, 54-97, 151-311 /home/admin/.local/lib/python3.8/site-packages/tqdm/gui.py 10 1 90% 181 /home/admin/.local/lib/python3.8/site-packages/tqdm/std.py 696 579 17% 46-49, 93-100, 103-104, 107-108, 111, 114, 118-121, 127-128, 154-161, 165, 169-184, 187-211, 227-229, 238-242, 390-398, 415-420, 437-439, 448-465, 537-663, 667-680, 685-687, 699-717, 722-726, 735-756, 761, 766-768, 805-950, 963-1104, 1107-1111, 1114, 1122-1130, 1133-1134, 1137, 1140-1146, 1149, 1152, 1156, 1159, 1165-1197, 1225-1264, 1268-1308, 1312-1324, 1339-1351, 1355-1359, 1371-1381, 1393-1395, 1399-1401, 1416-1432, 1438-1440, 1444-1445, 1450-1455, 1478-1499, 1515-1520, 1525 /home/admin/.local/lib/python3.8/site-packages/tqdm/utils.py 175 113 35% 22, 28-31, 70, 81-96, 108-109, 112-113, 119, 122, 125, 128, 131, 134, 139, 142, 146-149, 153, 159, 169-170, 176, 179, 191-210, 213-218, 222, 231-248, 252-262, 266-269, 273-278, 374, 382, 389-398 /home/admin/.local/lib/python3.8/site-packages/tqdm/version.py 8 6 25% 4-9 /home/admin/.local/lib/python3.8/site-packages/typing_extensions.py 1914 1300 32% 158, 165-166, 170-171, 189, 194-196, 199-201, 213-215, 223, 229, 256-263, 267, 272, 279, 290-294, 297, 306, 313-315, 319-325, 348-350, 387-395, 400-406, 410, 432, 451, 466-490, 542-545, 551, 558, 561-562, 576-578, 600, 612-635, 640-667, 674, 685-706, 731, 753, 773-774, 780, 791-797, 806, 815, 824, 833, 841, 852, 863, 869-874, 889, 895, 906, 909, 915, 926, 929, 955-957, 964-981, 994-1096, 1102, 1165-1210, 1215, 1229-1231, 1235, 1251, 1255, 1260-1280, 1314-1333, 1340-1345, 1352-1403, 1407-1410, 1422-1426, 1429-1431, 1434, 1438, 1443-1447, 1450, 1488, 1492-1503, 1506, 1514-1515, 1545, 1550, 1564-1573, 1578, 1581-1593, 1612-1613, 1618-1620, 1633, 1637, 1648-1672, 1675, 1680-1681, 1689, 1692, 1707, 1710, 1713-1715, 1730, 1733, 1736-1738, 1742, 1748-1791, 1849, 1853, 1857-1871, 1874-1882, 1885, 1888, 1891, 1895, 1906-1916, 1927-1929, 1932-1933, 1937, 1941, 1945, 1952-1959, 1963-2063, 2072-2091, 2097-2107, 2112, 2115-2127, 2132, 2149, 2152-2197, 2202-2204, 2253, 2256-2295, 2300-2302, 2345, 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/home/admin/.local/lib/python3.8/site-packages/uritemplate/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/uritemplate/api.py 11 3 73% 43, 66, 85 /home/admin/.local/lib/python3.8/site-packages/uritemplate/orderedset.py 59 40 32% 28-32, 35, 38, 42-47, 52-55, 59-63, 67-71, 74-78, 81-83, 86, 89-92 /home/admin/.local/lib/python3.8/site-packages/uritemplate/template.py 48 33 31% 30-34, 72-83, 86, 89, 92-94, 97, 102-120, 147, 169 /home/admin/.local/lib/python3.8/site-packages/uritemplate/variable.py 195 171 12% 56-71, 74, 77, 89-122, 130-143, 153-190, 204-240, 250-295, 304-325, 356-386, 392-399, 403, 407, 411-413, 417-419 /home/admin/.local/lib/python3.8/site-packages/zipp.py 123 73 41% 28, 47-50, 62, 73-75, 78-79, 82, 89-92, 100-111, 121-124, 127-130, 223-224, 232-242, 246, 250, 254, 258, 262, 265-266, 269-270, 273, 276, 279, 282, 285, 288-291, 294, 297, 300-301, 307-312 /home/admin/mtr/.credentials/credentials.py 1 0 100% /home/admin/workarea/git/Velours/python/dev/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/dev/angular_coefficients_to_crops.py 371 201 46% 12-41, 49-51, 66-68, 79-91, 97-104, 109-179, 185-186, 188-189, 194, 232, 237-238, 271, 290-304, 312-313, 316-317, 331, 346, 351-354, 365-367, 406, 417-504 /home/admin/workarea/git/Velours/python/dev/conditional_crop_copy.py 414 151 64% 14, 24, 29, 35-39, 51, 55-56, 83, 93-94, 103-104, 106, 113-114, 118, 121, 126-129, 136, 150, 157, 163, 167, 172-173, 177, 179, 182-231, 240-241, 270-274, 276-279, 289-295, 298-299, 304-306, 311-312, 321-323, 340, 343-344, 358-370, 400, 408, 418-419, 422-423, 435, 437, 442-445, 456-458, 471-473, 506, 509, 512, 524-540 /home/admin/workarea/git/Velours/python/dev/generate_new_image.py 477 245 49% 27-28, 31-36, 72-77, 83-84, 87-88, 94, 99-106, 135-147, 156-157, 162-175, 223-227, 245-248, 251, 254-261, 268, 279-280, 296-297, 302-312, 316-333, 337-357, 362-370, 404-405, 407-408, 411-412, 414, 420, 423-431, 437-438, 440, 459-466, 498-508, 543-578, 585-586, 593, 611, 655-656, 664-671, 678-771 /home/admin/workarea/git/Velours/python/dev/poly_crop_reduction.py 238 157 34% 9-20, 40, 45, 54-56, 58-59, 117, 119-120, 127-168, 172-226, 229-244, 260-310, 330-381 /home/admin/workarea/git/Velours/python/file_uploader.py 73 35 52% 14-15, 23-24, 28-30, 36-37, 54-56, 62-64, 70-80, 84-95, 98 /home/admin/workarea/git/Velours/python/misc/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/misc/split_time_score.py 929 713 23% 30-47, 51-60, 67-224, 311, 314-329, 353-354, 368-376, 386-387, 412-413, 420-424, 432, 441-442, 447-448, 457-463, 468-499, 515, 528, 533-535, 560-594, 600-644, 774-795, 800, 811-813, 836-838, 840, 843, 861, 863, 883-944, 958-1806 /home/admin/workarea/git/Velours/python/mtr/Gan2/pre_ops.py 265 175 34% 14-16, 19-21, 24, 27, 30, 33-35, 51-52, 76-86, 89-105, 141-201, 215-293, 317-320, 322-325, 327-334, 337, 361-415 /home/admin/workarea/git/Velours/python/mtr/Rubbia_Report.py 577 544 6% 95-134, 141-202, 211-225, 234-252, 260-300, 309-315, 323-338, 352-379, 387-394, 397-429, 437-446, 450-465, 471-476, 481-527, 530-541, 545-607, 612-673, 677-771, 774-791, 797-804, 808-828, 832-930 /home/admin/workarea/git/Velours/python/mtr/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/cnn/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/cnn/classifier_new.py 289 77 73% 24-39, 124, 184, 213-215, 221, 225-227, 240, 245-254, 263-270, 277, 291, 337-338, 354, 356-357, 365-369, 378-379, 395, 427-430, 456-457, 465, 485, 507, 522-523, 536-550 /home/admin/workarea/git/Velours/python/mtr/cnn/ordonner.py 73 39 47% 20-29, 36, 44, 54, 66-101, 104 /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelConfig.py 63 45 29% 15-18, 23-26, 30, 35-48, 54-68, 73-77, 81-85, 88-95, 98, 101 /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelData_queries.py 180 77 57% 19, 26, 35-42, 61, 66-82, 102, 111-133, 144, 148, 150, 154-159, 161-162, 164, 166, 168, 171-172, 205-206, 226-227, 231-232, 240-256, 293 /home/admin/workarea/git/Velours/python/mtr/database_queries/CachePhotoData_queries.py 364 90 75% 35-37, 56, 58, 63, 88, 101-110, 117, 119, 133, 139-141, 149-150, 160, 169, 182-184, 201-202, 225-227, 253-255, 275-277, 288-289, 304, 327-333, 337, 350-352, 378-387, 397-398, 489-490, 501, 517, 524-529, 537, 594-597, 618-620, 634-654 /home/admin/workarea/git/Velours/python/mtr/database_queries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/admin_queries.py 457 380 17% 32-39, 44-50, 56-66, 71-87, 92, 96-99, 102-116, 120-135, 138-143, 146-148, 156-165, 168-177, 180-187, 192-206, 211-227, 232-250, 254-271, 275-291, 294-295, 298-299, 302-308, 326-331, 334-337, 340-349, 353-357, 360-367, 370-376, 379-389, 392-399, 402-404, 407-414, 417-430, 433-444, 447-469, 476, 485, 488-495, 498-504, 507-510, 514-518, 522-540, 543-548, 551-556, 559-564, 568-577, 580-588, 591-600, 603-612, 615-621, 625-648 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_admin_tools.py 87 53 39% 27-28, 30-34, 45, 61, 64, 76-92, 97-105, 110-137, 142, 147-163, 166-171 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_queries.py 291 200 31% 22-42, 45-49, 52-71, 74-82, 85-91, 94-98, 101-106, 109-117, 124-134, 139-148, 152-159, 162-172, 176-197, 200-220, 223-233, 236-248, 253-261, 267-283, 301-363, 379, 382, 390, 411, 414, 423, 489-511, 514-528 /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/objet_thcl.py 146 114 22% 32-50, 56-65, 70-77, 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, 120, 123, 126, 129-132, 138-139, 143-147, 152-171, 177-196, 200-202, 205-212, 226-232, 237-269 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_queries.py 1475 764 48% 44, 57, 69, 87, 95-97, 102, 121-127, 132, 149-153, 162-170, 186-295, 305-308, 311-316, 319-326, 329-348, 351-359, 364, 369, 391, 394-397, 406-409, 420-461, 481, 484-487, 503-523, 528, 534-541, 551-572, 577, 584, 587, 590, 597, 630, 646, 663, 744, 751, 764, 801, 831, 868, 873-874, 894-897, 901-906, 909-914, 918-921, 960, 964, 968-969, 977, 979, 989-990, 994, 1006, 1011, 1016-1017, 1024-1044, 1083, 1090, 1135, 1144, 1158, 1164, 1179, 1191-1195, 1206, 1217, 1228-1232, 1250-1310, 1342-1349, 1366-1374, 1384-1396, 1400-1407, 1410-1416, 1419-1427, 1432-1439, 1449, 1456, 1460-1473, 1483, 1486-1493, 1505-1506, 1516-1517, 1524-1534, 1541, 1548-1554, 1556, 1568-1573, 1585-1590, 1602-1607, 1613-1617, 1624-1630, 1635-1649, 1655-1663, 1671, 1678-1680, 1685, 1691-1710, 1714-1729, 1733-1753, 1757-1775, 1781-1803, 1808-1818, 1821-1829, 1835-1852, 1858-1868, 1877, 1881, 1891-1908, 1912-1920, 1923-1927, 1945, 1950, 1966, 1979, 1990, 2022, 2047, 2075-2083, 2087-2095, 2100-2109, 2112-2121, 2124-2129, 2137, 2157, 2175, 2200, 2204-2211, 2257-2281, 2293-2295, 2299-2308, 2313-2331, 2334-2342, 2364, 2366, 2369-2371, 2385-2386, 2394, 2413-2444, 2449-2477, 2481-2487, 2491-2510, 2514-2523, 2527-2531, 2540, 2547, 2575, 2578, 2582-2609, 2644, 2650, 2654-2690, 2706-2708, 2713-2728, 2739-2775, 2784-2820 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_utils/util_portfolio_hashtag_ids.py 213 95 55% 18-19, 23-24, 29-128, 143, 146-147, 150, 154, 160-161, 169, 217-218, 223-226, 238-239, 276-277, 301, 304-305, 310, 313-314, 328, 331-332, 336, 339-340 /home/admin/workarea/git/Velours/python/mtr/database_queries/descriptor_queries.py 354 238 33% 23-42, 56, 63, 67, 73, 77, 82-103, 106-145, 163, 166, 169, 184, 218, 226-264, 270-301, 304-321, 333, 338, 349-352, 360-387, 390-400, 404-407, 412-435, 444-471, 474-477, 480-495, 499-556 /home/admin/workarea/git/Velours/python/mtr/database_queries/general_queries.py 148 61 59% 12-13, 33-34, 36-37, 45-46, 49, 59-61, 74, 83-95, 103-114, 122-133, 137-140, 151, 163-167, 182 /home/admin/workarea/git/Velours/python/mtr/database_queries/graph_nodes_queries.py 77 60 22% 28-34, 38-54, 59-130 /home/admin/workarea/git/Velours/python/mtr/database_queries/hashtag_queries.py 158 103 35% 46-50, 64-65, 72, 80-91, 94-110, 113-125, 128-133, 136-142, 145-155, 158-165, 168-183, 188, 196-207, 211-218, 221-226, 229-235 /home/admin/workarea/git/Velours/python/mtr/database_queries/mission_queries.py 520 478 8% 26-38, 42-250, 255-272, 275-314, 317-414, 418-430, 433-445, 448-460, 463-475, 479-491, 495-507, 510-522, 525-548, 551-552, 555-567, 570-582, 586-622, 625-644, 647-662, 665-671, 674-681, 697-741, 747-756, 773-799, 803-810, 815-822, 828-838, 841-843, 848-855, 859-873 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_insert_queries.py 105 81 23% 30-71, 79, 84-91, 94-103, 106-113, 118-138, 141-145, 149-163, 173-192, 203-218 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_retrieval_queries.py 558 401 28% 12, 51-61, 71-75, 96-101, 107-123, 129-142, 148-161, 180-181, 188, 199-200, 212, 217, 221, 224-226, 229-231, 234-237, 248, 254, 261-266, 269, 271, 274, 277-278, 288-326, 332-348, 351-425, 428-475, 481-492, 495-544, 547-548, 555-605, 608-631, 634-668, 675, 683, 695, 703, 711, 714, 719, 724-742, 750, 756-758, 761, 770, 774-776, 781-787, 790, 796-800, 805-826, 832-849, 852-864, 868-922, 946, 968, 975-986, 1002 /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py 511 261 49% 39, 41, 56-72, 88, 97-114, 122, 130, 134, 140-158, 164, 170, 172-174, 176, 179, 188, 192, 198, 202-212, 215-222, 225-235, 240-255, 261, 270, 274-284, 287-299, 302-311, 321, 325, 339, 351, 354, 360-361, 365, 369-375, 378-383, 389, 393-397, 400-410, 425, 441, 447, 473-497, 516-517, 525, 548-571, 576-584, 589, 594, 598-608, 616, 620-622, 630, 637, 642-662, 684, 717-748, 750-759, 780-781, 783-786, 793-794, 796-799 /home/admin/workarea/git/Velours/python/mtr/datou/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/calcul_brightness_image.py 76 46 39% 4, 7-8, 22-24, 42-68, 71-78, 81-88, 91-98 /home/admin/workarea/git/Velours/python/mtr/datou/count_refus.py 64 7 89% 15, 61-69, 72-73, 94 /home/admin/workarea/git/Velours/python/mtr/datou/darker_image.py 39 4 90% 15, 19, 24, 60 /home/admin/workarea/git/Velours/python/mtr/datou/data_augmentation_imgaug.py 244 194 20% 16, 19-22, 25, 27-176, 188-193, 203, 241-303 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py 1757 984 44% 43-44, 75-101, 108-109, 112-113, 117-118, 153, 158, 188-189, 206-209, 212-213, 227-229, 241-243, 246-247, 277, 302-306, 311, 328-332, 335, 375, 397-398, 434, 462-463, 481-495, 509-516, 522-573, 580-624, 652-653, 660-663, 674-677, 700-702, 721, 729-730, 738-739, 773-776, 810, 818, 821, 823-825, 833-853, 863-870, 886-942, 968-1161, 1165, 1171-1174, 1178-1180, 1185-1186, 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2617, 2619, 2621, 2625, 2638, 2641, 2643, 2645, 2647, 2651, 2653, 2659, 2661, 2663, 2666-2672, 2706-2708, 2723-2725, 2741, 2747-2749, 2751, 2756, 2765-2767, 2776-2779, 2788-2792, 2808, 2813, 2816, 2824-2838, 2842-2848, 2860-2878 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_object.py 478 150 69% 16-23, 35-37, 46, 51-62, 101-122, 178, 194, 212, 215, 222-246, 252-290, 317, 335, 363-369, 374, 376, 385, 389, 396, 400, 412, 495, 499-500, 512-513, 522-523, 570, 579, 589, 616, 635-652, 660, 675-679, 688-689, 694, 723-743, 747-771 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_data_increase.py 204 104 49% 32, 34-35, 93, 102, 125-162, 214-216, 221-294, 297-339 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_save.py 1287 803 38% 24, 33-38, 45, 49-96, 101-145, 150, 155, 160-178, 183, 200-260, 269-305, 316, 324-325, 339-344, 347, 349, 361, 368-369, 374-436, 445-486, 489-553, 569, 580, 582, 593, 595, 604-608, 613-619, 655, 669-670, 674, 676, 695, 712-717, 722, 726-728, 738-744, 747-761, 764-779, 783-809, 813-864, 883, 885, 902, 917, 919, 936, 957, 962-965, 971-976, 979, 981, 1006-1008, 1012-1014, 1017-1018, 1047-1078, 1086-1087, 1095, 1098, 1102, 1110-1114, 1138-1140, 1155-1157, 1172-1175, 1194-1198, 1223-1253, 1257-1279, 1295-1332, 1338-1357, 1362-1387, 1393-1457, 1472-1500, 1523-1534, 1538-1619, 1625, 1631-1632, 1654-1655, 1667, 1671, 1675, 1677, 1683, 1689-1690, 1694, 1696, 1698, 1703, 1705, 1708, 1710, 1712, 1716, 1719, 1721, 1730-1739, 1743-1745, 1749-1769, 1775-1783, 1786-1818, 1821-1836, 1850, 1854, 1858-1861 /home/admin/workarea/git/Velours/python/mtr/datou/datou_local_cache_db.py 157 117 25% 62-70, 73-84, 88-102, 105-113, 117-122, 126-136, 139-143, 167-175, 178-194, 197-201, 204-205, 214-218, 233-257, 287-301, 304-307, 311 /home/admin/workarea/git/Velours/python/mtr/datou/datou_step_finale.py 344 269 22% 9-77, 82-130, 135-351, 371-373, 376-377, 396, 414-427, 431, 436, 440-444, 469, 471-490, 497 /home/admin/workarea/git/Velours/python/mtr/datou/detect_blur_image.py 109 72 34% 12-15, 18-20, 24-34, 55-64, 77, 87-145 /home/admin/workarea/git/Velours/python/mtr/datou/image_blanchir.py 30 2 93% 27, 31 /home/admin/workarea/git/Velours/python/mtr/datou/image_temperature.py 22 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_deprecated.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_end_or_aggreg.py 484 469 3% 17, 19, 21, 24, 27, 34-274, 288-767, 908-918 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_initialisation.py 372 326 12% 15-244, 249-267, 284-289, 304, 313-314, 317-331, 337-346, 358-360, 376-558 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_post_processing.py 1089 513 53% 29-62, 90, 99-103, 110-115, 121-133, 137, 153-157, 179-180, 187-191, 199, 201, 217, 235-236, 266, 272-273, 294, 297-302, 311-317, 365-366, 394-401, 407, 409-487, 492-496, 519-520, 535-541, 544, 549-551, 576, 579-580, 585, 593, 606, 638-639, 642-643, 648-650, 653, 676-685, 691-693, 703, 713-719, 722-723, 727-732, 738-739, 742-743, 752-759, 762-770, 773-774, 790-795, 801-835, 840-853, 859-861, 881-883, 888-895, 912-962, 970-1044, 1049-1119, 1160-1162, 1166-1167, 1227, 1248-1269, 1281, 1287-1293, 2574-2576, 2579-2580, 2583-2590, 2600, 2607-2619, 2622, 2626-2630, 2637-2644, 2705-2707, 2800-2801, 2815-2816, 2827-2829, 2836-2846, 2854, 2886, 2897 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py 1403 793 43% 34-89, 124, 127, 133, 136-137, 150, 157-161, 172-177, 188-193, 233-235, 260-275, 281, 283, 289, 350, 354, 362-376, 391-399, 408-422, 425-426, 428-430, 432, 438, 440, 447, 485, 490-491, 497, 506, 510-696, 703-838, 843-885, 898-899, 908, 913-920, 928-929, 932-934, 956-960, 1003-1010, 1014-1021, 1024-1026, 1034, 1076-1077, 1087, 1095-1105, 1109-1111, 1119-1133, 1142-1143, 1156, 1174-1176, 1180, 1199-1200, 1204, 1220-1231, 1242, 1245-1246, 1251, 1255, 1259-1306, 1322-1323, 1344-1345, 1362-1459, 1485, 1502, 1508-1551, 1580-1581, 1586, 1596, 1620, 1623-1624, 1663-1666, 1670, 1677-1678, 1683, 1686, 1693-1699, 1703-1708, 1719-1733, 1780, 1813-1816, 1855-1857, 1930-1931, 1934-1935, 1940, 1949, 1955, 1960-1961, 1981-1986, 1990-2178 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py 2032 1319 35% 40, 47-59, 75, 84-86, 89, 98, 104, 139, 143, 149-150, 162-174, 211, 232, 253-257, 280-308, 325-326, 331, 334-335, 338, 350, 386-387, 392-393, 401, 410-411, 415, 427-556, 560-628, 637, 651-652, 663-664, 669, 697, 702, 710, 724, 729, 736, 745, 749, 755-761, 764, 803-807, 816, 819-823, 827, 830, 832-845, 855-889, 917-918, 924, 927-929, 937-943, 970, 982, 992-1000, 1005, 1009, 1016-1026, 1032-1077, 1098, 1103-1106, 1109-1111, 1118-1137, 1161, 1163, 1166-1187, 1196-1272, 1279-1466, 1470-1499, 1503-1579, 1586-1674, 1678-1855, 1867, 1914, 1921-1924, 1931-1933, 1936, 1967-1968, 1972, 1977-1987, 1994-1995, 2016-2017, 2031-2078, 2133, 2166-2169, 2214-2216, 2223-2237, 2240-2249, 2254-2255, 2258-2267, 2270-2291, 2293-2294, 2358-2370, 2374-2419, 2441-2442, 2457, 2459, 2461, 2463, 2469, 2477, 2480, 2496, 2507, 2511, 2516-2620, 2627-2815, 3023-3034, 3039, 3042-3043, 3048-3050, 3053, 3077-3079, 3088, 3099-3111, 3122-3123, 3136, 3166, 3182, 3187-3194, 3453-3556, 3560-3599, 3603-3660 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_send_or_copy.py 554 378 32% 19-195, 200-268, 273-332, 336-379, 397, 415, 424, 427-428, 430, 437, 444-449, 456, 462, 485-486, 493-623, 665-666, 671, 675-676, 680, 689-692, 700-716, 719-720, 728-741, 749, 751-754, 770, 809, 814-816, 833-834, 839 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_sort.py 193 163 16% 12-115, 125-127, 143-144, 161, 178-183, 189-287, 291-305 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_util.py 298 130 56% 16-17, 21, 26, 40, 47-48, 70, 97-98, 106, 125-126, 143-155, 179-183, 194-196, 214, 219, 224-231, 241-257, 261-284, 289, 293-294, 297-300, 303-305, 307-309, 319-324, 327-333, 366-371, 395, 405, 409-411, 423-467 /home/admin/workarea/git/Velours/python/mtr/datou/merge_rubbia.py 50 46 8% 12-36, 40-86 /home/admin/workarea/git/Velours/python/mtr/datou/send_mail_dechet.py 227 129 43% 19, 21, 27-28, 60, 66-119, 126-131, 146, 155, 157, 164-170, 175-179, 183-188, 191-193, 195-197, 207-221, 230, 234, 249-251, 255-259, 263, 269, 282-340, 344-345, 350, 354-355 /home/admin/workarea/git/Velours/python/mtr/lib/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/fotonower_connect.py 322 216 33% 73, 78-85, 88-90, 96-119, 123-184, 187-213, 220-221, 225, 230-231, 233-240, 252-253, 258-261, 267-279, 282, 285, 288-290, 307, 318, 323-325, 329, 332-335, 338-384, 389-412, 415-433, 436-461 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py 299 238 20% 35-43, 49-298, 304-342, 359, 373-374, 383-387, 402, 423-429, 444-549 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_segment.py 67 16 76% 40, 87, 107-117, 173, 190-191, 196-197, 222 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/prepare_maskdata.py 359 81 77% 28, 51, 62, 78, 83, 120-121, 140-141, 145-146, 148-149, 154-155, 164, 176-177, 187, 195, 198, 202, 218-220, 241-243, 246, 269-270, 290-293, 315, 317-318, 321, 326-332, 335-342, 347-348, 360-369, 391-393, 400-403, 411-414, 424-427, 429, 460-462, 502-504, 513-514, 545 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/svm_subroutines.py 69 63 9% 21-43, 50-99, 104-136 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/class_split_time_score.py 546 192 65% 48-49, 54, 73, 77, 85, 97, 102-114, 117-123, 142, 166-167, 173-174, 183, 265, 289, 295, 298, 311-320, 323, 326, 350, 353, 356, 359-370, 380-384, 395, 408-413, 440, 470, 472, 514, 526, 529, 541, 589-594, 598, 607, 623-627, 632-638, 643-649, 654-670, 673, 676, 679-695, 698-701, 704-707, 710-713, 716-723, 726, 730-764, 768-789 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/lib_split_time_score.py 1943 930 52% 19-36, 42, 78, 84-85, 96-104, 108-115, 125-136, 139-141, 182-185, 207-231, 236-411, 416-647, 654-729, 745-747, 752-776, 784-864, 868-886, 897-1001, 1006-1088, 1103, 1108, 1115, 1121-1134, 1139-1206, 1209-1250, 1255-1264, 1267-1351, 1354-1372, 1377-1414, 1419-1443, 1448-1467, 1695-1700, 1703-1721, 1768-1772, 1821, 1844-1864, 1948-1949, 1999, 2028, 2050-2105, 2175, 2178-2182, 2194, 2217, 2230, 2251-2252, 2271-2275, 2357, 2423, 2428, 2454-2458, 2544-2546, 2581-2582, 2613-2614, 2628-2630, 2647-2674, 2756-2757, 2774, 2821-2823, 2840-2842, 2876-2877, 2885-2898, 2913-2914, 2921, 2943-2944, 2951, 2959-2963, 2969-2973, 2983, 2996-3000, 3011, 3055-3106, 3114-3122, 3149-3151, 3170, 3176-3177, 3186-3188, 3190, 3217-3219, 3225, 3237-3238, 3246-3267, 3274, 3320, 3326-3395, 3404-3462, 3493-3494, 3540-3542, 3576, 3631, 3644, 3677-3722, 3754-3760, 3828-3829, 3854, 3860-3861, 3867, 3880-3881, 3895, 3902, 3917-3935, 3945-3963, 3970, 3974-4008 /home/admin/workarea/git/Velours/python/mtr/mem_info.py 76 30 61% 33-34, 41, 49, 59-63, 72, 95-124 /home/admin/workarea/git/Velours/python/mtr/monitor_sys.py 131 53 60% 40, 44, 47-50, 52, 61, 65-68, 98, 102, 104, 108, 110, 112, 114, 116, 124, 131, 143-144, 150, 162, 164-167, 170-194 /home/admin/workarea/git/Velours/python/mtr/ses_mailer.py 55 34 38% 35-36, 42-44, 47-85 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/flip_images.py 241 88 63% 20-91, 98-105, 111-114, 138-139, 155, 164-171, 177-180, 204-205, 220, 236, 243, 246, 253-254, 263, 268, 306-307, 311, 333, 338, 349, 378 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/image_utils.py 328 225 31% 21-28, 37-52, 88, 91-113, 121, 129, 142, 144-162, 181-191, 194-236, 242-253, 265-298, 301-314, 343, 348-354, 363-365, 368-381, 385-397, 401-441, 446-465, 470-473, 476-484 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/rotate_crop_and_images.py 894 306 66% 62, 65, 80-81, 97, 120, 139-140, 150-151, 176, 208, 212, 217-218, 223, 227-230, 239-241, 262, 268, 275, 297-310, 318-319, 333-334, 359-360, 370, 400-401, 412-413, 442, 457, 460, 481-482, 494, 497-498, 501, 521-524, 528, 531, 534, 543-546, 553-554, 571-572, 579-580, 584, 603, 606, 609, 618, 624, 634, 679-681, 690, 712-725, 733, 751, 794-831, 835, 884, 904-906, 911, 913-914, 920-924, 957-961, 964-967, 989, 1005-1013, 1062-1070, 1074, 1093, 1098, 1102-1109, 1140, 1180-1190, 1198, 1200, 1204, 1207-1212, 1235, 1259, 1274, 1282-1283, 1300-1304, 1308, 1323-1383, 1394-1512 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/simple_image_editor.py 2091 1596 24% 24-25, 43-51, 60-81, 86-126, 131-134, 140-324, 329-332, 335-359, 365-387, 391-422, 429-446, 451-469, 475-485, 492-598, 605-613, 619-793, 798-815, 821-853, 859-907, 910-911, 916-936, 942-972, 979-1100, 1109-1145, 1151-1183, 1189-1227, 1232-1251, 1259-1567, 1575-1639, 1643-1654, 1660-1683, 1690-1756, 1762-1828, 1832-1907, 1913-1990, 2023-2024, 2035, 2041-2042, 2044-2045, 2057-2064, 2077, 2081, 2097, 2102, 2113-2120, 2127-2128, 2137, 2145-2146, 2172, 2176, 2181-2194, 2216, 2223, 2233-2234, 2239, 2244, 2250, 2262, 2303, 2315, 2328-2331, 2335, 2349-2355, 2379-2384, 2395-2421, 2431-2465, 2479-2741, 2852-2854, 2868, 2939, 2944, 2949, 2952-2953, 2959, 2961, 2964, 2979, 3007-3008, 3041-3042, 3080, 3102, 3140-3156, 3164-3189, 3200-3304, 3339, 3359-3360, 3362-3363, 3387, 3409-3417, 3508-3540, 3562, 3579-3590, 3594-3600, 3603-3682, 3685-3688, 3691-3723, 3726-3752, 3757-3819, 3825-3877 /home/admin/workarea/git/Velours/python/mtr/split_time_gps_score.py 723 514 29% 14-26, 36-68, 78-89, 106-107, 119-141, 151-182, 232, 236, 244-256, 299-300, 311, 314, 360-379, 399-401, 451-484, 488-509, 526-554, 558-596, 614, 631-634, 646, 649-650, 658, 665-671, 691-700, 713-721, 732-799, 812-865, 869-877, 881-893, 903-938, 948-987, 999-1034, 1046-1070, 1079, 1092-1093, 1097-1310 /home/admin/workarea/git/Velours/python/mtr/tfhub2/data_ops.py 228 196 14% 18-26, 29-37, 41-49, 52-60, 63-68, 71-82, 85-93, 96-117, 120-129, 132-149, 152-183, 187-189, 193-211, 215-229, 232-249, 271-302 /home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py 162 60 63% 42, 44-52, 83, 86, 99, 110-111, 115, 128-131, 139, 173-181, 186-231, 246, 255-273, 281-282, 293, 295 /home/admin/workarea/git/Velours/python/mtr/tfhub2/foto_datasets.py 242 131 46% 23-25, 41-51, 64-66, 82, 85, 95-108, 117-119, 122-123, 131, 136-137, 139-140, 153-168, 173-240, 246, 251, 256, 264, 271, 287-322, 341, 343, 366, 379-381, 386, 391, 395-401 /home/admin/workarea/git/Velours/python/mtr/tfhub2/fotonower_data_ops.py 111 94 15% 19-23, 26-30, 33-38, 41-44, 48-84, 91-144, 148-182, 186-192 /home/admin/workarea/git/Velours/python/mtr/tfhub2/ops.py 201 170 15% 29-31, 40, 44, 48, 60, 65-72, 76-129, 139-151, 155-167, 171-177, 182-186, 191-202, 207-210, 219-244, 254-280, 290-319, 324-326, 334-343, 346-348, 351-358, 362-373, 377-394 /home/admin/workarea/git/Velours/python/mtr/utils/MTRMongoClient.py 99 87 12% 21-92, 97-208, 213-241 /home/admin/workarea/git/Velours/python/mtr/utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cd.py 11 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cdn/copy_to_ovh.py 14 2 86% 16, 27 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/s3_bucket_manager.py 112 88 21% 33-40, 43-48, 51-54, 57-69, 75-84, 97-104, 119-125, 128-132, 140-159, 162-166, 169-175, 179-182, 185-186, 189-190 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/swift_upload_manager.py 151 73 52% 44, 47-48, 54, 63, 72-73, 76-79, 105, 107, 125-127, 130, 133-142, 145-156, 163, 180-184, 187-193, 197-210, 213, 216-217, 223-239 /home/admin/workarea/git/Velours/python/mtr/utils/general_util.py 57 32 44% 11-12, 20-27, 30, 33-57, 61-63, 69-70, 75, 86-90 /home/admin/workarea/git/Velours/python/mtr/utils/kmean_cloud_storage.py 15 5 67% 19-20, 23, 26, 29 /home/admin/workarea/git/Velours/python/mtr/utils/load_caffe.py 61 26 57% 23, 29, 43, 48, 55, 61-62, 65, 70, 76-94 /home/admin/workarea/git/Velours/python/mtr/utils/prepare_photo_learning.py 201 125 38% 14-15, 62-81, 89, 94-97, 103, 114, 123-124, 131, 137, 140, 154, 156-158, 176, 189-232, 238-365 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100% /home/admin/workarea/git/Velours/python/tests/cache_photo_data_test.py 74 20 73% 41, 49-62, 84, 90-94 /home/admin/workarea/git/Velours/python/tests/cod_main_test.py 75 12 84% 32, 57, 92, 98-101, 122, 128, 131, 134, 138 /home/admin/workarea/git/Velours/python/tests/datou_test.py 1923 633 67% 37, 43-45, 51-52, 58-60, 66-70, 101-104, 108, 122-126, 157-159, 182-184, 191-192, 203-205, 213-215, 221-224, 227-228, 241, 271-276, 283, 302-304, 317, 333, 352-354, 360-364, 378-416, 448-450, 457, 464-466, 499-501, 510-513, 540-542, 546, 549, 553, 568-569, 579, 582-583, 588, 626-628, 631, 645-646, 656, 660-661, 674-675, 681, 707-709, 729, 731, 737-739, 743-745, 777-779, 790-791, 814, 816, 822-824, 828-830, 866-868, 886-887, 915-917, 935-936, 962, 968-970, 983-987, 998, 1025, 1031-1033, 1046-1050, 1066, 1071, 1099-1101, 1111, 1118, 1174, 1291, 1298-1301, 1317, 1323, 1333, 1374, 1380-1383, 1807-1809, 1846, 1852-1855, 1874-1878, 1920, 1926-1929, 1935, 1948, 1959-2067, 2100-2102, 2119-2123, 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/usr/lib/python3/dist-packages/keystoneclient/v3/limits.py 21 9 57% 61-73, 93, 115, 133, 150 /usr/lib/python3/dist-packages/keystoneclient/v3/policies.py 24 12 50% 31-42, 64, 79, 92, 108, 124 /usr/lib/python3/dist-packages/keystoneclient/v3/projects.py 106 76 28% 41-55, 58, 61, 64, 67, 70, 73, 105-108, 136-157, 161-164, 168-171, 201-222, 225-227, 246, 264, 274, 283-285, 298-302, 311, 323-326, 337-345 /usr/lib/python3/dist-packages/keystoneclient/v3/regions.py 20 5 75% 61, 75, 87, 112, 129 /usr/lib/python3/dist-packages/keystoneclient/v3/registered_limits.py 22 10 55% 61-72, 99, 120, 140, 157-158 /usr/lib/python3/dist-packages/keystoneclient/v3/role_assignments.py 69 48 30% 40-42, 45-47, 50-52, 55-57, 60-62, 95-124, 127, 131, 135, 139, 143, 147 /usr/lib/python3/dist-packages/keystoneclient/v3/roles.py 149 101 32% 61-92, 95-113, 116-121, 137-141, 156, 190-203, 217, 235, 275-284, 327-336, 377-386, 395, 401, 407, 413, 419, 430-433, 456-458, 481-482, 501-503, 521-523, 543-544, 559, 562, 566, 570 /usr/lib/python3/dist-packages/keystoneclient/v3/services.py 23 11 52% 57-58, 75, 89-90, 111-112, 130-134 /usr/lib/python3/dist-packages/keystoneclient/v3/tokens.py 37 27 27% 18-21, 37-39, 54-58, 78-94, 116-121 /usr/lib/python3/dist-packages/keystoneclient/v3/users.py 57 34 40% 43-45, 82-92, 126-132, 148, 187-197, 213-226, 240-243, 259-262, 278-281, 295 /usr/lib/python3/dist-packages/netaddr/__init__.py 19 1 95% 16 /usr/lib/python3/dist-packages/netaddr/compat.py 60 37 38% 39, 50-53, 56-59, 62-113 /usr/lib/python3/dist-packages/netaddr/contrib/__init__.py 1 0 100% /usr/lib/python3/dist-packages/netaddr/contrib/subnet_splitter.py 17 11 35% 23, 27-38, 42, 46 /usr/lib/python3/dist-packages/netaddr/core.py 73 40 45% 61-74, 89, 112-113, 122-124, 136, 145-149, 158-161, 169-170, 184-196, 199-200, 203, 206 /usr/lib/python3/dist-packages/netaddr/eui/__init__.py 361 276 24% 24, 28, 32, 37-39, 44, 52, 72-101, 104-109, 112-117, 121, 125, 129-152, 157, 169, 173-174, 181, 202-216, 230-268, 271-276, 279-284, 288, 292, 296-308, 312, 316-318, 327, 357-390, 394, 401-413, 416, 419-450, 456, 459-468, 477-480, 485-488, 492, 500-501, 506, 515-525, 529-548, 552, 559-564, 571-576, 583-588, 595-600, 607-612, 619-624, 633, 638, 643, 652, 663-671, 685-687, 699-700, 710, 718-722, 726, 730 /usr/lib/python3/dist-packages/netaddr/ip/__init__.py 822 596 27% 33-38, 47, 54, 60, 69-72, 81-84, 93-96, 105-108, 117-120, 129-132, 136, 140-143, 151-154, 162-174, 181-184, 191-199, 206, 213, 222-223, 228, 262-266, 275, 278, 285-293, 305, 316-319, 323, 330-339, 348-371, 377-378, 384-385, 396-400, 411-415, 426-429, 442-445, 456-459, 468, 472, 480, 485-487, 492, 500, 508, 513, 521, 530, 535, 544-557, 570-586, 596-600, 609, 618, 627, 636, 645, 651, 657, 661, 676-678, 685, 693-697, 705-734, 743-752, 760, 768-776, 782, 787-788, 796-798, 804-816, 820-823, 827-828, 906-908, 911-913, 915-917, 919-921, 924, 934-935, 938, 946, 953-968, 972-977, 989, 994, 999-1002, 1010, 1018-1019, 1024-1025, 1030, 1035-1036, 1041, 1049, 1064-1072, 1085-1093, 1102-1123, 1129, 1135-1138, 1146-1161, 1174-1193, 1202-1205, 1214-1217, 1229-1240, 1255-1281, 1297-1318, 1322-1323, 1327, 1361, 1365, 1371-1375, 1378-1397, 1402, 1407, 1413, 1419-1420, 1427, 1431, 1435, 1446-1448, 1477-1531, 1549-1576, 1589-1591, 1606-1650, 1663-1684, 1701-1730, 1746-1767, 1782-1796, 1811-1823, 1838-1852 /usr/lib/python3/dist-packages/netaddr/ip/glob.py 137 117 15% 26-67, 79-97, 109-127, 141-201, 213, 225-231, 283-285, 289, 293-294, 297, 300-301, 308, 312 /usr/lib/python3/dist-packages/netaddr/ip/nmap.py 64 55 14% 22-45, 51-62, 69-87, 96-101, 115-117 /usr/lib/python3/dist-packages/netaddr/ip/rfc1924.py 28 18 36% 32-42, 49-61 /usr/lib/python3/dist-packages/netaddr/ip/sets.py 350 300 14% 27-53, 65-81, 105-122, 126, 133, 145-210, 216-217, 226, 238-245, 249, 257, 263, 281-296, 317-350, 361, 371-372, 376-378, 392-413, 417, 426-429, 438-441, 450-453, 462-465, 476-479, 488-494, 505-507, 518-551, 566-619, 631-673, 683-688, 696, 700, 711-718, 729-735, 744-748 /usr/lib/python3/dist-packages/netaddr/strategy/__init__.py 113 90 20% 44-56, 70-83, 97-106, 121-138, 154-160, 177-194, 207-226, 238-257, 270-273 /usr/lib/python3/dist-packages/netaddr/strategy/eui48.py 135 70 48% 144-152, 163-197, 209-216, 226, 237-245, 249-251, 255-257, 261-263, 267-269, 273-275, 279-281, 286-288, 292, 296 /usr/lib/python3/dist-packages/netaddr/strategy/eui64.py 122 66 46% 121-124, 133-139, 149-176, 187-192, 202-203, 214-222, 226-228, 232-234, 238-240, 244-246, 250-252, 256-258, 263-265, 269, 273 /usr/lib/python3/dist-packages/netaddr/strategy/ipv4.py 103 51 50% 16, 91-107, 121, 141-148, 158-161, 171, 182, 186, 196-199, 212-214, 218, 222, 226-228, 232, 236, 240, 261-278 /usr/lib/python3/dist-packages/netaddr/strategy/ipv6.py 106 47 56% 18, 24-25, 119-126, 141-142, 154-172, 182-187, 197-198, 221, 225-229, 233, 237, 241, 245-247, 251, 255, 259 /usr/lib/python3/dist-packages/oauthlib/__init__.py 10 2 80% 27, 34 /usr/lib/python3/dist-packages/oauthlib/common.py 212 144 32% 22-24, 27-28, 59, 64-70, 74-80, 84-89, 96-101, 108-113, 129-165, 176-194, 209, 221, 232-233, 237-251, 255-257, 266, 271-275, 280-285, 297-303, 308-328, 338-340, 343, 346-348, 351-352, 355, 358-359, 362-364, 385-430, 433-436, 439-447, 452, 456-458, 463-468 /usr/lib/python3/dist-packages/oauthlib/oauth1/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/__init__.py 125 91 27% 51, 86-101, 104-110, 122-151, 156-186, 199-223, 256-327 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/__init__.py 8 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/access_token.py 59 48 19% 44-54, 104-119, 131-217 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/authorization.py 38 25 34% 50-57, 111-139, 154-163 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/base.py 87 75 14% 23-24, 32-66, 70-106, 110-112, 117-177, 182-216 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/pre_configured.py 8 4 50% 11-14 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/request_token.py 56 45 20% 42-49, 99-110, 122-211 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/resource.py 43 35 19% 70-165 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/signature_only.py 34 26 24% 35-84 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/errors.py 36 15 58% 39-46, 49, 53-58, 62 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/parameters.py 36 23 36% 48-91, 105-112, 124, 136-139 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/request_validator.py 108 48 56% 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 162-163, 170-171, 178-179, 186-187, 194-195, 200, 207-208, 232, 248, 264, 300, 333, 366, 383, 398, 416, 440, 467, 504, 539, 574, 625, 659, 678, 713, 745, 764, 788, 812, 833, 854 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/signature.py 149 117 21% 84-106, 131-205, 279-340, 423-438, 442, 468-495, 499, 525-552, 559-562, 580-586, 590-592, 616-627, 631, 651-661, 681-691, 695-697, 716-729, 739-743 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/utils.py 40 22 45% 31-32, 40-44, 55-60, 64-66, 72, 78, 83-90 /usr/lib/python3/dist-packages/os_service_types/__init__.py 9 3 67% 39-41 /usr/lib/python3/dist-packages/os_service_types/data/__init__.py 8 0 100% /usr/lib/python3/dist-packages/os_service_types/exc.py 10 2 80% 24-25 /usr/lib/python3/dist-packages/os_service_types/service_types.py 111 69 38% 28-29, 59, 64-71, 75-78, 83, 88, 93, 98, 103, 108, 114, 120, 129-133, 141-144, 152, 160-161, 169, 191-202, 210-211, 221-234, 242-245, 254-258, 269-270, 281-285 /usr/lib/python3/dist-packages/oslo_config/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_config/cfg.py 1258 889 29% 38-39, 77, 80, 87, 94-97, 104-105, 108-109, 116, 119, 126, 129, 136-137, 140-141, 149, 156, 159, 167, 170, 178, 181, 188-189, 192, 211, 237-248, 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2332-2335, 2340-2341, 2351-2354, 2364-2380, 2385-2386, 2404-2405, 2422-2423, 2438-2441, 2459-2462, 2468-2471, 2484-2485, 2497-2498, 2502-2506, 2510-2511, 2515-2517, 2521-2523, 2542-2554, 2567-2591, 2603-2605, 2617-2619, 2622-2633, 2646-2735, 2749-2768, 2783-2786, 2806-2815, 2818-2830, 2839-2863, 2871-2879, 2893-2897, 2906-2937, 2940-2955, 2958-2965, 2975-2987, 2996, 3009-3032, 3040-3054, 3065-3081, 3089-3094, 3112-3114, 3129-3130, 3134, 3138, 3142, 3146-3147, 3151, 3167-3169, 3173-3185, 3202-3204, 3212-3229 /usr/lib/python3/dist-packages/oslo_config/iniparser.py 75 57 24% 18-20, 23, 31-32, 35-40, 43-56, 59-97, 101, 105, 109, 112, 116, 119, 123, 127 /usr/lib/python3/dist-packages/oslo_config/sources/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oslo_config/sources/_environment.py 22 11 50% 70, 73, 81-82, 85-92 /usr/lib/python3/dist-packages/oslo_config/types.py 419 300 28% 44-53, 56-61, 65, 113, 120-123, 129, 133-139, 142-167, 173-180, 183, 194, 201, 209-215, 218, 238, 241-250, 253, 256, 259, 282-304, 307-324, 327-338, 341, 352, 388, 411, 437-446, 479, 487-519, 522, 525, 531-540, 562-565, 568-586, 589, 596, 618-626, 629-680, 683, 686, 692-694, 713-722, 725-729, 732, 735, 738-739, 742-743, 746-748, 751, 766, 785-799, 802, 805, 808, 829-831, 841-848, 851, 854, 857, 880-882, 885-906, 910, 914, 918, 921, 924-932, 935 /usr/lib/python3/dist-packages/oslo_i18n/__init__.py 4 0 100% /usr/lib/python3/dist-packages/oslo_i18n/_factory.py 75 32 57% 83, 99-121, 136-152, 169, 182, 185, 190, 195, 200, 205 /usr/lib/python3/dist-packages/oslo_i18n/_gettextutils.py 41 20 51% 48-50, 61-101 /usr/lib/python3/dist-packages/oslo_i18n/_lazy.py 4 1 75% 38 /usr/lib/python3/dist-packages/oslo_i18n/_locale.py 2 0 100% /usr/lib/python3/dist-packages/oslo_i18n/_message.py 95 71 25% 59-69, 94-104, 115-132, 137-179, 194-215, 221-227, 238-251, 254-259, 262-264, 267 /usr/lib/python3/dist-packages/oslo_i18n/_translate.py 17 13 24% 39-49, 67-73 /usr/lib/python3/dist-packages/oslo_log/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_serialization/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_serialization/jsonutils.py 82 52 37% 85-181, 217, 235-236, 248, 260, 268-270 /usr/lib/python3/dist-packages/oslo_utils/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_utils/_i18n.py 4 0 100% /usr/lib/python3/dist-packages/oslo_utils/encodeutils.py 60 53 12% 38-63, 84-104, 114-119, 135-188 /usr/lib/python3/dist-packages/oslo_utils/importutils.py 40 24 40% 29-34, 44, 60-65, 92-97, 117-122 /usr/lib/python3/dist-packages/oslo_utils/reflection.py 107 83 22% 44-47, 55-58, 63, 78-96, 107-111, 121-153, 158-163, 168-186, 191, 196, 208-214, 219-220 /usr/lib/python3/dist-packages/oslo_utils/strutils.py 183 135 26% 127, 146-165, 177-178, 214-247, 265-272, 333-359, 416-441, 453-456, 470-488, 503-520, 545-569, 579-586 /usr/lib/python3/dist-packages/oslo_utils/timeutils.py 230 155 33% 35, 56-64, 71-74, 95-97, 102, 109, 120-125, 135-140, 151-165, 177-180, 182, 201, 217, 226-231, 240, 249, 258-267, 283-297, 306-307, 318-319, 333-334, 339, 344, 347-349, 379-396, 421-428, 435-441, 446, 450-459, 465-468, 473, 477-486, 490-491, 495-498, 508-516, 520-525, 529, 533, 537-541, 546-553 /usr/lib/python3/dist-packages/paramiko/__init__.py 34 0 100% /usr/lib/python3/dist-packages/paramiko/_version.py 2 0 100% /usr/lib/python3/dist-packages/paramiko/agent.py 223 152 32% 64-72, 75-78, 81-85, 88-96, 105-107, 110-126, 129-150, 153-155, 165, 173-180, 189-190, 193, 210-213, 216, 222-241, 248-252, 263-269, 272, 275-279, 286-290, 299, 302, 328-331, 334, 337, 340-341, 363-368, 370-375, 379, 385, 396-399, 402, 405, 408, 411-419 /usr/lib/python3/dist-packages/paramiko/auth_handler.py 521 373 28% 105-108, 111-118, 132-140, 146-155, 158-167, 170-177, 180-181, 192-198, 201-207, 219-220, 233-236, 239-240, 249, 254-270, 282-284, 290-291, 300-397, 402, 408-428, 432-441, 444-615, 630-632, 634-641, 654-656, 660-678, 681-694, 699-706, 737, 752-753, 756-757, 761, 765, 769, 773, 776, 779-802, 805-826, 829-830, 833-834, 847 /usr/lib/python3/dist-packages/paramiko/ber.py 84 66 21% 34-35, 38, 41, 44, 47, 50-93, 97-104, 108-114, 117-129, 135-138 /usr/lib/python3/dist-packages/paramiko/buffered_pipe.py 93 45 52% 55-59, 64, 77-90, 99-106, 118-124, 155, 162-164, 171-174, 188-196, 208, 218-222 /usr/lib/python3/dist-packages/paramiko/channel.py 597 342 43% 71, 139-142, 148-161, 190-203, 223-230, 275-283, 300-309, 330-335, 355-362, 377, 402-404, 419-425, 474-492, 509-516, 522, 532, 538, 549, 572-584, 612, 632-635, 645, 654-671, 683, 700-701, 706-710, 727, 748-749, 754-758, 775-781, 798-801, 821-825, 845-848, 866-869, 933-944, 959, 967, 979, 997, 1032-1039, 1042-1047, 1050-1060, 1067, 1082-1155, 1157-1163, 1173, 1192-1209, 1223-1226, 1238, 1254, 1267-1274, 1281-1292, 1305-1333, 1358, 1364-1365, 1379-1380 /usr/lib/python3/dist-packages/paramiko/client.py 275 127 54% 100-108, 126-127, 142-148, 161, 170, 191, 215-216, 345-348, 352-360, 368, 385, 387, 389, 394, 401-404, 419-423, 426, 430-433, 459-466, 510, 513, 545-548, 556, 566, 580-581, 596-597, 632, 637-641, 648-653, 656-670, 674-687, 694-707, 727, 730, 745, 749-765, 789, 801, 817-823, 835 /usr/lib/python3/dist-packages/paramiko/common.py 93 4 96% 186-187, 207-211 /usr/lib/python3/dist-packages/paramiko/compress.py 12 4 67% 29, 32, 37, 40 /usr/lib/python3/dist-packages/paramiko/config.py 130 107 18% 50, 58-94, 126-147, 154-157, 160-166, 180-239, 245-248, 257-259, 262-303, 342, 358-361, 372 /usr/lib/python3/dist-packages/paramiko/dsskey.py 115 86 25% 55-81, 84-90, 93, 96, 99, 102, 105, 108-130, 133-159, 162-172, 180-190, 207-219, 224-225, 228-229, 234-247 /usr/lib/python3/dist-packages/paramiko/ecdsakey.py 158 107 32% 77, 80-82, 85-87, 90-92, 119-168, 172, 175-192, 195, 198, 207, 210, 213, 216-223, 226-239, 242, 250, 266-273, 278-279, 282-283, 286-296, 299-302, 305-308 /usr/lib/python3/dist-packages/paramiko/ed25519key.py 122 36 70% 42-47, 68, 79-80, 86, 102, 113-123, 126, 132, 139-154, 160, 165, 184, 198-202, 208, 221, 225-226 /usr/lib/python3/dist-packages/paramiko/file.py 250 156 38% 76-78, 98-111, 125-128, 138, 148, 158, 168-170, 189-229, 255, 257, 268-273, 277-283, 289, 292-293, 298-334, 352-355, 376, 386, 397-422, 433-435, 442, 446, 456, 463, 474, 492, 494-496, 506-508, 512-516, 522-529, 536-545 /usr/lib/python3/dist-packages/paramiko/hostkeys.py 200 138 31% 27, 63, 75-77, 95-110, 125-129, 144-146, 149-150, 153, 156-160, 163-166, 169-180, 183, 191-192, 195, 203-211, 223-229, 235, 238-239, 242, 248, 251-258, 262-273, 277-282, 285-288, 301-310, 315-317, 343-374, 382-388, 391 /usr/lib/python3/dist-packages/paramiko/kex_curve25519.py 82 29 65% 31-32, 39, 47-48, 62, 65, 70-100 /usr/lib/python3/dist-packages/paramiko/kex_ecdh_nist.py 87 59 32% 26-30, 33-47, 50-54, 59-63, 66-106, 109-137 /usr/lib/python3/dist-packages/paramiko/kex_gex.py 181 153 15% 61-68, 71-91, 94-105, 111-125, 128-163, 168-190, 193-210, 213-251, 254-282 /usr/lib/python3/dist-packages/paramiko/kex_group1.py 85 62 27% 51-54, 57-69, 72-77, 89-96, 100-121, 125-154 /usr/lib/python3/dist-packages/paramiko/kex_group14.py 11 0 100% /usr/lib/python3/dist-packages/paramiko/kex_group16.py 9 0 100% /usr/lib/python3/dist-packages/paramiko/kex_gss.py 345 294 15% 84-89, 95-110, 124-135, 148-153, 162-166, 175-189, 199-234, 243-288, 303-307, 342-351, 357-370, 379-394, 400-414, 423-458, 466-486, 499-551, 562-566, 574-588, 596-638, 651-655, 674, 677, 680 /usr/lib/python3/dist-packages/paramiko/message.py 104 24 77% 60, 86-89, 111, 123, 138-142, 156, 164, 241-246, 254-255, 291, 293, 295 /usr/lib/python3/dist-packages/paramiko/packet.py 372 118 68% 59-62, 132, 173-174, 207, 210, 213-214, 217, 220, 223, 226, 242-244, 247, 272, 300-302, 306, 310, 315-328, 331, 333, 344-359, 361-363, 371, 374, 377, 405, 410, 413-417, 421, 437, 451-457, 471-484, 489, 494, 503, 515, 527, 532, 540, 546, 558-566, 573-580, 586, 588, 597-603, 613-616, 624, 626-637, 655, 660 /usr/lib/python3/dist-packages/paramiko/pipe.py 84 60 29% 34-38, 43-46, 49-52, 55, 58-61, 64-67, 70-71, 81-93, 96-99, 102, 105-108, 111-114, 117-118, 123-125, 128-130, 133-135, 144-148 /usr/lib/python3/dist-packages/paramiko/pkey.py 183 97 47% 82, 90, 93, 107-111, 114, 124, 133, 140, 161, 171, 183, 227-228, 242, 255, 287, 289, 297-298, 307-308, 313-338, 362-367, 370-375, 402, 415-426, 447-457, 487-489, 496-498, 505-525, 535-536, 539-542, 546, 549 /usr/lib/python3/dist-packages/paramiko/primes.py 69 58 16% 32-49, 60-61, 64-107, 113-122, 125-148 /usr/lib/python3/dist-packages/paramiko/proxy.py 51 33 35% 53-59, 68-76, 86-109, 112, 116, 121, 124 /usr/lib/python3/dist-packages/paramiko/py3compat.py 101 59 42% 32-102, 124, 132-133, 148-151, 154, 162, 165 /usr/lib/python3/dist-packages/paramiko/rsakey.py 89 34 62% 52-53, 57-67, 73, 80, 94-99, 102, 110, 113, 126-139, 142, 150, 167-170, 179-180, 187-188 /usr/lib/python3/dist-packages/paramiko/server.py 98 60 39% 88, 105, 124, 149, 181, 206, 237, 265-267, 297-299, 310-311, 332, 343, 373, 398, 414, 433, 457-463, 483, 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/usr/lib/python3/dist-packages/simplejson/scanner.py 64 53 17% 9-10, 21-83 /usr/lib/python3/dist-packages/six.py 491 216 56% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 308, 488, 496, 501-507, 519-525, 530-532, 538-540, 545, 550, 554-568, 583, 592, 600-616, 631, 645-647, 653-673, 679, 683, 687, 691, 698-721, 737-738, 743-795, 797-804, 814-834, 853-855, 871, 873, 893-898, 913, 915, 933, 937, 948-955, 976-977 /usr/lib/python3/dist-packages/stevedore/__init__.py 9 0 100% /usr/lib/python3/dist-packages/stevedore/driver.py 29 17 41% 51-53, 66, 100-105, 108-118, 139-141, 147-148 /usr/lib/python3/dist-packages/stevedore/enabled.py 13 7 46% 64-65, 77-84 /usr/lib/python3/dist-packages/stevedore/exception.py 3 0 100% /usr/lib/python3/dist-packages/stevedore/extension.py 104 71 32% 46-49, 58, 99-107, 141-146, 150-152, 155-156, 160-165, 176-179, 183, 187-214, 220-230, 237, 259-265, 269, 290, 294-301, 309, 317, 326, 331 /usr/lib/python3/dist-packages/stevedore/hook.py 11 6 45% 59, 74-78, 87-89 /usr/lib/python3/dist-packages/stevedore/named.py 34 24 29% 74-89, 123-129, 134-140, 143-146, 154-156 /usr/lib/python3/dist-packages/swiftclient/__init__.py 7 2 71% 31-32 /usr/lib/python3/dist-packages/swiftclient/client.py 959 551 43% 53-63, 71-72, 75-76, 89, 128-135, 146-156, 164-190, 195, 197-208, 214-217, 220-221, 232, 242, 257, 275-276, 279, 282, 285-288, 291, 294, 320-328, 331-368, 411, 417, 421-424, 427-430, 441, 459, 490, 495, 524-526, 536-567, 573-574, 594-600, 604, 611-612, 615, 639-646, 658-659, 694-700, 702, 715, 720, 725-727, 738, 743, 796, 798, 801, 803-813, 817, 819, 821, 823, 825, 836, 838, 857-875, 896-921, 953, 955-970, 975, 977, 981, 983, 985, 987, 990, 992, 1004, 1007, 1027-1048, 1068-1092, 1111-1133, 1155-1180, 1213, 1216, 1221, 1228, 1231-1234, 1236, 1262-1285, 1332, 1339, 1341, 1347, 1349, 1351, 1355, 1357, 1362, 1364, 1367, 1375, 1379-1387, 1397, 1420-1439, 1465-1503, 1529-1557, 1568-1578, 1656, 1660, 1677-1679, 1694-1702, 1722-1727, 1741-1742, 1748, 1753-1791, 1795, 1812, 1818, 1836, 1842, 1848, 1855, 1873, 1886, 1892, 1903-1905, 1914, 1920, 1927, 1933-1944, 1947-1950 /usr/lib/python3/dist-packages/swiftclient/exceptions.py 51 45 12% 25-36, 40-43, 48-81 /usr/lib/python3/dist-packages/swiftclient/utils.py 229 158 31% 41, 51-68, 100-197, 202-204, 220-239, 248-253, 264, 290, 304-305, 310, 332-339, 342, 345, 348, 351-363, 367-369, 373-378, 382-387, 391-392, 396-397, 401-405, 410-416, 419, 424-427 /usr/lib/python3/dist-packages/swiftclient/version.py 6 3 50% 24-28 /usr/lib/python3/dist-packages/urllib3/__init__.py 33 8 76% 56-62, 86 /usr/lib/python3/dist-packages/urllib3/_collections.py 187 87 53% 5-6, 9-16, 70, 73, 76-80, 83-84, 87, 102-103, 145, 149, 152-153, 160, 166-170, 175, 178-179, 184, 198-206, 209-212, 228, 236, 243-244, 249-250, 256, 261-268, 275-286, 297, 300-305, 308-310, 321-323, 334-354 /usr/lib/python3/dist-packages/urllib3/connection.py 173 40 77% 17-21, 27-30, 153, 163-171, 178-184, 187-188, 194, 215, 221, 224, 226, 298-301, 319-327, 331, 335, 364, 378, 391, 411-420, 428 /usr/lib/python3/dist-packages/urllib3/connectionpool.py 318 127 60% 76, 83, 86, 89-91, 97, 215, 221-236, 254-263, 267-273, 294-303, 313, 318, 325, 330-348, 378-381, 401, 405, 418-425, 443-444, 454, 461, 479-493, 602, 605, 612, 618, 637-638, 663, 697-726, 734-735, 741, 745-748, 765-777, 782-804, 823-838, 954-955, 970, 977-978, 1004, 1035-1040, 1057 /usr/lib/python3/dist-packages/urllib3/contrib/__init__.py 0 0 100% /usr/lib/python3/dist-packages/urllib3/contrib/_appengine_environ.py 11 1 91% 36 /usr/lib/python3/dist-packages/urllib3/contrib/pyopenssl.py 248 246 1% 47-498 /usr/lib/python3/dist-packages/urllib3/contrib/socks.py 75 66 12% 55-210 /usr/lib/python3/dist-packages/urllib3/exceptions.py 96 21 78% 21-22, 26, 33-34, 38, 79-83, 90-92, 147-150, 222, 225, 241-242, 249-250 /usr/lib/python3/dist-packages/urllib3/fields.py 90 29 68% 18-20, 38-61, 83, 114, 155, 177-181, 221, 242-243 /usr/lib/python3/dist-packages/urllib3/filepost.py 43 6 86% 34, 57-60, 85, 88 /usr/lib/python3/dist-packages/urllib3/packages/__init__.py 8 2 75% 10-11 /usr/lib/python3/dist-packages/urllib3/packages/ssl_match_hostname/__init__.py 11 6 45% 7, 10-16 /usr/lib/python3/dist-packages/urllib3/poolmanager.py 172 80 53% 96, 102, 170, 173-175, 189, 199-200, 225, 299-306, 318-372, 411-431, 434-439, 448-456, 460-469, 473 /usr/lib/python3/dist-packages/urllib3/request.py 39 27 31% 54, 70-79, 88-97, 144-171 /usr/lib/python3/dist-packages/urllib3/response.py 399 219 45% 34-36, 39, 42-61, 73-74, 77, 80-98, 103-118, 131, 134, 137-139, 143-152, 190, 217, 236, 251, 258, 268-271, 283-287, 291, 294, 302, 315-322, 332, 337-338, 341, 347-348, 352, 365, 367-373, 377, 388-391, 397, 406-410, 427-443, 455-462, 495, 518-529, 539, 560-561, 585, 603, 610, 615, 618, 621, 626, 628, 631-634, 637-642, 648-653, 657, 661-666, 675, 680-689, 692-711, 727-781, 789-792, 795-809 /usr/lib/python3/dist-packages/urllib3/util/__init__.py 10 0 100% /usr/lib/python3/dist-packages/urllib3/util/connection.py 66 18 73% 19, 21, 25-26, 53, 73, 77-86, 91, 118, 130-131 /usr/lib/python3/dist-packages/urllib3/util/queue.py 14 1 93% 7 /usr/lib/python3/dist-packages/urllib3/util/request.py 50 22 56% 13, 63, 65, 71, 74, 77, 80, 85, 96, 98-103, 119-133 /usr/lib/python3/dist-packages/urllib3/util/response.py 35 17 51% 19-35, 55, 71, 83-86 /usr/lib/python3/dist-packages/urllib3/util/retry.py 150 95 37% 186-187, 202-218, 223-232, 240-249, 253-265, 270-275, 278-283, 286-289, 300-305, 311, 317, 339, 350-355, 376-442, 445 /usr/lib/python3/dist-packages/urllib3/util/ssl_.py 148 78 47% 31-34, 43-44, 50-56, 61-63, 104-149, 162-174, 193, 198, 201, 211-217, 332, 337-348, 354, 357-360, 372-383, 395, 401-407 /usr/lib/python3/dist-packages/urllib3/util/timeout.py 63 25 60% 102, 121, 126-153, 192, 204-208, 223-226, 252-254, 256 /usr/lib/python3/dist-packages/urllib3/util/url.py 205 82 60% 102, 112, 117-122, 127-129, 150-169, 172, 193-207, 239, 252, 258-259, 264, 269, 277, 282-294, 299, 304-314, 354, 358, 372, 374-376, 379-381, 391-394, 401-404, 411, 431-432 /usr/lib/python3/dist-packages/urllib3/util/wait.py 76 37 51% 8-9, 48-68, 72-87, 92, 97, 111, 121-122, 135-138, 153 /usr/lib/python3/dist-packages/yaml/__init__.py 184 118 36% 15-16, 31-37, 45, 62-67, 73-78, 85-89, 96-101, 123-132, 142, 152, 162, 172, 182, 192, 201-213, 224-243, 250, 262-283, 290, 298, 306, 316-322, 331-337, 345-350, 359-364, 373, 382, 391-397, 418, 425 /usr/lib/python3/dist-packages/yaml/composer.py 92 31 66% 18-22, 26-27, 40-41, 65-70, 74-75, 82, 96, 100-115, 126 /usr/lib/python3/dist-packages/yaml/constructor.py 479 295 38% 32, 38-39, 44-45, 52, 61, 69, 71-72, 74, 82-98, 102, 107-108, 114, 119, 125-129, 134, 141, 148-157, 175-177, 186-204, 208-209, 213, 221-222, 234-235, 242, 244, 246, 248, 250, 252, 254-261, 271-292, 295-307, 323-350, 356-373, 377-394, 397-400, 406-408, 417-424, 427, 487, 490-492, 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/usr/lib/python3/dist-packages/yaml/events.py 61 6 90% 9-13, 17-19 /usr/lib/python3/dist-packages/yaml/loader.py 47 24 49% 14-19, 34-39, 44-49, 58-63 /usr/lib/python3/dist-packages/yaml/nodes.py 29 7 76% 4-7, 9-23 /usr/lib/python3/dist-packages/yaml/parser.py 352 210 40% 101, 110-111, 157, 163, 167-180, 197-199, 209-215, 218-246, 268, 275-277, 283-291, 293-300, 302-310, 320-323, 333, 338-341, 343-346, 348-351, 357-369, 377-379, 382-398, 403-415, 434-435, 437-438, 453-458, 472-474, 477-500, 503-510, 513-524, 527-529, 538-540, 543-567, 570-581, 584-585, 588 /usr/lib/python3/dist-packages/yaml/reader.py 122 69 43% 27-31, 34-40, 76-85, 90-92, 96, 108-109, 119, 123-135, 141-143, 149-175, 178-185 /usr/lib/python3/dist-packages/yaml/representer.py 248 176 29% 19-24, 27-31, 34-63, 78-83, 86-101, 104-129, 132, 137-142, 145, 148, 151-155, 158-162, 165, 172-189, 199, 207, 210-213, 216-217, 220-221, 224-228, 231, 275-283, 286, 289-290, 293, 313-356, 360-364 /usr/lib/python3/dist-packages/yaml/resolver.py 135 78 42% 30, 33, 51-89, 94-112, 117-118, 122-141, 146, 153, 155-159, 163 /usr/lib/python3/dist-packages/yaml/scanner.py 753 487 35% 119, 129, 133, 138, 177, 181, 185, 195, 199, 203, 207, 211, 215, 219, 227, 231, 235, 239, 243, 251, 258, 290-293, 315-321, 341, 355, 393-400, 403, 406, 411-422, 425, 428, 433-445, 448, 451, 456-468, 473-482, 487-515, 520-543, 572-593, 604-610, 615-621, 626-632, 635, 638, 643-649, 655, 687-688, 693-696, 701-704, 709, 714-719, 725, 773, 779-780, 782-783, 789-804, 808-825, 829-842, 846-855, 859-865, 869-874, 878-883, 887-897, 908-933, 937-974, 979-1049, 1054-1090, 1094-1104, 1108-1119, 1123-1132, 1142, 1151-1152, 1197-1198, 1200-1201, 1203-1223, 1230-1250, 1254-1268, 1288, 1296, 1299, 1308, 1318, 1323-1343, 1345, 1352-1370, 1375-1395, 1399-1414, 1427-1431, 1433-1434 /usr/lib/python3/dist-packages/yaml/serializer.py 85 70 18% 17-25, 28-34, 37-41, 47-58, 61-72, 75-76, 79-110 /usr/lib/python3/dist-packages/yaml/tokens.py 76 17 78% 7-12, 20-23, 78-80, 85-87, 92-94 /usr/local/lib/python3.8/dist-packages/Cython/Shadow.py 292 147 50% 21-26, 29-35, 44-74, 92, 95, 101, 103, 133-135, 142-143, 149-152, 155-158, 164-169, 172, 175, 179, 182-188, 194-198, 201, 203, 215, 230, 232, 236, 239-241, 244-246, 249-254, 257, 262, 268-278, 281-284, 290-305, 308-313, 321-324, 327-331, 334-338, 347-348, 351, 360-377, 382, 458, 461-464, 467 /usr/local/lib/python3.8/dist-packages/Cython/__init__.py 6 2 67% 11-12 /usr/local/lib/python3.8/dist-packages/IPython/__init__.py 32 12 62% 31, 90-98, 125-126, 151-152 /usr/local/lib/python3.8/dist-packages/IPython/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/alias.py 107 66 38% 71-88, 101-108, 130-134, 138-160, 163, 166-186, 199-202, 206-209, 213, 218-221, 229-230, 235-236, 240, 243-246, 249-250, 254-258 /usr/local/lib/python3.8/dist-packages/IPython/core/application.py 251 170 32% 35-39, 58-63, 91-100, 122, 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/usr/local/lib/python3.8/dist-packages/IPython/core/excolors.py 22 4 82% 164, 174-178 /usr/local/lib/python3.8/dist-packages/IPython/core/extensions.py 63 41 35% 52-56, 60, 63, 72-90, 102-110, 120-130, 133-135, 138-140, 148 /usr/local/lib/python3.8/dist-packages/IPython/core/formatters.py 361 221 39% 45, 49-53, 58, 63, 71-87, 143-193, 198, 211-214, 223-236, 261, 269-271, 276, 334-348, 352-357, 364-368, 391-395, 413-429, 456-468, 498-507, 528-548, 556-564, 640-670, 675, 679-681, 685, 692-704, 829-845, 906-919, 941-947, 958-973, 1018-1020 /usr/local/lib/python3.8/dist-packages/IPython/core/getipython.py 5 1 80% 24 /usr/local/lib/python3.8/dist-packages/IPython/core/history.py 384 277 28% 13-17, 40, 43, 46, 49, 55-58, 65-70, 86-119, 125, 129, 132, 135, 191-198, 214-229, 242, 247-268, 273, 294-303, 330-331, 340-341, 362-369, 395-411, 441-448, 467-469, 488-491, 536-551, 558-559, 564-570, 574-579, 583-584, 590-599, 628-631, 636-651, 681-685, 707-739, 751-758, 761-763, 767-769, 775-802, 816-819, 824-836, 845-847, 868-899, 904-906 /usr/local/lib/python3.8/dist-packages/IPython/core/hooks.py 59 39 34% 66-82, 86, 97-100, 109-117, 120, 124-125, 132, 142, 155, 165, 170, 176-190 /usr/local/lib/python3.8/dist-packages/IPython/core/inputtransformer2.py 359 284 21% 27-32, 40-47, 74, 77-82, 92-99, 108-117, 124-129, 154-155, 182, 185-186, 199, 207, 215-221, 226-238, 247-260, 265-278, 302-312, 320-323, 330-333, 337-338, 342-343, 347-348, 352-353, 370-380, 385-405, 422-424, 430-437, 442-463, 478-503, 507-513, 525-534, 555-571, 574-579, 584-591, 611-714, 718-721 /usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py 1477 1139 23% 95-97, 116-117, 123-124, 139-163, 210-222, 238-248, 252, 260-262, 272-274, 295-298, 301-304, 320, 324, 328-331, 334-335, 388, 392-403, 455-456, 466-467, 475, 487, 501-505, 553-554, 609-611, 630-703, 707, 714, 720-723, 726, 733-737, 740-743, 747-771, 775, 781-784, 790-791, 795, 799-801, 804, 810-815, 824-825, 833-836, 841, 853-856, 861-863, 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3611-3660, 3680-3699, 3702, 3707 /usr/local/lib/python3.8/dist-packages/IPython/core/latex_symbols.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/logger.py 108 92 15% 34-51, 55-57, 60, 70-127, 132-152, 156-165, 182-185, 191-201, 210-215 /usr/local/lib/python3.8/dist-packages/IPython/core/macro.py 28 19 32% 25-36, 39, 42, 46, 49-53 /usr/local/lib/python3.8/dist-packages/IPython/core/magic.py 256 154 40% 55, 64-74, 142, 203, 220-250, 324, 334-339, 343, 351, 363-375, 399-411, 442-445, 469-475, 510-541, 545-546, 552-573, 610-654, 659-661, 675-683, 687-703 /usr/local/lib/python3.8/dist-packages/IPython/core/magic_arguments.py 102 16 84% 130, 135-136, 153, 164, 172, 190, 203-204, 206, 232, 262, 269, 272-274 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/magics/auto.py 38 27 29% 29-31, 51-60, 107-128 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/basic.py 249 188 24% 22-23, 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360-390 /usr/local/lib/python3.8/dist-packages/IPython/utils/module_paths.py 11 8 27% 61-70 /usr/local/lib/python3.8/dist-packages/IPython/utils/openpy.py 45 35 22% 24-40, 46-58, 75-79, 100-103 /usr/local/lib/python3.8/dist-packages/IPython/utils/path.py 191 148 23% 27, 30-53, 57, 67, 76-81, 87-90, 99-109, 146-162, 190-211, 220-230, 239-249, 254-256, 260-262, 266-268, 272-274, 278-280, 297-302, 307-311, 321-327, 341-351, 362-363, 375-382, 395-419, 429-436 /usr/local/lib/python3.8/dist-packages/IPython/utils/process.py 29 17 41% 15, 17, 47-50, 55-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/py3compat.py 108 72 33% 18-19, 22-23, 27-29, 32-34, 38-40, 45-59, 66-76, 93-140, 147, 156-158, 165-168, 179, 189 /usr/local/lib/python3.8/dist-packages/IPython/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/IPython/utils/strdispatch.py 33 23 30% 25-26, 31-33, 38-40, 44-52, 55, 58-61, 65-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/sysinfo.py 40 24 40% 54-65, 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127-128, 154, 158-165, 180, 193-207, 211-215, 221, 239, 241, 243, 245, 247, 249, 251, 258-267, 272, 281-286, 294-296, 300, 317, 337, 342-346, 351, 369-378, 392-398, 402, 408, 412, 419-422, 428, 432-445, 453, 468-472, 476-481, 485, 492, 496-509, 513-516, 530-532, 545-552, 555-564 /usr/local/lib/python3.8/dist-packages/absl/flags/_defines.py 120 65 46% 50-56, 107, 136-142, 169-181, 199-206, 289-292, 359, 378-380, 402-405, 436, 460-462, 487-489, 514-516, 542-544, 577, 598-622 /usr/local/lib/python3.8/dist-packages/absl/flags/_exceptions.py 31 13 58% 64-75, 91-99 /usr/local/lib/python3.8/dist-packages/absl/flags/_flag.py 201 104 48% 88, 106, 121, 124, 127-129, 132, 135, 139-141, 154, 162-167, 182-183, 187-189, 193, 197-208, 228, 247-280, 284, 299, 333-337, 345-349, 352-356, 377-378, 388-393, 396-406, 410-420, 424, 427-432, 444-448, 454-458, 462-466 /usr/local/lib/python3.8/dist-packages/absl/flags/_flagvalues.py 525 404 23% 135-136, 139, 169, 201-206, 217-227, 238-247, 260-263, 276-288, 303-314, 329-340, 349, 365-375, 383-393, 403-404, 410, 414, 416, 418, 420, 423-430, 438, 440, 446, 449, 459, 467, 471-491, 495-503, 506-510, 525-531, 554-561, 578-583, 587, 590, 593, 613-637, 640, 643, 647-649, 661, 684-796, 800, 809, 813-819, 823, 827, 840-857, 870-879, 883-886, 890-892, 903-905, 916-918, 926, 929-963, 976-980, 984-995, 1013-1018, 1041-1086, 1126-1168, 1183-1190, 1203-1204, 1219-1255, 1259, 1264 /usr/local/lib/python3.8/dist-packages/absl/flags/_helpers.py 164 117 29% 29-30, 34-35, 127, 132, 157-158, 174-186, 191-204, 210-233, 238-261, 284-318, 339-356, 371-398, 407-424, 428-431 /usr/local/lib/python3.8/dist-packages/absl/flags/_validators.py 94 61 35% 64-68, 80-82, 90, 93, 104, 128-129, 132, 135, 145, 171-172, 183, 186-190, 193, 222-223, 252-257, 286-288, 320-327, 355-360, 383-384, 405-420, 435-449, 462-463 /usr/local/lib/python3.8/dist-packages/absl/logging/__init__.py 406 221 46% 96, 169, 181-194, 208, 215-216, 220, 223, 266, 280-284, 298-303, 312, 317, 322, 326, 331-333, 338, 343, 348, 382-383, 408-414, 429-430, 473, 476, 499, 515-524, 529, 534, 539, 544, 552, 568-575, 596-620, 633-647, 656-668, 704-718, 722, 727, 744-772, 776-779, 783-790, 803-808, 827-850, 854-870, 883, 886, 889, 892, 895-896, 899-902, 906, 913, 916, 931-938, 989, 993-996, 998, 1002, 1006, 1010, 1014-1017, 1021, 1025, 1029, 1046-1047, 1064-1065, 1083-1086, 1099-1100, 1110, 1119-1121, 1133-1148, 1156 /usr/local/lib/python3.8/dist-packages/absl/logging/converter.py 62 32 48% 107-114, 129-135, 151, 153, 157, 169, 184-200, 215 /usr/local/lib/python3.8/dist-packages/absl/testing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/testing/_pretty_print_reporter.py 50 28 44% 31-32, 35-40, 43-44, 47-48, 51-52, 55-56, 59-60, 63-64, 67-68, 84-87, 91-93, 96 /usr/local/lib/python3.8/dist-packages/absl/testing/absltest.py 885 696 21% 52-54, 75-76, 82-88, 94-97, 131-135, 210-223, 270, 276-278, 300-303, 315, 321, 348-350, 363-371, 383, 391-417, 423, 428-429, 434-435, 449-452, 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/usr/local/lib/python3.8/dist-packages/absl/testing/xml_reporter.py 247 180 27% 64, 78-80, 92-99, 112-117, 127, 151-186, 189, 192, 206-223, 226-231, 239-243, 246-269, 272-304, 312-316, 324, 332, 346-353, 356-357, 361-373, 376-377, 380-398, 413-415, 439-447, 450-452, 455-456, 459-460, 463-465, 468-470, 473-474, 477-480, 483-488, 491-501, 504-506, 531-533, 548, 551-554 /usr/local/lib/python3.8/dist-packages/absl/third_party/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/case.py 187 177 5% 15-63, 70-235, 239-273 /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/result.py 16 9 44% 16-25 /usr/local/lib/python3.8/dist-packages/appdirs.py 257 211 18% 29-39, 77-97, 131-163, 195-203, 236-254, 291-300, 302-304, 310, 345-353, 388-404, 411-415, 419, 424, 429, 434, 439, 444, 449, 460-476, 480-503, 507-530, 533-556, 559-571, 577-608 /usr/local/lib/python3.8/dist-packages/astunparse/__init__.py 13 3 77% 18-20 /usr/local/lib/python3.8/dist-packages/astunparse/printer.py 37 28 24% 10-12, 16, 19, 23-51 /usr/local/lib/python3.8/dist-packages/astunparse/unparser.py 718 440 39% 77-78, 81-82, 85, 93-97, 100-101, 123-126, 129-139, 151, 154, 157-158, 161-165, 168-175, 178-189, 192-193, 196-197, 200-205, 208-213, 216-221, 232-243, 253-256, 258-261, 264-275, 278-290, 295-296, 298-302, 308-344, 350, 362-363, 369, 372, 375-386, 389-408, 411-420, 427-430, 439, 443, 446-459, 463-479, 483-486, 489-491, 494-495, 498-500, 503-518, 524, 527-529, 534, 541-547, 549, 552, 556-568, 576-580, 583-587, 590-594, 597-603, 606-615, 618-624, 627-630, 633-649, 663-677, 701-704, 712, 729-738, 748-749, 753, 759-766, 769, 775-776, 793, 797-810, 814-820, 824-835, 857, 866-875, 880-895, 898-903, 906 /usr/local/lib/python3.8/dist-packages/backcall/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/backcall/backcall.py 66 48 27% 12-13, 18-25, 40, 44, 51-106 /usr/local/lib/python3.8/dist-packages/boto/__init__.py 281 191 32% 75, 88-97, 102-111, 125-126, 140-141, 155-156, 170-171, 185-186, 206-207, 223-224, 239-240, 254-255, 270-271, 286-287, 302-303, 317-318, 332-333, 351-352, 366-367, 381-382, 397-398, 414-415, 437-453, 470-471, 494-508, 530-545, 562-563, 577-578, 599-607, 626-627, 643-644, 660-661, 683-684, 702-703, 720-721, 737-738, 747-748, 767-768, 788-789, 811-812, 834-835, 856-857, 878-879, 901-902, 924-925, 947-948, 970-971, 993-994, 1016-1017, 1039-1040, 1062-1063, 1078-1079, 1094-1095, 1143-1197, 1209-1214 /usr/local/lib/python3.8/dist-packages/boto/auth.py 584 467 20% 49-51, 102-105, 108-115, 118-121, 124-128, 132-134, 137-140, 143-144, 155, 158, 167-169, 172-173, 176-193, 203-205, 208-209, 212-221, 232-233, 236-247, 258-259, 266-271, 280-282, 290-298, 309-322, 334-340, 343-350, 357-367, 370-374, 377-383, 388-395, 404-414, 417-419, 422-430, 433-441, 444-451, 454-459, 462, 465-479, 482-487, 490-504, 512-516, 519-525, 536-582, 592-595, 598-601, 606-611, 617-622, 625-629, 636-645, 658-690, 696, 703-738, 741-744, 747-753, 764-806, 825, 828-834, 837-845, 856-874, 885-896, 908-910, 913-924, 935-955, 967-982, 1007-1033, 1037-1054, 1059-1098 /usr/local/lib/python3.8/dist-packages/boto/auth_handler.py 10 2 80% 52, 60 /usr/local/lib/python3.8/dist-packages/boto/cacerts/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/compat.py 47 26 45% 28-29, 35-36, 45-47, 68-102 /usr/local/lib/python3.8/dist-packages/boto/connection.py 605 493 19% 79-80, 84-85, 123, 131, 138, 146-158, 173-181, 189-190, 197-199, 234-238, 243-246, 249, 255, 264-269, 276-280, 290-300, 341-358, 361, 367-384, 390-391, 402-413, 474-572, 575, 578, 581, 587, 591, 594, 598, 602, 608, 614, 622-642, 645-662, 665-698, 701-705, 708-721, 724-778, 781, 784-851, 854-855, 858-859, 863-874, 877-881, 884, 897-1033, 1037-1059, 1066-1070, 1077-1078, 1091, 1103, 1106, 1109-1116, 1119-1122, 1158-1162, 1168-1186, 1190-1208, 1211-1227 /usr/local/lib/python3.8/dist-packages/boto/endpoints.py 79 57 28% 44-50, 55-56, 61-78, 81-82, 87-92, 100-103, 118-121, 126, 130, 149, 163-166, 177, 186, 197, 210-222, 227-232, 237-239 /usr/local/lib/python3.8/dist-packages/boto/exception.py 287 166 42% 42-43, 46, 49, 79-135, 138-142, 145-148, 151, 155, 159, 162-170, 173-176, 181-185, 188, 191-196, 204-205, 208-211, 254-256, 259, 262-267, 270-272, 280-281, 284, 287, 295-296, 299, 303-306, 310-312, 334-340, 343-347, 350-353, 356-359, 376-383, 403-405, 408, 411-416, 458-459, 466-467, 474-475, 482-483, 490-491, 532-534, 537, 549-551, 554, 565-566, 574-575, 578, 592-593 /usr/local/lib/python3.8/dist-packages/boto/gs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/gs/acl.py 187 133 29% 58-59, 63, 67-75, 80-82, 87-88, 91-93, 96-97, 100-107, 110-115, 118-126, 132-135, 138-141, 144-149, 152-155, 158-164, 172-175, 178, 181-205, 208-216, 219-223, 243-250, 254-264, 267-271, 274-284, 287-308 /usr/local/lib/python3.8/dist-packages/boto/gs/user.py 26 20 23% 25-29, 32, 35, 38-43, 46-54 /usr/local/lib/python3.8/dist-packages/boto/handler.py 29 19 34% 30-32, 35-38, 41-46, 49, 54-57, 60 /usr/local/lib/python3.8/dist-packages/boto/https_connection.py 49 34 31% 41-44, 47, 59-62, 75-83, 105-114, 118-135 /usr/local/lib/python3.8/dist-packages/boto/jsonresponse.py 108 89 18% 30-32, 35-41, 44-47, 50, 53-55, 64-74, 77-86, 89-91, 94-109, 112-119, 127-132, 135-137, 140-155, 158-168 /usr/local/lib/python3.8/dist-packages/boto/plugin.py 38 22 42% 53-56, 60-66, 70-78, 86, 91-93 /usr/local/lib/python3.8/dist-packages/boto/provider.py 247 178 28% 183-214, 217-219, 222, 227-229, 232, 237-239, 242, 247-263, 267-378, 383-402, 413-434, 437-441, 444-465, 468-473, 476, 479, 484 /usr/local/lib/python3.8/dist-packages/boto/pyami/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/pyami/config.py 158 108 32% 42, 47-49, 59, 61, 65-69, 78, 88-93, 96-103, 111-121, 124, 127, 130-134, 137-141, 144-148, 151, 166-169, 172-180, 183-186, 189-191, 194-202, 205-217, 220-235 /usr/local/lib/python3.8/dist-packages/boto/regioninfo.py 89 66 26% 44, 56-57, 77-82, 100-115, 121-134, 161-182, 208-220, 226-229, 235-239, 247-249, 259-262, 265, 268, 271-276, 289-290 /usr/local/lib/python3.8/dist-packages/boto/resultset.py 111 98 12% 47-62, 65-76, 79-82, 85-134, 140-142, 145-148, 151, 154, 157-160, 163-176 /usr/local/lib/python3.8/dist-packages/boto/s3/__init__.py 17 11 35% 43-44, 54-55, 63-74 /usr/local/lib/python3.8/dist-packages/boto/s3/acl.py 111 89 20% 34-36, 39-51, 54-64, 67-72, 75-82, 88-89, 92, 95-97, 100-101, 104-108, 111-114, 117-121, 130-135, 138-140, 143-156, 159-171 /usr/local/lib/python3.8/dist-packages/boto/s3/bucket.py 700 576 18% 71, 95-97, 100, 103, 106, 109, 112-117, 131, 143, 175-194, 197-231, 282, 328, 363, 369-390, 394-411, 424-426, 469-472, 521-522, 535, 606-609, 623-625, 630, 662-730, 757-759, 766-788, 847-889, 894-908, 912-922, 926-936, 940-944, 948-963, 989-1002, 1028-1040, 1043-1046, 1072-1080, 1110-1119, 1123-1124, 1134-1146, 1162-1171, 1193-1197, 1206-1207, 1216-1227, 1235-1240, 1243-1249, 1253-1260, 1288-1308, 1323-1339, 1350-1366, 1377-1389, 1396-1404, 1438-1441, 1448, 1454-1461, 1483, 1489-1493, 1522-1526, 1530-1538, 1544-1551, 1560-1563, 1570-1576, 1586-1594, 1598-1606, 1618-1632, 1644, 1651-1658, 1669-1673, 1679-1687, 1736-1767, 1775-1806, 1815-1822, 1826, 1829-1835, 1838-1845, 1849-1864, 1867, 1870-1878 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlistresultset.py 67 54 19% 29-41, 54-59, 62, 73-86, 99-105, 108, 121-134, 147-151, 154 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlogging.py 50 41 18% 28-33, 36-47, 50, 53-57, 60-65, 69-83 /usr/local/lib/python3.8/dist-packages/boto/s3/connection.py 282 212 25% 58-62, 67-69, 76, 79-82, 85-88, 91-95, 98-99, 106, 113, 119, 122-126, 132-135, 176-199, 205-208, 211-212, 215, 226, 232-237, 298-355, 361-380, 386-438, 443-453, 468-469, 508-511, 528-555, 577-581, 606-627, 644-647, 653-667 /usr/local/lib/python3.8/dist-packages/boto/s3/cors.py 80 69 14% 65-78, 81, 84, 87-100, 103-117, 126-130, 133, 140-144, 194-210 /usr/local/lib/python3.8/dist-packages/boto/s3/deletemarker.py 28 23 18% 26-31, 34-38, 41-55 /usr/local/lib/python3.8/dist-packages/boto/s3/key.py 690 590 14% 106-135, 138-147, 150, 154-157, 160, 163, 168-169, 172-175, 180-184, 187-192, 197-207, 210, 219-223, 226-231, 234-242, 246-259, 262-273, 280, 304-334, 348, 352-361, 381-385, 397-402, 408-415, 441-451, 501-507, 515-519, 522-542, 551, 557, 561, 566-573, 576, 580-581, 584-585, 588-589, 592-593, 596, 605-610, 624-635, 639, 695-713, 760, 767-966, 969-1021, 1036-1045, 1108-1132, 1214-1311, 1374-1375, 1437-1444, 1493, 1503-1575, 1602, 1658-1664, 1722-1739, 1795-1804, 1829-1831, 1853-1856, 1859-1867, 1875-1889, 1893-1912, 1929-1935 /usr/local/lib/python3.8/dist-packages/boto/s3/keyfile.py 74 53 28% 35-44, 47-49, 52-85, 88-89, 92-94, 97, 102, 107, 110, 113, 116, 119, 122, 125, 128, 131, 134 /usr/local/lib/python3.8/dist-packages/boto/s3/lifecycle.py 148 113 24% 48-65, 68, 71-76, 79-86, 89-99, 111-112, 115, 118-121, 124-128, 131-137, 152-154, 157-161, 164-171, 178-182, 185, 188-203, 210-213, 230-231, 234-236, 242, 246, 250, 259-263, 266, 273-278, 310-311 /usr/local/lib/python3.8/dist-packages/boto/s3/multidelete.py 64 48 25% 41-44, 47-50, 53, 56-66, 82-85, 88-92, 95, 98-107, 121-123, 126-134, 137 /usr/local/lib/python3.8/dist-packages/boto/s3/multipart.py 160 133 17% 46-52, 55, 59, 62-71, 86-90, 93-96, 99, 102-111, 118-125, 134-146, 149, 152, 155-162, 165-175, 178-200, 211-226, 253-261, 290-301, 317-318, 330 /usr/local/lib/python3.8/dist-packages/boto/s3/prefix.py 16 10 38% 24-25, 28, 31-34, 38-41 /usr/local/lib/python3.8/dist-packages/boto/s3/tagging.py 52 34 35% 7-8, 11, 14-17, 20, 24, 29-33, 36, 39-40, 43-47, 54-58, 61, 64-68, 71 /usr/local/lib/python3.8/dist-packages/boto/s3/user.py 23 18 22% 24-28, 31, 34-39, 42-49 /usr/local/lib/python3.8/dist-packages/boto/s3/website.py 122 85 30% 24-26, 57-63, 66-72, 75, 78-89, 94-98, 101, 104-106, 109-114, 131-133, 136, 152-153, 156-159, 162, 165, 168-171, 191-192, 195-198, 201, 204-209, 213, 218-223, 245-247, 250, 283-288, 291 /usr/local/lib/python3.8/dist-packages/boto/storage_uri.py 488 377 23% 57, 62, 66, 69-70, 77-78, 82-83, 87-89, 103-149, 152, 158-160, 165-172, 176-177, 180-184, 187-191, 194-196, 199-203, 209-218, 224-227, 231-234, 237-240, 288-299, 302-318, 321, 328-332, 335-344, 348-355, 365-366, 379-390, 402-413, 417-421, 425-429, 433-438, 441-443, 446-453, 457-464, 468-470, 475-491, 496-504, 508-515, 518-520, 524, 528, 536, 540, 544, 548, 552, 556, 560, 564, 568-576, 579-581, 584-585, 588-591, 596-605, 610-619, 624-625, 630-631, 636-640, 646-649, 653-655, 661-675, 680-696, 700-706, 713-724, 733-735, 738-740, 743-745, 749-754, 757-759, 762-764, 767-769, 773, 779-787, 791-795, 800-802, 805-811, 816-822, 826-833, 838-840, 845-849, 875-880, 889, 893, 897, 901, 905, 909-911, 915, 919, 923, 927, 932, 937, 944 /usr/local/lib/python3.8/dist-packages/boto/utils.py 575 467 19% 108-111, 119-169, 173-183, 187-202, 212-237, 241, 246-266, 269-270, 273-337, 340-343, 346-347, 350-351, 354-355, 358-359, 383, 399-405, 413-427, 432-441, 454-460, 464-466, 470-481, 485-498, 505-508, 518-543, 549-554, 557-576, 579, 582, 588, 616-619, 629-646, 689-691, 694, 697-700, 703, 706-709, 712, 715-717, 720-728, 731, 734-741, 744-750, 753-765, 780-782, 785-787, 790, 793-797, 800-803, 808-859, 863-872, 876-881, 897-899, 920-944, 959-972, 1000, 1004-1029, 1038, 1048-1049, 1060, 1070-1083, 1093-1098 /usr/local/lib/python3.8/dist-packages/boto/vendored/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/exceptions.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/regions.py 81 60 26% 58, 65, 85, 94-96, 99-102, 106-116, 120-124, 128-152, 156-160, 163-177, 180-182, 186 /usr/local/lib/python3.8/dist-packages/boto/vendored/six.py 444 208 53% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /usr/local/lib/python3.8/dist-packages/cachetools/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/cachetools/abc.py 25 14 44% 21-24, 29-36, 39-43 /usr/local/lib/python3.8/dist-packages/cachetools/cache.py 60 38 37% 6, 9, 12, 21-27, 30, 38-41, 44-57, 60-62, 65, 68, 71, 74, 79, 84, 89 /usr/local/lib/python3.8/dist-packages/cachetools/decorators.py 73 69 5% 11-44, 52-88 /usr/local/lib/python3.8/dist-packages/cachetools/keys.py 24 14 42% 17-20, 23, 26, 29, 40-43, 49-52 /usr/local/lib/python3.8/dist-packages/cachetools/lfu.py 22 14 36% 10-11, 14-16, 19-20, 23-24, 28-33 /usr/local/lib/python3.8/dist-packages/cachetools/lru.py 27 18 33% 10-11, 14-16, 19-20, 23-24, 28-33, 36-39 /usr/local/lib/python3.8/dist-packages/cachetools/rr.py 19 11 42% 8, 15-20, 25, 29-34 /usr/local/lib/python3.8/dist-packages/cachetools/ttl.py 156 118 24% 12-13, 16, 19-22, 28-29, 32-35, 38-43, 46, 49, 52, 59-64, 67-72, 75-84, 87-99, 102-106, 109-116, 119-126, 129-136, 139-141, 145-147, 152, 157, 161-172, 175-177, 180-181, 184-185, 188-189, 196-203, 206-208 /usr/local/lib/python3.8/dist-packages/cycler.py 177 107 40% 73, 110, 118, 122, 127, 131, 157-178, 185-189, 218-223, 229, 240-243, 255-262, 265, 268-273, 284-292, 303-311, 317-322, 325-333, 337-347, 396-397, 425, 454-465, 509, 513-516, 519-526, 548-556 /usr/local/lib/python3.8/dist-packages/cython.py 10 6 40% 9-17 /usr/local/lib/python3.8/dist-packages/decorator.py 270 120 56% 49-60, 64-67, 70-73, 100, 114-115, 117, 119-120, 133, 139, 143, 155-156, 168, 174, 186-189, 217, 233-235, 241, 246, 271, 279, 282-283, 291, 299-300, 306, 314-316, 318, 321, 329, 339-348, 357-454 /usr/local/lib/python3.8/dist-packages/defusedxml/ElementTree.py 64 25 61% 21-23, 52, 79-105, 108, 113, 120 /usr/local/lib/python3.8/dist-packages/defusedxml/__init__.py 21 15 29% 25-51 /usr/local/lib/python3.8/dist-packages/defusedxml/common.py 65 42 35% 23, 31-34, 37-38, 46-52, 55-56, 64-68, 71-72, 81-90, 98-105, 115-122, 125-132 /usr/local/lib/python3.8/dist-packages/gast/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/gast/ast3.py 192 145 24% 10-160, 174, 189-194, 200-207, 214-232, 239, 260-264, 270-306, 310-376, 391 /usr/local/lib/python3.8/dist-packages/gast/astn.py 24 1 96% 21 /usr/local/lib/python3.8/dist-packages/gast/gast.py 78 46 41% 9-11, 292, 302-304, 308-316, 327-331, 342-366, 375-380 /usr/local/lib/python3.8/dist-packages/h5py/__init__.py 57 23 60% 27-32, 37, 89-90, 97-113 /usr/local/lib/python3.8/dist-packages/h5py/_hl/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/h5py/_hl/attrs.py 133 92 31% 63, 76-78, 90, 100, 105, 122-221, 232-247, 253, 257-275, 284-286 /usr/local/lib/python3.8/dist-packages/h5py/_hl/base.py 216 108 50% 23-24, 40-45, 53-63, 88, 112, 126-128, 131, 134, 139-141, 144, 155-162, 185, 188-193, 197-199, 204-207, 219-221, 227, 237-239, 251, 265, 281, 285-287, 291, 294-295, 308, 312, 326, 340-344, 347-349, 359-363, 366-368, 381-404, 409, 413, 417, 421, 447, 450-452, 455 /usr/local/lib/python3.8/dist-packages/h5py/_hl/compat.py 70 44 37% 12-41, 51, 56-57, 67-72, 80-85, 94-95, 99-100, 113-115, 127-134 /usr/local/lib/python3.8/dist-packages/h5py/_hl/dataset.py 474 340 28% 47-54, 68-172, 183-205, 216-217, 221, 225, 229-244, 260, 263-267, 272-274, 280, 291, 297-299, 311-313, 319-322, 328-331, 337, 343, 349, 358-361, 369-376, 383-385, 391-393, 400, 423-438, 447-450, 458-462, 470-474, 488-582, 592-708, 720, 724, 730, 743-758, 770, 777-790, 800, 811, 817, 821-824, 841 /usr/local/lib/python3.8/dist-packages/h5py/_hl/datatype.py 24 11 54% 37, 43-45, 49-56 /usr/local/lib/python3.8/dist-packages/h5py/_hl/files.py 238 113 53% 46-48, 52, 78, 89, 95, 103-107, 115, 117, 119, 125-127, 130-137, 143-146, 158-167, 172, 174-206, 210-215, 239, 245-253, 259, 266-268, 274-275, 281-282, 287-298, 304, 310-314, 379, 382-383, 386-393, 400-402, 411, 418, 438-439, 451-452, 465-478 /usr/local/lib/python3.8/dist-packages/h5py/_hl/filters.py 175 141 19% 78-84, 88-101, 113-244, 263-283, 302-342 /usr/local/lib/python3.8/dist-packages/h5py/_hl/group.py 234 168 28% 41, 62-69, 132-140, 158-174, 189-206, 223-238, 247-253, 260-262, 271-274, 301-342, 368-393, 399, 404, 409-410, 415, 455-494, 504-507, 530-534, 560-565, 569-579, 603, 606, 609, 622, 627, 630-631, 634 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections.py 295 221 25% 58-95, 111, 117-118, 150-151, 171, 176, 180-182, 185, 197-207, 211-218, 222, 226, 230, 252-269, 279-283, 293, 299, 304, 315-320, 337, 340-341, 345-414, 417-419, 424-442, 453-475, 482-488, 495-508, 519-598 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections2.py 43 33 23% 26-45, 57-74, 83-91, 94-95, 102-105 /usr/local/lib/python3.8/dist-packages/h5py/_hl/vds.py 52 33 37% 56-90, 94, 97-99, 120-123, 126-127 /usr/local/lib/python3.8/dist-packages/h5py/h5py_warnings.py 19 6 68% 16-17, 34-36, 39 /usr/local/lib/python3.8/dist-packages/h5py/version.py 21 3 86% 32, 34, 36 /usr/local/lib/python3.8/dist-packages/imagecodecs/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/imagecodecs/imagecodecs.py 300 226 25% 330, 350-351, 356, 371, 375-383, 387, 390, 395, 401-402, 408-479, 484, 489-510, 515-603, 608-641, 646-666, 675, 684, 689, 698, 703-706, 711-722, 727-734, 745-788, 796-802, 814 /usr/local/lib/python3.8/dist-packages/imageio/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/fetching.py 105 88 16% 62-114, 150-184, 210-225, 230-232, 237-247 /usr/local/lib/python3.8/dist-packages/imageio/core/findlib.py 74 65 12% 23-29, 37-81, 99-160 /usr/local/lib/python3.8/dist-packages/imageio/core/format.py 276 178 36% 94, 103, 108, 111, 115, 118, 126, 142, 149, 155, 164-170, 179-185, 192, 199, 216-221, 228, 235, 238-239, 242-244, 247-248, 256-261, 267, 272-275, 289, 300, 331, 343-349, 359, 367-370, 386-392, 401-414, 419, 422-425, 437, 447, 458, 484-502, 517-521, 527, 531, 551, 557, 560-565, 569-609, 633, 635, 655, 657, 659-665, 678-697, 705-725, 735 /usr/local/lib/python3.8/dist-packages/imageio/core/functions.py 157 124 21% 100-121, 139-142, 172-186, 213-231, 259-267, 291-308, 354-374, 397-427, 453-455, 479-496, 542-561, 586-615 /usr/local/lib/python3.8/dist-packages/imageio/core/request.py 319 274 14% 19-20, 89-128, 133-262, 271, 279, 289, 295, 310-350, 359-372, 381-421, 427-428, 435-437, 440-467, 476-482, 492-496, 501-530, 533, 539-561, 564-565, 568, 571 /usr/local/lib/python3.8/dist-packages/imageio/core/util.py 263 210 20% 30-34, 38-42, 55-108, 122-134, 139-143, 149, 155-158, 164-169, 180-185, 205-211, 214-225, 228-230, 252-257, 265-273, 281, 290-309, 316, 324-328, 335-339, 346-350, 355, 358, 361, 364, 376-383, 387-395, 398-400, 404-408, 423-466, 479-491, 506-520, 531-542, 548-555 /usr/local/lib/python3.8/dist-packages/imageio/plugins/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/imageio/plugins/_freeimage.py 603 405 33% 63-67, 74-87, 422, 429-432, 439-447, 450-454, 461-480, 485-514, 518-521, 526-527, 530-531, 537, 544-549, 555-557, 564, 573-603, 609, 615, 620-625, 628, 631-640, 645-652, 660-723, 728-797, 807-839, 842-856, 876-895, 941-975, 980-1031, 1041-1083, 1089-1110, 1113-1163, 1170-1187, 1220-1245, 1271-1295, 1298-1299, 1305-1321, 1326-1328 /usr/local/lib/python3.8/dist-packages/imageio/plugins/bsdf.py 140 113 19% 14-52, 62-63, 66-73, 76, 126-130, 133-135, 143-180, 185, 188-194, 198-231, 240-249, 256-257, 261-289 /usr/local/lib/python3.8/dist-packages/imageio/plugins/dicom.py 147 122 17% 31-33, 41-56, 89-100, 105, 111-155, 159-161, 165-168, 171-199, 202-231, 239-266 /usr/local/lib/python3.8/dist-packages/imageio/plugins/example.py 43 23 47% 54-56, 67-69, 82-84, 89, 93, 97-106, 111, 124, 129, 133, 138 /usr/local/lib/python3.8/dist-packages/imageio/plugins/feisem.py 41 32 22% 27, 38-41, 55-84 /usr/local/lib/python3.8/dist-packages/imageio/plugins/ffmpeg.py 304 258 15% 27, 30-31, 57-66, 178-189, 192-194, 204-239, 257-327, 339-344, 357-358, 361, 370-386, 389, 394-468, 472-474, 478-502, 523-527, 530-532, 537-572, 575, 582-617, 629-636, 639-641, 646-649, 653-661, 667-698 /usr/local/lib/python3.8/dist-packages/imageio/plugins/fits.py 40 24 40% 15-23, 80, 84, 90-102, 105, 108, 112-116, 120 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimage.py 177 122 31% 48, 52-59, 63-70, 76, 79-80, 83, 86-88, 91-93, 99-102, 106-109, 113-132, 135, 168-174, 177-178, 215-219, 225-240, 243-259, 303-309, 312-314, 323-340, 349-362, 365-368, 393-397, 485-510 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimagemulti.py 144 104 28% 27-32, 35, 38, 41-45, 48-55, 62-67, 71, 75-86, 90-94, 97, 137-140, 197-200, 203-205, 225-254, 263-299, 308-322 /usr/local/lib/python3.8/dist-packages/imageio/plugins/gdal.py 35 19 46% 15-23, 40-43, 46, 52-54, 57, 60, 63-65, 68 /usr/local/lib/python3.8/dist-packages/imageio/plugins/grab.py 63 37 41% 25, 28-40, 44, 47, 50, 66-70, 73-79, 95-99, 102-111 /usr/local/lib/python3.8/dist-packages/imageio/plugins/lytro.py 304 225 26% 63, 69, 74, 78, 83, 101-103, 109-136, 142-143, 148, 152, 157-171, 177-195, 212-214, 220-280, 285, 289, 295-302, 308-312, 319-325, 348-371, 375-383, 388-391, 410-412, 418-435, 441-442, 447, 451, 456-470, 476-494, 511-513, 519-559, 564, 568, 574-582, 588-592, 599-607, 630-653, 657-665, 670-673 /usr/local/lib/python3.8/dist-packages/imageio/plugins/npz.py 37 17 54% 40, 44, 51-55, 58, 61, 65-69, 73, 81, 85, 88, 91 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillow.py 365 285 22% 76, 79-99, 102-108, 111-115, 119-146, 149-150, 153-155, 159, 162-165, 168-181, 184-186, 190-197, 200, 203-218, 221, 298, 301-315, 323-357, 360-364, 429, 433-440, 443-455, 461-478, 486-496, 499-503, 564, 568-575, 578-590, 596-613, 621-639, 642-648, 653-655, 667-680, 684-695, 705-792, 797-835 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillow_info.py 5 1 80% 100 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillowmulti.py 164 134 18% 60, 74-101, 106, 109-118, 138-151, 156-177, 181-186, 189, 193-223, 228-232, 245-262, 268-279, 297-305, 317-337, 347-364 /usr/local/lib/python3.8/dist-packages/imageio/plugins/simpleitk.py 62 38 39% 15-37, 95-98, 101-104, 110-112, 115, 118, 122-127, 130-131, 136-137, 140, 143-144, 147-148 /usr/local/lib/python3.8/dist-packages/imageio/plugins/spe.py 126 96 24% 255, 260, 264-295, 298-303, 307, 310-370, 373-399, 402-405, 408-411, 414-438, 456-465 /usr/local/lib/python3.8/dist-packages/imageio/plugins/swf.py 179 147 18% 25-27, 68-71, 74-76, 82-138, 141, 144, 147-150, 154-179, 188-215, 218, 224-239, 242-267, 270-288, 292-298, 302-320, 323 /usr/local/lib/python3.8/dist-packages/imageio/plugins/tifffile.py 91 65 29% 19-23, 208, 212, 218-229, 232-234, 237-240, 243-256, 259-276, 281-297, 300, 303-311, 314-322 /usr/local/lib/python3.8/dist-packages/ipykernel/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipykernel/_version.py 11 5 55% 7-11, 13 /usr/local/lib/python3.8/dist-packages/ipykernel/comm/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipykernel/comm/comm.py 86 52 40% 21-22, 28, 40, 51-59, 63-66, 76, 82-99, 103-118, 122, 135, 144, 150-152, 156-163 /usr/local/lib/python3.8/dist-packages/ipykernel/comm/manager.py 72 52 28% 37-40, 44, 48-51, 56, 66-72, 77-99, 104-113, 117-129 /usr/local/lib/python3.8/dist-packages/ipykernel/connect.py 58 41 29% 31-37, 58-81, 91-103, 128-137, 163-178 /usr/local/lib/python3.8/dist-packages/ipykernel/jsonutil.py 81 59 27% 73-106, 133-197 /usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py 441 316 28% 20-22, 61-63, 80, 163-171, 176-209, 216-223, 228-282, 287, 291, 295-331, 343-349, 358-365, 377-383, 393, 397-408, 412-441, 450, 459, 466, 482-483, 495, 505, 514, 520-569, 575, 579-585, 591, 599-611, 616, 620-627, 633, 636-643, 647, 657-661, 664-679, 683-693, 699, 703-710, 715, 723-742, 747, 755-767, 771-773, 778, 786-788, 794-798, 805-807, 811-817, 825, 835-843, 856-860, 868-904, 909-912 /usr/local/lib/python3.8/dist-packages/ipython_genutils/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/ipython_genutils/_version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipython_genutils/encoding.py 23 7 70% 30, 53-58, 64-68 /usr/local/lib/python3.8/dist-packages/ipython_genutils/importstring.py 12 10 17% 27-39 /usr/local/lib/python3.8/dist-packages/ipython_genutils/path.py 70 55 21% 55-71, 90-95, 100-101, 110-117, 130-154, 165-172 /usr/local/lib/python3.8/dist-packages/ipython_genutils/py3compat.py 196 141 28% 16-17, 20-21, 25-27, 30-32, 36-40, 45-57, 64-79, 96-143, 158-167, 175, 183-185, 195-198, 203-204, 212, 220, 224-293, 298-307 /usr/local/lib/python3.8/dist-packages/ipython_genutils/text.py 68 51 25% 19, 49-60, 74-87, 101-113, 125, 134-135, 140-146, 155-158, 215-217, 238-243 /usr/local/lib/python3.8/dist-packages/ipywidgets/__init__.py 21 7 67% 31-33, 38-40, 50 /usr/local/lib/python3.8/dist-packages/ipywidgets/_version.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/docutils.py 6 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/domwidget.py 27 14 48% 26-28, 36-38, 41-50 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/interaction.py 289 226 22% 11-12, 17-18, 33-34, 51-62, 73-85, 90-93, 99-128, 132-152, 177-232, 245-268, 272, 278-290, 295-307, 312-343, 348-359, 364-380, 388-393, 444, 507-538, 555-556, 570, 576 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/trait_types.py 78 33 58% 45-52, 84-87, 100-103, 125-128, 137-140, 162-165, 168, 195-206, 220 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/util.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/valuewidget.py 12 5 58% 20, 24-27 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget.py 403 283 30% 13-15, 32-39, 42-49, 59, 65-71, 80-116, 129-131, 141-162, 173-174, 183-195, 208-211, 216-223, 243-256, 259-265, 281-283, 302-303, 312, 317-318, 323-339, 349-354, 357-369, 372, 398, 411-415, 419, 427-438, 443-448, 455, 467-471, 481-489, 506-523, 526, 529-533, 540-545, 557, 571, 586, 590-594, 600-606, 609, 624-628, 633-642, 646-662, 668-689, 693, 697, 702, 707, 712-732, 736-737, 740-755, 758-763 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_bool.py 31 3 90% 22-24 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_box.py 37 5 86% 63-65, 68-69 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_button.py 41 12 71% 61-63, 68-73, 86, 94, 104-105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_color.py 14 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_controller.py 29 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_core.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_date.py 13 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_description.py 23 6 74% 28-34 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_float.py 168 62 63% 24-26, 36-39, 44-49, 54-59, 70-73, 78-83, 88-93, 262, 266, 270, 274, 278-281, 290-294, 298-308, 312-315 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_int.py 185 67 64% 42-44, 53-61, 73-75, 85-93, 98-101, 106-111, 116-121, 202, 206, 210, 214, 218-221, 243-247, 251-261, 265-268 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_layout.py 61 7 89% 82-85, 93-96 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_link.py 36 15 58% 24-34, 50-52, 56, 75, 105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_media.py 102 46 55% 44-51, 71-75, 86-88, 92-96, 101-111, 116-133, 159, 163, 166, 194, 197, 223, 226 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_output.py 64 33 48% 76-77, 97-105, 109-113, 117-127, 131-132, 136, 142, 146, 157-159 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selection.py 295 148 50% 11-12, 119-132, 136-139, 175-191, 196-200, 205-218, 222-225, 230-235, 239-243, 247-254, 258-260, 264-271, 274-280, 316-327, 331-335, 340-344, 349-352, 357-363, 368-371, 375-377, 381-383, 387-389, 392-395, 529-531, 535-540, 544-547, 553-555, 559-565, 615-619, 624-629 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selectioncontainer.py 36 14 61% 27-30, 44-46, 57-61, 65-68 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_string.py 68 15 78% 30-32, 79-81, 91-92, 106-108, 120-123 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_style.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_templates.py 198 138 30% 80-85, 89-91, 95-96, 100-106, 157-158, 162-167, 172, 176-242, 247, 281-289, 293-295, 300-312, 315-330, 333-345, 349-355, 397-398, 403-450, 454 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_upload.py 39 11 72% 24-26, 58-64, 68 /usr/local/lib/python3.8/dist-packages/jedi/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jedi/_compatibility.py 339 252 26% 18-19, 34-45, 52-53, 56, 59, 63-88, 92-111, 115-150, 158-162, 169-205, 223-224, 230-233, 246-248, 266, 273-275, 285, 311-312, 319, 324-325, 329-330, 334-335, 339-340, 350-352, 360-370, 376, 387-396, 404-410, 414-426, 432-440, 445-451, 456-466, 483-530, 536-582, 591-624 /usr/local/lib/python3.8/dist-packages/jedi/api/__init__.py 341 241 29% 58-62, 127-203, 209-248, 251, 254, 275, 278-283, 286-291, 310-311, 314-319, 322-342, 345-350, 370-371, 375-408, 422, 425, 429-431, 454, 471-480, 483-488, 502-514, 517-522, 541-558, 569-600, 603-634, 648-649, 657, 661-671, 683, 686-687, 715, 719-727, 765, 769-777, 800-801, 830-845, 849-855, 863-869, 884-886, 898-901 /usr/local/lib/python3.8/dist-packages/jedi/api/classes.py 297 193 35% 37, 47-49, 53, 81-86, 93, 98-104, 115, 175-187, 202, 208-211, 216-219, 224-227, 260-270, 273, 276, 311-329, 355-368, 374-377, 384-387, 402-403, 406-411, 416-427, 447-448, 451-466, 472-489, 497-522, 525, 542-552, 555-561, 570, 582, 594, 604-613, 616-624, 647-649, 664, 670-675, 678-684, 687-693, 696-697, 709-716, 719, 728, 732-738, 747-748, 758-761, 764, 770, 773, 782-783, 793, 803, 812-814, 824, 836, 839, 853, 861, 870, 879-883 /usr/local/lib/python3.8/dist-packages/jedi/api/completion.py 348 296 15% 30, 35-40, 44-68, 72-73, 80-81, 85-89, 96, 102-114, 117-151, 174-282, 285-295, 298-317, 320-323, 326-337, 340-355, 358-360, 363-365, 371-389, 401-421, 424-431, 442-448, 455-499, 503-516, 539-577, 583-619 /usr/local/lib/python3.8/dist-packages/jedi/api/completion_cache.py 19 11 42% 5-9, 14-19 /usr/local/lib/python3.8/dist-packages/jedi/api/environment.py 219 166 24% 36-37, 41-45, 49, 65-67, 70-107, 110-111, 114, 129, 134-136, 145, 148, 157-169, 173-177, 190-198, 202-238, 242-252, 257-264, 284-321, 334-338, 351-363, 374-377, 385-393, 398-420, 424-425, 432-454, 458-473, 477-480 /usr/local/lib/python3.8/dist-packages/jedi/api/errors.py 19 7 63% 8, 16, 21, 26, 31, 36, 39 /usr/local/lib/python3.8/dist-packages/jedi/api/exceptions.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/api/file_name.py 115 103 10% 17-54, 64-81, 85-99, 103-156 /usr/local/lib/python3.8/dist-packages/jedi/api/helpers.py 319 272 15% 26, 30-35, 39-42, 47, 51-61, 66-71, 77, 83-117, 124-156, 163-179, 183-201, 206-209, 213, 217, 220-268, 272-336, 343-357, 361-371, 375-421, 427-439, 449-465, 474-493, 497-500 /usr/local/lib/python3.8/dist-packages/jedi/api/interpreter.py 23 12 48% 12, 19, 24-25, 28, 34-41 /usr/local/lib/python3.8/dist-packages/jedi/api/keywords.py 34 25 26% 8-15, 22, 30-57 /usr/local/lib/python3.8/dist-packages/jedi/api/project.py 214 164 23% 40-51, 56-61, 65, 78, 82, 92-98, 106-118, 139-151, 156-161, 169-205, 208-213, 236, 248, 251, 256-343, 346, 350-353, 358-362, 375-411, 415 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/__init__.py 133 110 17% 19-23, 26-36, 39, 42-48, 51, 56-58, 64-73, 89, 92-98, 104-108, 112-118, 122-138, 142-218, 225 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/extract.py 239 210 12% 20-29, 36-41, 49-93, 100-126, 130, 134-138, 147-149, 153, 160-164, 172-200, 204, 209-292, 296-306, 310-316, 320-337, 341-353, 357-363, 371-379, 383-386 /usr/local/lib/python3.8/dist-packages/jedi/api/strings.py 64 47 27% 27-50, 54-58, 68-77, 81-86, 90-93, 97-98, 102-109 /usr/local/lib/python3.8/dist-packages/jedi/cache.py 65 40 38% 32-43, 60-73, 84-93, 105-113 /usr/local/lib/python3.8/dist-packages/jedi/common/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jedi/common/utils.py 24 18 25% 6-13, 21-26, 31-36 /usr/local/lib/python3.8/dist-packages/jedi/common/value.py 54 31 43% 3-4, 7-11, 48, 55, 59-61, 68-74, 77, 80, 83-84, 87, 90, 93, 96, 99-104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/jedi/debug.py 80 50 38% 22, 36-56, 74-75, 81-82, 89-97, 103-109, 113-120, 124-127, 136-140 /usr/local/lib/python3.8/dist-packages/jedi/file_io.py 54 30 44% 8, 11, 14, 17, 20, 23, 28, 31, 34, 37, 40-57, 62, 68-69, 72-75 /usr/local/lib/python3.8/dist-packages/jedi/inference/__init__.py 107 76 29% 86-108, 111, 117-121, 126-130, 135-136, 139-140, 144, 147-179, 183-194, 197 /usr/local/lib/python3.8/dist-packages/jedi/inference/analysis.py 126 101 20% 32-37, 41, 45, 50-51, 54, 58, 61, 65, 68, 71, 81-91, 98-109, 116-127, 138-217 /usr/local/lib/python3.8/dist-packages/jedi/inference/arguments.py 228 156 32% 19-31, 53-68, 76-108, 135, 138, 148-172, 180-183, 188, 191-231, 234-239, 242-247, 250, 253-279, 284, 287-288, 291, 296, 300, 304, 308, 311, 314, 317, 321-333, 337-349 /usr/local/lib/python3.8/dist-packages/jedi/inference/base_value.py 285 179 37% 27-34, 39, 42, 45-47, 50, 53, 56, 62-70, 73-77, 84-98, 101-104, 107-120, 123-126, 130-132, 136, 152, 155-163, 166, 169-176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 210, 213-218, 221-223, 226-227, 230-231, 234-235, 241, 244-245, 249, 253, 256, 260, 263, 266, 274, 283-289, 294, 297-298, 305-306, 309, 312, 317, 320, 325-326, 329, 334-335, 338, 341, 344, 349-371, 376, 379-382, 387, 390, 393, 396, 399, 402-410, 413, 416, 419-436, 440-448, 456 /usr/local/lib/python3.8/dist-packages/jedi/inference/cache.py 72 44 39% 25-45, 80, 90-121 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/__init__.py 38 26 32% 8-16, 25-26, 29-32, 35-37, 40, 48-53, 57, 63-68 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/access.py 327 243 26% 82-98, 109-112, 117, 121-140, 145, 151, 154, 158-159, 167-175, 180-181, 184, 187, 190, 193, 196-199, 202, 205-219, 222, 225-227, 230-234, 237-250, 253, 256, 259-265, 270-285, 288, 291, 294, 297, 300, 303-313, 316, 319-323, 327-351, 354-396, 399-401, 404, 407-409, 412-421, 424-425, 428-456, 459-461, 467-482, 485, 488-490, 493, 507-516, 519-538, 541, 548-552, 557-564 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/getattr_static.py 97 81 16% 16-21, 25-31, 35-39, 43-54, 58-65, 73-127, 131, 135, 152-184 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/mixed.py 155 108 30% 47-49, 52, 58, 63-66, 69-72, 75-78, 81-83, 86, 96, 108-109, 113-117, 121-129, 134-135, 138, 146, 156-175, 179-249, 256-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/subprocess/__init__.py 236 176 25% 20-21, 37-38, 45-50, 54, 58-70, 75-77, 80-87, 90, 93, 103, 108-110, 113-129, 132-147, 150-151, 160-162, 165-166, 176-206, 211-219, 222, 225-226, 229-273, 283, 288-292, 295-308, 311-327, 330-357, 362-364, 367, 370-374, 377, 380, 383-388, 396-398, 402 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/subprocess/functions.py 66 45 32% 15, 19, 23-24, 28, 35-43, 47, 54, 61-66, 74-78, 82, 86, 91-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/value.py 394 269 32% 36-41, 46-47, 50-69, 73, 77, 84, 90, 93, 96, 99, 102, 105, 108, 111, 114, 118-133, 136-137, 140, 144-148, 152, 155, 159, 162-170, 173-178, 188-198, 201, 205-208, 211-228, 231-236, 239-245, 248-267, 270, 273, 276, 280, 283, 289-291, 298, 301-304, 307, 312-315, 318, 323-326, 329-330, 333-336, 339-343, 346-350, 354-358, 362, 365, 370-371, 375, 378-383, 386, 389-397, 402-404, 407, 410-413, 416, 421-423, 433-434, 437, 442-444, 447-448, 460-478, 482-485, 488-507, 510, 517, 537-587, 591-600, 606, 611-618, 624-629 /usr/local/lib/python3.8/dist-packages/jedi/inference/context.py 294 199 32% 20-21, 25, 28-34, 41-86, 89-106, 109-112, 115, 118, 121, 124, 127, 130, 133, 137, 140, 144, 147, 150, 154-159, 167-168, 172, 176, 179, 182, 185, 188, 191, 194, 197, 200, 204, 207, 210, 213, 216, 221-222, 225-248, 251-287, 290-297, 302, 312, 315-327, 330, 334, 338, 346, 351, 354, 358, 361, 366, 369, 378-380, 383, 386, 389, 392, 397, 404, 408, 411, 418-432, 483-500 /usr/local/lib/python3.8/dist-packages/jedi/inference/docstrings.py 144 116 19% 52-56, 61-75, 82-100, 108-133, 154-161, 179-183, 187-235, 244-245, 256-268, 273-290, 296-307 /usr/local/lib/python3.8/dist-packages/jedi/inference/filters.py 215 115 47% 26-28, 32, 36, 43, 46, 49, 52, 56-68, 75-78, 81, 86, 89, 98, 110-115, 118-120, 123-127, 130-141, 146-151, 154, 158-166, 171-172, 175, 180, 185-189, 193-195, 198, 206, 209-214, 217-223, 226, 229-230, 235, 238, 241, 244, 252-254, 258, 270-278, 281-291, 296-299, 307, 331-334, 340 /usr/local/lib/python3.8/dist-packages/jedi/inference/flow_analysis.py 84 65 23% 15-20, 23-26, 29, 39-42, 46-83, 87-110, 114-123 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/annotation.py 228 193 15% 34-45, 49-59, 63-76, 88-107, 112-132, 139-182, 186-195, 204-230, 247-272, 276-282, 295-304, 308-313, 351-370, 374, 378-381, 385, 389-408, 414-425, 429-433, 437-443 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/base.py 217 149 31% 18-20, 23-32, 35, 38, 53-54, 57-63, 68, 73-81, 86, 89, 93, 96-117, 122-137, 147, 162-163, 166, 169-178, 181, 184-185, 188, 192-193, 196, 199-201, 205-242, 247-248, 252-261, 264-279, 284-293, 296, 310-314, 318, 322-332, 338, 342, 345, 348, 353-355, 359, 362, 365, 370-373, 376, 379 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/conversion.py 138 122 12% 11-47, 51-59, 64-92, 96-98, 109-142, 146-153, 158-167, 175-208 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/generics.py 67 38 43% 15-23, 28-32, 35, 40-41, 45, 48, 53-63, 70-71, 74-78, 81, 86, 89, 92, 95, 98, 101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/stub_value.py 69 43 38% 13-14, 17, 25-34, 37, 43-50, 53, 60, 65-70, 73, 78-81, 88-101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/type_var.py 84 65 23% 9-18, 27-48, 53-71, 74, 77, 80-85, 89, 93, 98-105, 108, 111-114, 117 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typeshed.py 158 130 18% 23-26, 33-53, 57-69, 81-89, 95-124, 130-143, 154-229, 233-249, 258-264, 271, 281-295 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typing.py 236 154 35% 40, 43-96, 105-130, 137, 140, 151, 159, 174, 179, 183, 189-228, 237-240, 244, 247, 250, 253-266, 269, 278-286, 293, 296-303, 306-310, 313-316, 321-323, 327-355, 368-369, 376, 381-386, 397-399, 402, 406-407, 413, 426-429, 433, 436-442, 445-449, 455-457 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/utils.py 17 14 18% 11-30 /usr/local/lib/python3.8/dist-packages/jedi/inference/helpers.py 122 97 20% 17-21, 29-43, 65-109, 113-121, 125-129, 133, 137-139, 143, 147, 151, 160-163, 167-192, 196, 200-207 /usr/local/lib/python3.8/dist-packages/jedi/inference/imports.py 284 237 17% 39, 42-43, 46, 53-70, 75-96, 100-118, 122-125, 134-152, 169-224, 229, 235-238, 247-260, 269-273, 284-326, 331-356, 365-427, 432-440, 450-464, 473-510, 514-519, 523-548, 558-563 /usr/local/lib/python3.8/dist-packages/jedi/inference/lazy_value.py 37 18 51% 7-9, 12, 15, 21, 27, 32, 35, 40-44, 47-48, 52-55, 61 /usr/local/lib/python3.8/dist-packages/jedi/inference/names.py 450 314 30% 16-23, 38, 44, 47-54, 58, 61, 64, 67-69, 73, 76, 80, 87, 99-101, 104, 109-110, 113-126, 129-132, 135-139, 142-211, 214-215, 219, 223, 228, 231-237, 240, 243-245, 248-251, 255, 260-261, 264, 278-279, 287-290, 307-331, 334-346, 351-354, 357-360, 363-368, 371, 378, 381, 392, 396-401, 409-416, 419-424, 427, 432-434, 437, 441, 444-450, 453-456, 460, 463-488, 491-496, 502, 506-525, 530-531, 534-538, 541-543, 548, 551, 554, 562-563, 566-574, 578-584, 588-590, 593, 597, 600, 609, 612, 615, 620-634, 640-644, 651-652, 656 /usr/local/lib/python3.8/dist-packages/jedi/inference/param.py 130 113 13% 14-18, 23-26, 29, 32-47, 50, 73-225, 246, 250-257 /usr/local/lib/python3.8/dist-packages/jedi/inference/parser_cache.py 4 1 75% 6 /usr/local/lib/python3.8/dist-packages/jedi/inference/recursion.py 67 47 30% 55, 64-75, 81-90, 100-105, 108-109, 112-153 /usr/local/lib/python3.8/dist-packages/jedi/inference/references.py 182 157 14% 29-42, 46, 53-69, 73-77, 81-96, 100-113, 117-160, 164-175, 179-193, 197-218, 222-224, 228-245, 257-270, 274-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/signature.py 108 72 33% 9-34, 39-40, 44, 48, 51-54, 57, 60, 63-65, 70-71, 74, 80-82, 86-89, 93-97, 100-117, 122-124, 128, 132-134, 137, 146, 149 /usr/local/lib/python3.8/dist-packages/jedi/inference/syntax_tree.py 545 485 11% 45-63, 69-137, 144-150, 155, 161-237, 241-261, 270-352, 357-366, 385-444, 448-469, 477-487, 493-502, 506-518, 526-538, 542, 546, 550, 554-567, 571-642, 647-730, 741-782, 789-809, 814, 821-846 /usr/local/lib/python3.8/dist-packages/jedi/inference/sys_path.py 148 125 16% 18-29, 43-72, 79-97, 105-135, 139-147, 151-170, 174-177, 189-207, 211-215, 230-271 /usr/local/lib/python3.8/dist-packages/jedi/inference/utils.py 55 23 58% 13, 20, 26, 32, 74-78, 84-86, 89, 92, 96, 99-103, 112-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/value/decorator.py 7 3 57% 11-12, 15 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/dynamic_arrays.py 114 87 24% 36-40, 52-123, 128-130, 144-145, 148-149, 152-165, 168, 173-175, 178, 181-187, 192-194, 197, 202-204 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/function.py 305 223 27% 32-33, 37, 40, 45-54, 61-64, 67-71, 74, 79-81, 84, 87, 90-114, 117-118, 121-123, 126, 132-161, 164-165, 168, 171, 176-177, 180, 185-186, 189, 194-197, 201, 206, 211-248, 251-263, 268-312, 315, 321, 327-360, 365-366, 369, 377-378, 381, 391, 394, 401, 406-407, 410-421, 424, 427, 431-470 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/instance.py 353 227 36% 28-30, 33, 36, 41-42, 45-58, 63-64, 67, 74-81, 86-87, 94-97, 100, 103, 106, 109, 113, 117, 120-121, 125, 128, 134, 137, 144-146, 149-155, 159, 162, 168-172, 176, 179-199, 203-215, 223-239, 242-250, 253-275, 278-283, 291-297, 303-307, 314-322, 328-349, 352, 355-370, 373-393, 396, 406-414, 418-422, 427-428, 431, 434, 437-438, 446-448, 451, 455, 461-462, 465-466, 469-473, 476-480, 483, 489, 492, 497, 500, 508-510, 514, 517, 522-523, 527-529, 532, 535, 545-546, 549, 552, 555, 561, 569-575, 578-580, 583-592, 595-597, 605, 608, 613-614, 617-619 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/iterable.py 404 270 33% 27, 37-40, 49-52, 55, 58, 62, 68, 71, 75, 81-82, 85, 88, 91, 95-122, 133, 136-163, 168-170, 173-174, 177, 182, 190, 193, 196-203, 206, 210, 213-215, 220-224, 231-237, 251, 255, 262-267, 270-271, 274-280, 283, 286, 290-291, 295-306, 311, 321-329, 332-334, 338-343, 350-358, 362, 365-407, 414-417, 420, 427-429, 433-439, 447-453, 457-458, 462-470, 473, 479, 490-491, 494-499, 502, 505, 508, 523-524, 527-528, 531-548, 552, 558, 561, 564, 567, 572-574, 577-579, 582, 589-620, 625-630, 633-635, 642-658 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/klass.py 204 151 26% 59-61, 66-75, 81-87, 90, 100-105, 112-126, 130-131, 136, 139-144, 147, 151, 154, 158-187, 190-221, 227-229, 232, 235-237, 243-267, 275-289, 292-296, 300-309, 314-317, 329-330, 336-356, 360-361, 365-379 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/module.py 129 85 34% 22-24, 27-35, 45-56, 63-73, 76-77, 80, 83, 88, 92-98, 101-104, 111-128, 136, 144-156, 159-164, 167-169, 175-178, 181, 184-186, 194-219, 222, 225 /usr/local/lib/python3.8/dist-packages/jedi/parser_utils.py 194 159 18% 26-56, 60-69, 79, 83-94, 99-109, 113-124, 128-142, 158-175, 182-188, 196-219, 223-228, 235-244, 252-269, 280, 287-292, 299-311, 322-326 /usr/local/lib/python3.8/dist-packages/jedi/plugins/__init__.py 31 1 97% 21 /usr/local/lib/python3.8/dist-packages/jedi/plugins/flask.py 11 8 27% 7-20 /usr/local/lib/python3.8/dist-packages/jedi/plugins/pytest.py 99 71 28% 21-25, 31-41, 44-61, 67-77, 83-90, 95-98, 108-112, 117, 123-142, 147-153, 156-164 /usr/local/lib/python3.8/dist-packages/jedi/plugins/registry.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/plugins/stdlib.py 440 292 34% 106-132, 138-143, 157-173, 181-188, 194, 200-208, 213-217, 223-224, 227, 230-235, 238-241, 246-253, 258-259, 263, 268, 278-289, 294-328, 336, 341, 346-347, 350, 358-360, 363, 366, 371-372, 375-377, 382, 391-392, 395-397, 403, 408, 423-470, 475-478, 481-485, 488-502, 505-509, 514, 519, 524-526, 529-530, 535-537, 540-549, 553, 560, 568, 573, 581-586, 591-612, 617-618, 621, 626-628, 631, 634-637, 642-643, 647-661, 666, 675, 680-681, 685, 688, 693, 702-711, 717-734, 801-808, 814-817, 821, 824-825, 828-833, 838-842 /usr/local/lib/python3.8/dist-packages/jedi/settings.py 19 2 89% 72, 75 /usr/local/lib/python3.8/dist-packages/joblib/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/joblib/_compat.py 15 3 80% 11, 24-25 /usr/local/lib/python3.8/dist-packages/joblib/_memmapping_reducer.py 180 132 27% 28, 38-39, 70, 73-78, 81-95, 98, 107-119, 152-178, 183, 189-202, 213-236, 243-252, 281-286, 293-298, 301-361, 374-434 /usr/local/lib/python3.8/dist-packages/joblib/_memory_helpers.py 65 63 3% 5-105 /usr/local/lib/python3.8/dist-packages/joblib/_multiprocessing_helpers.py 34 11 68% 20-21, 34-37, 51-53, 61-64 /usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py 271 174 36% 38-39, 78-79, 92, 99, 123, 132-136, 153, 163-184, 188, 203-205, 209-212, 216-220, 230-238, 242-245, 249, 253, 258-260, 282-284, 288-344, 348-360, 367-368, 392-399, 407-409, 432-462, 467-489, 493-497, 509-519, 523-547, 551-555, 561-564, 567-574, 579-583, 590, 593, 604, 607-624, 631 /usr/local/lib/python3.8/dist-packages/joblib/_store_backends.py 196 137 30% 26-31, 152-174, 179-193, 198-200, 205-208, 212, 217-223, 227-238, 242-243, 247-249, 253-260, 264-270, 274, 278, 282-294, 298-322, 326-328, 332, 345-348, 352, 356-388, 395-415 /usr/local/lib/python3.8/dist-packages/joblib/backports.py 48 37 23% 22-30, 37-76, 80-81 /usr/local/lib/python3.8/dist-packages/joblib/compressor.py 315 209 34% 12-13, 17-18, 22-23, 27-28, 61, 65, 73, 78, 107-110, 115, 127, 130-131, 136-140, 145-152, 164, 168-172, 178-186, 202, 206-209, 220, 225-235, 239-243, 248-249, 289-321, 330-348, 353, 357-358, 362, 366-367, 371-372, 377-383, 386-388, 391-393, 396-401, 406-424, 430-440, 446-470, 478-485, 492-493, 502-511, 515-520, 537-562, 566-568 /usr/local/lib/python3.8/dist-packages/joblib/disk.py 59 42 29% 27-38, 44-52, 59-63, 90-101, 106-124 /usr/local/lib/python3.8/dist-packages/joblib/executor.py 32 21 34% 28-50, 58-59, 63-65, 68-69, 72-73 /usr/local/lib/python3.8/dist-packages/joblib/externals/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle.py 623 480 23% 65-66, 85, 88-95, 109-115, 119-124, 128-139, 151-164, 169-202, 209-227, 257-274, 334-339, 343-384, 388, 406, 410-432, 440-443, 448-465, 473-477, 480-488, 491, 496-499, 505-509, 517-541, 551-556, 579-582, 591-608, 617-692, 706-760, 767-812, 820-847, 850-851, 854, 865-879, 886-892, 900-941, 944, 948, 953-954, 961-967, 974-989, 996-1033, 1036, 1039, 1050, 1055, 1060, 1066, 1073, 1083-1089, 1093-1094, 1109, 1122-1128, 1138-1139, 1143-1145, 1149, 1153, 1157-1161, 1186, 1194-1251, 1260, 1271-1281, 1296-1297, 1305-1315, 1335-1348, 1357-1397 /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle_fast.py 227 169 26% 47, 60-63, 70-78, 83-84, 91, 103-132, 136-158, 162-174, 190-198, 203-208, 212-213, 218-260, 264, 268, 272, 276-279, 283, 287, 291, 295, 306-312, 320-330, 346-370, 374-385, 419-431, 463-475, 481-483, 498-501, 504-534, 537-547 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/_base.py 287 271 6% 34-615, 623-627 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/__init__.py 10 2 80% 9-10 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/_posix_reduction.py 41 21 49% 20, 29-31, 36-43, 51-52, 55-58, 67-68, 71-74 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat.py 18 8 56% 14, 19, 23, 29-38 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat_posix.py 4 1 75% 11 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/context.py 135 95 30% 37-85, 91-97, 101, 118-153, 164-165, 170-171, 175-206, 214-215, 219-220, 224-225, 229-230, 234-235, 239-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/process.py 57 42 26% 20-31, 35-39, 42-64, 67-81, 89, 100-108 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/queues.py 131 102 22% 36-62, 66-67, 72-75, 79-111, 121-175, 182-183, 187-189, 195-210, 214-215, 219, 225-229, 234-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/reduction.py 126 59 53% 27-30, 63-66, 79-82, 87, 91, 101, 109, 113, 121, 127-129, 146, 149, 154-165, 176-177, 185-197, 202-210, 218, 223, 232-234, 240, 246-250, 256 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/utils.py 94 75 20% 11-12, 20-21, 25-28, 32-46, 52-60, 67-116, 125-138, 143-145, 149-172 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/cloudpickle_wrapper.py 60 44 27% 7-8, 16-17, 20-24, 29-31, 38, 42-44, 48-49, 55-83, 95-113 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py 507 411 19% 88-89, 94, 132-138, 143, 146-147, 150-155, 158-159, 171-174, 177-179, 182-184, 189-197, 214, 217, 223-229, 232, 236-237, 245-248, 254-256, 262-268, 271-272, 275, 283-286, 289-312, 317-325, 337, 342-347, 372-465, 487-505, 542-757, 766-785, 794-797, 803-810, 882-933, 939-951, 955-994, 998-1014, 1019-1022, 1025-1048, 1073-1081, 1084-1117 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/reusable_executor.py 91 70 23% 34-37, 84-142, 150-155, 158-159, 163-194, 199-207, 212-213 /usr/local/lib/python3.8/dist-packages/joblib/format_stack.py 209 188 10% 34-35, 45-68, 72, 88-94, 104-116, 120-147, 151-176, 181-322, 337-365, 371-401 /usr/local/lib/python3.8/dist-packages/joblib/func_inspect.py 176 154 12% 47-79, 84-93, 110-162, 173-177, 190-193, 198-203, 228-318, 322-325, 330-349, 356-359 /usr/local/lib/python3.8/dist-packages/joblib/hashing.py 117 85 27% 24, 33-42, 49, 58-64, 67-75, 78-94, 101-103, 111-127, 141-150, 155, 174-182, 189-242, 258-267 /usr/local/lib/python3.8/dist-packages/joblib/logger.py 76 55 28% 28-31, 35-36, 40-44, 48-57, 77, 81, 85, 96-124, 136-156 /usr/local/lib/python3.8/dist-packages/joblib/memory.py 374 270 28% 57-63, 92-100, 106, 109, 112, 114-133, 135, 146-148, 153-160, 166-182, 225-244, 248-253, 257-277, 281, 284, 293-295, 306-307, 310-313, 316-317, 320-325, 329, 332-333, 358, 361, 365, 415-453, 483-545, 562-563, 568, 574-576, 583, 588-590, 594-595, 605-625, 636-713, 717-724, 730-745, 764-795, 804, 884, 887-901, 905, 914-921, 952, 956-962, 971-974, 978-979, 990-992, 999, 1008-1010 /usr/local/lib/python3.8/dist-packages/joblib/my_exceptions.py 53 20 62% 24, 27-33, 46-48, 51-59, 75, 80, 84, 89, 94-99, 112-113 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle.py 204 161 21% 13-14, 78-82, 91-104, 112-161, 165-178, 195-209, 234-249, 253-260, 272-295, 320-332, 342-355, 361, 415-515, 526-548, 588-607 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_compat.py 105 75 29% 21-25, 38-61, 71-75, 90-92, 96-120, 140-142, 148-154, 164-173, 176, 185-192, 198, 227-247 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_utils.py 92 65 29% 22-23, 27-28, 36-37, 45-49, 54-56, 73-90, 95-100, 105-112, 144-182, 187-197, 229-245 /usr/local/lib/python3.8/dist-packages/joblib/parallel.py 362 293 19% 40-41, 65-73, 83-124, 181-209, 212, 215, 218-222, 234, 241-249, 254-255, 259, 267-270, 282-291, 297-311, 327-329, 332-340, 360-363, 388-389, 620-696, 699-701, 704-705, 709-725, 728-730, 733-734, 744-759, 769-771, 783-836, 842-849, 855-887, 895-940, 943-1032, 1035 /usr/local/lib/python3.8/dist-packages/joblib/pool.py 116 83 28% 42-43, 75-87, 91-99, 120-127, 130-131, 135-137, 140, 143-177, 199-207, 210-216, 296-313, 316-329 /usr/local/lib/python3.8/dist-packages/jupyter_client/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/adapter.py 256 210 18% 17-25, 38-52, 64, 67, 70-74, 81, 84-96, 103-109, 125-127, 132-146, 149-151, 154-157, 160-170, 173-179, 182-190, 194-195, 200-202, 205-214, 219-220, 231-234, 239-258, 261-266, 269-282, 285-290, 297-307, 310-317, 321-339, 344-346, 349-358, 363-364, 386-398 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/channels.py 45 28 38% 29-32, 35-37, 41-48, 52-58, 62, 65-70, 74, 79, 82 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/client.py 188 138 27% 23-29, 34, 69, 77, 81, 85, 89, 94-101, 112-150, 162-177, 194-212, 216-224, 231-235, 239-253, 311-388 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/channels.py 50 32 36% 11-12, 35-38, 41-43, 47-57, 61-67, 71, 74-79, 83, 88, 91 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/client.py 161 121 25% 25-31, 36, 77-115, 127-142, 159-179, 183-191, 198-202, 262-339 /usr/local/lib/python3.8/dist-packages/jupyter_client/channels.py 119 82 31% 61-80, 87-88, 91-99, 109-132, 136-170, 174, 178, 182-185, 189-192, 195-200, 210 /usr/local/lib/python3.8/dist-packages/jupyter_client/channelsabc.py 25 7 72% 16, 20, 24, 37, 41, 45, 49 /usr/local/lib/python3.8/dist-packages/jupyter_client/client.py 172 120 30% 26-30, 53, 78, 82, 86, 90, 105-118, 125-134, 139, 148-155, 160-167, 172-179, 184-190, 195-202, 206-218, 258-277, 295-300, 322-329, 363-370, 379-381, 390-396, 404-406, 410-412, 420-422, 441-443 /usr/local/lib/python3.8/dist-packages/jupyter_client/clientabc.py 45 14 69% 32, 36, 40, 44, 48, 52, 60, 64, 68, 72, 76, 80, 84, 88 /usr/local/lib/python3.8/dist-packages/jupyter_client/connect.py 247 186 25% 79-167, 191-224, 253-274, 296, 319-325, 329-330, 350, 355-356, 377-396, 402-406, 413-419, 423-430, 438-446, 455-461, 465-481, 492-497, 511-523, 531-538, 542-551, 555-557, 561, 565, 569, 573 /usr/local/lib/python3.8/dist-packages/jupyter_client/jsonutil.py 50 34 32% 38-44, 53-59, 63-72, 76-84, 88-92 /usr/local/lib/python3.8/dist-packages/jupyter_client/kernelspec.py 196 139 29% 44-47, 50-58, 65, 73, 82, 90-103, 108, 111, 130, 134, 146-159, 163-183, 190-200, 204-222, 229-237, 252-270, 277-289, 292-297, 315-347, 351-354, 359, 366, 370, 376 /usr/local/lib/python3.8/dist-packages/jupyter_client/launcher.py 59 51 14% 56-158 /usr/local/lib/python3.8/dist-packages/jupyter_client/localinterfaces.py 170 129 24% 29-30, 34-42, 48-52, 58-59, 68-89, 96-108, 113-121, 127-135, 140-164, 173-196, 201-203, 216-248, 254, 259, 264, 269, 274 /usr/local/lib/python3.8/dist-packages/jupyter_client/manager.py 349 261 25% 42, 48, 52, 61, 64, 75-77, 83-85, 102, 109, 113, 126-127, 134, 137, 141-143, 147-149, 157-166, 174-205, 212, 217-219, 222-225, 239-267, 274-283, 286-287, 301-306, 311-315, 323-338, 342-347, 368-379, 406-418, 423, 430-456, 464-478, 488-498, 502-509, 523-524, 538-543, 551-562, 583-594, 621-634, 641-675, 683-697, 707-717, 721-728, 735-736, 744-755, 760-771, 784-789 /usr/local/lib/python3.8/dist-packages/jupyter_client/managerabc.py 27 8 70% 21, 29, 33, 37, 41, 45, 49, 53 /usr/local/lib/python3.8/dist-packages/jupyter_client/multikernelmanager.py 185 104 44% 31-39, 60-63, 67, 73, 76-90, 94-105, 109, 119, 123, 126, 130-147, 157-160, 174-188, 198, 212, 216-222, 233, 247, 258, 275-276, 286-287, 402, 422-428, 442-456, 461-463, 473-475, 488-490, 500-502, 506-512 /usr/local/lib/python3.8/dist-packages/jupyter_client/session.py 442 311 30% 27, 65-75, 148-153, 157, 172, 177, 184, 187-191, 201-205, 209, 212, 215, 218, 221, 226-228, 232-247, 305-315, 323-333, 338-340, 344, 364, 368, 376-384, 388, 393-396, 411-412, 423-425, 432-434, 488-496, 508-514, 519-521, 525-568, 571, 580-590, 600-605, 630-664, 711-766, 784-794, 810-828, 853-865, 869-876, 883-889, 916-954, 957-961, 965-978 /usr/local/lib/python3.8/dist-packages/jupyter_core/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_core/paths.py 194 155 20% 33-38, 47-51, 59-68, 78-98, 109-114, 118-122, 147-166, 170-174, 186-202, 209-213, 232-247, 266-287, 291, 316-344, 361-377, 394, 415-448, 452-456 /usr/local/lib/python3.8/dist-packages/jupyter_core/version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/__init__.py 18 5 72% 22, 37-40 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/affine_transformations.py 107 85 21% 16-17, 22-24, 28-31, 55-59, 84-90, 114-118, 145-156, 171-180, 194-195, 212-219, 236-242, 246-251, 280-336 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py 120 97 19% 93-99, 124-173, 180-223, 227-233, 237-261, 273-284, 288, 292-295, 299 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/directory_iterator.py 65 46 29% 68-74, 94-151, 158, 162, 167 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/image_data_generator.py 213 190 11% 17-18, 279-366, 421, 526, 649-666, 707-743, 757-833, 859-890, 902-903, 933-988 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/iterator.py 146 112 23% 37-45, 48-50, 53-65, 68, 71, 74, 78-95, 101, 104, 112-116, 127, 172-211, 222-272, 277, 285, 292 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/numpy_array_iterator.py 87 75 14% 46-52, 68-150, 156-183 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/utils.py 121 80 34% 16-18, 47, 71-76, 107-109, 111, 118-119, 121-122, 125-127, 129-138, 153-154, 172-182, 209-228, 251-286, 305, 312-319 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/sequence.py 155 135 13% 55-110, 143-149, 197-240, 254-259, 334-354, 360, 364-378, 386-402, 427-432, 446-454 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/text.py 198 166 16% 21, 42-63, 88, 128-138, 179-197, 212-251, 263-267, 281, 298-324, 338, 355-370, 382-383, 400-438, 449-455, 482-487, 500-519 /usr/local/lib/python3.8/dist-packages/parso/__init__.py 8 3 62% 56-58 /usr/local/lib/python3.8/dist-packages/parso/_compatibility.py 36 15 58% 27-29, 38-43, 49-51, 60-64, 69 /usr/local/lib/python3.8/dist-packages/parso/cache.py 103 67 35% 66, 68, 89-94, 101-111, 120-144, 148-160, 164-174, 178-179, 183-186, 190-193, 197-202 /usr/local/lib/python3.8/dist-packages/parso/file_io.py 20 11 45% 6, 12-13, 19-23, 26, 31-32, 35 /usr/local/lib/python3.8/dist-packages/parso/grammar.py 127 89 30% 33-40, 77-79, 90-155, 158-161, 169-172, 175, 178-183, 190-191, 194-196, 199-201, 210-216, 219, 223, 234-260 /usr/local/lib/python3.8/dist-packages/parso/normalizer.py 137 74 46% 19-24, 27-33, 36-39, 42-48, 52-53, 56-57, 60-65, 68, 71, 74-77, 101, 117-120, 125-138, 141, 144, 147, 150, 158, 161, 164, 167-171, 174-181, 184-186, 191, 194-197, 200-203 /usr/local/lib/python3.8/dist-packages/parso/parser.py 106 79 25% 37-38, 49-54, 59-71, 76-77, 81, 84, 89-96, 119-121, 124-143, 146-151, 154-160, 163-166, 174-198, 201-211 /usr/local/lib/python3.8/dist-packages/parso/pgen2/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/parso/pgen2/generator.py 178 152 15% 45-47, 56-57, 60, 74-87, 90-93, 96-98, 102-112, 117, 130, 133, 145-157, 170-207, 211-224, 228-232, 244-274, 282-294, 303-344, 352-377 /usr/local/lib/python3.8/dist-packages/parso/pgen2/grammar_parser.py 101 81 20% 18-23, 27-38, 42-58, 62-69, 73-94, 98-111, 115-122, 125-126, 129-135, 141-142, 145, 150-151, 154-156, 159 /usr/local/lib/python3.8/dist-packages/parso/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/parso/python/diff.py 502 437 13% 52, 56-58, 62-64, 68, 72, 81-118, 122-138, 142-146, 154-167, 171-173, 177-184, 192-195, 199-203, 207-220, 224-230, 238-247, 256-258, 261-264, 282-340, 343-344, 347-388, 391-405, 412-431, 439-453, 456-514, 523-527, 530-550, 553, 557-562, 565-586, 589, 594-599, 603, 606-609, 612-624, 627-640, 646-665, 672-685, 693-719, 723-861, 864-886 /usr/local/lib/python3.8/dist-packages/parso/python/errors.py 758 567 25% 26-33, 37-48, 56-57, 65-72, 76, 83-93, 97-121, 125-131, 135-136, 141-148, 153, 157, 160-170, 176-202, 205-247, 251-253, 256, 259, 267-269, 272-282, 285-291, 295-315, 318-349, 352, 355, 360-362, 365-368, 375-376, 384-385, 389, 400-401, 409, 413, 421, 425, 433-437, 446-456, 464, 467, 477-483, 491-522, 530-535, 546-548, 558, 561-570, 579-581, 584-590, 599-600, 608, 616-633, 643-661, 667-693, 703, 706-730, 736-750, 758, 765-768, 771-823, 833-845, 853-860, 871, 874-889, 892, 895-897, 902-983, 991-995, 1004-1009, 1015, 1021-1024, 1030-1031, 1038-1040, 1049-1108 /usr/local/lib/python3.8/dist-packages/parso/python/parser.py 102 85 17% 66-71, 74-80, 90-108, 112-118, 121-188, 191-200, 203-217 /usr/local/lib/python3.8/dist-packages/parso/python/pep8.py 492 426 13% 39-40, 43, 46-51, 56-105, 114-119, 126-144, 148, 153-172, 176-178, 182-280, 283-286, 289-290, 293-341, 344-375, 378-519, 522-537, 545-629, 632-685, 688-700, 727 /usr/local/lib/python3.8/dist-packages/parso/python/prefix.py 56 35 38% 11-16, 20-25, 28-29, 35, 69-94 /usr/local/lib/python3.8/dist-packages/parso/python/token.py 12 1 92% 10 /usr/local/lib/python3.8/dist-packages/parso/python/tokenize.py 458 395 14% 60-61, 70, 75, 80-111, 119-124, 136-265, 274-278, 283, 289-295, 298, 301-304, 307, 310, 313, 317-331, 335-371, 376-377, 384-389, 401-672, 676-705, 709-722 /usr/local/lib/python3.8/dist-packages/parso/python/tree.py 642 403 37% 48-49, 79-97, 111-119, 126, 134-143, 154, 180, 192, 204, 211, 221-249, 267, 270-275, 311-314, 318, 321, 343, 349, 355, 361, 364-372, 378, 381-386, 400-401, 412-417, 426-429, 436-453, 469, 475-485, 496, 503-509, 523-566, 586-588, 591, 597, 601, 607-624, 630-639, 645-654, 660, 667-673, 692-694, 701, 704, 711, 714, 734-736, 744-752, 758-763, 779, 782, 794-798, 810-815, 818-821, 833-842, 845, 848, 861, 865, 869-877, 882-888, 891-905, 914-918, 932, 937, 940, 944-960, 969, 976, 996, 1000, 1003-1008, 1016, 1023, 1040-1061, 1072-1075, 1084-1090, 1096-1105, 1117-1120, 1128-1131, 1139-1144, 1152-1159, 1165-1166, 1173-1176, 1179, 1186-1201, 1207, 1216-1222, 1228-1229, 1241, 1254, 1257, 1260, 1263, 1266, 1270 /usr/local/lib/python3.8/dist-packages/parso/tree.py 195 124 36% 18-20, 38-41, 48-58, 66-75, 82-101, 108-127, 186-197, 203, 207-208, 211-216, 219, 222, 225-228, 232-239, 243-246, 253-254, 266-271, 278, 281, 285, 288-292, 295, 306-328, 331, 334, 338-341, 350-351, 354, 376-377, 380 /usr/local/lib/python3.8/dist-packages/parso/utils.py 86 68 21% 38-68, 80-109, 117-119, 123-140, 146-152, 155-159, 162, 171-176 /usr/local/lib/python3.8/dist-packages/pickleshare.py 194 152 22% 43-45, 50-51, 62, 65, 73-86, 91-107, 111-123, 127-133, 139-154, 158-177, 186-198, 204-211, 215, 220-224, 227, 230, 240-243, 259-275, 279, 282, 297, 300, 302, 304-306, 311-347, 350 /usr/local/lib/python3.8/dist-packages/pooch/__init__.py 20 14 30% 36-50 /usr/local/lib/python3.8/dist-packages/pooch/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/pooch/core.py 120 84 30% 197-230, 410, 413, 424, 545-568, 575-576, 589-590, 610-637, 658-673, 702-711, 725-733 /usr/local/lib/python3.8/dist-packages/pooch/downloaders.py 82 69 16% 14-15, 47-60, 139-143, 161-203, 253-261, 277-310 /usr/local/lib/python3.8/dist-packages/pooch/processors.py 75 50 33% 38, 64-81, 88, 117-133, 162-182, 213, 240-252, 262-279 /usr/local/lib/python3.8/dist-packages/pooch/utils.py 101 54 47% 33, 96, 150, 173-193, 214-216, 246, 248, 255-256, 261-271, 308-315, 346-362, 386-393, 430-436 /usr/local/lib/python3.8/dist-packages/pooch/version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/application.py 442 346 22% 93-94, 220-336, 343-353, 366-374, 385, 395-399, 410-431, 437-477, 482, 492-523, 530-537, 546-559, 569, 579-581, 585-591, 614-778, 801-811, 826-839, 849-851, 862-869, 875-884, 916-929, 938-939, 959-981, 993-1006, 1019, 1030, 1034-1036, 1043-1053, 1064-1065, 1073, 1078, 1087-1132, 1136-1140, 1145, 1148, 1158-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/current.py 64 39 39% 7-8, 11-13, 50, 54-58, 62-66, 75, 97-103, 111-112, 127-134, 148-165 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/dummy.py 17 5 71% 21, 28, 35, 44, 47 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/run_in_terminal.py 44 33 25% 13-14, 50-57, 73-116 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/auto_suggest.py 57 28 51% 23, 44, 47, 82, 93, 98, 107-110, 121, 132-145, 155-156, 161-164, 175, 180-181, 186-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/buffer.py 775 644 17% 82-90, 93, 106-108, 114-128, 136-138, 153-155, 158, 251-316, 319-324, 332-383, 389-405, 409-412, 416, 426-442, 446, 453-464, 468, 472-478, 482-498, 504-516, 524, 536, 557-571, 578, 589-596, 619-628, 636-639, 656-658, 667, 670, 674-680, 684-690, 699-708, 717-726, 733-748, 754-762, 769-774, 784-799, 808-814, 820-822, 831-845, 856-867, 873-875, 883-890, 897-926, 932-943, 950-956, 963-967, 974, 984-1000, 1006-1021, 1035-1074, 1081, 1089, 1103-1108, 1114, 1125-1135, 1141-1144, 1150-1157, 1163-1169, 1185-1221, 1228-1237, 1240-1246, 1256-1277, 1295-1319, 1327-1330, 1342-1418, 1428-1441, 1456-1464, 1476-1483, 1486, 1493-1502, 1506-1531, 1539-1578, 1588-1614, 1628-1630, 1648-1768, 1776-1795, 1803-1807, 1813-1825, 1839-1862, 1873-1883, 1892-1909, 1920-1956 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/cache.py 56 25 55% 37-51, 55-56, 93-101, 117-121 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/base.py 38 13 66% 29-30, 52, 72, 75, 78, 81, 92, 95, 98, 101, 104, 107 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/in_memory.py 22 13 41% 23-29, 32-35, 38-41, 44-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/base.py 116 71 39% 48-62, 65-72, 80-82, 90, 95-97, 102-104, 109-111, 120-122, 147-153, 156, 185, 196-197, 212, 217, 225-228, 231, 242, 245, 256, 261-262, 267-272, 275, 284, 290-292, 299-301, 308, 318-333, 338-349 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/filesystem.py 46 36 22% 34-38, 43-98, 107 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/fuzzy_completer.py 70 52 26% 54-60, 65-68, 71-75, 81-116, 131-159, 180-188, 193 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/nested.py 40 28 30% 32-33, 36, 63-75, 81-109 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/word_completer.py 33 26 21% 41-49, 55-83 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/data_structures.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/document.py 525 407 22% 52, 68-71, 98-121, 130, 133-136, 145, 150, 155, 160, 165, 169, 173, 178-179, 184-185, 193-196, 205-224, 231, 237, 243, 248-250, 256-259, 266, 273, 280-281, 291-292, 301-304, 312-315, 324-340, 345, 350, 356, 372-397, 404-405, 420-434, 446-453, 458-461, 475-496, 510-536, 546-547, 556-572, 581-604, 613-625, 634-652, 661-671, 680-690, 696-699, 705-708, 720-725, 742-747, 761-783, 794-816, 828-834, 838, 842, 846-854, 858, 864, 872-876, 890-897, 908-947, 958-997, 1005-1033, 1048-1098, 1104-1111, 1118-1129, 1136-1145, 1154, 1165-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/enums.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/async_generator.py 26 18 31% 32-67 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/inputhook.py 65 42 35% 50-52, 61-63, 78-80, 83, 86, 89, 94-143, 149-154, 157, 166-167, 170 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/utils.py 37 25 32% 9-10, 30-33, 57-83, 90-100 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/app.py 162 97 40% 13, 54-98, 106, 114, 122-123, 131-132, 140, 148, 154, 160, 168, 182, 191-195, 200, 205, 213-225, 234-248, 253-267, 272-286, 291-295, 300-304, 309-313, 319-323, 329, 334-341, 346-347, 354-355, 364-365, 371-375, 383 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/base.py 92 37 60% 20, 26, 38, 49, 67-77, 88, 90, 101-103, 117-123, 126, 129, 142, 147, 150, 159, 162, 165, 174, 177, 186, 189, 210, 213 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/cli.py 27 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/utils.py 14 2 86% 32, 41 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/ansi.py 152 133 12% 30-47, 53-109, 115-187, 193-211, 214, 217, 225-228, 246 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/base.py 66 49 26% 6, 30-37, 68-99, 108-114, 126, 129, 144-145, 148-160, 168-174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/html.py 70 59 16% 30-96, 99, 102, 110-113, 119-123, 129-132 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/pygments.py 14 6 57% 8, 22, 25-30 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/utils.py 24 16 33% 28-29, 40-41, 56-57, 68-85 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/history.py 95 61 36% 33-34, 64-74, 80, 84-85, 101, 120-123, 126-139, 142, 145, 150, 153, 156, 165, 168, 177, 180, 184, 193-194, 197-221, 225-232 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/ansi_escape_sequences.py 12 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/base.py 53 14 74% 48, 52, 60, 65, 95, 104, 107, 110, 114, 117, 120, 123, 126, 131 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/defaults.py 24 17 29% 26-43, 51-58 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/typeahead.py 17 8 53% 54-55, 64-67, 75-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/vt100_parser.py 98 73 26% 48-61, 87-88, 91-92, 98-99, 108-118, 124-168, 176-188, 199-226, 240, 246-247 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/auto_suggest.py 30 22 27% 26-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/basic.py 157 147 6% 27, 31-248 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/completion.py 96 79 18% 21-22, 37-43, 61-79, 90-171, 178-203 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/cpr.py 12 6 50% 14-28 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/emacs.py 270 255 6% 41-335, 339-401, 409-558 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/focus.py 7 2 71% 16, 24 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/mouse.py 63 54 14% 21-146 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/named_commands.py 279 156 44% 56-59, 73-74, 82-83, 91-92, 102-103, 111-112, 118-119, 128-132, 141-145, 153, 162, 176, 184, 192, 200, 208-210, 219-223, 236, 244-246, 254-262, 270, 281-289, 297-302, 310-315, 323-328, 337, 354-364, 373-382, 391-411, 420, 428-436, 444-452, 460, 471-472, 481-482, 491-498, 511, 520, 528, 541, 550, 566-569, 578-586, 599, 609-627, 635, 643, 657, 666-678, 686-687 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/open_in_editor.py 14 7 50% 20, 29-35, 42-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/page_navigation.py 26 19 27% 38, 51-59, 67-79 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/scroll.py 80 69 14% 26-50, 57-80, 87, 94, 101-113, 120-144, 151-160, 169-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/vi.py 949 904 5% 74-76, 80-85, 91-94, 105-126, 133-141, 147-166, 181-289, 304-369, 388-2161, 2165-2210 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/defaults.py 11 2 82% 35-49 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/digraphs.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/emacs_state.py 17 7 59% 18-19, 22, 27, 31, 35-36 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_bindings.py 215 138 36% 57, 101, 104, 126, 137, 149, 158, 189-196, 199-201, 205, 209, 238-281, 297-325, 340-364, 376-389, 397-417, 461-462, 469, 475-476, 480-481, 484-485, 488-489, 514-517, 521-541, 555-556, 563-572, 583, 596-599, 602-607, 617-618, 625-635 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_processor.py 238 184 23% 23-24, 44-47, 53, 56-58, 91-98, 101-117, 124-127, 134-145, 152-204, 213-216, 222-225, 236-292, 298-303, 306-354, 362-375, 382-387, 397-420, 443-451, 454, 462, 466-469, 476, 483, 490-499, 506, 514-525, 530 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/vi_state.py 47 27 43% 7-8, 28-29, 40-76, 81, 86-91, 98-106 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/keys.py 165 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/containers.py 956 776 19% 62-64, 140, 148, 155, 160-166, 174, 215-227, 230, 233, 236, 289-308, 311-318, 321-327, 330-331, 339-364, 381-414, 428-474, 526-545, 548-555, 558-579, 582-583, 591-616, 623-667, 684-730, 769-775, 778-781, 784, 792, 803-841, 867-986, 1000-1011, 1014, 1017, 1020-1022, 1069-1090, 1093-1095, 1098-1100, 1103, 1149-1162, 1166, 1177-1185, 1195-1202, 1221, 1230-1236, 1243-1246, 1252-1255, 1263, 1277, 1284, 1294, 1301, 1309-1312, 1319-1324, 1342-1345, 1349, 1353, 1357, 1361, 1364, 1378-1379, 1490-1524, 1527, 1530-1543, 1551-1558, 1564, 1573-1593, 1604-1620, 1637-1671, 1680-1684, 1688-1695, 1710-1726, 1738-1898, 1924-2140, 2150-2162, 2169-2181, 2188-2191, 2203-2214, 2224-2262, 2277-2281, 2287-2292, 2303-2413, 2424-2507, 2525-2528, 2532-2541, 2545-2558, 2561, 2564, 2578-2579, 2582, 2585, 2588-2591, 2594-2597, 2608-2609, 2614, 2626, 2635-2636, 2639, 2642, 2645, 2648, 2651, 2656, 2663, 2670-2675, 2682-2687, 2695-2699 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/controls.py 311 237 24% 51-58, 88, 91, 100, 106, 126, 154, 179-186, 190-193, 216-266, 323-341, 344, 347, 350, 358, 367-369, 379-380, 384-430, 442-469, 472, 475, 488-491, 496, 545-574, 577, 583-589, 593-596, 606-610, 613, 626, 638-657, 666-670, 681-722, 730-819, 825-883, 886-887, 890-891, 897, 904-908, 929-939 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/dimension.py 91 53 42% 44, 56, 58, 60, 71, 75, 78, 94, 98, 101-111, 118-122, 130-167, 184-193, 201-207 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/dummy.py 17 7 59% 26-37 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/layout.py 188 143 24% 43-70, 73, 79-81, 84-85, 101-166, 175-193, 200, 207-212, 217, 222, 227, 236-241, 248-250, 258-259, 266-269, 276-280, 289-290, 297-300, 306-307, 313-323, 329-339, 345-346, 355-360, 366-375, 382-384, 391-394, 406-417 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/margins.py 119 90 24% 18, 43, 63, 80-81, 84-85, 90-132, 141-142, 145-148, 153-156, 173-175, 178, 183-243, 276-277, 282-283, 288-305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/menus.py 281 228 19% 43-45, 71, 74-81, 91-95, 101-132, 138, 144, 159-167, 173-181, 190-205, 218-232, 245-265, 281-284, 327-340, 343, 346, 353-371, 383-390, 396-507, 513, 519-557, 564-607, 626-664, 680-685, 694, 697-702, 705-720 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/mouse_handlers.py 12 4 67% 18-24, 39-40 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/processors.py 378 291 23% 38, 80, 109-115, 122, 155-157, 168, 187, 193-243, 264-267, 278-312, 323, 326-334, 354-357, 368-398, 404-439, 451-489, 502-503, 509-522, 529, 541, 544-550, 557, 570-571, 575-580, 583, 593, 597-607, 622-629, 632-645, 660-667, 670-684, 707-710, 713-764, 790-798, 803-855, 858-903, 930-931, 937-940, 943, 958, 961-962, 969-975, 985, 988-1029 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/screen.py 100 71 29% 9, 107-120, 123, 128, 131, 152-192, 198, 204, 211-214, 221-227, 234, 242-249, 256-263, 272-293, 300-307, 310 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/utils.py 32 17 47% 23, 26, 29, 35, 39, 48-53, 67-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/base.py 31 15 52% 38, 50, 53-62, 73-74, 77-78, 81-82 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/pygments.py 112 79 29% 29, 69, 86, 94-110, 117-129, 141-143, 193-202, 214-222, 229-335 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/mouse_events.py 14 3 79% 41-42, 45 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/base.py 130 33 75% 164, 174, 177, 180, 183, 186, 189, 192, 195, 198, 201, 204, 207, 210, 213, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/color_depth.py 29 12 59% 52-74 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/defaults.py 27 20 26% 31-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/vt100.py 289 192 34% 123-145, 169-175, 181-190, 237-256, 273, 276-300, 304-312, 321-369, 383-396, 420-432, 449-473, 476, 480, 484, 490, 497, 503-507, 512, 519, 522, 525, 528-534, 540-542, 548, 555, 558, 567-570, 573, 576, 579, 582, 588, 591-596, 599-606, 609-614, 617-622, 625, 628, 634-673, 679-680, 684-685 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/patch_stdout.py 61 43 30% 52-68, 81-93, 100-116, 128-139, 142-144, 147-150, 156-157, 164, 167 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/renderer.py 299 255 15% 27-28, 70-243, 262-263, 266-270, 303-326, 331-369, 377, 386, 395-402, 417-457, 464-480, 488, 494-514, 525-637, 648-657, 664-673, 686-716 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/search.py 86 66 23% 16-17, 56-58, 61, 73-78, 94-119, 126-150, 157-181, 190-217, 226 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/selection.py 19 5 74% 48-50, 53, 56 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/dialogs.py 89 60 33% 53-69, 86-99, 116-140, 152-159, 176-196, 213-231, 246-285, 290-294, 305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/base.py 177 119 33% 58-59, 74-85, 128-148, 152-233, 237-247, 265-269, 272, 281-283, 286-300, 303, 306, 326-342, 345-358, 366-367, 379, 383-387, 405, 409-415, 419-422, 429-432, 439-444 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/formatters.py 158 91 42% 22, 52, 55, 64, 72, 75, 89-90, 93-94, 103-113, 116-124, 141, 144, 164-172, 180-206, 211, 228, 233-237, 244-247, 262-263, 268-273, 291-297, 300-306, 325-326, 329-335, 352-353, 358, 391, 402-413, 416, 423 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/prompt.py 449 368 18% 133, 159-184, 193, 406-458, 470-475, 481-490, 516, 522-685, 693-761, 767-824, 912-994, 1010-1040, 1091-1173, 1178-1189, 1193, 1197, 1202-1219, 1222, 1235-1249, 1262-1270, 1274-1282, 1286-1292, 1303, 1307, 1359-1361, 1412-1435, 1442-1443 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/utils.py 59 41 31% 27, 96-147, 160-173, 180-183, 190-191, 198 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/base.py 37 10 73% 129, 148, 151, 155, 166-167, 172-174, 177, 181 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/defaults.py 15 2 87% 213, 223 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/named_colors.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/pygments.py 18 11 39% 14-15, 39-43, 51-56, 66-67 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/style.py 165 126 24% 39-74, 97-103, 112-167, 201, 227-242, 246, 256-264, 272-313, 316, 329-336, 352-353, 373-374, 380-383, 387-390, 395, 398 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/style_transformation.py 124 76 39% 54, 80-83, 94, 111-112, 115-121, 124, 159-160, 163-189, 196-205, 220, 223, 236, 240, 256, 259-262, 265-268, 280-281, 284-286, 289, 294, 297-299, 302, 311, 349-375 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/utils.py 115 73 37% 64-68, 72-73, 77, 86, 92-93, 99-100, 106-107, 116, 119, 141-160, 170, 178, 185, 193-195, 202, 209, 214, 235-271, 276-279, 284-287, 295-298, 308-311 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/validation.py 66 34 48% 32-34, 37, 65, 73-76, 100, 112-114, 117, 120-126, 137, 140, 147-150, 159, 169-170, 174-175, 186, 189-190, 193-194 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/base.py 292 180 38% 192-256, 274, 278, 285, 289, 296, 300, 303, 326-342, 351, 367-382, 393-402, 411-419, 422, 448-486, 516, 529, 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/usr/local/lib/python3.8/dist-packages/ptyprocess/util.py 35 32 9% 3-67 /usr/local/lib/python3.8/dist-packages/pyasn1/__init__.py 4 1 75% 7 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/decoder.py 817 685 16% 33, 39, 45, 48-55, 65-79, 85-98, 112-122, 129, 141-190, 197-226, 237-263, 269-293, 304-314, 324-371, 381-478, 490, 493, 496-534, 540-737, 743-946, 983-1023, 1029-1074, 1084-1109, 1115-1171, 1312-1626 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/eoo.py 12 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/cer/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/cer/decoder.py 32 11 66% 22-35, 57 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/der/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/der/decoder.py 19 1 95% 37 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/compat/binary.py 18 15 17% 10-31 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/calling.py 7 3 57% 13-16 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/dateandtime.py 9 3 67% 16-17, 22 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/integer.py 68 58 15% 14-15, 20-94, 99, 102-107 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/octets.py 22 10 55% 10-27 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/string.py 11 8 27% 11-21, 26 /usr/local/lib/python3.8/dist-packages/pyasn1/debug.py 85 31 64% 52, 55, 63-65, 72-101, 104, 107, 110, 113, 122, 138, 151, 154 /usr/local/lib/python3.8/dist-packages/pyasn1/error.py 10 3 70% 47-49 /usr/local/lib/python3.8/dist-packages/pyasn1/type/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/type/base.py 249 74 70% 65, 70, 80, 108, 132, 138-141, 144, 149, 152, 155, 158, 162, 214, 233-236, 239, 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/usr/local/lib/python3.8/dist-packages/pyasn1_modules/rfc2459.py 418 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1_modules/rfc5208.py 18 0 100% /usr/local/lib/python3.8/dist-packages/pygments/__init__.py 26 17 35% 41-49, 60-73, 84 /usr/local/lib/python3.8/dist-packages/pygments/filter.py 24 14 42% 19-24, 36, 50, 53, 66-69, 73-74 /usr/local/lib/python3.8/dist-packages/pygments/filters/__init__.py 159 124 22% 25-30, 39-43, 48-51, 56-64, 78-81, 86-95, 113-116, 119-123, 151-157, 160-164, 184-191, 194-197, 231-245, 248-277, 295-296, 299-302, 305-316, 326, 329-340 /usr/local/lib/python3.8/dist-packages/pygments/formatter.py 29 15 48% 21-23, 67-75, 85, 92-95 /usr/local/lib/python3.8/dist-packages/pygments/formatters/__init__.py 85 49 42% 31-34, 48-53, 61-68, 76-79, 98-115, 123-134, 147 /usr/local/lib/python3.8/dist-packages/pygments/formatters/_mapping.py 2 0 100% /usr/local/lib/python3.8/dist-packages/pygments/formatters/html.py 363 319 12% 40, 44-47, 51-59, 405-449, 454-457, 462-466, 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/usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/base.py 99 80 19% 7-23, 118-151, 155-158, 170-191, 201-209, 212, 215, 231-234, 250-253, 256, 266-267, 270-275 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/bdf.py 245 222 9% 21-26, 31-34, 39-70, 188-242, 245-295, 298-438, 441, 447-451, 454-467 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/common.py 214 189 12% 13-17, 22-24, 39-40, 46-57, 62, 100-120, 153-176, 181-190, 206-238, 292-320, 325-363, 367-432 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/dop853_coefficients.py 153 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/ivp.py 162 143 12% 31-51, 77-78, 109-128, 146-154, 504-662 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/lsoda.py 57 46 19% 108-138, 141-161, 164-172, 177-180, 183-188 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/radau.py 262 230 12% 88-137, 169-177, 286-334, 337-387, 390-529, 532-533, 536, 541-545, 548-562 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/rk.py 190 127 33% 62-72, 89-104, 107, 110, 113-177, 180-181, 480-485, 488-494, 497-503, 506-524, 529-533, 536-549, 554-557, 560-576 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ode.py 507 401 21% 348-353, 357, 361-369, 382-394, 422-437, 441-445, 532-536, 540-541, 545-546, 562-567, 584-587, 620-625, 628-633, 637-656, 660, 673-686, 690-694, 722-723, 739-742, 751-754, 765-769, 786-787, 790-791, 804, 809, 814, 826-831, 862-881, 912-939, 942-987, 990-1014, 1017-1021, 1024-1028, 1044-1100, 1132-1143, 1146-1151, 1154-1168, 1171-1179, 1182-1187, 1210, 1216-1230, 1267-1283, 1287-1333, 1336-1353, 1356-1360, 1363-1367 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_quad_vec.py 262 227 13% 16, 19-24, 28, 36-41, 44-48, 51-56, 64-67, 70-71, 74-79, 83, 87-93, 98-99, 102, 202-400, 404-429, 436-449, 461-504, 512-569, 580-622 /usr/local/lib/python3.8/dist-packages/scipy/integrate/odepack.py 32 22 31% 229-260 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadpack.py 199 172 14% 41, 334-432, 436-465, 469-516, 581-585, 665-674, 799-810, 815, 823, 828, 832, 837-844, 847-883 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadrature.py 312 274 12% 45-49, 117-123, 151-168, 235-252, 256-258, 315-350, 354-381, 454-506, 568-623, 650-660, 668-669, 674-686, 773-806, 934-975 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_bsplines.py 324 291 10% 20-22, 27-30, 38-43, 182-226, 235-239, 245, 303-308, 332-355, 358, 367-370, 391-397, 425-437, 488-571, 581-588, 593, 597-604, 608-617, 735-861, 969-1022 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_cubic.py 259 230 11% 28-72, 142-158, 235-240, 245-255, 268-304, 343-350, 405-435, 438, 445, 450, 619-770, 784-837 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_fitpack_impl.py 413 388 6% 44-48, 215-311, 443-524, 580-607, 654-668, 710-733, 773-791, 890-988, 1039-1057, 1079-1080, 1128-1146, 1196-1229, 1288-1311 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_pade.py 27 22 19% 46-67 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack.py 65 49 25% 156-158, 289-290, 353-368, 417-429, 477-491, 531-534, 586-601, 654-657, 719-722 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack2.py 366 304 17% 171-196, 201-208, 211-232, 235-241, 244-256, 265-276, 303-317, 324-326, 330-332, 342, 380, 405-408, 415-421, 468-471, 524-525, 602-618, 741-770, 793, 801, 805, 841-880, 955-961, 983, 1002-1004, 1048-1069, 1110-1136, 1172-1198, 1273-1281, 1304, 1385-1396, 1486-1504, 1670-1727 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/interpolate.py 920 819 11% 32-34, 84-94, 201-252, 279-313, 318-328, 335, 431-540, 547, 552-581, 585, 591-614, 623-631, 637-646, 649, 652-654, 660-669, 687-700, 708-751, 754-758, 770-777, 784-787, 808-870, 902-923, 986, 1013-1031, 1063-1085, 1108-1167, 1217-1239, 1268, 1284-1296, 1313-1332, 1422, 1443-1477, 1502-1533, 1558-1597, 1600-1603, 1621-1639, 1705-1757, 1814-1843, 1873-1883, 1946-1965, 1978-1984, 1987-1991, 1994-1997, 2028-2062, 2069-2096, 2103-2136, 2163-2169, 2197-2203, 2236-2264, 2287-2312, 2415-2452, 2468-2501, 2505-2516, 2519-2521, 2525-2541, 2601-2676, 2689-2704, 2707, 2710-2712, 2717-2724 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/ndgriddata.py 47 38 19% 59-65, 77-81, 193-228 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/polyint.py 203 169 17% 18, 56-60, 78-80, 86, 90-92, 96-103, 106-111, 114-131, 134-139, 177-188, 216-218, 293-316, 319-326, 329-355, 400-406, 448-461, 502-513, 532-537, 559-577, 599, 602-617, 666 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/rbf.py 105 83 21% 145, 148, 151, 154, 157, 160, 163, 167-216, 223-268, 274-275, 278, 281-290 /usr/local/lib/python3.8/dist-packages/scipy/io/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/_fortran.py 79 61 23% 111-126, 129-136, 161-169, 245-294, 317, 340, 349, 352, 355 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/_fortran_format_parser.py 165 124 25% 34, 62-66, 69-71, 74-80, 84-90, 94, 123-132, 141-144, 147-153, 157-163, 167, 172-174, 177, 180, 185-186, 189-191, 194-205, 234, 237-250, 253-257, 260-261, 264-305, 308-312 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/hb.py 265 215 19% 45, 70-120, 141-205, 217-281, 285-299, 303-308, 313-332, 336-360, 391-400, 403-412, 416, 421, 437-443, 447, 451, 455, 459, 463, 466, 469, 496-504, 534-547 /usr/local/lib/python3.8/dist-packages/scipy/io/idl.py 429 382 11% 81-84, 89-90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 152-153, 163-170, 175-182, 187-224, 233-269, 278-316, 322-425, 431-445, 451-492, 498-546, 552-565, 570-631, 652, 655, 658, 705-873 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/byteordercodes.py 19 12 37% 57-69 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio.py 65 47 28% 19-22, 32-47, 71-80, 215-225, 266-279, 314-317 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio4.py 281 222 21% 91-95, 102-106, 110-127, 135-149, 168-185, 202-207, 221-223, 254-267, 273-301, 313-314, 317-326, 333-334, 351-357, 375, 386-406, 410-422, 442-446, 451-452, 455, 458, 478-490, 504-519, 522-541, 544-560, 567-584, 590-594, 616-618 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5.py 371 312 16% 151-167, 172-175, 179-186, 196-198, 217-233, 252, 261-307, 311-331, 375-400, 422-455, 471-477, 480, 483, 487-496, 500-504, 508-516, 536-559, 562-570, 587-590, 601-630, 633-655, 660-698, 703-718, 721-726, 729-733, 736-738, 742-755, 761-765, 789-798, 802-809, 829-849 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5_params.py 77 11 86% 195-197, 227-231, 235, 242-243, 249-250 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/miobase.py 108 70 35% 20, 179-184, 221-241, 305-318, 324, 328, 332, 363-377, 381-383, 387, 390-393, 398, 403-415 /usr/local/lib/python3.8/dist-packages/scipy/io/mmio.py 444 375 16% 54, 75, 101, 115, 119, 123, 127, 131, 135, 139, 150-151, 164-165, 178-179, 191, 196, 226-269, 294-327, 332-384, 389, 398, 417-425, 451-460, 469-479, 483-485, 490-657, 662-802, 812-828, 833-839 /usr/local/lib/python3.8/dist-packages/scipy/io/netcdf.py 486 393 19% 239-284, 289-293, 297-319, 323, 326, 348-352, 385-397, 408-409, 413-421, 425-428, 431-439, 442, 445-452, 455-479, 482-512, 515-553, 556-558, 561-602, 606-616, 619, 622-631, 634-635, 638-647, 650-733, 736-758, 761-776, 779-782, 785, 789, 793, 796, 799-802, 805-808, 867-876, 881-885, 896, 905, 919, 938-946, 958, 970, 973-988, 991-1021, 1027-1028, 1037-1045, 1058-1065, 1077-1093 /usr/local/lib/python3.8/dist-packages/scipy/linalg/__init__.py 36 4 89% 222-223, 227-228 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_ldl.py 85 74 13% 123-156, 207-241, 268-297, 335-354 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_polar.py 16 11 31% 98-112 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_qz.py 127 110 13% 19-34, 38-43, 47-52, 56-61, 65-71, 76-145, 262-265, 358-405 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_expm_frechet.py 153 138 10% 91-114, 122-127, 166-173, 177-187, 191-203, 207-222, 226-278, 298, 334-350, 393-411 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_matfuncs_sqrtm.py 85 74 13% 52-116, 163-196 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_procrustes.py 18 13 28% 76-91 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_sketches.py 15 8 47% 49-54, 167-168 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_solvers.py 216 194 10% 86-107, 159-199, 214-218, 228-233, 306-324, 446-528, 652-736, 778-844 /usr/local/lib/python3.8/dist-packages/scipy/linalg/basic.py 385 346 10% 27-37, 137-258, 330-359, 433-472, 568-596, 669-702, 706-711, 862-907, 952, 979, 981, 1034-1043, 1157-1246, 1304-1318, 1373-1391, 1451-1470, 1575-1619 /usr/local/lib/python3.8/dist-packages/scipy/linalg/blas.py 86 12 86% 301-310, 341, 352, 381-384 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp.py 364 338 7% 41-47, 51-73, 78-115, 214-267, 374-489, 503-530, 640-695, 767, 858, 951, 1031, 1124-1194, 1199-1203, 1252-1283, 1368-1431 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_cholesky.py 72 61 15% 19-44, 90-92, 154-156, 194-213, 274-286, 334-353 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_lu.py 48 38 21% 71-86, 135-148, 209-223 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_qr.py 130 121 7% 16-25, 121-173, 251-320, 386-424 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_schur.py 104 85 18% 119-178, 191-197, 201-210, 266-295 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_svd.py 88 60 32% 111, 116, 118, 132, 134, 139, 225-232, 273-281, 323-330, 384-391, 459-496 /usr/local/lib/python3.8/dist-packages/scipy/linalg/flinalg.py 30 23 23% 14-19, 23, 32-58 /usr/local/lib/python3.8/dist-packages/scipy/linalg/lapack.py 45 4 91% 808, 814, 826, 830 /usr/local/lib/python3.8/dist-packages/scipy/linalg/linalg_version.py 5 0 100% /usr/local/lib/python3.8/dist-packages/scipy/linalg/matfuncs.py 130 103 21% 52-55, 84-89, 136-138, 195-208, 255-256, 291-295, 330-334, 371-372, 409-410, 447-448, 485-486, 551-590, 626-670 /usr/local/lib/python3.8/dist-packages/scipy/linalg/misc.py 42 30 29% 141-181, 192-194 /usr/local/lib/python3.8/dist-packages/scipy/linalg/special_matrices.py 223 197 12% 62-73, 104-106, 138-140, 193-203, 239-244, 290-301, 344-358, 413-430, 465-471, 535-553, 600-617, 654-662, 698-700, 759-777, 840-863, 938-973, 1033-1043, 1109-1119, 1172-1196 /usr/local/lib/python3.8/dist-packages/scipy/misc/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/scipy/misc/common.py 73 65 11% 35-47, 85-120, 154-159, 195-204, 297-303 /usr/local/lib/python3.8/dist-packages/scipy/misc/doccer.py 29 8 72% 15, 21, 27, 33, 39, 44, 49, 54 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_docstrings.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_support.py 43 13 70% 42, 44, 46, 48, 52, 64-65, 78, 80-81, 83, 89, 91 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/filters.py 399 298 25% 81, 85, 90, 129-133, 141, 156-164, 332-340, 369-377, 404-419, 447-449, 487-495, 525-544, 583-591, 598-623, 648, 754, 783-795, 842-857, 897-909, 954-966, 971-1033, 1069, 1106, 1113-1162, 1201-1202, 1240, 1280, 1348-1364, 1421-1448 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/fourier.py 69 58 16% 42-55, 59-70, 120-129, 179-187, 241-249, 298-306 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/interpolation.py 210 182 13% 93-105, 127-139, 245-263, 329-351, 433-487, 520-539, 589-616, 677-746 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/measurements.py 315 288 9% 178-236, 298-305, 377-458, 463-466, 500-573, 617-618, 669-670, 721-722, 773, 782-884, 946, 1025, 1086, 1155-1164, 1210-1219, 1275-1293, 1355-1364, 1419-1424, 1459-1498 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/morphology.py 403 368 9% 50-53, 106-122, 208, 210, 218-285, 381, 501-513, 629-636, 775-782, 871-892, 1022, 1097-1107, 1213-1216, 1338-1364, 1443-1447, 1526-1530, 1636-1643, 1681-1695, 1739-1752, 1797-1810, 1872-1947, 1986-2063, 2175-2230 /usr/local/lib/python3.8/dist-packages/scipy/optimize/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py 219 182 17% 20, 23-24, 27-31, 34, 61-92, 102-146, 151-173, 177-178, 208-216, 219, 222-232, 238-242, 246-247, 264-265, 268-270, 278-280, 283-286, 304-305, 313-315, 321, 626-701, 705-707, 711-716, 720-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_constraints.py 180 157 13% 94-101, 136-139, 168-170, 173-176, 215-251, 267-274, 284-294, 304-307, 312-317, 324-411, 419-450 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentiable_functions.py 351 317 10% 31-160, 163-165, 168-170, 173-175, 178-181, 184-187, 190-193, 196-200, 223-420, 423-425, 428-429, 432-434, 437-439, 442-444, 447-449, 452-454, 458-461, 472-489, 492-494, 497-501, 504-505, 508-510, 521-528 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentialevolution.py 364 310 15% 294-308, 475-599, 607-637, 644-652, 666-687, 694, 702-704, 711, 730-849, 869-889, 894-908, 928, 948-959, 962, 965, 969-970, 974-975, 1011-1020, 1035-1144, 1150, 1154, 1158-1159, 1163-1195, 1199-1200, 1205-1206, 1211-1216, 1220-1224, 1228-1233, 1237-1242, 1249-1253, 1261-1262, 1265, 1294-1322, 1325, 1341-1346 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_dual_annealing.py 285 250 12% 55-71, 79-111, 115-127, 153-159, 166-195, 199-205, 209-210, 240-256, 259-277, 280-306, 315-355, 362-370, 373-374, 388-405, 409-425, 602-689 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_hessian_update_strategy.py 134 94 30% 52, 70, 87, 100, 136-145, 151-159, 162, 180-201, 217-220, 231-237, 279, 282-288, 311-312, 329-330, 334-375, 407-408, 412-430 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog.py 78 65 17% 78-110, 155-161, 510-581 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_ip.py 247 217 12% 84-119, 189-329, 345-349, 368-375, 399-412, 427-432, 447-453, 471-502, 534-544, 698-822, 1083-1127 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_rs.py 190 171 10% 47-99, 108-134, 161-237, 249-269, 277, 285-288, 295-310, 327-402, 522-558 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_simplex.py 107 98 8% 89-95, 154-166, 212-229, 355-435, 591-659 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_util.py 489 470 4% 57-68, 92-99, 118-121, 183-386, 497-778, 873-881, 971-1085, 1092, 1100-1130, 1138-1145, 1170-1173, 1241-1293, 1350-1396, 1473-1485 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsap.py 20 17 15% 79-105 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/bvls.py 116 109 6% 13-16, 20-177 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/common.py 295 261 12% 36-56, 108-170, 196-221, 235-247, 284-301, 316-324, 350-363, 371, 392-400, 418-439, 448-466, 499-510, 515-541, 548, 555-565, 571, 578-588, 597-600, 605-615, 620-631, 637-648, 660-671, 679-689, 697-707, 712-722, 730-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/dogbox.py 149 138 7% 65-77, 94-106, 125-149, 154-330 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/least_squares.py 255 229 10% 43-92, 98-105, 109-126, 130-149, 153-162, 169-177, 181-186, 190-195, 199-204, 212-237, 748-940 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/lsq_linear.py 82 70 15% 16-24, 218-317 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf.py 290 278 4% 121-126, 133-205, 210-402, 410-564 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf_linear.py 144 132 8% 53-69, 74-90, 95-142, 147-248 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_minimize.py 176 155 12% 479-636, 756-794, 799-806, 811-829 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_numdiff.py 254 237 7% 46-91, 100-103, 107-114, 147-175, 330-398, 404-441, 445-481, 486-561, 625-639 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_remove_redundancy.py 147 136 7% 30-31, 53-54, 83-92, 96-104, 139-230, 266-357, 393-449 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root.py 89 69 22% 153-203, 207-208, 246-257, 266-305, 369, 434, 476, 513, 550, 590, 654 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root_scalar.py 124 104 16% 30-33, 38-43, 47-49, 53-55, 58, 181-287, 306, 325, 343, 366, 392, 423, 442, 461 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo.py 657 595 9% 417-447, 456-655, 673-707, 721-737, 744-757, 762-778, 782-789, 793-795, 799-800, 803-804, 814-835, 838-845, 855-868, 871-879, 888-899, 907-910, 917-955, 974-1020, 1024-1028, 1031-1034, 1044-1052, 1070-1086, 1102-1104, 1124-1182, 1190-1199, 1203-1206, 1225-1240, 1245-1282, 1290-1292, 1302-1357, 1366-1375, 1384-1390, 1395-1403, 1407-1409, 1415-1421, 1431-1458, 1466-1472, 1476-1508, 1514-1531, 1534-1542, 1550, 1555-1568, 1574-1596, 1601-1605, 1610-1617, 1620-1628, 1631-1645, 1651-1671 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/sobol_seq.py 122 114 7% 30-40, 53-58, 97-102, 140-145, 197-372 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/triangulation.py 359 321 11% 8-46, 49, 56-85, 89-115, 119-141, 146-159, 163-174, 182, 195-225, 231-243, 258-298, 315-362, 372-451, 456-464, 467, 470-471, 477-480, 487-490, 497, 503-504, 513-517, 528-530, 536-569, 572, 575-585, 588-592, 596-600, 603-609, 616-626, 629-661 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_spectral.py 106 93 12% 66-164, 207-238, 245-251, 255, 259 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trlib/__init__.py 6 3 50% 7-12 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion.py 135 114 16% 25-35, 38, 43-45, 50-52, 57-59, 62-65, 70-72, 80-95, 98, 133-266 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/canonical_constraint.py 253 234 8% 43-48, 53-69, 78-91, 101-149, 153-181, 185-221, 225-261, 265-327, 337-390 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py 105 96 9% 14-15, 50-218 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py 175 152 13% 29-30, 33-36, 46-48, 51-57, 62-100, 107-112, 314-544 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/projections.py 164 145 12% 10, 41-55, 62-90, 96-172, 179-233, 240-287, 364-406 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/qp_subproblem.py 215 201 7% 45-63, 99-149, 189-234, 286-303, 308, 313, 364-409, 492-638 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/report.py 32 10 69% 11-16, 23-28, 32 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py 148 124 16% 38-57, 60-61, 64, 67, 80-86, 93-96, 100, 108-114, 130-136, 140, 143-163, 179-195, 199-205, 209-221, 226-240, 251-264, 287-347 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_dogleg.py 40 30 25% 31-35, 47-51, 57-62, 98-124 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_exact.py 139 124 11% 35-41, 80-122, 137-143, 173-185, 218-254, 264-285, 290-432 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_krylov.py 11 7 36% 22-59 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_ncg.py 51 42 18% 33-39, 71-128 /usr/local/lib/python3.8/dist-packages/scipy/optimize/cobyla.py 72 60 17% 139-170, 196-258 /usr/local/lib/python3.8/dist-packages/scipy/optimize/lbfgsb.py 136 123 10% 174-208, 265-380, 412-422, 440-456, 468-478 /usr/local/lib/python3.8/dist-packages/scipy/optimize/linesearch.py 320 299 7% 69-103, 150-186, 267-322, 384-468, 481-502, 512-523, 532-603, 644-659, 666-668, 685-726, 773-802, 852-883 /usr/local/lib/python3.8/dist-packages/scipy/optimize/minpack.py 284 255 10% 26-45, 139-165, 206-259, 384-457, 461-478, 482-491, 495-508, 683-813, 821-844, 848, 852, 856-870, 914-916 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nnls.py 21 16 24% 60-82 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nonlin.py 628 491 22% 139, 144-147, 152-154, 158-160, 269-367, 375-415, 434-453, 456-472, 521-530, 533, 536, 539, 542-547, 552-558, 562, 566, 573-658, 667-678, 681, 684-688, 703-708, 712-718, 723-746, 750-752, 756-758, 762-764, 768-770, 773-781, 784-790, 794-797, 803-808, 814-819, 850-884, 951-973, 977-978, 981, 984-988, 991, 994, 997, 1000-1006, 1046-1051, 1115-1121, 1124-1144, 1147-1167, 1170-1192, 1225-1226, 1229-1230, 1233, 1236, 1239, 1242, 1245, 1248, 1275-1276, 1279, 1282, 1285, 1288, 1291, 1294, 1325-1328, 1331-1332, 1335, 1338, 1341, 1344, 1347, 1350-1353, 1438-1476, 1479-1481, 1484-1491, 1494-1498, 1501-1508, 1511-1524 /usr/local/lib/python3.8/dist-packages/scipy/optimize/optimize.py 1239 1166 6% 59-61, 64-67, 70-74, 115-118, 124-129, 132, 140-145, 152, 159-164, 197-200, 229-238, 270-277, 310-317, 321-329, 435-453, 498-682, 690-707, 765, 809-813, 818-820, 841-869, 945-964, 990-1098, 1253-1271, 1297-1406, 1500-1519, 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/usr/local/lib/python3.8/dist-packages/scipy/signal/_max_len_seq.py 31 25 19% 104-137 /usr/local/lib/python3.8/dist-packages/scipy/signal/_peak_finding.py 225 201 11% 66-81, 138, 194, 248-250, 264-267, 281-293, 307-319, 459-462, 584-590, 625-640, 673-678, 713-723, 935-1006, 1055-1126, 1164-1190, 1283-1299 /usr/local/lib/python3.8/dist-packages/scipy/signal/_savitzky_golay.py 81 70 14% 98-141, 153-165, 179-209, 219-223, 328-353 /usr/local/lib/python3.8/dist-packages/scipy/signal/_upfirdn.py 40 30 25% 59-63, 67-69, 75-86, 90-100, 207-210 /usr/local/lib/python3.8/dist-packages/scipy/signal/bsplines.py 202 177 12% 19, 28-43, 60-114, 125-129, 148-149, 157-167, 175-185, 189-193, 197, 202-206, 210-237, 241-252, 256-267, 292-295, 319-322, 337-358, 373-394 /usr/local/lib/python3.8/dist-packages/scipy/signal/filter_design.py 1094 1014 7% 47-56, 94-117, 180-193, 255-272, 424-477, 562-583, 662-693, 698-706, 821-828, 881-932, 987-1000, 1064-1071, 1095-1130, 1168, 1193-1200, 1230-1240, 1245-1250, 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439, 468, 479, 561-578, 583-592, 596, 606, 610-617, 622, 626, 638-639, 651, 663, 676, 697-702, 722-727, 800, 940-958, 963-972, 976, 987, 991-998, 1003, 1007, 1012, 1016, 1028-1030, 1042, 1055, 1067, 1131, 1207-1208, 1304-1317, 1322-1333, 1337, 1347, 1365-1404, 1411-1421, 1428, 1434-1473, 1479-1482, 1485-1488, 1491-1494, 1501-1508, 1513, 1517, 1522, 1526-1527, 1532, 1536-1537, 1542, 1546, 1558-1561, 1578, 1596, 1609, 1677, 1799-1853, 1861-1867, 1926-2032, 2058-2064, 2114-2133, 2200-2222, 2277-2292, 2358-2373, 2432-2437, 2494-2522, 2529, 2540-2580, 2590-2600, 2613-2636, 2655-2710, 2720-2763, 2777-2886, 2896-2912, 3095-3263, 3322-3379, 3431-3465, 3516-3550, 3615-3648, 3712-3722 /usr/local/lib/python3.8/dist-packages/scipy/signal/signaltools.py 1148 1072 7% 27, 47-50, 55-58, 77-93, 197-259, 265-270, 305-331, 365-391, 419-428, 523-544, 572-655, 736-860, 876-881, 889-892, 904-935, 963-977, 984-985, 996-1002, 1019-1037, 1146-1174, 1267-1296, 1353-1359, 1394-1409, 1437-1460, 1537-1549, 1627-1644, 1681-1692, 1817-1885, 1928-1953, 1997-2009, 2098-2120, 2145-2180, 2208-2210, 2265-2297, 2356-2372, 2377-2397, 2401-2424, 2503-2539, 2598-2644, 2648-2675, 2733-2749, 2832-2924, 3054-3134, 3184-3212, 3254-3297, 3391-3433, 3489-3505, 3557-3684, 3844-3885, 3890-3920, 3924-3927, 4000-4039, 4130-4151, 4202-4245 /usr/local/lib/python3.8/dist-packages/scipy/signal/spectral.py 363 339 7% 142-158, 268-289, 452-457, 584-601, 734-771, 870-895, 996-1022, 1172-1178, 1348-1456, 1566-1576, 1669-1870, 1896-1920, 1959-1981, 2001-2002 /usr/local/lib/python3.8/dist-packages/scipy/signal/waveforms.py 120 107 11% 58-88, 139-162, 224-262, 427-430, 440-483, 577-580, 591-593, 669-681 /usr/local/lib/python3.8/dist-packages/scipy/signal/wavelets.py 136 123 10% 29-76, 90-92, 127-198, 253-261, 301-308, 384-388, 462-473 /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/windows.py 289 243 16% 21-23, 28-31, 36-39, 112-121, 168-174, 226-238, 290-302, 349-357, 442, 501, 548, 609-610, 700-708, 790, 795, 856-878, 925-933, 1020, 1098, 1207-1216, 1271-1279, 1342-1349, 1438-1476, 1545-1563, 1616-1622, 1698-1710, 1876-1970, 1975-1982, 2095-2124 /usr/local/lib/python3.8/dist-packages/scipy/sparse/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/_index.py 221 190 14% 24-27, 35-75, 78-126, 129-150, 157-180, 185-191, 196-202, 205, 208, 211, 214, 217, 220, 223, 226, 229, 232, 235, 238, 242-244, 252-283, 288-322, 326-328 /usr/local/lib/python3.8/dist-packages/scipy/sparse/_matrix_io.py 42 32 24% 16, 63-80, 131-156 /usr/local/lib/python3.8/dist-packages/scipy/sparse/base.py 455 344 24% 71-75, 81-82, 86, 123-131, 157, 182-189, 194-203, 207-208, 212, 223, 239, 250, 254, 257-258, 263-281, 284-287, 295, 313-328, 340, 344, 348, 363, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 407, 410-424, 427, 430-443, 446-455, 466-530, 534, 537, 540, 543, 546-554, 561-564, 567-570, 577-617, 620, 624, 628, 632, 635, 638, 641, 644, 647, 650, 653-675, 678-691, 718, 736-741, 744, 756, 759, 762, 780-782, 791-799, 808-816, 851, 883, 894, 902, 910, 918, 926, 937, 945, 953, 993-1025, 1064-1097, 1124, 1146-1149, 1152-1176, 1179-1189 /usr/local/lib/python3.8/dist-packages/scipy/sparse/bsr.py 315 266 16% 123-214, 225-271, 279, 283-287, 292-293, 299-308, 317, 320, 330, 336, 339, 342-351, 354-364, 367-421, 436-441, 444-463, 468, 479-505, 508, 513-534, 546-561, 568-594, 599-607, 613-627, 635-676, 684-688, 722 /usr/local/lib/python3.8/dist-packages/scipy/sparse/compressed.py 737 654 11% 31-108, 111-123, 130-136, 148-195, 212-215, 219-248, 252-279, 283-313, 316, 322, 328, 334, 344-352, 355, 358, 365-458, 465-475, 478-489, 492-528, 531-539, 548-568, 571, 577, 592-611, 630-635, 642-647, 650-653, 657-668, 672-673, 678-698, 704-730, 736-758, 764-775, 782-793, 797-798, 801-802, 806-823, 826-853, 856-872, 880-911, 918-931, 942-1003, 1010-1017, 1023-1037, 1050-1053, 1068-1074, 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/usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_validation.py 31 25 19% 17-58 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csr.py 135 103 24% 129-137, 143-156, 161-164, 169-186, 191-222, 231, 234-242, 248-256, 263-271, 275, 278-307, 311-313, 316, 319, 322-325, 351 /usr/local/lib/python3.8/dist-packages/scipy/sparse/data.py 184 138 25% 23, 26, 29, 33-35, 38, 41, 44, 47, 50-53, 56-60, 63-68, 71-79, 84-89, 94, 99, 113-119, 126, 135-136, 148-156, 166-187, 191-215, 218-252, 255-289, 321, 353, 376, 399 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dia.py 224 188 16% 79-146, 149-150, 158-164, 167-168, 171-181, 187-225, 230-241, 244, 247-276, 279-282, 287-305, 311-318, 323-343, 349-365, 374-377, 380-392, 420 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dok.py 275 213 23% 23, 79-111, 115, 122, 125-128, 133-136, 139, 145, 151-158, 161, 164, 167, 170-191, 194, 197, 200-201, 204-205, 209-216, 220-227, 230-234, 237-246, 249-277, 280-301, 304-309, 312-316, 320-323, 327-332, 335-338, 341-346, 349-352, 358, 365-374, 380-384, 387-389, 394-404, 409-411, 416, 421-429, 457 /usr/local/lib/python3.8/dist-packages/scipy/sparse/extract.py 22 14 36% 38-42, 101-103, 162-164, 168-171 /usr/local/lib/python3.8/dist-packages/scipy/sparse/lil.py 291 232 20% 89-132, 135-136, 139-140, 143-147, 150-154, 160-172, 175, 181-185, 190-193, 198-206, 210-216, 220-226, 229-231, 234-235, 238, 241, 244-245, 248, 251-252, 255-256, 260-261, 265-271, 289-300, 303, 307-308, 314-324, 328-336, 339-350, 353-360, 363-369, 374-401, 406-426, 431-435, 440, 445-448, 454-484, 512-527, 553 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_expm_multiply.py 255 225 12% 18-23, 28-33, 38-43, 48-55, 140-144, 172-197, 204-223, 313, 342-346, 352, 358-360, 366-369, 375, 399, 414-417, 455-475, 506-511, 556-629, 639-648, 655-677, 684-713 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_norm.py 70 63 10% 15-19, 110-184 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_onenormest.py 199 177 11% 86-119, 130-139, 154-157, 162, 166-174, 178-180, 187-190, 194-197, 204-211, 215, 219, 261-322, 366-468 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/_add_newdocs.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py 197 173 12% 56-59, 63-82, 132-233, 302-324, 386-410, 442-469, 528-607 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py 726 642 12% 280-281, 297-299, 307, 313-364, 367-377, 435-533, 536-573, 576-595, 636-719, 722-759, 762-896, 900-904, 914-917, 922-927, 937-939, 942, 949-951, 961-974, 977-982, 992-1020, 1023-1028, 1032, 1037-1044, 1048-1054, 1058-1089, 1251-1349, 1554-1689, 1694-1713, 1717, 1721, 1804-1910 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py 323 310 4% 34-44, 52-57, 64-72, 77-79, 84-114, 119-125, 287-711 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/interface.py 346 245 29% 140-152, 160-168, 173-175, 184, 196, 222-243, 269-290, 294-298, 324-339, 364-377, 381-384, 387, 390, 407-419, 423-426, 429-432, 435-438, 441-444, 447-450, 453, 456, 459-465, 481, 491, 497, 501, 509-518, 521-524, 527, 530-533, 536-539, 542, 553-556, 559, 562, 565, 568, 573-576, 580, 583, 587, 590, 593-598, 603-610, 613, 616, 619, 622, 625-626, 631-639, 642, 645, 648, 651, 654-655, 660-666, 669, 672, 675, 678, 681-682, 687-695, 698-701, 704, 707, 710, 713, 716-717, 722-725, 728, 731-733, 737-740, 744, 747, 752, 755, 758, 761, 764, 767, 795-823 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/_gcrotmk.py 192 182 5% 66-182, 267-490 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/iterative.py 421 386 8% 73-77, 97-118, 137-198, 209-265, 276-337, 347-414, 514-647, 717-802 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lgmres.py 69 59 14% 128-235 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsmr.py 185 177 4% 197-482 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsqr.py 200 192 4% 81-95, 311-570 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/minres.py 203 196 3% 71-343, 347-363 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/utils.py 56 46 18% 23-27, 31, 65-123 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/matfuncs.py 354 279 21% 77-82, 103-115, 120-130, 155-173, 179-188, 191-193, 196-200, 203-205, 209, 218-235, 238-240, 243-246, 249-251, 255-256, 296, 338, 367-383, 387-390, 394-397, 401-404, 408-411, 415-418, 422-424, 428-430, 434-436, 440-442, 446-454, 458-466, 470-478, 482-490, 493-498, 501-506, 509-514, 517-525, 528-547, 595, 603-677, 700-709, 736-740, 762-764, 783-813, 833-855 /usr/local/lib/python3.8/dist-packages/scipy/sparse/sputils.py 176 146 17% 42-53, 58-63, 70, 80-90, 94, 105-118, 143-171, 176-180, 185, 194-207, 215-225, 229, 235, 241, 245-264, 269-314, 326-331, 338-339, 346-349, 353-356, 360-363 /usr/local/lib/python3.8/dist-packages/scipy/spatial/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/_plotutils.py 78 65 17% 11-27, 31-35, 81-90, 136-148, 212-264 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_procrustes.py 25 20 20% 101-132 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_spherical_voronoi.py 68 57 16% 133-166, 171-206, 217-243, 273-277 /usr/local/lib/python3.8/dist-packages/scipy/spatial/distance.py 633 503 21% 135-165, 170-172, 176-178, 182-183, 194-195, 200-220, 224-240, 244-259, 263-269, 273-286, 290-292, 307-309, 314-329, 334-339, 343-346, 350-357, 451-457, 508-524, 580-581, 620, 661-674, 708-721, 766, 813-820, 883-894, 941-950, 985-991, 1031-1037, 1079-1084, 1122-1130, 1170-1178, 1221-1236, 1286-1296, 1337-1342, 1353, 1397-1412, 1457-1462, 1507-1519, 1565-1578, 1624-1638, 1718, 1722, 1724-1731, 1999, 2006-2012, 2017, 2023, 2027, 2031, 2039-2050, 2069-2094, 2149-2212, 2254-2302, 2334-2339, 2344-2358, 2378-2380, 2403, 2407, 2708-2793 /usr/local/lib/python3.8/dist-packages/scipy/spatial/kdtree.py 419 379 10% 39-55, 78-83, 93-95, 98, 102, 120-126, 140, 154, 168, 182, 243-251, 256, 259, 262, 265, 268, 272-273, 277-281, 284-323, 329-407, 492-547, 550-572, 625-636, 663-705, 731-812, 842-889, 912-942, 978-996 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_groups.py 56 48 14% 6-58, 62-76, 80-90, 94-99, 103-105, 109-140 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_spline.py 176 159 10% 18-25, 30, 48-65, 83-104, 124-151, 168, 188, 220-248, 331-361, 364-404, 424-456 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/rotation.py 470 416 11% 15-17, 29-142, 146-150, 154-158, 162-173, 369-394, 407, 474-479, 568-612, 618, 674-707, 803-860, 907-910, 964-999, 1004, 1052-1073, 1159-1179, 1298-1329, 1397-1406, 1439-1443, 1467-1475, 1510-1527, 1560-1617, 1655, 1703, 1722-1727, 1768-1775, 1783-1838, 1915-1968, 2049-2070, 2090-2114 /usr/local/lib/python3.8/dist-packages/scipy/special/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/_basic.py 524 449 14% 100-112, 179-213, 250-254, 279-285, 305, 325, 345, 365, 397-401, 433-437, 469-473, 481-487, 515-519, 547-551, 602-606, 635-639, 667-671, 699-703, 747-755, 799-807, 888, 926-928, 941-943, 956-958, 971-973, 988, 1032-1034, 1072-1093, 1129-1150, 1201-1226, 1284-1307, 1343-1360, 1373-1380, 1424-1431, 1449-1460, 1476-1487, 1535-1538, 1586-1589, 1624-1639, 1666-1676, 1703-1713, 1740-1749, 1762-1764, 1777-1779, 1792-1794, 1800-1802, 1815-1817, 1830-1832, 1845-1847, 1860-1862, 1878-1880, 1904-1911, 1928-1935, 1984-1996, 2035-2050, 2065-2070, 2120-2158, 2195-2215, 2260-2270, 2333-2336 /usr/local/lib/python3.8/dist-packages/scipy/special/_ellip_harm.py 16 6 62% 97, 155-156, 160, 208-209 /usr/local/lib/python3.8/dist-packages/scipy/special/_logsumexp.py 34 12 65% 96-99, 105-106, 109-110, 118-119, 123, 127 /usr/local/lib/python3.8/dist-packages/scipy/special/_spherical_bessel.py 18 12 33% 53-56, 104-107, 153-156, 202-205 /usr/local/lib/python3.8/dist-packages/scipy/special/lambertw.py 4 1 75% 107 /usr/local/lib/python3.8/dist-packages/scipy/special/orthogonal.py 525 464 12% 128-151, 154-157, 160-173, 191-216, 264-290, 331-346, 394-404, 442-457, 503-524, 569-586, 630, 662-676, 744-760, 789-796, 825-831, 860-871, 898-910, 947-1020, 1050-1067, 1113-1124, 1157-1172, 1229-1248, 1282-1297, 1345-1364, 1400-1408, 1455-1463, 1499-1512, 1557-1566, 1603-1608, 1652-1659, 1696-1714, 1758-1765, 1802-1821, 1865-1866, 1894-1902, 1945-1952, 1980-1985, 2029-2039, 2080-2094, 2137-2143, 2170-2182 /usr/local/lib/python3.8/dist-packages/scipy/special/sf_error.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/spfun_stats.py 14 8 43% 86-95 /usr/local/lib/python3.8/dist-packages/scipy/stats/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_binned_statistic.py 159 145 9% 167-182, 336-349, 514-634, 640-675, 681-706 /usr/local/lib/python3.8/dist-packages/scipy/stats/_constants.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_continuous_distns.py 2994 1970 34% 35-36, 50-54, 97, 100, 103, 106, 109, 152, 155, 158, 161, 164, 180, 184, 188, 192, 196, 200, 204, 208, 235, 239, 242, 245, 248, 251, 254, 257, 260, 263, 266, 273-300, 342, 345, 348, 351, 354, 382, 385, 388, 391, 394, 422, 425, 428, 431-435, 438, 449, 461-463, 470-472, 482-486, 514, 520, 523-525, 528, 531, 534-539, 542-553, 563-658, 690-693, 697, 700, 703, 706-724, 754, 757, 760, 763-775, 778-779, 834-840, 843-852, 855, 858, 861, 864, 867, 870-887, 890-894, 948, 951, 954, 957, 960, 963, 969, 972-973, 1011, 1014, 1017, 1021, 1024, 1027, 1030, 1033, 1036, 1039, 1068, 1071, 1074, 1077, 1080, 1083, 1086, 1090-1091, 1130-1131, 1137, 1140-1141, 1144, 1147, 1150-1155, 1187, 1191, 1194, 1197, 1200, 1203, 1206, 1209-1213, 1242, 1245, 1248, 1251, 1281-1284, 1288-1289, 1292-1293, 1296-1297, 1300-1301, 1304-1305, 1308-1309, 1338-1341, 1345-1347, 1350-1351, 1354-1355, 1358-1360, 1363, 1369, 1401, 1405, 1408, 1411, 1414, 1417, 1420, 1423, 1426, 1429, 1436-1473, 1518-1520, 1525-1530, 1533-1535, 1538-1540, 1543-1545, 1548-1552, 1602, 1605-1609, 1612-1613, 1616, 1652, 1655-1657, 1660, 1663, 1666, 1669, 1705-1709, 1714, 1717, 1721, 1724-1725, 1733-1739, 1766, 1771, 1774, 1777, 1808, 1814, 1817-1821, 1824, 1827, 1830, 1833-1860, 1897, 1900, 1904, 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5044-5045, 5051, 5092, 5095, 5098, 5101, 5104-5105, 5108-5112, 5208, 5211-5249, 5254, 5257-5289, 5295, 5298-5327, 5333-5352, 5358-5368, 5408, 5412, 5415, 5418, 5421-5422, 5471-5473, 5476, 5479, 5482, 5485, 5488-5492, 5495-5513, 5544, 5547, 5550, 5553-5558, 5592, 5595, 5598-5599, 5604-5605, 5608-5609, 5612-5613, 5616-5617, 5660, 5669-5676, 5681, 5684, 5687-5691, 5697-5707, 5739, 5742, 5748-5751, 5754-5757, 5760, 5763, 5766, 5769, 5772-5782, 5814, 5817-5820, 5823-5837, 5840, 5843, 5852-5876, 5906, 5909, 5912, 5915, 5918-5942, 5945, 5977, 5980, 5983, 5986, 5989, 5992, 5995-5996, 5999, 6053-6074, 6081, 6084-6090, 6097-6103, 6111-6116, 6119-6124, 6127-6139, 6142-6146, 6178, 6181, 6184, 6187, 6190, 6193, 6199, 6234, 6238, 6241, 6272, 6275, 6278, 6281, 6323, 6326, 6329, 6332, 6335, 6338-6339, 6370, 6374, 6377, 6380, 6383, 6386, 6389, 6392, 6395-6396, 6402, 6458, 6461, 6465, 6468, 6471, 6474, 6477, 6480, 6517, 6521-6523, 6526, 6529, 6538, 6541-6544, 6579, 6582, 6585-6588, 6591, 6629, 6632, 6635, 6638, 6643-6645, 6648, 6651, 6684, 6687, 6690-6700, 6703, 6706-6710, 6713-6725, 6754, 6757-6759, 6768, 6778-6783, 6812, 6815, 6823-6832, 6835-6844, 6847, 6850, 6856, 6885, 6888, 6892, 6895, 6898, 6901, 6906-6913, 6916-6917, 6933, 6939-6946, 6950-6967, 6972-6973, 6978-6981, 6986-7004, 7010-7013, 7018-7037, 7042-7045, 7050-7063, 7069-7072, 7077-7114, 7139, 7142, 7145, 7148, 7151, 7154, 7157, 7160, 7163, 7166-7185, 7189-7213, 7244, 7247-7250, 7253, 7256, 7259, 7262-7264, 7272, 7293, 7296, 7299, 7302, 7305, 7308, 7383-7455, 7493, 7497, 7500, 7503, 7506, 7538, 7542, 7545, 7548, 7551, 7580, 7584, 7587-7604, 7607-7610, 7613, 7655, 7658, 7661-7663, 7666-7668, 7671, 7674, 7677-7678, 7681, 7728, 7731, 7734, 7737, 7740, 7743, 7746, 7799-7809, 7815-7824, 7830-7841, 7844-7861, 7867-7885, 7893, 7904, 7952-7953, 7959, 7965, 8053-8070, 8076, 8082, 8088, 8092-8093, 8097-8101, 8107-8109 /usr/local/lib/python3.8/dist-packages/scipy/stats/_discrete_distns.py 446 296 34% 43, 46, 49, 52-54, 58, 61-63, 66-67, 70-73, 76-84, 87-89, 119, 122, 126, 129, 134, 137, 140, 143, 146, 149, 193-194, 197, 200, 203-205, 208, 211-230, 263, 266, 270, 273-274, 277-278, 282-283, 286-289, 292-298, 331, 334, 337, 340, 343-344, 347, 350-351, 354-356, 359-364, 433, 436, 439-441, 444-449, 454, 459-471, 474-476, 483-489, 492-501, 504-513, 545, 548, 552, 555-567, 598, 601, 604-605, 609, 612-613, 616-617, 620-623, 626-631, 666, 669, 672-673, 676, 679-680, 683-686, 690-691, 694-698, 701-702, 731, 734, 739-740, 743-744, 747-751, 754-764, 794, 797, 801-802, 805-806, 809-812, 815-821, 825-836, 839, 871, 874, 878-879, 882, 915, 918-921, 924-929, 932-935, 938, 976-977, 981-985, 988-992, 995-999, 1040-1043, 1046, 1049, 1052, 1055, 1058, 1061, 1064-1077 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distn_infrastructure.py 1393 1036 26% 42, 361-363, 367-395, 402-406, 413-417, 424-432, 436, 440, 443, 446, 449, 452, 455, 458, 461-463, 466, 469, 472-474, 477, 480, 483, 486, 489, 492, 495, 498, 501, 508-512, 516, 541-545, 567-571, 575, 579, 607, 611, 614, 617-620, 640-649, 665, 668, 671, 679, 733-734, 742-751, 768, 771, 778, 785-788, 801-859, 871-874, 895, 898-899, 902-903, 912-914, 917-918, 921, 924-925, 928, 931, 961-995, 1024-1110, 1135-1146, 1165-1192, 1219, 1241-1245, 1267-1271, 1293-1295, 1321-1328, 1349-1351, 1362-1368, 1612, 1621, 1629, 1643-1651, 1654, 1657-1678, 1683, 1686-1687, 1692, 1695, 1698, 1702, 1705-1706, 1709, 1736-1752, 1778-1795, 1819-1837, 1861-1880, 1904-1922, 1949-1968, 1992-2014, 2038-2060, 2063, 2066-2072, 2082-2089, 2092-2102, 2109-2114, 2118-2121, 2132-2176, 2276-2314, 2336-2380, 2402-2412, 2415-2438, 2516-2540, 2546-2550, 2554-2609, 2663-2676, 2822, 2851, 2878, 2885, 2893, 2912-2921, 2924, 2927, 2930, 2933-2935, 2938-2939, 2968-2969, 2991-3006, 3028-3044, 3066-3084, 3106-3125, 3147-3164, 3189-3207, 3229-3248, 3270-3294, 3297-3301, 3356-3386, 3394-3428, 3449-3463, 3475-3517, 3532, 3535, 3539-3541, 3544-3546, 3551-3557, 3560, 3563-3564, 3593-3602 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distr_params.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_hypotests.py 43 36 16% 80-132 /usr/local/lib/python3.8/dist-packages/scipy/stats/_multivariate.py 972 690 29% 50-53, 81-88, 108, 156-173, 177-179, 203, 207, 210-213, 223, 227, 361, 373-426, 434-444, 469-471, 493-497, 519-523, 550-555, 587-594, 626-633, 657-661, 681-683, 733-741, 744-747, 750, 753, 756-759, 762, 774-776, 938, 948-1005, 1013-1019, 1050-1055, 1078-1085, 1107, 1131-1142, 1175-1179, 1182-1186, 1189, 1192, 1227-1233, 1237-1277, 1299, 1412, 1429-1430, 1448-1452, 1470-1474, 1490-1493, 1510-1514, 1531-1539, 1559-1561, 1569-1570, 1573, 1576, 1579, 1582, 1585, 1588, 1739, 1742-1768, 1775-1805, 1808-1819, 1851-1864, 1887-1894, 1917, 1933, 1948-1950, 1966-1970, 1988-1990, 2006-2010, 2025-2027, 2051-2072, 2098-2118, 2142-2150, 2169, 2197-2199, 2223-2225, 2250-2253, 2256-2260, 2263, 2266-2267, 2270-2271, 2274-2275, 2278-2281, 2284, 2325-2357, 2471, 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/usr/local/lib/python3.8/dist-packages/scipy/stats/kde.py 183 151 17% 194-209, 231-265, 293-320, 344-355, 375-388, 411-438, 465-475, 485, 495, 548-565, 571-581, 593, 600-632, 636-640, 644-648 /usr/local/lib/python3.8/dist-packages/scipy/stats/morestats.py 829 760 8% 127-136, 194-211, 278-306, 349-358, 416-421, 440-452, 457-471, 585-627, 705-718, 797-812, 894-910, 916-945, 1034-1059, 1129-1165, 1173-1202, 1270, 1345-1363, 1370-1385, 1470-1481, 1533-1536, 1606, 1661-1678, 1771-1825, 1853-1869, 1897-1906, 2014-2070, 2113-2166, 2225-2247, 2307-2371, 2421-2465, 2472-2475, 2548-2600, 2670-2725, 2847-2938, 3080-3162, 3167-3186, 3225-3255, 3295-3307, 3349-3361 /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats_basic.py 947 845 11% 61-67, 71-79, 83-89, 116-128, 151-160, 196-211, 235-259, 299-324, 328-330, 335, 373-399, 459-528, 569-663, 676-727, 759-782, 798-828, 870-884, 924-938, 942-949, 982-996, 1037-1066, 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96-119, 153-172, 208-241, 283-289, 378-434 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw.py 112 92 18% 35-43, 114-143, 177-181, 225-226, 277-305, 345-369, 411, 453, 500, 563, 624, 695, 752, 840-848, 912-921, 936-949 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw3d.py 35 31 11% 33-63, 88-114 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw_nd.py 19 16 16% 45-51, 97-108 /usr/local/lib/python3.8/dist-packages/skimage/exposure/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/skimage/exposure/_adapthist.py 106 95 10% 78-98, 126-233, 256-287, 312-317 /usr/local/lib/python3.8/dist-packages/skimage/exposure/exposure.py 138 117 15% 23-35, 61-74, 122-144, 181-184, 217-223, 254-266, 292-307, 405-428, 433-434, 485-494, 528-537, 577-586, 629-640 /usr/local/lib/python3.8/dist-packages/skimage/exposure/histogram_matching.py 20 17 15% 9-19, 53-70 /usr/local/lib/python3.8/dist-packages/skimage/feature/__init__.py 25 4 84% 35-36, 46-47 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56% 20-23, 35 /usr/local/lib/python3.8/dist-packages/skimage/io/_io.py 44 22 50% 45, 51, 55-56, 59-61, 92, 126-136, 157-159, 179, 201 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/imageio_plugin.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/matplotlib_plugin.py 86 67 22% 44-58, 70-78, 97-112, 148-165, 176-200, 207-208 /usr/local/lib/python3.8/dist-packages/skimage/io/collection.py 152 119 22% 47-52, 77-78, 85-95, 175-205, 209, 213, 216-239, 258-313, 317-323, 327-328, 332, 335, 347, 366, 390, 435-443, 448 /usr/local/lib/python3.8/dist-packages/skimage/io/manage_plugins.py 136 22 84% 78, 115-116, 123, 188, 192-196, 202-206, 257, 293, 304-306, 330-331, 344-347 /usr/local/lib/python3.8/dist-packages/skimage/io/sift.py 27 20 26% 41-69, 73, 77 /usr/local/lib/python3.8/dist-packages/skimage/io/util.py 27 13 52% 24-41 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/usr/local/lib/python3.8/dist-packages/skimage/morphology/_skeletonize.py 142 125 12% 80-90, 166-176, 184-225, 327-356, 436-510, 518, 559-576, 628-659 /usr/local/lib/python3.8/dist-packages/skimage/morphology/_util.py 57 50 12% 37-54, 92-126, 167-184, 207-210, 252-261 /usr/local/lib/python3.8/dist-packages/skimage/morphology/binary.py 25 14 44% 40-43, 75-78, 110-112, 144-146 /usr/local/lib/python3.8/dist-packages/skimage/morphology/convex_hull.py 58 46 21% 15-18, 50-93, 134-164 /usr/local/lib/python3.8/dist-packages/skimage/morphology/extrema.py 81 70 14% 23-31, 37-45, 114-170, 236-263, 373-426, 530 /usr/local/lib/python3.8/dist-packages/skimage/morphology/grey.py 106 80 25% 32-49, 80-81, 106-127, 181-186, 241-252, 299-302, 349-352, 405-426, 480-489 /usr/local/lib/python3.8/dist-packages/skimage/morphology/greyreconstruct.py 59 56 5% 126-211 /usr/local/lib/python3.8/dist-packages/skimage/morphology/max_tree.py 56 43 23% 118-143, 244-254, 335-346, 457-472, 565-579, 662-670 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464 15% 193-202, 217-227, 254-310, 332-347, 358-377, 382-384, 408, 430, 442-448, 460-466, 494-495, 512-513, 520-522, 525, 528, 532-536, 539, 551-555, 558-559, 563-579, 583, 600-615, 634-659, 662-665, 689-695, 703-709, 712-721, 737-787, 791, 805-825, 843-879, 893-895, 899, 917-927, 955-1060, 1073-1150, 1154-1159, 1165-1171, 1185-1247, 1272-1282, 1286-1294, 1318-1326 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/optimize/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/optimize/calibrator.py 32 20 38% 47-55, 75-78, 102-105, 118-121 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/util.py 175 140 20% 68-71, 83-92, 110-129, 145-160, 172-176, 196-218, 223-229, 252-269, 284-288, 302-327, 341-356, 370-387, 413-507 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/wrap_toco.py 10 2 80% 32, 43 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/model_flags_pb2.py 45 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/toco_flags_pb2.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/types_pb2.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/__init__.py 116 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/arg_defaults.py 30 7 77% 66-70, 81, 87-88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/asserts.py 18 8 56% 31-47, 51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/break_statements.py 68 47 31% 30-31, 34, 41-48, 52-63, 66-71, 74-105, 108-149 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46, 53-57, 60-65, 90-107, 110-123, 130-148, 151-180, 190-199, 202, 208-211, 214-217, 220-223, 226-229, 232-234, 238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/logical_expressions.py 66 23 65% 56-59, 72-77, 90-112, 119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/return_statements.py 187 49 74% 87, 99-102, 105-109, 122-126, 130-131, 144, 147, 166, 177, 258-266, 282-293, 296-315, 323-327, 330-331, 376 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/slices.py 35 22 37% 37-45, 49-56, 59-80, 85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/ag_ctx.py 37 3 92% 58, 70, 73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config_lib.py 28 3 89% 48, 60, 64 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785-788, 831-836 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/conversion.py 322 94 71% 87, 117, 134, 151, 210, 308-310, 345, 350, 355, 388, 397-398, 406-410, 418-419, 440, 443-444, 451-453, 459-465, 474-476, 482-484, 509, 514-521, 526, 528-529, 538-632, 638-639, 683-690, 700, 704, 715, 724, 751, 758-759 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/directives.py 16 7 56% 44-46, 95-98 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/special_functions.py 33 20 39% 33-45, 53, 83-88, 113-119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/__init__.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/control_flow.py 449 379 16% 109-119, 128-135, 142-191, 204-234, 241-263, 272-297, 341-372, 377-401, 407-439, 452-485, 497-526, 538-586, 606, 621-677, 684-704, 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428-436, 440, 444-446, 454-462, 466, 470-472, 477-497, 501-507, 514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/slices.py 55 31 44% 55-67, 72, 77-81, 86, 91-92, 97, 117-125, 130, 135, 140, 145-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/special_values.py 27 10 63% 52, 55, 58-63, 66, 81, 93, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/anno.py 59 7 88% 40, 107, 134-138 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/ast_util.py 214 117 45% 101-105, 108-109, 112-113, 130-132, 137-142, 149-151, 154-157, 160-161, 164-170, 173-206, 222-227, 262-275, 294, 299, 309, 315-316, 318-319, 325, 330, 341, 350-351, 354-359, 362-378, 381-388, 392-394 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/cfg.py 416 121 71% 86-93, 132, 136-143, 188, 200, 334-335, 340, 356, 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258, 279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/pretty_printer.py 87 69 21% 30-33, 36-38, 41, 44, 47, 50, 53, 56-57, 62-125, 129-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/qual_names.py 141 45 68% 48, 51, 58, 61, 70, 77, 80, 88, 96, 104, 107, 120-122, 134-138, 153-161, 173, 180, 187-191, 198, 203, 210-215, 244, 252, 265-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/activity.py 341 103 70% 121-124, 134-135, 189, 204-205, 244-249, 261-264, 269-274, 279, 281, 290-300, 324, 327, 330-332, 347, 352-361, 364, 372-382, 387, 400-404, 407, 445-456, 461-463, 466, 469, 472, 475, 481-497, 554-568, 571-579, 586 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/annos.py 17 1 94% 30 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/context_managers.py 18 10 44% 38-49 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/misc.py 26 15 42% 42-52, 57-59, 63-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/py_func.py 48 37 23% 64-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/tensor_list.py 32 16 50% 28-40, 47-49, 52, 55-56, 59, 62, 65, 68 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/tensors.py 16 3 81% 39, 47, 53 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/testing.py 17 8 53% 30-37 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/type_check.py 7 1 86% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/client_lib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/device_lib.py 15 8 47% 34-42 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/pywrap_tf_session.py 31 11 65% 53-59, 66-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py 620 490 21% 57, 62, 66, 70, 74, 78, 84, 141, 192, 201, 206-207, 231, 245, 261-278, 301-316, 319, 322-326, 349-362, 374-379, 382, 386-396, 408-413, 416, 419-422, 434-437, 440, 443-446, 476-498, 501-502, 515, 523, 544-570, 582, 597-600, 604, 608, 612, 616, 619, 650-704, 731-742, 753-755, 759-772, 777, 787, 791, 846, 952-967, 1014, 1038-1089, 1095-1184, 1222-1309, 1340-1361, 1364-1384, 1387-1388, 1396-1411, 1417-1437, 1441, 1446, 1454-1462, 1467-1479, 1485-1487, 1504-1505, 1586-1589, 1592-1601, 1604-1641, 1669-1675, 1738-1769, 1773-1784 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/timeline.py 288 227 21% 60-62, 81-88, 97-102, 112-118, 132-135, 148-150, 163-165, 180-183, 198-200, 215-217, 230-232, 244-246, 257-262, 285-292, 297, 302, 307, 312, 317, 322, 327, 335, 343, 362-372, 376-378, 382-384, 389-397, 401-414, 424-441, 453-455, 459-463, 467, 471-481, 485-512, 517-561, 569-608, 611-617, 633-636 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/compat.py 32 7 78% 49, 54, 119, 162-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/v2_compat.py 60 19 68% 92-113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/mlir.py 9 1 89% 41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/trt_convert.py 455 365 20% 58, 80-81, 86-88, 93-95, 105-108, 199-245, 258-284, 309-371, 384, 389-395, 487-542, 547-559, 562-569, 573-580, 587-592, 596-641, 649-654, 683-747, 763-830, 834-835, 842-844, 847-849, 861-867, 870, 873, 995-1022, 1033-1036, 1041-1047, 1051-1056, 1075-1124, 1146-1187, 1195-1266, 1340-1357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/jit.py 36 21 42% 36-37, 90-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/xla.py 240 189 21% 115-122, 153-157, 160-168, 173-193, 198-266, 270-283, 286-288, 296, 301-303, 329-406, 430-441, 460-493, 507-528, 542-548, 555, 558-563, 566, 571-581, 598-629 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/__init__.py 64 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/batching.py 107 64 40% 81-86, 133-136, 179-192, 243-255, 282-285, 293-310, 315, 323-361, 364, 368, 394-422, 426 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/cardinality.py 27 8 70% 66, 95-98, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/counter.py 23 6 74% 51-54, 60, 66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute.py 70 45 36% 48-67, 71, 75, 88-119, 123, 130-133, 137, 150-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute_options.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/enumerate_ops.py 12 3 75% 55-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/error_ops.py 17 6 65% 50-53, 61-66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/get_single_element.py 13 3 77% 62-66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/grouping.py 177 125 29% 60-64, 106-124, 177-244, 252-268, 272-276, 283, 293-349, 353, 359, 362, 367, 375-388, 393-401, 407-413, 418-428, 433, 436, 439, 453-455, 459, 463, 467 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/interleave_ops.py 86 46 47% 95-101, 108-135, 138, 142, 169-226, 231, 272-276, 281, 290-291 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/iterator_ops.py 64 37 42% 32-38, 94-95, 184-221, 227-238, 243, 266-277, 281-284, 287, 290, 301-302, 313 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/optimization_options.py 76 10 87% 57, 63-66, 191, 209, 211, 232, 256, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/parsing_ops.py 61 41 33% 37-117, 121, 161-180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/prefetching_ops.py 103 71 31% 52-56, 72-80, 97-212, 220-225, 233-247, 250, 254, 257, 278-281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/random_ops.py 30 7 77% 37-40, 44, 53-54, 60 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/readers.py 300 228 24% 50-55, 59-63, 67-70, 87-107, 113-127, 134-151, 159-179, 184-203, 209-213, 268-311, 424-558, 584, 677-720, 724, 742-745, 854-929, 948, 970-985, 1016-1027, 1031, 1040-1041, 1050-1053 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/resampling.py 88 63 28% 56-106, 123-129, 147-174, 180-196, 201-202, 218-227, 295-304 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/scan_ops.py 63 44 30% 38-144, 147, 151, 154, 178-181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/shuffle_ops.py 28 11 61% 34-50, 106-109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_aggregator.py 23 6 74% 64-77, 128, 140 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_ops.py 32 14 56% 44-48, 67-71, 90-94, 101-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_options.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/take_while_ops.py 25 11 56% 33-49, 52, 69-72 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/threading_options.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/unique.py 20 8 60% 45-48, 56-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/writers.py 22 7 68% 77-79, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/dataset_ops.py 1381 743 46% 203, 227-240, 252-279, 285, 289, 293, 306-318, 329, 354, 357, 367, 385, 406, 420, 423-427, 475-484, 640, 658-665, 668-673, 676, 734, 736, 744, 785-823, 840-846, 913, 958, 987, 1057-1084, 1105, 1137-1138, 1198, 1248, 1267, 1286, 1354, 1387, 1489-1497, 1620-1623, 1748-1751, 1780, 1804-1810, 1889-1891, 1919-1996, 2023-2024, 2051, 2064-2078, 2087, 2103, 2106-2150, 2186, 2189-2204, 2218, 2232, 2246, 2254, 2260, 2265, 2278, 2283, 2289, 2294, 2298, 2302, 2307, 2311, 2315, 2320, 2324, 2328, 2332, 2336, 2345, 2351-2355, 2393-2401, 2412, 2421, 2427, 2446, 2450, 2454, 2459, 2463, 2469, 2476-2477, 2480, 2483, 2486, 2489, 2492, 2496, 2499, 2505-2524, 2540-2545, 2574-2579, 2597-2600, 2619, 2639, 2659, 2750, 2754, 2756, 2758, 2761, 2771-2774, 2782, 2858-2873, 2877, 2885-2902, 2906, 2913-2914, 2917, 2921, 2927-2929, 2933, 2948, 2961, 2985, 2988, 2992, 2995, 2999-3002, 3013, 3016, 3021-3023, 3026-3028, 3031, 3034, 3037, 3088-3092, 3097, 3104, 3144, 3168, 3172-3173, 3181-3202, 3226, 3230-3231, 3244, 3250, 3256, 3325-3342, 3345, 3349, 3357-3390, 3393, 3397, 3405-3415, 3423-3430, 3434-3451, 3454, 3458, 3471-3474, 3477-3485, 3495-3501, 3506, 3514-3529, 3540-3543, 3546-3554, 3562-3566, 3571, 3601-3627, 3635-3641, 3649-3655, 3663-3672, 3680-3705, 3709, 3716, 3719, 3722, 3725, 3765-3774, 3794-3826, 3843-3849, 3855-3875, 3885-3952, 3956, 3974-3989, 3992, 3996, 3999, 4014-4051, 4054, 4058, 4061, 4073, 4085, 4100-4120, 4123, 4127, 4130, 4146-4209, 4213, 4217, 4220, 4228-4246, 4249, 4252, 4273, 4289-4313, 4317, 4324-4331, 4334, 4356, 4358, 4373-4383, 4391-4400, 4408-4415, 4437-4448, 4455-4459, 4463, 4471-4488, 4492, 4498-4534, 4545-4553, 4564-4572 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/iterator_ops.py 253 111 56% 70-72, 99-117, 189-220, 273-298, 311-316, 333-371, 418-428, 439-442, 457, 471, 485, 497, 501-504, 512-516, 540-541, 581, 591, 597, 628, 647-661, 671-672, 686, 700, 714, 740-741, 745-748, 762, 785, 797-806, 809-810, 828 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py 291 224 23% 43-140, 144, 148, 160-182, 186, 190, 196-208, 232-302, 306-320, 324-332, 335-340, 344-346, 350-365, 370, 381-385, 388-400, 412-414, 418, 421, 425-431, 435-437, 440, 450, 493-566, 574-582, 585, 588, 591-594, 597-602, 606, 610 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/optional_ops.py 77 26 66% 59, 75, 85, 98-103, 121, 132-133, 136, 141-143, 155, 159, 170, 174, 177, 181, 184, 188, 192, 195, 198, 201 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/readers.py 198 115 42% 46-64, 80-90, 114-127, 131, 160-172, 176, 188-190, 196, 200, 216-229, 233, 242-291, 295, 299, 302, 336-349, 356, 362, 366, 378-380, 390, 398, 402, 430-447, 451, 488-505, 509, 524-527, 533, 537, 545-547 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/convert.py 23 13 43% 30-34, 49-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/nest.py 101 35 65% 47-50, 69-70, 75, 88-89, 91, 176, 180, 186, 225-244, 297, 302, 308, 314-320, 462 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/options.py 59 30 49% 23, 38-43, 46-49, 52-55, 81-84, 115, 120, 124, 131-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/random_seed.py 21 10 52% 42-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/structure.py 178 72 60% 45, 51, 57, 64, 100-114, 146-172, 198, 252, 350, 394-404, 435-439, 445-450, 456-462, 476, 486, 489, 493, 496, 499, 502, 506, 509, 512, 515, 518, 521, 524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/traverse.py 21 14 33% 39-56 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/__init__.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/analyzer_cli.py 581 520 10% 84-127, 151-158, 173-418, 444-458, 473-476, 479, 502-598, 616-639, 656-674, 690-738, 757-823, 834, 850-872, 891-908, 925-1050, 1069-1086, 1089-1095, 1107, 1113-1168, 1183-1240, 1244-1271, 1301-1361, 1404-1469, 1495-1520, 1533-1544, 1556-1579, 1601-1659 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_config.py 81 60 26% 41-50, 53-55, 71-97, 114-118, 121, 124-128, 139-147, 150-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_shared.py 177 136 23% 69-82, 99-110, 126-140, 144-147, 185-208, 229, 248-258, 264-273, 301-383, 405-431, 445-493 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/command_parser.py 207 177 14% 37-40, 43-47, 50, 72-101, 118-148, 164-171, 187, 204-216, 234-240, 259-281, 299-310, 328-339, 359-403, 426-439, 454-468, 486-491, 506-550 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/debugger_cli_common.py 451 353 22% 44-45, 49, 73-77, 94-107, 110, 123-132, 145-151, 198-212, 217, 221, 225, 228, 246-267, 285-300, 310-332, 343-345, 348, 359-362, 373-375, 403-431, 456-527, 563-585, 625-655, 682-716, 727, 741-757, 767, 783-790, 793-795, 807-812, 825-843, 850, 883-896, 909-915, 929-934, 948-953, 974-981, 992-1001, 1019-1023, 1026-1040, 1043-1047, 1051, 1063-1075, 1088, 1103-1105, 1125-1127, 1131, 1135, 1139, 1142, 1145, 1148, 1161-1162, 1170, 1173, 1176, 1179, 1194-1199, 1221-1248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/evaluator.py 52 37 29% 69-103, 115-116, 131-152 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/profile_analyzer_cli.py 302 252 17% 58-77, 103-131, 134, 137, 140, 143, 173-195, 209-220, 236-380, 396-439, 447-474, 504-575, 586-592, 613-733, 737-742, 756-762, 765, 786-802 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/tensor_format.py 240 215 10% 67-69, 103-199, 233-279, 321-403, 407-426, 449-481, 485, 503-568 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/ui_factory.py 23 16 30% 51-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/check_numerics_callback.py 122 89 27% 103-106, 114-119, 156-212, 216, 226-232, 242-289, 314-328, 414-419, 437-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/common.py 22 11 50% 44, 59-71, 86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_data.py 530 403 24% 53-56, 70-71, 74-76, 80, 99-102, 121-140, 144-148, 152-156, 160, 164, 168, 182, 199, 219-231, 241-242, 252-254, 266-268, 307-330, 334, 341, 350, 361, 375, 385, 395, 405, 415, 425, 435, 442, 455, 485-498, 502-522, 555-576, 580-581, 584-590, 594-598, 602-606, 621-623, 636-655, 669-673, 684, 717-718, 723-730, 739, 748, 771-799, 802-809, 812-817, 834-886, 906-918, 922, 933-935, 953-956, 970-971, 985-986, 1007-1020, 1036-1046, 1062-1066, 1085-1093, 1121-1137, 1141-1151, 1193-1229, 1249-1258, 1266, 1284-1295, 1313-1322, 1339-1344, 1363-1378, 1397-1415, 1448-1464, 1490-1497, 1522-1528, 1560-1567, 1594-1601, 1618-1625 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_events_writer.py 52 29 44% 51-54, 64-66, 76-79, 89-92, 102-104, 113-115, 125-128, 132, 137, 146, 150, 154, 157-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_gradients.py 120 82 32% 38-39, 53-65, 85-98, 102, 106, 109, 112, 157-169, 215-222, 267-284, 287-290, 304-306, 324-329, 338, 341-346, 353, 360-363, 369, 403-417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_graphs.py 237 183 23% 40-46, 50-51, 66-67, 83, 98, 116-139, 170-178, 191-214, 217, 220, 225-234, 241-266, 277-309, 320-329, 333-337, 346-354, 358-365, 372-393, 401-405, 413-431, 435, 440, 445-446, 450, 454, 458, 462, 466, 470, 474, 478, 503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_utils.py 69 60 13% 61-79, 137-197, 252-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/dumping_callback.py 301 237 21% 61, 67-68, 73, 77, 89-115, 125-133, 137, 141-143, 147, 151, 155-159, 176-189, 202-205, 216-232, 242-267, 288-301, 334-421, 453-514, 526-571, 590-608, 611-617, 632-643, 740-807, 819-826 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/op_callbacks_common.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/profiling.py 41 25 39% 44-56, 62, 76-80, 90-100, 104, 108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/source_utils.py 135 110 19% 44, 48-49, 53-54, 58, 78-84, 112-125, 145-158, 192-225, 262-325, 353-383 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/dumping_wrapper.py 42 28 33% 69-90, 110-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/framework.py 279 191 32% 130-131, 148-149, 177-178, 204-209, 259-270, 298-305, 314, 346-379, 383, 387, 391, 395, 428-517, 530-588, 605-634, 640-641, 645, 650, 654, 658, 661, 667-676, 679-684, 688, 691, 703, 733-734, 753, 806, 809-811, 814, 818-819, 822, 828-831, 867-874, 878, 915-923, 928, 948-951, 975-984, 989 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/grpc_wrapper.py 58 38 34% 57-66, 100-118, 137, 140, 146-151, 155-159, 193-210, 220-224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/hooks.py 95 67 29% 60-65, 83-86, 89, 92-141, 146-148, 176-180, 183, 186-217, 220, 256-264, 277-301, 335-349, 352-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/local_cli_wrapper.py 237 197 17% 80-131, 136, 139-206, 218, 230, 242-277, 280-285, 289-304, 320-374, 377-378, 399-446, 449-453, 462-468, 471-485, 488-513, 525-576, 579-603, 613, 633-642 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/all_reduce.py 392 355 9% 44-57, 71-75, 95-128, 145-157, 176-190, 223-251, 277-294, 317-356, 370-374, 394-423, 469-477, 496-518, 532-555, 582-589, 608-626, 640-645, 666-682, 701-711, 730-762, 767-776, 781-785, 790-791, 797-800, 820-842, 848-853, 860-865 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/central_storage_strategy.py 34 11 68% 56-70, 75, 103, 144, 162, 180, 246, 255-260 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py 173 108 38% 36-39, 44-56, 99, 117, 144-154, 172, 183-198, 202, 218-223, 227, 231, 235, 239, 243, 262-265, 269, 273, 305-321, 347-395, 411-415, 419, 423, 427, 431, 435, 441, 446, 450 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py 77 49 36% 30-31, 83-104, 116-149, 152-162, 166, 170, 174, 180, 184, 188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py 52 34 35% 29, 76-94, 112-120, 135-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py 160 132 18% 39-86, 96-107, 121-125, 135, 146-155, 164, 233-275, 280, 284, 288, 298-301, 321-358, 372, 386-394, 401-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py 78 42 46% 36-39, 43, 47-48, 76-79, 83-87, 91-95, 99, 103, 107, 111-114, 118, 124-126, 135-138, 160-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py 106 67 37% 40, 76-85, 90-95, 150-167, 170, 173, 201-214, 217, 220, 246-256, 278-300, 305, 308-319, 324 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py 228 165 28% 99-110, 116-118, 135, 148-158, 169-176, 180-183, 187-242, 247-344, 356-391, 399-408, 411-412, 421-422, 434-435, 456-470, 473-508, 511-530, 537-540, 545, 549, 553, 557, 561, 565, 577 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_util.py 11 3 73% 62-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_ops.py 452 356 21% 56-59, 64-73, 79-102, 107-116, 122-138, 144-151, 157-161, 165, 169-174, 179-188, 194-211, 219, 224, 251-266, 298-318, 331-332, 359, 387, 402, 426-428, 432-443, 447, 472-479, 504-513, 529-531, 535-583, 587-617, 622-628, 633-635, 652-655, 659-663, 668-672, 679-690, 695-720, 724-730, 760-764, 791-795, 835-861, 867-907, 948-953, 957, 961-989, 993-1004, 1012-1024, 1031-1101, 1106-1137, 1151-1183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_utils.py 400 345 14% 44-53, 80-135, 158-170, 188-197, 214-229, 270-275, 280-282, 293-306, 318-319, 323-325, 329-331, 365-387, 414-433, 465-535, 545-576, 594-618, 635-655, 675-690, 705-713, 743-769, 785-801, 806-809, 813-818, 822-830, 835-842, 846-849, 863-874, 887-897, 910-916, 937-958, 976-986 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/device_util.py 47 29 38% 47-67, 72, 79-80, 87-90, 93, 96, 102-108, 113-114, 121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_coordinator.py 315 243 23% 74-79, 83-92, 97-99, 137-146, 149-153, 156-162, 167, 172-192, 196-205, 213-216, 244-260, 268, 273, 278, 283, 288, 293, 298, 303, 308, 313, 318, 323, 336-360, 373-382, 395-451, 457-494, 500-526, 538-552, 590-625, 752-868 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_coordinator_context.py 11 4 64% 28-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py 687 352 49% 149-152, 159-160, 163-164, 167-168, 186-194, 206, 209-214, 220-225, 232-240, 249, 254-258, 277-285, 289-300, 303-332, 374-376, 381, 386, 391, 407-411, 414, 463-464, 469, 474, 477, 507, 617-625, 640-644, 661, 666, 673-677, 705, 711-713, 874, 943, 957, 998-1056, 1078, 1097, 1102, 1107, 1126, 1132, 1136-1144, 1147, 1201, 1264, 1314, 1388, 1429, 1470, 1501, 1536, 1540, 1559, 1754-1777, 1785, 1789, 1818, 1857-1865, 1869, 1872, 1877, 1881, 1885, 1888, 1891, 1894, 1897, 1901, 1922-1931, 1934, 1953-1959, 1964, 2007-2013, 2016, 2033-2039, 2042, 2045, 2060, 2064-2072, 2077, 2082, 2089, 2095, 2110, 2118, 2121, 2140, 2165-2166, 2169, 2182-2186, 2189, 2233-2235, 2240, 2293, 2308, 2317, 2322, 2327, 2332, 2374, 2415-2420, 2424-2429, 2434, 2447-2448, 2458-2459, 2489-2511, 2526-2530, 2544, 2550-2551, 2571, 2583-2584, 2603-2604, 2613, 2616, 2619, 2624-2625, 2628-2637, 2640-2643, 2653-2654, 2659, 2664-2669, 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870-957, 966, 975-977, 982-990, 994, 1001-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/tpu_values.py 143 89 38% 41-46, 51-61, 68-75, 78-81, 85-88, 92, 95-98, 101-104, 108, 114-118, 124, 127-129, 132-135, 138-141, 144-147, 151, 159-166, 171-183, 190-196, 200-202, 205-207, 210-212, 215, 218, 221, 224, 227, 230, 233, 236, 243-246, 251-254, 259-262, 266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/values.py 749 470 37% 48-55, 125, 129-133, 136, 142-151, 156, 160, 163-165, 168-170, 191-198, 203, 212, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300-304, 307-311, 314-318, 321-325, 335, 341, 352, 355, 359, 362-367, 370, 380, 383-387, 391-392, 396-397, 401-402, 416-433, 445-454, 458-465, 468, 472, 476, 480, 484, 488, 493, 497, 501, 505, 509, 513-518, 521, 525, 528, 531-532, 536, 540, 544, 547, 550, 557-560, 564, 567-568, 571, 575, 582-584, 591-595, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/backprop.py 415 331 20% 70-77, 87-97, 104-108, 111-115, 118, 144-159, 170, 226-249, 294-298, 303-326, 394-400, 424-432, 491-500, 539-572, 576-582, 589-612, 625-634, 639-647, 651, 659-681, 685-699, 715-717, 821-829, 833-834, 838-839, 843-852, 855-858, 861-867, 878-892, 917-924, 958-960, 964-966, 998-1056, 1108-1160, 1219-1281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/backprop_util.py 12 5 58% 26-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py 1014 426 58% 84-86, 89-95, 98, 101, 143-149, 170-172, 176-178, 332-335, 407, 440-451, 463, 498, 508, 511, 513, 522-523, 526-527, 534-536, 539, 560-572, 589-604, 620-623, 640, 653-656, 669-678, 705-721, 726, 732-735, 738-745, 771, 775, 815, 817, 822, 835, 840-857, 860-863, 867-868, 873-874, 884, 887, 891, 893, 896, 901, 904, 911, 921, 942, 963, 965-970, 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171-202, 237-359 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/monitoring.py 112 35 69% 119, 125-132, 137, 164, 200, 208, 212, 236, 248, 256, 260-263, 287, 311, 347, 355, 363-368, 380, 383, 402, 425, 430 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/profiler.py 68 37 46% 72-84, 99-108, 120-126, 136-142, 158-160, 174, 177, 180-181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/remote.py 76 53 30% 70-77, 136-219, 223 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/tape.py 67 26 61% 39, 42, 47-48, 53, 58, 63-70, 84, 97-109, 114, 155, 177, 184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/wrap_function.py 228 179 21% 49-52, 56, 60-83, 86, 90-94, 99-119, 124-142, 160-208, 223-229, 257-362, 377-389, 433-443, 447, 451, 501, 510-539, 599-603, 630-635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/dense_features.py 36 18 50% 92, 102, 111-113, 116, 137-150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/dense_features_v2.py 18 6 67% 82-87, 90-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/feature_column.py 877 622 29% 182-231, 297, 322-329, 332, 342, 346, 350, 354, 358, 362, 366, 492-504, 516-526, 543-548, 551-566, 569-577, 590-592, 595-601, 604, 608-612, 616, 633-661, 669, 672-700, 706-708, 744-752, 801-814, 904-929, 997-1009, 1083-1097, 1144-1155, 1242-1268, 1357-1385, 1444-1453, 1488, 1558-1560, 1670-1688, 1718-1722, 1731, 1734-1742, 1745, 1768, 1790, 1810, 1815, 1840, 1861, 1885, 1910, 1921-1931, 1947-1963, 1981, 2008, 2046-2067, 2085, 2128-2129, 2149-2171, 2191-2217, 2226-2233, 2254-2274, 2297-2323, 2335, 2339, 2346-2353, 2357, 2374-2378, 2388, 2392, 2395-2396, 2402, 2406-2409, 2418, 2423-2448, 2470, 2484-2486, 2490, 2493, 2497-2499, 2507-2531, 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101, 110, 113, 133-153, 176-202, 247, 293, 355, 418, 474-482, 492-499, 511, 516, 521, 537-540, 545, 556-576, 582, 586-588, 593-596 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/serialization.py 35 19 46% 84-90, 118-146, 166, 187-188, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/utils.py 64 47 27% 32-52, 56-57, 62-63, 94-122, 129-131, 135-137, 145-154 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/auto_control_deps.py 163 58 64% 171-175, 177-181, 184-186, 197, 244-268, 272, 275, 283, 329, 339, 342-349, 361, 366, 371-374, 377, 388, 466, 489-495 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/auto_control_deps_utils.py 82 21 74% 46-47, 53-54, 63, 67-69, 75, 121, 141-149, 166-169 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/c_api_util.py 100 27 73% 33, 38-39, 104, 107, 118-128, 133-134, 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705-708 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/cpp_shape_inference_pb2.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/device.py 57 8 86% 29, 42, 53, 55, 95, 117, 120, 169 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/device_spec.py 156 25 84% 46, 210, 333, 339, 344, 389, 403-404, 408-409, 413-414, 418-419, 423-424, 427-429, 432-436, 439-442, 453 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/dtypes.py 215 49 77% 79-82, 88, 95-101, 116-130, 140-154, 167-170, 200-201, 216, 628-629, 633-642 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/error_interpolation.py 202 168 17% 86-98, 119-140, 145, 168-189, 195, 214-223, 243-251, 256-257, 274-281, 299-337, 372-401, 416-425, 438-452, 467-484, 502-542 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/errors.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/errors_impl.py 210 71 66% 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_shape.py 375 190 49% 93-94, 126, 172-176, 190, 196-197, 202, 205, 208-209, 215-216, 219, 223-229, 232, 237, 241, 275, 337-344, 355, 379-386, 397-401, 425-433, 444, 468-475, 486-490, 506, 521, 537, 553, 578-582, 593-594, 615-619, 640-644, 665-669, 690-694, 697, 767-769, 776, 780-786, 789-800, 807, 829, 841-847, 867, 872-897, 901-907, 927, 935-936, 939-941, 944-946, 965-969, 984, 996-997, 1014-1017, 1033-1036, 1052-1055, 1098, 1101, 1117, 1141, 1146, 1160-1161, 1188-1192, 1196-1204, 1207, 1210, 1235, 1237, 1241 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_spec.py 143 57 60% 61, 79, 84, 92, 98, 102, 108, 111, 114, 117, 142, 146-151, 158-159, 162, 167, 181-183, 186, 191-193, 236-262, 266-269, 274, 279, 282-283, 287-288, 292, 295, 299 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/tensor_util.py 480 358 25% 39-40, 45, 49, 55, 61, 66, 71, 124-159, 190, 199-202, 215, 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63-64, 96-126, 152-174, 179, 185-191, 244-247, 250-251, 265, 269, 285-304, 311-316, 320-325, 328-330, 333-335, 347-350, 363-365, 377-379, 388-390, 400-402, 411-413, 424-425, 435-436, 445-447, 456-458, 467-469, 478-480, 505, 601, 615, 771-772, 775-776, 779-793, 796-803, 812-817, 839-855, 858-865, 869, 872-873, 876, 879-881, 884, 887-888, 891, 894, 897-898, 901, 904-905, 909-928, 931-935, 952, 955-962, 1046-1100, 1103-1108, 1112-1140, 1145-1155, 1158-1163, 1166, 1169-1182, 1191-1231, 1238-1257, 1264-1267, 1315-1358, 1362, 1427-1457, 1461-1466, 1469-1485, 1488-1489, 1492-1498, 1529-1535, 1538-1559, 1594-1608, 1612-1613, 1709-1739, 1743-1765, 1770-1801, 1806-1840, 1847-1850, 1865-1869, 1873-1886, 1890-1899, 1905-1909, 1928-1947, 1950-1952, 1955, 1968-1983, 1988-1990, 1993, 1997-2003, 2006-2018, 2026-2030, 2038-2039, 2046-2054, 2062-2063, 2073-2106, 2110-2122, 2126-2146, 2149-2151, 2200-2220, 2225-2237, 2240, 2243-2271, 2274, 2299-2311, 2314-2321, 2326-2363, 2366-2367, 2431-2456 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/callbacks_v1.py 218 186 15% 130-163, 167-175, 180-230, 235-310, 313-314, 325-343, 351-367, 370-372, 378-385, 394-451, 454-457 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/constraints.py 102 47 54% 38, 41, 68-69, 72-75, 78, 87, 109, 112, 118, 152-155, 158-163, 166, 204-216, 221-249, 268, 273, 284-292 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/boston_housing.py 25 16 36% 58-79 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar.py 19 12 37% 37-51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar10.py 31 19 39% 50-82 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar100.py 27 15 44% 59-84 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/fashion_mnist.py 26 15 42% 66-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/imdb.py 56 42 25% 99-158, 171-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/mnist.py 15 6 60% 57-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/reuters.py 47 33 30% 106-151, 164-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/distributed_training_utils.py 473 386 18% 68-80, 113-137, 142-166, 193-213, 236, 253-272, 308-322, 343-359, 363-366, 372-375, 381-399, 404-410, 424-426, 435, 447-449, 459-469, 502-573, 577-581, 585-588, 592-595, 600-620, 636-669, 684-689, 694-703, 732-773, 779-783, 790-825, 830-835, 840-849, 854-880, 885-902, 907-913, 918-947, 953-983, 993-1025, 1033-1040, 1045-1049, 1054-1062, 1066-1071, 1075-1076, 1080-1081, 1085-1086, 1090-1091, 1095-1096, 1101-1102, 1121-1133, 1137, 1151-1169, 1176-1192, 1197-1201 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_preprocessing_layer_v1.py 26 14 46% 51-54, 58-61, 66-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/compile_utils.py 318 253 20% 41-44, 63-68, 85-98, 101, 104, 126-132, 136-149, 153-161, 183-247, 261-269, 272, 275, 295-297, 301-335, 342-370, 375-383, 387-416, 420-421, 434-477, 481-484, 488-490, 495, 500, 528-545, 575-592, 597-602, 607-617, 623-632 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/data_adapter.py 639 341 47% 56-57, 61-62, 115, 140, 156, 172, 185, 198, 203, 211, 216, 220-225, 229, 241, 249, 264-371, 388-408, 411, 414, 417, 420, 423, 427, 452, 467, 470-476, 493-523, 533, 539, 542, 546-548, 562-593, 596, 599, 602, 605, 608, 611, 622, 628, 630, 641-650, 660, 663, 666, 669, 672, 675, 691-699, 702, 705, 708, 711, 714, 720, 726-735, 762, 765, 779-780, 789, 801-802, 823, 830-831, 836-849, 855, 858, 861, 864, 867, 870, 891, 894, 917-922, 925-932, 944, 960, 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123-336, 349-362, 398-418, 449-484, 493-509, 514-530, 535-538, 571-574, 604-606, 626-627, 659-666, 692-695, 706-707, 738-766, 791-800, 816-819 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_utils.py 834 682 18% 79-83, 92, 105, 110, 124, 131, 135-140, 143-145, 156-157, 161, 168-174, 178-192, 209-212, 241-244, 253-258, 262-286, 289-300, 303-310, 321-342, 345-347, 350-353, 358-364, 385-400, 428-436, 441-457, 486-583, 601-626, 633, 638, 657-697, 713-750, 771-808, 844-891, 907-915, 944-1042, 1046-1048, 1052-1056, 1069-1091, 1106-1133, 1142-1157, 1162-1186, 1210-1213, 1216-1228, 1233-1235, 1256-1270, 1278-1289, 1297-1306, 1333-1355, 1359-1364, 1380-1391, 1405, 1420-1421, 1435-1461, 1482-1503, 1525-1544, 1552, 1556, 1584-1613, 1639-1670, 1682-1689, 1694, 1700-1705, 1709-1711, 1723-1725, 1737-1759, 1787-1817, 1827-1840, 1850, 1857-1887, 1891-1892, 1896, 1923-1925, 1929, 1947, 1951, 1965-1970, 1975-1977, 1997-2007, 2012-2030, 2049-2078, 2110, 2123, 2133 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_v1.py 1067 853 20% 71-72, 143-163, 167, 175-180, 228-233, 301-463, 475-476, 481-486, 494-506, 522-545, 549, 556-586, 754-766, 874-879, 953-957, 970-976, 1025-1070, 1110-1146, 1166-1192, 1217, 1249-1251, 1276, 1288-1299, 1313-1337, 1348-1350, 1362-1380, 1386-1423, 1427-1437, 1460-1468, 1471-1478, 1494-1515, 1528, 1532, 1546-1627, 1632-1642, 1646-1650, 1673-1742, 1747-1756, 1760-1768, 1791-1799, 1805, 1818-1827, 1831-1856, 1876-1882, 1914-1938, 1949-1953, 1959-2003, 2006-2030, 2033-2041, 2049-2057, 2099-2170, 2242-2302, 2312-2436, 2440-2498, 2501-2534, 2572-2591, 2596-2636, 2643-2647, 2652, 2660, 2668, 2676, 2684, 2692, 2696-2702, 2706, 2710, 2714, 2732-2735, 2743-2746, 2753-2754, 2774-2775, 2781-2787, 2791, 2794-2802, 2806, 2813-2814, 2817, 2820, 2825-2829, 2832-2835, 2842-2846, 2886-2893, 2897, 2901, 2905, 2909, 2913, 2917, 2921, 2925, 2942-2972, 2979, 2983, 2987, 2991, 2995, 2999, 3002, 3005, 3009, 3012, 3016-3018, 3023-3042, 3048, 3054-3075, 3097-3099, 3103, 3107, 3111, 3115, 3135-3151, 3165-3175, 3179-3180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/estimator/__init__.py 23 12 48% 115-122, 212-219 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/initializers.py 89 8 91% 93, 118, 142, 166, 181, 194, 202, 207 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/__init__.py 180 21 88% 54-59, 157-159, 214-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/advanced_activations.py 135 78 42% 68-70, 73, 76-78, 82, 126-136, 140-157, 160-162, 165-172, 176, 203-205, 208, 211-213, 217, 244-246, 249-250, 253-255, 259, 279-281, 284, 287-289, 293, 345-358, 363, 369-375, 379 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py 730 587 20% 121-149, 152-191, 194-222, 225-250, 254-273, 277-282, 285-288, 291-295, 298-304, 319-325, 429, 582, 726, 874-898, 903-934, 937-994, 997-1027, 1030-1032, 1167-1190, 1195-1226, 1229-1295, 1298-1332, 1335-1338, 1430-1453, 1456-1496, 1499, 1502-1543, 1656, 1680-1719, 1841, 1866-1887, 1985-2001, 2004-2036, 2039-2056, 2060-2078, 2081-2093, 2134-2136, 2139-2141, 2144-2145, 2148-2150, 2216-2223, 2226-2239, 2243, 2248-2254, 2302-2305, 2308-2325, 2329, 2333-2335, 2384-2386, 2389-2393, 2396, 2399-2401, 2472-2492, 2495-2516, 2520, 2524-2526, 2584-2610, 2613-2642, 2646, 2650-2652, 2692-2694, 2697-2702, 2705-2708, 2711-2713, 2768-2788, 2791-2802, 2813-2833, 2838-2840, 2899-2925, 2928-2958, 2964-3014, 3020-3022 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional_recurrent.py 345 261 24% 165-181, 185-222, 228-275, 279-292, 302-350, 353-420, 510-537, 541-584, 587-644, 647-654, 657-660, 663-692, 842-869, 872-873, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 963-995, 999 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/core.py 458 303 34% 101-104, 107, 110-115, 118, 121-123, 179-183, 189-196, 199-212, 215, 218-224, 260-261, 264-266, 311-318, 321-325, 369-376, 379-383, 415-417, 420, 423, 426-428, 472-473, 495-515, 518-527, 530, 534-536, 571-578, 581-586, 589, 592-594, 630-632, 635-667, 670-679, 682-684, 714-716, 719-720, 723, 726-728, 825-841, 845-870, 874-890, 893-932, 937, 940-942, 945-966, 969-985, 989-1016, 1022-1048, 1129, 1150, 1154, 1176, 1183-1188, 1192, 1199, 1202-1208, 1211-1225, 1246-1250, 1253, 1256-1258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/cudnn_recurrent.py 189 143 24% 65-81, 84-121, 124-133, 137, 141-143, 147-149, 153, 156, 215-236, 240, 243-269, 272-316, 319-337, 400-422, 426, 429-465, 468-518, 521-540 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/dense_attention.py 132 95 28% 77-80, 92, 119-134, 138-167, 170-176, 180-196, 201-206, 307-308, 312-321, 332-335, 338-340, 443-444, 447-460, 473-480, 484-486, 491-495, 499-503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/embeddings.py 71 46 35% 101-123, 133-148, 151-154, 158-178, 181-185, 188-203 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/kernelized.py 80 56 30% 138-154, 157-200, 203-207, 210-216, 219-228, 234-251, 255-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/local.py 209 172 18% 151-171, 175-259, 263-274, 277-298, 301-334, 465-485, 489-581, 585-600, 603-625, 628-661, 704-724, 770-778, 807-816, 834-841 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/merge.py 342 244 29% 49, 67-87, 92-118, 122, 124-181, 187-202, 205-217, 251-254, 284-286, 290-293, 320-323, 357-360, 387-390, 417-420, 493, 496, 504-519, 526-536, 541-564, 567-571, 639-654, 659-674, 680-700, 704-722, 725, 728-733, 767, 796, 810, 846, 878, 892, 927, 947 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/noise.py 80 47 41% 60-62, 66-73, 76-78, 82, 111-113, 116-127, 130-132, 136, 170-174, 177, 180-203, 206-208, 212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization.py 511 442 14% 199-247, 258-271, 275-279, 283, 287-289, 292-295, 300-303, 306, 311-503, 506-516, 519-521, 525-635, 640-693, 696, 699-708, 711-722, 725-890, 893, 896-928, 1010-1034, 1042-1055, 1058-1105, 1109-1193, 1196, 1199-1212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization_v2.py 43 23 47% 136, 161-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/pooling.py 256 162 37% 60-70, 73-81, 84-98, 101-108, 193, 235, 272-282, 285-297, 300-315, 319-326, 458, 508, 544-554, 557-574, 577-596, 600-607, 654, 704, 714-717, 720-724, 727, 730-732, 775-777, 780-789, 792, 840-841, 849-852, 855-859, 862, 865-867, 906-909, 947-950, 957-960, 963-967, 970, 973-975, 1008-1011, 1043-1046 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding.py 218 164 25% 83-129, 136, 139-145, 163-170, 173-178, 181-187, 190-193, 196-205, 208-217, 220-234, 237-292, 317-318, 322-341, 345-364, 379, 394-412, 416, 421-429, 433-447, 452-458 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding_v1.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py 485 384 21% 78-83, 86-89, 92-96, 99-100, 104-110, 135-138, 141-144, 147-171, 174-175, 179-184, 214-219, 222-277, 280-281, 285-291, 315-316, 319-320, 323, 326-330, 367-383, 386-402, 405, 408-413, 467-506, 509-541, 544, 547-555, 570-578, 647-670, 696-704, 768-790, 793-816, 819, 822-829, 879-918, 921-952, 955, 958-966, 987-997, 1043-1054, 1057-1067, 1070, 1073-1078, 1118-1132, 1135-1157, 1160-1161, 1165-1171, 1213-1227, 1230-1252, 1255-1256, 1260-1266, 1270-1273, 1277-1282 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup.py 230 173 25% 104-190, 193-208, 211-212, 215, 218-222, 225-231, 234-235, 239-241, 245-246, 250-257, 260, 263-268, 283-285, 288-294, 297-305, 312, 333-353, 356-358, 361-389, 392-401, 404-429, 451, 455-465, 469-477, 489-495, 499, 504-507, 511-518, 523-524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup_v1.py 38 22 42% 63-66, 69, 72-76, 79-81, 84-85, 89-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization.py 95 56 41% 63-71, 75-104, 109-111, 114, 117, 120-122, 126-128, 149, 155-173, 178-197, 202, 212-220, 225-230, 234-235, 241 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization_v1.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py 309 235 24% 214-320, 325, 328-329, 332, 336-338, 342-343, 347, 350-359, 362-364, 379-404, 407, 410-420, 427, 461-521, 528-534, 537-543, 546-592, 595-633, 663-665, 669-694, 698-715, 730, 745-762, 766, 771-781, 785-802, 807-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization_v1.py 26 9 65% 84, 87, 91-97 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent.py 1046 802 23% 85-104, 108, 113-118, 121-131, 135-160, 165-180, 183-191, 195-200, 397-439, 444-447, 454, 457-505, 513-519, 522-592, 606-621, 625-645, 648-707, 717-814, 820-854, 857-866, 871-873, 890-943, 946-965, 969-974, 978, 1052, 1061, 1066, 1069, 1096-1099, 1110, 1121, 1124, 1131, 1153-1156, 1174-1177, 1266-1290, 1294-1319, 1322-1340, 1343, 1346-1378, 1489-1526, 1529-1530, 1535, 1539, 1543, 1547, 1551, 1555, 1559, 1563, 1567, 1571, 1575, 1579, 1583, 1587, 1590-1625, 1629-1631, 1709-1740, 1744-1778, 1781-1879, 1882-1907, 1910, 2032-2071, 2074-2075, 2080, 2084, 2088, 2092, 2096, 2100, 2104, 2108, 2112, 2116, 2120, 2124, 2128, 2132, 2136, 2140, 2144, 2147-2188, 2192-2194, 2275-2312, 2316-2353, 2357-2367, 2371-2376, 2379-2436, 2439-2477, 2480, 2518-2530, 2536-2549, 2552-2559, 2678-2717, 2720-2721, 2726, 2730, 2734, 2738, 2742, 2746, 2750, 2754, 2758, 2762, 2766, 2770, 2774, 2778, 2782, 2786, 2790, 2793-2834, 2838-2840, 2844-2852, 2877-2913, 2918, 2923-2926, 2931-2944, 2964-2991, 3008-3011 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent_v2.py 321 259 19% 165, 345-385, 388-398, 403-454, 462-503, 547-587, 593-673, 713-791, 895, 1059-1102, 1107-1207, 1233-1238, 1288-1321, 1359-1444, 1485-1569, 1596-1602, 1625-1626, 1631-1635, 1642-1645, 1649-1650 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/rnn_cell_wrapper_v2.py 43 17 60% 42-43, 66, 71-72, 75-82, 86-89, 98-100, 113, 124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/serialization.py 57 18 68% 68, 84-105 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/wrappers.py 370 318 14% 52-54, 57-60, 64-67, 70-77, 81-86, 126-137, 163-172, 175-184, 187-195, 199-251, 290-327, 400-456, 460-469, 480-495, 499-518, 522-593, 602-677, 680-681, 684-688, 691-707, 711-715, 718-728, 733-745 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/losses.py 266 127 52% 105, 107, 140-144, 157, 161, 177, 181-201, 243-246, 249-253, 312, 372, 433, 494, 570-576, 729, 793, 855, 916, 974, 1032, 1093, 1162, 1196-1198, 1228-1230, 1262-1266, 1300-1304, 1309-1319, 1347-1350, 1379-1382, 1411-1415, 1438-1444, 1481-1487, 1517-1527, 1555-1557, 1585-1594, 1632-1636, 1667-1669, 1714-1716, 1782, 1797-1803, 1816, 1831, 1853-1863 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/metrics.py 759 457 40% 151, 164-167, 186-207, 212, 216, 224, 244, 253, 268, 274, 290, 328-374, 377-385, 425, 516-518, 533-547, 551-554, 595-604, 608-618, 624-628, 665, 710, 765, 809, 889, 913-918, 936, 944-948, 951-952, 956-958, 1004, 1054, 1104, 1154, 1225-1237, 1256, 1269-1271, 1274-1275, 1279-1285, 1351-1363, 1382, 1395-1397, 1400-1401, 1405-1411, 1423-1450, 1465, 1478-1479, 1537-1541, 1546-1556, 1561-1566, 1623-1627, 1632-1642, 1647-1652, 1701-1705, 1713-1723, 1728-1730, 1782-1786, 1796-1807, 1810-1813, 1920-1981, 1985-2023, 2038-2067, 2129-2164, 2167-2214, 2219-2223, 2228-2244, 2295, 2326, 2359, 2390, 2423, 2456, 2491, 2523, 2555, 2570-2575, 2579, 2610, 2641, 2672, 2729-2734, 2754-2776, 2780-2799, 2803, 2806-2808, 2840-2844, 2847-2857, 2861, 2865, 2877-2908, 2911-2916, 2919-2920, 2973, 3112, 3136, 3155-3157, 3160-3166, 3170-3174, 3178-3184, 3200-3202, 3219, 3239-3252, 3268, 3285-3294, 3310-3312, 3327-3330, 3335, 3340, 3345, 3355-3364, 3368 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py 243 135 44% 63-69, 73-75, 82-85, 90, 93-96, 99-100, 104-105, 109-110, 113, 117-127, 131, 134-143, 161, 165, 169, 173, 176, 179, 183, 187, 190-191, 194-195, 198-199, 202-203, 206-207, 210-211, 214-215, 218-219, 222-223, 226-227, 230-231, 234-235, 238-239, 242-243, 246, 250, 254, 258, 262, 266, 270, 274, 277, 284, 289, 292, 304, 308, 312, 316, 325, 328, 331, 334, 337, 340, 343, 346, 349, 352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388-392, 395-399, 402-406, 409-413, 439-462, 481-483 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/device_compatibility_check.py 66 48 27% 54-61, 73-128, 154-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/get_layer_policy.py 11 3 73% 38-41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale.py 15 4 73% 29, 33-38, 48 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale_optimizer.py 140 81 42% 48, 123-155, 160, 182-189, 212-214, 218-224, 227-229, 232, 238-245, 251-272, 279, 284-286, 293-298, 301-303, 313, 317, 320, 323, 327, 330, 333, 336, 345, 349, 353, 357, 366, 372, 395-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/policy.py 133 60 55% 328, 331, 339, 341, 348, 361-371, 374, 376, 378, 382-388, 460, 463-471, 475-479, 508-509, 519-520, 549-558, 573-578, 582-586, 606, 612-617, 621-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/models.py 277 231 17% 57, 61, 67-74, 90-129, 162-215, 234-247, 265-276, 307-381, 419-426, 452-533, 550-557, 570-589, 638-723 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adadelta.py 49 28 43% 100-104, 108-111, 114-115, 121-127, 130-136, 147-153, 165-172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adagrad.py 60 35 42% 91-100, 103-107, 110-111, 118-124, 143-147, 150-155, 164-169, 179-186 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adam.py 84 61 27% 144-150, 155-161, 164-173, 185-192, 195-217, 232-269, 272-281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adamax.py 63 41 35% 103-108, 112-115, 118-126, 137-144, 157-181, 184-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/ftrl.py 60 42 30% 108-136, 141-146, 149-150, 162-181, 194-214, 228-245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/gradient_descent.py 53 24 55% 112, 118-120, 123-124, 128-143, 148-156, 161-166, 177-184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/learning_rate_schedule.py 249 184 26% 44, 48, 60, 135-140, 143-154, 158, 225-233, 236-256, 259, 360-367, 370-392, 399, 480-486, 489-502, 505, 573-578, 581-594, 597, 668-675, 678-716, 719, 803-810, 813-831, 835, 925-934, 937-965, 969, 983, 988 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/nadam.py 96 72 25% 92-105, 108-124, 127-146, 164-165, 168-188, 191-228, 231-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py 448 310 31% 72-76, 268, 272, 293, 301, 333-336, 340-356, 385-402, 418-430, 472-504, 520-545, 550-604, 607-613, 618, 622-630, 633-643, 646, 661-663, 675, 677, 684, 686, 692, 696-730, 733-735, 738-754, 757-759, 763-765, 768-782, 787-795, 799-803, 807-815, 829-834, 853-859, 863-870, 874, 879, 908-909, 941-958, 969-1000, 1004, 1019-1023, 1034, 1041, 1055, 1080-1082, 1104, 1107-1109, 1112-1114, 1119, 1124, 1132-1140, 1163-1199, 1206-1224, 1243-1247, 1253-1254, 1269-1270, 1274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/rmsprop.py 101 76 25% 133-146, 149-156, 159-162, 171-214, 217-272, 275-281, 284-293 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizers.py 434 336 23% 62-72, 88, 91, 107-121, 137-151, 159, 162-167, 171, 189-196, 199-202, 205-230, 233-240, 258-267, 270-272, 275-297, 300-307, 332-340, 343-346, 349-371, 374-380, 411-420, 423-427, 430-459, 462-469, 495-506, 509-516, 519-555, 558-567, 590-600, 604-610, 613-644, 647-655, 681-691, 694-699, 702-742, 745-753, 760-767, 772, 775, 778, 781-806, 810, 813, 816, 832, 849-865, 892-902 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/linear.py 59 39 34% 88-96, 99-127, 130-147, 150-160, 164-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/wide_deep.py 98 75 23% 87-91, 94-109, 113-136, 140-194, 197-205, 209-215 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/image.py 95 37 61% 27-28, 80-87, 152-154, 230-238, 299-307, 516-524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/sequence.py 18 1 94% 156 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/text.py 19 2 89% 42-43 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/regularizers.py 65 23 65% 152, 172, 192, 215-222, 225, 244, 280, 285, 290, 302, 304-311, 315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/hdf5_format.py 354 269 24% 43-44, 82-130, 158-217, 251-258, 272, 287-310, 316, 318, 320, 323-393, 397-404, 446, 468-473, 483-519, 527-572, 585-598, 610-613, 623-644, 661, 665, 682, 699, 729-789, 809-831, 851-856, 880 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/model_config.py 28 14 50% 29-30, 50-55, 86-90, 114-116 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/save.py 45 21 53% 39-40, 113-137, 181-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/base_serialization.py 33 10 70% 34, 43, 54, 74, 87-95, 106, 122, 172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/constants.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/json_utils.py 32 19 41% 38-41, 44, 48-56, 60, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/layer_serialization.py 74 40 46% 36, 41, 48-69, 72, 76, 81-96, 100-105, 111-119, 127, 131, 141, 144, 152, 155-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/load.py 460 367 20% 116-137, 143-148, 175-202, 209-215, 219, 223-234, 239-291, 295-313, 317-344, 348-363, 367-393, 401-431, 434-450, 454-466, 470-472, 486-510, 514-520, 523-539, 544-572, 577-579, 609-623, 627-646, 650-654, 662-687, 694-719, 723-725, 730-738, 745-753, 758-778, 786-791, 802-831, 835, 838-841, 848-863, 872-882, 885, 890-902, 915-922, 931-948, 953-958, 966-968, 972 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/metric_serialization.py 18 7 61% 30, 33-40, 43 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/model_serialization.py 27 11 59% 32, 35-39, 42-55, 62 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/network_serialization.py 14 5 64% 30, 33-39 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/save.py 29 14 52% 59-81 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/save_impl.py 271 210 23% 72-78, 97-116, 146-197, 201-205, 233-290, 296-302, 309-316, 320-323, 338-363, 375-397, 406-422, 427-434, 437-441, 445-448, 452, 457-491, 495-504, 509-527, 534-537, 540-542, 545-547, 564-567, 572-576, 582-586, 590-594, 603-607, 614-616, 624-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/serialized_attributes.py 78 34 56% 145-154, 158-160, 165, 171, 177, 183-186, 190-203, 207-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/utils.py 112 88 21% 56-96, 101-113, 117-120, 125-128, 150-199, 214-220, 224-230, 234-239, 243-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model_experimental.py 135 89 34% 133-145, 151-155, 160-163, 168-221, 226-227, 231, 255-326, 331-361, 371, 416-430 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saving_utils.py 138 110 20% 48-53, 77-87, 91, 112-142, 147-191, 197-199, 204-233, 245-264, 270-275, 280-285, 291-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/all_utils.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/conv_utils.py 172 152 12% 29-48, 68-87, 103-113, 128-137, 160-186, 190-197, 201-208, 227-233, 279-309, 358-400, 439-456, 475-482 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/data_utils.py 425 321 24% 56-57, 62-64, 69-104, 111-114, 133-160, 214-282, 286-294, 316-325, 342-350, 357-367, 370, 373, 376-384, 389-393, 453, 462, 467, 471-472, 485-486, 506-514, 519-525, 531-533, 538, 559-645, 662, 689-715, 718, 728-738, 743, 753-759, 762-763, 768, 780, 791, 807-808, 819-826, 831-833, 837-860, 872-880, 894-907, 923, 946-947, 958-964, 968-974, 987-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/generic_utils.py 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/mode_keys.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/multi_gpu_utils.py 82 65 21% 31, 35-36, 157-266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/np_utils.py 26 16 38% 49-61, 76-78 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/tf_utils.py 203 105 48% 61-64, 83-91, 95-99, 116-152, 178, 181, 226, 231, 248, 251, 266-295, 319, 345, 352, 355-356, 359, 392, 397-404, 408, 427-428, 452, 458-462, 467-472, 478, 481, 485, 490-496, 521 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/version_utils.py 34 4 88% 69, 79-85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/vis_utils.py 150 125 17% 45-53, 57-59, 64-65, 98-249, 278-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/scikit_learn.py 106 77 27% 75-77, 88-106, 117-119, 130-132, 150-168, 181-187, 214-223, 240-242, 263-270, 293-308, 332-333, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/base.py 218 171 22% 105-110, 148, 153, 195-234, 244-246, 250-258, 262-269, 273-279, 282-294, 300-302, 305-314, 376-481, 507-552, 555-569, 573, 578, 582-593 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/convolutional.py 84 21 75% 98, 198-218, 297, 404-424, 504, 612-632, 717, 829, 947-971, 1072-1096, 1164, 1260-1279, 1344, 1434-1453 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/core.py 39 9 77% 98, 173-187, 219, 226, 270-271, 331-332 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/layers.py 46 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/normalization.py 22 4 82% 147, 172, 312-336 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/pooling.py 78 30 62% 50-52, 90-95, 120-122, 160-165, 194-196, 235-238, 267-269, 308-311, 342-344, 385-388, 419-421, 460-463 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/utils.py 129 109 16% 28-47, 67-86, 90-95, 99-103, 119-129, 144-153, 168-175, 197-200, 219-227, 232-233, 238-243, 260-285 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/file_io.py 257 108 58% 58, 66, 71, 76, 84, 120, 139-165, 169-170, 174-181, 189-191, 202, 205-208, 211, 220-221, 232, 316-320, 333-334, 350, 366-374, 396, 412, 458, 474, 526-535, 589-590, 610-614, 653, 681, 701-729, 782-790, 808-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/python_io.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/tf_record.py 81 48 41% 90-99, 114-125, 129-149, 170-171, 212, 294-298, 313, 317, 321 /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/module.py 96 58 40% 107-121, 130, 135-139, 154, 169, 193, 249-252, 287-291, 295, 299, 303, 310, 314, 326-378 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_grad.py 573 409 29% 44, 50, 72-212, 218, 228, 247-260, 266-285, 301-305, 319, 324-329, 337, 342, 347, 352, 358, 364-368, 374-382, 389-399, 407-424, 430-460, 466-500, 505-507, 516, 525, 531-538, 552-564, 569-585, 593-615, 627-687, 692-700, 705-713, 719, 728, 737, 742, 747, 755, 767, 773, 778, 784-785, 791-792, 810-832, 842-854, 864-865, 876-877, 882-883, 889-890, 898, 906-907, 915, 923-928, 934-939, 947-948, 953-954, 959, 964, 970, 975-1027, 1032-1090, 1095-1097, 1102-1107, 1112-1115, 1120-1123, 1128-1130, 1135-1150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_ops.py 1121 797 29% 193-195, 277, 341-344, 426, 475, 503, 530, 576, 602, 620-630, 649, 683, 715, 732-753, 787, 802-811, 825-836, 840, 902-973, 1037, 1137-1178, 1224, 1270-1277, 1328-1342, 1357-1393, 1407-1414, 1419-1425, 1446-1455, 1501-1511, 1594, 1601-1605, 1659-1693, 1746, 1786-1790, 1832, 1882, 1943-1961, 2042, 2112-2129, 2190-2210, 2368-2371, 2514-2517, 2653, 2661-2667, 2710, 2712, 2721-2723, 2728-2730, 2732, 2774, 2819, 2825-2850, 2883, 2918, 2923-2931, 2959-2981, 3023, 3092-3139, 3199, 3260-3294, 3311-3316, 3362-3392, 3404-3438, 3443-3456, 3519-3527, 3541, 3553, 3565, 3607-3650, 3661-3670, 3678, 3687, 3695, 3704, 3712, 3720-3729, 3845-3848, 3966-4009, 4016-4025, 4064-4090, 4141-4145, 4197, 4243-4251, 4340-4348, 4406-4410, 4424, 4512-4524, 4535, 4554-4560, 4589-4682, 4835-4846, 4852, 4860-4934, 4956-4975, 5007-5019, 5043-5060, 5106-5113, 5172-5184, 5307, 5350-5352, 5405, 5410-5416, 5441-5455, 5499-5565, 5571-5578, 5583-5591, 5641-5644 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/batch_ops.py 25 13 48% 79-111 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bitwise_ops.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/boosted_trees_ops.py 144 76 47% 63-66, 74-89, 93-95, 111-126, 129, 133, 138-140, 143, 147, 150, 153-156, 159, 162, 176-190, 203-204, 214-227, 234, 238, 245-247, 250, 254-255, 259-262, 271-276, 290, 303 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/candidate_sampling_ops.py 37 12 68% 83-84, 148-149, 208-209, 299-300, 337-338, 386-387 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/check_ops.py 590 425 28% 73-78, 83-85, 91, 238-271, 285-297, 327-372, 392-402, 435, 442-455, 487, 494-506, 539, 547-560, 593, 601-614, 648, 654-658, 696, 705, 758, 810-838, 873, 879, 915, 923, 959, 966, 1003, 1012, 1037-1061, 1094, 1124-1156, 1189, 1222-1255, 1259, 1263-1269, 1294-1321, 1353, 1386-1416, 1436, 1462-1475, 1494, 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870, 873-942, 947-976, 986-1064, 1088-1124, 1138-1148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/confusion_matrix.py 62 42 32% 59-92, 152-201, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_grad.py 121 89 26% 42-88, 98-136, 143, 149-182, 194, 199, 209-232, 237, 243 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_ops.py 1318 1123 15% 86-104, 148-176, 189-198, 202-211, 240-259, 274-283, 305-317, 341-362, 390-424, 431-434, 438-442, 450-457, 473-492, 506-529, 545-562, 578-591, 596-618, 641-654, 663-679, 684, 689, 693, 697, 707, 718-724, 727, 732-734, 738-740, 744-745, 749-750, 754-758, 762-764, 768-785, 791-792, 796, 799, 802, 805, 808, 833-849, 858-867, 872, 876, 880, 884-886, 890-892, 895, 906-922, 927-933, 936, 940-972, 975, 979-1032, 1036-1050, 1053-1059, 1063-1083, 1086, 1090-1093, 1175-1296, 1310-1314, 1392, 1397-1401, 1433-1440, 1456-1475, 1484-1529, 1534, 1539, 1544, 1549, 1554, 1559, 1564, 1569, 1580-1609, 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57% 27-30, 52-55, 79-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/custom_gradient.py 189 135 29% 64, 206, 211-214, 251-255, 258, 264-281, 288-298, 304-402, 408-454, 480-507, 551-560 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/data_flow_grad.py 51 20 61% 33-45, 53-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/data_flow_ops.py 622 469 25% 50-56, 64-88, 92-99, 104-112, 160-182, 200-219, 228, 233-235, 240, 245, 250, 272-293, 304-309, 334-347, 377-395, 412-419, 441-457, 484-500, 528-541, 565-573, 590-595, 606-611, 614-616, 683-706, 754-765, 818-829, 834, 840, 904-919, 974-987, 1055-1075, 1080, 1085-1087, 1106-1108, 1153-1178, 1202-1204, 1219-1221, 1233-1235, 1259-1268, 1273, 1278, 1283, 1294-1297, 1313, 1346-1356, 1375-1379, 1404-1407, 1438-1444, 1464, 1508-1509, 1540, 1564-1566, 1581-1584, 1600, 1618-1647, 1652, 1657, 1662, 1667, 1672, 1677, 1707-1757, 1764-1772, 1790-1800, 1811-1816, 1894, 1915-1933, 1936-1940, 1964-1974, 1994-2004, 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341-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/dirichlet.py 93 44 53% 196-202, 215, 220, 223, 226, 229, 232, 235-240, 244, 248, 251-252, 255, 258-259, 268, 271-272, 278-280, 284, 292-302, 311-313, 327-329, 351-410 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/dirichlet_multinomial.py 87 43 51% 203-223, 236, 241, 246, 249, 252, 255, 259, 262-278, 282-286, 291, 294, 316-317, 323-325, 331-332, 336-340, 348-351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/distribution.py 405 242 40% 91, 135-140, 170, 179, 186, 189, 203, 245, 259, 463-477, 481, 493-495, 515-516, 540-555, 559, 564, 569, 577, 591, 608, 613, 630-631, 634, 649-654, 657, 671, 674, 686-691, 694, 705, 716-717, 730-731, 736, 740-750, 766, 769, 773-782, 795, 798, 802-811, 824, 827, 831-840, 863, 866, 870-879, 898, 901, 906-915, 939, 942, 946-955, 976, 979, 984-985, 988, 993-994, 997, 1001-1004, 1023, 1026, 1048-1055, 1058, 1081-1088, 1091, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/multinomial.py 88 46 48% 194-208, 222, 227, 232, 235, 238, 241, 244, 247-277, 281, 284-285, 288-289, 292, 295-297, 303-305, 309-312 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/normal.py 100 46 54% 146-153, 164, 171, 176, 179, 184, 189, 192, 195-198, 201, 204, 207, 210, 213, 216, 219, 223-224, 227, 230, 233, 236, 240-241, 245-246, 263-271, 287-294 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/special_math.py 113 89 21% 137-143, 148-156, 179-185, 195-279, 332-365, 376-379, 384-398, 415-421, 426, 458-474 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/student_t.py 103 52 50% 191-200, 211, 219, 224, 229, 232, 238, 244, 247, 256-266, 269, 272-273, 276, 284-287, 290-294, 306-316, 337-358, 368, 386-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/uniform.py 69 31 55% 114-123, 135, 142, 147, 151-152, 155, 160, 165, 168, 171-175, 178-180, 189-196, 199, 202, 205, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/util.py 438 377 14% 61-75, 85-86, 93-105, 118-134, 152-161, 193-242, 247, 256, 269, 274-283, 288-292, 297-299, 343-377, 430-485, 513-519, 572-581, 620-657, 686-701, 720-748, 760, 772, 784-787, 792-795, 841-910, 951-979, 1015-1047, 1114-1142, 1164-1199, 1209-1213, 1243-1280, 1308-1351, 1370-1388, 1427-1437, 1443, 1448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/embedding_ops.py 239 200 16% 57-78, 118-249, 314-320, 373, 456-547, 626, 683, 746-826, 854-872, 877-885, 890-894 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/functional_ops.py 275 203 26% 103-161, 230, 296-355, 424, 535-683, 797, 832, 860-861, 867-884, 914-949, 977-1024, 1055-1079, 1118, 1121, 1124, 1127, 1130-1144, 1179 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_array_ops.py 5009 4493 10% 35-62, 68-79, 92-118, 124-133, 146-172, 178-187, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_bitwise_ops.py 319 274 14% 56-95, 101-111, 147-186, 192-202, 238-277, 283-293, 348-387, 393-402, 449-488, 494-504, 524-550, 556-565, 615-654, 660-670 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_boosted_trees_ops.py 1223 1116 9% 46-80, 86-101, 121-162, 168-192, 241-285, 291-310, 362-409, 415-439, 484-532, 538-566, 588-619, 625-639, 657-681, 686-695, 715-744, 749-761, 781-805, 810-819, 833-869, 875-890, 913-953, 959-977, 996-1028, 1034-1044, 1068-1097, 1103-1113, 1134-1167, 1173-1189, 1215-1259, 1265-1286, 1308-1346, 1352-1369, 1389-1416, 1421-1434, 1452-1480, 1485-1498, 1523-1551, 1556-1567, 1586-1619, 1625-1635, 1649-1685, 1691-1706, 1727-1756, 1762-1772, 1816-1858, 1864-1881, 1934-1981, 1987-2008, 2045-2089, 2095-2115, 2156-2229, 2234-2291, 2338-2442, 2447-2533, 2547-2575, 2581-2591, 2607-2635, 2641-2651 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_candidate_sampling_ops.py 423 383 9% 65-108, 114-133, 169-209, 215-232, 314-400, 406-451, 499-549, 555-576, 624-673, 679-700, 748-799, 805-826, 874-922, 928-949 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_checkpoint_ops.py 103 84 18% 77-119, 125-142, 215-259, 265-284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_clustering_ops.py 116 94 19% 42-68, 74-85, 113-145, 151-164, 194-221, 227-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_collective_ops.py 215 191 11% 38-85, 91-109, 127-173, 179-197, 215-260, 266-284, 305-368, 374-406 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_control_flow_ops.py 434 371 15% 40-68, 73-84, 98-117, 122-127, 151-190, 196-213, 228-254, 260-269, 286-312, 318-327, 354-386, 392-406, 419-445, 451-460, 474-505, 510-515, 539-564, 570, 585-599, 605, 631-650, 656, 669-683, 689, 704-723, 729, 756-770, 776, 802-829, 835-845 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ctc_ops.py 214 186 13% 57-101, 107-127, 164-198, 204-217, 260-315, 321-345, 389-443, 449-472 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_cudnn_rnn_ops.py 910 857 6% 83-145, 151-184, 262-329, 335-364, 446-514, 520-550, 640-721, 727-765, 818-892, 898-948, 1003-1081, 1087-1137, 1183-1247, 1253-1289, 1349-1414, 1420-1456, 1519-1596, 1602-1644, 1711-1773, 1779-1812, 1886-1961, 1967-2007 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py 3989 3627 9% 39-47, 52, 64-78, 84, 101-110, 115, 138-154, 160, 195-236, 242, 265-277, 282, 294-308, 314, 338-347, 352, 364-378, 384, 425-462, 468, 497-528, 534, 547-566, 571-577, 634-678, 684-695, 774-827, 833-857, 885-926, 932, 959-1015, 1021-1051, 1064-1078, 1084, 1096-1122, 1128-1137, 1150-1176, 1182-1191, 1206-1233, 1239-1249, 1266-1306, 1311-1334, 1351-1401, 1407-1433, 1455-1508, 1514-1541, 1558-1608, 1614-1640, 1666-1708, 1713-1740, 1762-1815, 1821-1848, 1876-1930, 1936-1965, 1982-2022, 2027-2050, 2067-2119, 2125-2152, 2175-2229, 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829-849, 883-914, 920-934, 960-986, 992-1001, 1029-1073, 1079-1098 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_image_ops.py 1972 1820 8% 36-64, 70-82, 108-135, 141-151, 173-199, 205-215, 238-264, 270-280, 343-387, 393-414, 470-507, 513-532, 566-596, 602-618, 654-688, 694-711, 764-825, 831-861, 884-913, 919-931, 953-979, 985-994, 1045-1105, 1111-1140, 1172-1206, 1212-1227, 1256-1282, 1288-1298, 1329-1356, 1362-1373, 1423-1498, 1504-1543, 1560-1587, 1593-1604, 1631-1661, 1667-1679, 1736-1782, 1788-1812, 1830-1860, 1866-1878, 1926-1967, 1973-1992, 2014-2053, 2059-2068, 2097-2134, 2140-2157, 2197-2230, 2236-2250, 2292-2322, 2328-2340, 2384-2416, 2422-2435, 2489-2529, 2535-2552, 2616-2657, 2663-2677, 2719-2751, 2757-2771, 2803-2843, 2849-2869, 2904-2943, 2949-2958, 2987-3021, 3027-3043, 3074-3105, 3111-3124, 3146-3184, 3190-3207, 3228-3266, 3272-3289, 3311-3349, 3355-3372, 3393-3432, 3438-3455, 3475-3514, 3520-3537, 3557-3596, 3602-3620, 3709-3789, 3795-3838, 3927-4002, 4008-4048, 4066-4104, 4110-4128, 4146-4185, 4191-4209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_io_ops.py 1163 1009 13% 46-85, 91, 119-181, 187-217, 238-260, 266, 286-320, 326-340, 358-379, 385, 405-444, 450-459, 486-512, 517-527, 543-582, 588-597, 613-627, 633, 648-674, 680-690, 703-717, 723, 735-763, 769-779, 805-820, 826, 854-870, 876, 904-933, 939-950, 976-1004, 1010-1020, 1033-1040, 1045, 1057-1076, 1081-1087, 1106-1114, 1119, 1137-1157, 1162-1169, 1185-1199, 1205, 1220-1246, 1252-1262, 1301-1335, 1341-1355, 1387-1422, 1428-1443, 1487, 1493-1515, 1522, 1534, 1559-1579, 1584-1592, 1635-1657, 1662-1671, 1696-1718, 1723-1732, 1749-1776, 1782-1793, 1807-1834, 1840-1850, 1869-1895, 1901, 1919-1959, 1965-1983, 2003-2030, 2036, 2055-2096, 2102-2120, 2141-2163, 2169, 2189-2223, 2229-2243, 2264-2296, 2301-2308 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_linalg_ops.py 1226 1097 11% 33-59, 65-74, 88-114, 120-130, 143-169, 175-185, 199-229, 235-247, 262-293, 299-312, 328-360, 366-380, 396-431, 437-454, 467-493, 499-508, 529-560, 566-578, 601-639, 645-661, 692-731, 737-746, 769-795, 801-811, 848-880, 886-899, 991-1024, 1030-1045, 1076-1103, 1109-1118, 1161-1205, 1211-1223, 1244-1283, 1289-1298, 1311-1337, 1343-1352, 1382-1425, 1431-1443, 1473-1499, 1505-1514, 1543-1587, 1593-1606, 1663-1694, 1700-1714, 1746-1785, 1791-1800, 1873-1907, 1913-1929, 1968-2012, 2018-2030, 2052-2078, 2084-2093, 2129-2160, 2166-2178, 2221-2258, 2264-2280, 2306-2333, 2339-2349, 2379-2412, 2418-2431 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_list_ops.py 716 641 10% 42-74, 80-91, 119-155, 161-174, 189-218, 224-235, 270-304, 310-322, 339-367, 373-384, 403-431, 437-448, 471-502, 508-520, 540-571, 577-589, 605-631, 637-646, 675-707, 713-724, 744-771, 777-787, 801-828, 834-845, 865-897, 903-914, 932-958, 964-974, 998-1026, 1032-1044, 1067-1096, 1102-1114, 1142-1171, 1177-1190, 1210-1237, 1243-1254, 1277-1305, 1311-1323, 1345-1381, 1387-1401 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py 492 433 12% 49-53, 57-63, 68-78, 108-142, 148-162, 193-225, 231-245, 267-293, 299-309, 364-400, 406-422, 445-476, 482-496, 519-560, 566-586, 604-630, 635-647, 665-691, 697-707, 729-773, 779-802, 820-848, 854-865, 884-923, 929-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_lookup_ops.py 714 631 12% 47-78, 84, 110-157, 163-184, 200-208, 213, 247-266, 271, 305-341, 346-363, 379-399, 404-412, 434-452, 458, 479-510, 516-527, 548-564, 570, 590-618, 624-635, 653-661, 666, 683-703, 708-716, 734-742, 747, 764-784, 789-797, 814-834, 839-846, 859-873, 879, 891-917, 923-932, 970-1015, 1021, 1059-1128, 1134-1168, 1195-1227, 1233, 1258-1296, 1302, 1327-1383, 1389-1414, 1441-1489, 1495-1516 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_manip_ops.py 46 31 33% 64-92, 98-109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py 5995 5342 11% 37-63, 69-78, 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10486-10496, 10518-10557, 10563-10573, 10623-10667, 10673-10685, 10731-10775, 10781-10793, 10838-10882, 10888-10900, 10946-10990, 10996-11008, 11024-11063, 11069-11079, 11093-11119, 11125-11135, 11151-11190, 11196-11206, 11228-11267, 11273-11283 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nccl_ops.py 124 103 17% 49-85, 91-104, 127-154, 160-170, 193-227, 233-249 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py 5462 5063 7% 49-95, 101-125, 153-199, 205-229, 259-306, 312-337, 366-413, 419-444, 479-517, 523-537, 580-619, 625-639, 669-681, 690-693, 702-715, 741-772, 778-790, 811-837, 843-853, 917-983, 989-1027, 1072-1142, 1148-1187, 1232-1302, 1308-1347, 1387-1436, 1442-1469, 1495-1541, 1547-1571, 1613-1687, 1693-1722, 1748-1794, 1800-1824, 1863-1916, 1922-1950, 1971-2008, 2014-2029, 2049-2086, 2092-2107, 2160-2228, 2234-2261, 2306-2388, 2394-2423, 2467-2549, 2555-2584, 2632-2674, 2680-2702, 2728-2772, 2778-2801, 2827-2870, 2876-2899, 2917-2956, 2962-2971, 2987-3013, 3019-3029, 3088-3148, 3154-3185, 3224-3262, 3268-3283, 3366-3426, 3432-3463, 3498-3537, 3543-3558, 3603-3655, 3661-3685, 3735-3782, 3788-3808, 3858-3905, 3911-3934, 3988-4036, 4042-4065, 4110-4162, 4168-4193, 4239-4291, 4297-4322, 4359-4398, 4404-4423, 4465-4512, 4518-4542, 4574-4601, 4607-4618, 4651-4677, 4683-4694, 4714-4753, 4759-4768, 4803-4861, 4867-4889, 4913-4958, 4964-4987, 5001-5030, 5036-5048, 5066-5096, 5102-5115, 5133-5159, 5165-5174, 5201-5247, 5253-5277, 5305-5351, 5357-5381, 5413-5460, 5466-5492, 5524-5572, 5578-5603, 5634-5681, 5687-5712, 5743-5791, 5797-5822, 5853-5889, 5895-5911, 5939-5991, 5997-6025, 6056-6091, 6097-6113, 6141-6193, 6199-6226, 6253-6288, 6294-6309, 6350-6406, 6412-6439, 6467-6497, 6503-6516, 6550-6594, 6600-6623, 6688-6742, 6748-6773, 6807-6840, 6846-6861, 6909-6964, 6970-7001, 7032-7098, 7104-7144, 7177-7251, 7257-7299, 7332-7403, 7409-7451, 7489-7545, 7551-7583, 7615-7682, 7688-7729, 7761-7831, 7837-7878, 7912-7989, 7995-8038, 8072-8148, 8154-8197, 8234-8317, 8323-8370, 8403-8474, 8480-8522, 8559-8642, 8648-8695, 8733-8789, 8795-8827, 8866-8925, 8931-8964, 9004-9076, 9082-9123, 9168-9245, 9251-9294, 9343-9396, 9402-9432, 9457-9513, 9519-9549, 9599-9653, 9659-9689, 9743-9804, 9810-9842, 9875-9934, 9940-9972, 10006-10050, 10056-10079, 10105-10140, 10146-10160, 10186-10221, 10227-10241, 10268-10304, 10310-10325, 10345-10384, 10390-10399, 10412-10438, 10444-10453, 10470-10496, 10502-10512, 10529-10555, 10561-10571, 10594-10633, 10639-10648, 10664-10690, 10696-10706, 10724-10750, 10756-10765, 10792-10821, 10827-10838, 10853-10892, 10898-10907, 10923-10949, 10955-10965, 10980-11019, 11025-11034, 11050-11076, 11082-11092, 11123-11153, 11159-11170, 11210-11242, 11248-11261, 11299-11330, 11336-11349 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_parsing_ops.py 1066 994 7% 52-103, 109-137, 164-209, 215-227, 252-291, 297-306, 326-361, 367-381, 400-434, 440-453, 520-582, 588-628, 708-782, 788-834, 925-1095, 1101-1211, 1314-1499, 1505-1632, 1689-1755, 1761-1800, 1886-2018, 2024-2118, 2138-2179, 2185-2195, 2211-2250, 2256-2265, 2289-2319, 2325-2337 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_array_ops.py 58 42 28% 76-119, 125-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_conversion_ops.py 189 163 14% 61-106, 112-130, 157-192, 198-214, 279-325, 331-355, 386-425, 431-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_math_ops.py 53 37 30% 63-95, 101-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_random_ops.py 511 460 10% 43-83, 89-109, 141-180, 186-204, 232-267, 273-289, 303-329, 335-345, 361-396, 402-418, 453-493, 499-519, 548-581, 587-602, 626-662, 668-684, 717-745, 751-767, 799-835, 841-858, 884-920, 926-942 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_resource_variable_ops.py 631 526 17% 38-57, 62-69, 87-106, 111-118, 144-155, 160-167, 189-208, 213-219, 247-264, 269-278, 330-356, 362-371, 389-422, 428-442, 467-479, 486-488, 497-507, 538-579, 585-603, 618-647, 653-664, 701-721, 726-734, 771-791, 796-804, 841-861, 866-874, 911-931, 936-944, 981-1001, 1006-1014, 1051-1071, 1076-1084, 1111-1131, 1136-1144, 1173-1203, 1209-1226, 1247-1265, 1271-1280, 1303-1332, 1338-1350 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_rnn_ops.py 350 306 13% 84-130, 136-157, 220-257, 263-275, 338-375, 381-393, 460-501, 507-525, 597-625, 631-641, 753-782, 788-798, 866-912, 918-938, 987-1022, 1028-1039 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_script_ops.py 136 104 24% 39-80, 86-104, 128-140, 144, 152, 154-157, 165-179, 194-226, 232-247 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sdca_ops.py 322 287 11% 37-76, 82-91, 170-329, 335-421, 497-617, 623-709, 730-751, 756 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sendrecv_ops.py 97 79 19% 42-91, 97-116, 138-173, 178-193 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_set_ops.py 187 160 14% 56-94, 100-116, 167-208, 214-232, 258-292, 298-312, 378-423, 429-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sparse_ops.py 1272 1150 10% 68-108, 114-132, 169-209, 215-232, 298-327, 333-343, 409-439, 445-455, 484-519, 525-539, 559-594, 600-614, 663-695, 701-716, 750-781, 787-799, 870-924, 930-965, 1040-1103, 1109-1143, 1173-1200, 1206-1218, 1242-1269, 1275-1287, 1315-1342, 1348-1360, 1427-1456, 1462-1474, 1505-1533, 1539-1550, 1586-1621, 1627-1642, 1686-1723, 1729-1744, 1780-1815, 1821-1836, 1880-1917, 1923-1938, 1973-2002, 2008-2019, 2061-2089, 2095-2106, 2153-2181, 2187-2200, 2225-2256, 2262-2274, 2309-2336, 2342-2353, 2387-2418, 2424-2438, 2472-2503, 2509-2523, 2572-2607, 2613-2630, 2652-2680, 2686-2698, 2734-2773, 2779-2799, 2845-2882, 2888-2904, 2985-3025, 3031-3049 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_special_math_ops.py 172 145 16% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_spectral_ops.py 714 630 12% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290, 303-329, 335-344, 364-403, 409-418, 438-477, 483-492, 512-551, 557-566, 586-625, 631-640, 660-699, 705-714, 734-773, 779-788, 819-849, 855-868, 900-930, 936-949, 981-1011, 1017-1030, 1058-1089, 1095-1108, 1137-1168, 1174-1187, 1216-1247, 1253-1266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_state_ops.py 513 432 16% 46-69, 75, 96-114, 120, 141-159, 165, 181-197, 203, 226-242, 248, 263-277, 283, 300-329, 335-346, 403-427, 432-444, 501-525, 530-542, 602-627, 632-645, 689-709, 715, 756-776, 782, 825-845, 851, 894-914, 920, 961-981, 987, 1044-1064, 1070, 1129-1149, 1155, 1213-1233, 1239, 1282-1302, 1308, 1355-1375, 1381, 1413-1435, 1441, 1456-1482, 1488, 1512-1538, 1544 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateful_random_ops.py 353 314 11% 37-67, 73-85, 106-126, 131-139, 157-191, 197-214, 233-264, 270-284, 304-335, 341-356, 378-409, 415-430, 451-482, 488-502, 522-553, 559-574, 601-630, 636-649 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateless_random_ops.py 354 315 11% 40-75, 81-95, 123-155, 161-177, 200-228, 234-246, 268-299, 305-318, 343-373, 379-393, 416-447, 453-467, 489-521, 527-541, 565-593, 599-612, 636-667, 673-687 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_string_ops.py 1301 1175 10% 64-128, 134-159, 178-217, 223-232, 257-299, 305-317, 363-399, 405-421, 451-477, 483-493, 518-550, 556-570, 592-619, 625-635, 655-691, 697-712, 734-775, 781-800, 826-861, 867-884, 912-941, 947-959, 980-1022, 1028-1040, 1083-1137, 1143-1166, 1212-1243, 1249-1262, 1313-1343, 1349-1362, 1378-1417, 1423-1432, 1453-1481, 1487-1497, 1526-1569, 1575-1586, 1625-1675, 1681-1697, 1718-1760, 1766-1778, 1884-1913, 1919-1933, 1989-2043, 2049-2073, 2134-2190, 2196-2221, 2271-2313, 2319-2337, 2363-2402, 2408-2417, 2495-2570, 2576-2598, 2647-2699, 2705-2720 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_summary_ops.py 386 335 13% 33-52, 57-63, 80-104, 109-119, 136-160, 165-176, 189-208, 213-219, 233-252, 257-264, 278-312, 318-332, 350-376, 381-394, 409-429, 434-442, 458-478, 483-492, 510-536, 541-554, 569-589, 594-602, 618-638, 643-652, 669-689, 694-704 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_tpu_ops.py 3018 2798 7% 59-98, 104-118, 141-169, 175-185, 206-267, 273-300, 315-335, 340-346, 370-397, 403-413, 436-468, 473-489, 537-598, 603-647, 701-779, 784-841, 855-884, 890-900, 916-954, 960-978, 1002-1037, 1042-1062, 1079-1104, 1109-1119, 1144-1181, 1186-1208, 1237-1278, 1283-1304, 1335-1379, 1384-1406, 1435-1477, 1482-1503, 1534-1578, 1583-1605, 1632-1673, 1678-1698, 1727-1770, 1775-1796, 1827-1868, 1873-1895, 1924-1965, 1970-1991, 2022-2066, 2071-2093, 2124-2167, 2172-2194, 2221-2261, 2266-2286, 2315-2358, 2363-2384, 2411-2452, 2457-2477, 2506-2549, 2554-2575, 2604-2643, 2648-2669, 2700-2743, 2748-2770, 2795-2836, 2841-2860, 2880-2916, 2922-2935, 2958-3003, 3009-3031, 3044-3063, 3068-3074, 3089-3108, 3113-3119, 3138-3175, 3181-3200, 3221-3261, 3267-3288, 3311-3342, 3348-3358, 3388-3436, 3442-3463, 3494-3543, 3549-3570, 3600-3648, 3654-3675, 3706-3755, 3761-3782, 3811-3859, 3865-3886, 3916-3965, 3971-3992, 4023-4072, 4078-4099, 4129-4177, 4183-4204, 4235-4284, 4290-4311, 4342-4391, 4397-4418, 4447-4495, 4501-4522, 4552-4601, 4607-4628, 4657-4706, 4712-4733, 4763-4812, 4818-4839, 4869-4917, 4923-4944, 4975-5024, 5030-5051, 5073-5121, 5127-5148, 5175-5207, 5212-5231, 5245-5264, 5269-5275, 5291-5317, 5323-5331, 5359-5393, 5399-5412, 5428-5454, 5460-5468, 5486-5525, 5531-5550, 5580-5663, 5668-5722, 5749-5793, 5799-5820, 5844-5872, 5878-5888, 5905-5931, 5937-5946 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_user_ops.py 43 28 35% 32-57, 63-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker.py 145 117 19% 39-45, 49-54, 83-132, 160-193, 202-208, 221-242, 254-268, 321-335, 339-345, 393-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker_v2.py 140 116 17% 37-43, 59-64, 78-93, 110-127, 150-197, 221-261, 266-281, 287-293, 332-335, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_impl.py 59 25 58% 168-169, 298-299, 342-357, 392-424, 433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_util.py 446 380 15% 61-69, 97-133, 137, 162-227, 231-234, 250-254, 279-289, 295-299, 303, 308-318, 323-355, 362-374, 383, 388-392, 405-411, 428, 444-457, 471-476, 490-716, 721-728, 734-766, 771-781, 786-805, 810-813, 817-821, 826-838, 846-867, 932-987 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/histogram_ops.py 28 13 54% 77-100, 146-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_grad.py 91 69 24% 39-50, 64-69, 84-91, 105-113, 131-156, 167, 188-381 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops.py 51 32 37% 195-203, 228-239, 245-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops_impl.py 1010 802 21% 75-81, 93, 108-113, 133-150, 171, 193, 212-236, 258, 277-301, 315-320, 360, 401, 422-447, 481, 515, 537-546, 581-594, 610-625, 641-657, 706-715, 772-847, 907-956, 990-1036, 1070-1155, 1183-1255, 1315-1333, 1470-1511, 1523-1590, 1628-1631, 1668-1671, 1698-1717, 1748-1752, 1784-1792, 1830-1843, 1885-1899, 1945-1963, 2012-2062, 2086-2097, 2122-2132, 2168-2175, 2230-2241, 2277-2289, 2327-2339, 2377-2385, 2426-2437, 2455-2457, 2471-2473, 2540, 2586-2656, 2694-2728, 2823-2824, 2930-2931, 2988-2992, 3064-3079, 3129-3133, 3175-3179, 3211-3215, 3238-3242, 3278-3282, 3305-3309, 3329-3354, 3393-3408, 3444-3466, 3471-3483, 3519-3565, 3627-3640, 3690-3771, 3826-3844, 3861-3885, 3893, 3906, 3919, 4036, 4053-4055, 4137, 4222, 4295-4300, 4354-4356, 4408, 4427 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops.py 509 330 35% 68, 76, 97, 113, 117, 132-134, 137, 215-222, 225-229, 237, 260-263, 266-268, 272, 300-303, 306-308, 312, 346-349, 352-354, 358, 407-409, 412-427, 431, 480, 482, 487, 495-518, 522, 563-565, 568-595, 598, 626-628, 631-663, 666, 688-690, 693, 696, 708-715, 726-733, 758-771, 785, 803-813, 828-845, 860-877, 903-913, 926, 939-944, 959-973, 988-1002, 1028-1041, 1056, 1075-1093, 1109-1129, 1144-1165, 1185-1186, 1189-1201, 1204, 1236, 1265, 1269, 1318, 1345, 1370, 1394, 1410-1425, 1443 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops_v2.py 241 105 56% 59, 67, 88-89, 168-171, 239-243, 257-259, 263, 300-303, 316-319, 323, 364-367, 380-381, 385, 429-432, 445-446, 450, 509, 511, 515, 518, 541, 544, 546, 551-552, 554-555, 561, 612-614, 628-650, 653, 684, 698-708, 711, 757, 796, 803, 864, 907, 947, 987, 1004, 1006, 1013-1017, 1037, 1048, 1054-1058, 1064, 1072-1076 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/initializers_ns.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/inplace_ops.py 41 16 61% 53-64, 90, 116, 142, 160-161, 191, 221, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/io_ops.py 120 54 55% 68-71, 93-94, 131-137, 142, 161-171, 193-206, 224-228, 240-244, 259-262, 278-282, 287, 298-301, 337-338, 369-371, 410-417, 446-451, 479-481, 511-512 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/adjoint_registrations.py 50 18 64% 37, 48, 54, 60-64, 76-80, 92, 106, 118-123, 134 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/cholesky_registrations.py 31 6 81% 36, 46, 57, 70, 83, 96 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/inverse_registrations.py 68 31 54% 40, 51, 57, 68, 74, 87, 132-185, 200, 213, 225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg_impl.py 400 323 19% 95-97, 126-128, 135-145, 150-161, 166-178, 183-203, 208-229, 262-339, 439-490, 496-538, 591-618, 623-635, 657-673, 746-802, 848-896, 949-959, 1000-1036, 1041-1068, 1073-1094 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator.py 387 259 33% 188-213, 218-222, 227, 232, 238, 242, 246, 250, 257-264, 269, 282, 288, 303-308, 322, 338-339, 344-349, 365-366, 381-382, 387-391, 404-407, 424-425, 430-435, 448-451, 468-469, 474-479, 483-493, 500-508, 527-528, 532-544, 560-561, 564-568, 586-587, 591-592, 598, 629-653, 656, 659-661, 690-696, 699-704, 718-723, 726-733, 747-752, 756-763, 768-771, 814-846, 850-852, 893-900, 914-917, 937-945, 966-970, 974-986, 990-991, 995, 1022-1023, 1026, 1039-1040, 1044, 1056-1059, 1062, 1078-1081, 1084-1093, 1105-1106, 1109, 1122-1127, 1137, 1142, 1155, 1161, 1166-1169, 1174-1176, 1190-1202, 1212-1215, 1220 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_addition.py 160 105 34% 99-138, 144-149, 168-187, 195-211, 217-223, 233-235, 253, 258, 264, 280-290, 301-302, 307-317, 330-331, 334, 346-347, 350-355, 367, 371-375, 412-424 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_adjoint.py 72 39 46% 116-155, 160, 163, 166, 169, 173-174, 178-179, 183, 187, 190-192, 195, 198-200, 203, 207, 210-212, 215, 218-221, 224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_algebra.py 112 49 56% 37-48, 53, 58, 63, 68, 73, 90-96, 113-119, 138-144, 163-169, 186-192, 228, 231, 270, 273, 315, 318, 361, 364, 404, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_diag.py 153 119 22% 156-215, 219, 223-238, 242-260, 263-273, 276-279, 282-285, 288-299, 302-308, 311-314, 317-341, 344, 348, 352, 356-362, 378-386 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_lower_triangular.py 191 152 20% 224-241, 250-252, 259-283, 290-301, 304-310, 314-318, 324, 328-344, 348-367, 370-417, 420-425, 428-431, 460-510, 513-520, 523-526, 529-550, 553, 557-563, 579-587 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_circulant.py 204 144 29% 93-126, 130-138, 171, 177, 180-186, 190, 194, 210-224, 239-260, 272-273, 285-286, 300-302, 305-321, 324-331, 345-350, 356, 366-368, 373, 381-402, 405-425, 428-430, 433-436, 439-450, 455-478, 490-512, 747, 758, 927, 1077, 1089-1095 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_composition.py 86 62 28% 147-187, 191, 195-211, 215-233, 239-248, 251-254, 257-260, 270-279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_diag.py 83 49 41% 143-168, 172-173, 178-179, 182-184, 188, 191, 196-205, 210, 217-220, 223-224, 227, 230-234, 237-240, 243, 246, 249-251, 254, 257-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_full_matrix.py 38 17 55% 137-151, 155-172, 177, 180, 183, 187, 190 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_householder.py 86 52 40% 126-156, 160-162, 168-169, 172-174, 177, 180, 185, 200-207, 211-212, 219, 223, 228, 231-236, 240-243, 247-254, 258, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_identity.py 234 171 27% 48-74, 79-83, 87, 91-98, 255-292, 295-301, 304-308, 311, 314, 317, 322-350, 354-358, 361, 364, 367, 371-380, 384, 396-400, 403, 406, 411-437, 442-470, 599-630, 634-638, 641-644, 647, 651, 656-657, 664-668, 671-675, 678, 681, 685-689, 693-702, 706, 718-731, 734, 739, 747 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_inversion.py 52 27 48% 117-168, 173, 176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 206 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_kronecker.py 205 169 18% 38, 46, 55-62, 171-231, 235, 239-257, 260-277, 317-387, 399-405, 409-415, 419-422, 433-504, 507-522, 525-544, 550-563, 566-570, 575-579 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py 148 108 27% 185-263, 268-281, 286-294, 300, 305, 310, 315, 320, 325, 328-331, 334-340, 344-360, 363-374, 380-394, 397-435, 442-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py 49 22 55% 141-159, 164-165, 171, 175, 178, 181, 184, 189, 193, 196, 200-201, 205, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_permutation.py 81 48 41% 143-156, 166-178, 183-184, 187-189, 192, 195-196, 199-220, 226, 231, 234-235, 240-241, 247, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_toeplitz.py 82 50 39% 142-159, 163-170, 176-178, 181-187, 190, 210-224, 229, 234-235, 239-263, 267, 271, 275-278 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_tridiag.py 122 87 29% 175-190, 199-213, 216-230, 236-266, 270-287, 290-294, 299-331, 334-341, 344-359, 369, 373 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_util.py 150 122 19% 105-116, 122-125, 130-135, 151, 160-161, 182-187, 201-209, 228-236, 241-243, 251-255, 311-354, 359-373, 383-467, 491-502 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_zeros.py 157 118 25% 179-232, 235-241, 244-248, 251, 256, 261, 266-294, 297-320, 323-326, 330-333, 336, 348, 353-399, 404-432, 437-441, 445, 449-456, 459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/matmul_registrations.py 65 28 57% 37-50, 65-66, 73-74, 82, 102-105, 112-115, 125, 142, 159, 175, 191, 209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/registrations_util.py 29 21 28% 27-44, 49-62, 71-78, 86-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/solve_registrations.py 55 22 60% 37-50, 67, 75-76, 83-84, 92, 112, 129, 146, 162, 181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/conjugate_gradient.py 52 37 29% 76-136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/gen_sparse_csr_matrix_ops.py 624 562 10% 48-77, 83-95, 109-137, 143-154, 177-207, 213-224, 239-266, 272-283, 303-329, 335-347, 399-459, 465-495, 518-544, 550-560, 573-599, 605-614, 677-703, 709-719, 739-766, 772-782, 797-825, 831-843, 937-965, 971-983, 1083-1133, 1139-1164, 1183-1215, 1221-1234, 1248-1275, 1281-1291, 1307-1335, 1341-1353 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_grad.py 127 101 20% 30-34, 40-41, 65-66, 73, 80-81, 95-169, 175-222, 231-233 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_ops.py 157 96 39% 56, 61-69, 74, 80-87, 105-116, 124-144, 181-241, 253, 257, 261, 265, 269, 273, 277, 281, 286, 289, 294, 301, 307, 310, 330-352, 357, 360-370, 373, 376-377 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_grad.py 389 345 11% 53-54, 61-366, 372-378, 392-443, 449-454, 462-480, 486-510, 516-524, 537-601, 609-633, 639-675, 690-811, 816-819, 824-827, 833-848, 854-867, 881-898, 917-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops.py 166 121 27% 65-79, 139-140, 181-186, 227, 293-367, 393-398, 419-424, 446-447, 469-470, 535-540, 607, 682-761 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops_impl.py 35 24 31% 42-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/list_ops.py 141 87 38% 49-52, 60, 68, 76, 85, 97, 110-114, 127, 139, 148, 161-170, 176, 184-189, 194, 201-209, 214-217, 227-243, 249-259, 265-274, 279-281, 287-294, 301-311, 317-325, 350-371 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/logging_ops.py 147 89 39% 48, 112, 120-124, 129, 258-375, 383, 387-390, 426-429, 487-491, 542-552, 583-586, 606-610, 624-634, 667-670 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/lookup_ops.py 638 452 29% 63, 81-84, 99-103, 117-119, 122, 127, 132, 137, 141, 145, 165-179, 182, 187, 198-199, 217-236, 243, 279-291, 294-303, 307, 319-325, 364, 372, 385-386, 391, 396, 400, 405-411, 428-445, 461-467, 587-626, 641-654, 658-669, 712, 765, 807-814, 818-820, 896-933, 936-938, 941-944, 948-951, 956, 960-962, 966, 970-975, 979-988, 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189-190, 204, 217, 231-235, 264-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/manip_grad.py 12 4 67% 28-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/manip_ops.py 12 1 92% 30 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/map_fn.py 85 63 26% 149-287, 417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/math_grad.py 1177 911 23% 37, 42-43, 48-49, 56-64, 91-136, 144, 152-214, 219-235, 241, 246, 252-266, 277-315, 321, 327-334, 340-341, 349-350, 358-359, 366-367, 374-375, 382-383, 389-399, 405, 411, 426-445, 452-464, 470, 476, 482, 504-530, 535-536, 542, 548-549, 555-556, 561-566, 571-576, 581-586, 591-592, 597-605, 611-612, 618-625, 631-637, 643-650, 656-662, 668-674, 680-690, 697-707, 714-724, 731-734, 740-743, 749-752, 758-761, 767-773, 779-785, 790-793, 799-803, 809-814, 820-822, 829-831, 838-844, 850-857, 863-866, 872-874, 880-882, 888-890, 896-901, 907-914, 920-938, 944-963, 970-971, 978-1004, 1014-1032, 1040-1058, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/control_flow_ops.py 165 134 19% 64-106, 111-114, 122-132, 182-201, 206-219, 224-317, 398-407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/gradients.py 57 45 21% 48-80, 112-147 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/pfor.py 1990 1462 27% 75-80, 103-111, 126-225, 229, 234, 239-242, 247, 252, 257, 261-265, 269, 274, 297-340, 344-354, 369-396, 400-464, 474-483, 487-514, 520-559, 604-679, 694-696, 705-711, 721-742, 746, 750, 753-754, 757-768, 771-781, 785, 789, 792, 796, 799-800, 906, 915-919, 946-953, 957-982, 990-993, 997, 1001, 1022-1060, 1075, 1088, 1101, 1105-1106, 1167-1184, 1188-1192, 1224-1239, 1243-1257, 1262-1266, 1279-1292, 1296-1297, 1300-1302, 1306-1329, 1332-1516, 1521, 1526, 1530, 1534, 1544, 1556-1558, 1563-1565, 1570-1572, 1580-1582, 1587-1589, 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3455-3459, 3464-3485, 3492-3503, 3511-3520, 3532-3548, 3553-3580, 3587-3610, 3616-3628, 3632-3638, 3642-3662, 3668-3715, 3728, 3738-3741 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_config.py 305 240 21% 207-228, 303, 323, 358, 412-434, 454-463, 467, 471, 475, 491-516, 520-531, 535-537, 541-548, 553-568, 572-589, 593-603, 607-619, 623-627, 632-665, 704-758, 778-803, 827-885, 893-901 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_ops.py 165 110 33% 62-76, 304-313, 318, 339-370, 406, 442-447, 545-567, 597-689, 779-801, 830-833, 867-875, 907-918, 965, 1013-1020, 1033-1047 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/partitioned_variables.py 68 48 29% 104-154, 181-218, 233-237, 288-311 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/proto_ops.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_array_ops.py 187 158 16% 92-201, 230-243, 273-305, 332-373, 431-448, 474-477, 502-506, 520-528, 574-634, 660-689 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_ops.py 46 34 26% 64-124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py 61 44 28% 69-141, 147-183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_concat_ops.py 110 87 21% 67-70, 115-118, 136-202, 219-238, 253-289, 295-297, 302-310, 315-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_config.py 6 1 83% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_conversion_ops.py 58 39 33% 36-39, 49-52, 57, 64-106, 113-137, 141, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_dispatch.py 243 116 52% 82, 90-102, 114, 119-160, 175, 182-231, 246, 252-253, 258-261, 264-284, 417, 427, 435-437, 441, 445-447, 452-456, 515, 548, 550, 559-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_factory_ops.py 121 99 18% 78-84, 133-143, 168-240, 260-271, 277-310, 336-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_functional_ops.py 39 25 36% 70-91, 112-128 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_gather_ops.py 98 80 18% 88-118, 167-261, 270-295 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_getitem.py 161 136 16% 97-103, 124-187, 200-226, 244-340, 360-361, 381-389, 394-406, 437-456, 462-471 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_map_ops.py 132 103 22% 169-331, 360-364, 372-386, 395-403, 409-418, 423-427, 434-459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_math_ops.py 172 110 36% 88-105, 112-114, 193-256, 262, 271, 280, 289, 298-308, 313-324, 469-542, 551, 561, 572, 583, 594-608, 612, 618-619, 626-627 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_operators.py 44 1 98% 74 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_squeeze_op.py 53 40 25% 52-122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_string_ops.py 192 153 20% 59-78, 121-175, 219-220, 280-281, 322-325, 385-394, 400-452, 493-512, 563-575, 626-643, 648, 721-803 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_tensor.py 823 674 18% 268-311, 363-428, 472-497, 531-553, 587-608, 637-659, 718-795, 837-858, 888-897, 927-936, 959-983, 992, 1011-1020, 1031-1032, 1056, 1079, 1105, 1131-1134, 1161-1166, 1192-1196, 1227-1233, 1255-1266, 1290-1291, 1315-1316, 1345-1360, 1377-1383, 1410-1435, 1456-1466, 1491-1494, 1509-1533, 1570-1583, 1652-1793, 1828-1839, 1878-1905, 1927-1930, 1985-2001, 2030-2031, 2038-2041, 2075-2086, 2096-2099, 2105-2108, 2112-2117, 2140-2143, 2151, 2158, 2162, 2167, 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170-179, 206-212, 232-244, 247-260, 269, 274, 279, 282-309, 328-340, 344, 385, 420-436, 440, 444, 448-462, 466-471, 474-480, 526-544, 548, 552, 556-582, 586-603, 606-614, 633-637, 697-719, 723, 728, 732-746, 762-794, 797-805, 896-941, 945, 949, 953-993, 1019-1071, 1074-1089, 1100-1104, 1123, 1147, 1151-1158, 1162-1168, 1179, 1190, 1200, 1234-1255, 1261-1264, 1268, 1271-1277, 1281-1287, 1291-1301, 1305-1327, 1345-1346, 1350-1354 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell_wrapper_impl.py 224 164 27% 109-173, 179-183, 187, 191, 195, 198-199, 202-203, 209-215, 225-250, 271-289, 293-308, 312-319, 337-338, 342, 346, 349-350, 369-381, 385-396, 400-407, 424-425, 429, 433, 436-438, 442-443, 446-448, 453-465, 471-494, 498-504, 508-515 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_grad.py 14 5 64% 26-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/script_ops.py 174 76 56% 59-67, 83-85, 102-119, 124-150, 171-172, 183, 203-212, 230-253, 257, 282, 295, 319, 336, 347, 357-364, 452-457, 517-524, 536, 555 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sdca_ops.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/session_ops.py 128 85 34% 38, 56-60, 63-64, 67, 71-76, 85, 90, 94-99, 103-108, 117-118, 123-124, 129-130, 135, 173-178, 214-219, 239-243, 247, 251, 256-267, 272-288, 293-302 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets_impl.py 55 31 44% 51-57, 81-90, 118-133, 200-201, 277-280, 356-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/dct_ops.py 77 60 22% 33-47, 97-179, 223-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/fft_ops.py 217 146 33% 35-42, 48-60, 65-108, 116-140, 150-170, 198, 203-204, 209-212, 217-218, 223-226, 231-232, 237-240, 250-320, 336-360, 393-403, 434-444 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mel_ops.py 57 39 32% 46-48, 64-66, 73-89, 161-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mfcc_ops.py 20 9 55% 89-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/reconstruction_ops.py 60 48 20% 54-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/shape_ops.py 82 68 17% 33-54, 107-214 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/signal.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/spectral_ops.py 131 103 21% 70-94, 120-155, 224-276, 281-285, 331-365, 426-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/util_ops.py 33 20 39% 47-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/window_ops.py 77 52 32% 47-51, 70-90, 110-117, 135-141, 165, 192, 217-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sort_ops.py 53 33 38% 65-66, 107-109, 128-140, 155-204, 209-211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_grad.py 130 88 32% 50-60, 83-96, 101-103, 109-115, 136-145, 166-201, 206, 212-241, 247, 253, 273-286, 291, 297, 306-312, 317-322 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_ops.py 516 356 31% 66-70, 87-91, 95-101, 118-124, 147-179, 202-211, 324-331, 336-366, 430-434, 490-517, 550, 595, 614-655, 678-680, 720-731, 784-830, 840, 889-914, 955, 995-1008, 1062, 1129-1144, 1211-1216, 1259-1269, 1319-1333, 1387-1392, 1435-1445, 1490-1494, 1552-1564, 1663, 1672-1718, 1751-1765, 1829-1871, 1925-1935, 1958, 1977-1979, 2013, 2041-2043, 2107-2114, 2179-2187, 2401-2419, 2476-2480, 2508-2519, 2546-2557, 2595-2619, 2643-2645, 2683-2685, 2756-2777, 2795-2806 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/special_math_ops.py 357 299 16% 87-101, 129-130, 157-158, 186-187, 214-215, 242-243, 266-267, 290-291, 296-303, 319-324, 404, 409-468, 490-551, 580-679, 684-687, 693-700, 707-714, 723-726, 731-802, 807-853, 861-869, 881-973 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/standard_ops.py 83 1 99% 115 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_grad.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_ops.py 131 81 38% 44-52, 74, 101-114, 130-133, 161-164, 192-195, 224-228, 249-251, 301-304, 363-366, 415-418, 478-481, 532-535, 596-599, 648-651, 700-703, 755-758, 810-813, 872-914 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/stateful_random_ops.py 279 177 37% 90, 95-97, 110-136, 140-145, 149-151, 155, 168-182, 206-207, 212-216, 220-223, 230-231, 235, 239, 243-250, 254, 257, 270-278, 369-387, 410, 439-444, 467-473, 500-507, 517-519, 529-530, 541-548, 552, 557, 562, 565, 585-589, 600, 621-626, 629, 657-664, 667, 705-721, 737-741, 789-794, 810, 832-840, 880-890, 913-916, 939 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/build_info.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/device_context.py 6 1 83% 22 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/flags.py 59 18 69% 52-54, 56, 78, 81-86, 89, 92, 95, 98, 101, 104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/gfile.py 29 1 97% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/googletest.py 103 67 35% 56, 61-66, 72-94, 107, 112, 134-135, 143, 147, 150, 154-155, 184-218, 229-232, 251-258, 271-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/remote_utils.py 6 1 83% 22 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/resource_loader.py 47 22 53% 47-48, 60, 72-100, 121-125, 136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/self_check.py 22 12 45% 26-27, 44-53 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/traceme.py 27 16 41% 35-41, 44-45, 48-49, 53-60 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_mlir.py 14 4 71% 27, 34, 41, 47 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow.py 30 6 80% 38, 51, 61, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py 65 39 40% 19-21, 31, 35-36, 40-55, 59, 63-71, 74, 78-82, 87-90 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tfe.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder_impl.py 231 176 24% 93-112, 122-131, 145-153, 168-182, 201-214, 220-228, 267-300, 345-393, 412-428, 437, 442-446, 456-465, 478-492, 507-511, 525-555, 570-615, 638-667, 688-706, 710-716, 731-740, 752-754, 759-774, 786-792, 796-797 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/constants.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_deserialization.py 202 39 81% 74, 80-86, 94-95, 104-105, 110, 112, 114, 116, 118, 121, 132, 190, 243-255, 364-365, 376, 380, 387-392, 398-403, 441, 461 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_serialization.py 71 56 21% 31-49, 54-75, 81-83, 92-103, 122-161 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py 270 68 75% 55-64, 93, 98, 128-130, 205, 212-219, 231, 234-241, 256, 263, 273-279, 308, 326-333, 342-346, 369, 391-394, 409, 435-442, 445, 453-454, 457, 460, 464, 468-470, 476-482, 586, 595, 612-614 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load_v1_in_v2.py 137 105 23% 55-57, 60, 64, 72-83, 89-92, 95-102, 109-126, 133-184, 188-257, 262-263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader_impl.py 157 93 41% 63-67, 100-110, 135-161, 182-190, 194, 199-204, 208, 215-219, 244-246, 265, 298-299, 312-314, 319, 324, 329, 343-358, 379-381, 401-409, 425-433, 450-455 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/main_op.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/main_op_impl.py 23 7 70% 43-46, 70-72 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/method_name_updater.py 42 26 38% 71-72, 92-113, 130-148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/export_output.py 155 93 40% 53, 57-63, 86-99, 134-148, 152, 156, 159-166, 182-185, 189, 192-199, 223, 228, 231, 269-278, 294-298, 301-303, 325-361, 365, 369, 373, 378, 381-382, 395, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/export_utils.py 115 84 27% 93-140, 154-182, 211-226, 243-247, 277-290, 309-325, 342-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/mode_keys.py 43 5 88% 87, 100, 103, 106, 109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/nested_structure_coder.py 250 95 62% 70, 81, 96, 107-111, 132, 135-139, 152, 164-166, 175, 178-182, 198, 201-205, 225, 228-236, 256, 259-262, 268-269, 279, 282-285, 291-292, 306, 309-312, 329, 332-335, 352, 355-358, 375, 378-382, 399, 402-405, 422, 425-431, 482, 486-494, 497, 501-511 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/revived_types.py 70 21 70% 78, 92, 94, 96, 98-99, 122, 128, 133, 142-147, 171, 175-181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/save.py 392 316 19% 88-99, 103, 108-133, 136, 140-145, 171-210, 214, 218-234, 254-335, 339, 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100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer.py 128 95 26% 69-82, 90-96, 100, 110-112, 120-121, 132-137, 145-153, 160-163, 166-168, 193-204, 207-232, 244-254, 264-269, 285-294, 298-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer_v2.py 44 27 39% 74-96, 100, 110-112, 120-122, 131, 138-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer.py 133 90 32% 79-99, 119-142, 155-156, 159-161, 179-214, 217-227, 244-249, 263-273, 276-279, 360-372, 376, 380, 384, 387-388, 393-394, 402-403, 413-414, 421-422, 432-433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer_cache.py 24 8 67% 43-48, 60-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tf2.py 14 1 93% 40 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/module_util.py 31 12 61% 24, 47, 50-56, 60-61, 63 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/api.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/bfloat16.py 26 13 50% 49-68, 78-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/client.py 162 118 27% 34-35, 46, 50-54, 59-68, 75-81, 85-87, 106-141, 157-176, 181, 186-191, 196-200, 203, 206, 213-216, 220, 224, 228, 232, 236, 240, 244, 248-258, 270-281, 286-315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/device_assignment.py 180 148 18% 36-56, 81-102, 108, 113, 118, 129, 133, 148-151, 157-158, 162-163, 167-168, 175, 198-213, 257-413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/feature_column.py 220 158 28% 106-155, 219-285, 295-304, 310, 314, 318, 322, 330, 334, 338, 341, 344, 347, 351, 373, 397-402, 405, 409, 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196-212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/training_loop.py 82 70 15% 56-177, 201-222 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adadelta.py 46 28 39% 59-67, 70-72, 75-81, 84-86, 97-99, 110-112, 124-126 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad.py 48 26 46% 63-70, 73-80, 84-90, 93-94, 98-99, 107-108, 116-117, 126-127 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad_da.py 59 39 34% 75-88, 91-100, 104-109, 112-116, 128-132, 144-148, 161-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adam.py 93 67 28% 101-111, 114-119, 127-136, 139-147, 150-153, 167-170, 184-207, 210, 221-223, 226, 231-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_loops.py 21 13 38% 51-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_session_run_hooks.py 491 361 26% 61, 65, 69, 83, 87, 99-108, 111-112, 125-139, 142-152, 155, 162-163, 166-167, 170, 212-231, 234-237, 243-247, 250-263, 266-270, 273-275, 306-315, 352-360, 363-366, 369-371, 374-377, 382-386, 412-417, 420-422, 425-427, 430, 433-442, 500, 503, 506, 509, 546-557, 560, 563-569, 572-588, 591, 594-602, 605-609, 613-634, 637-656, 669-678, 681, 684-690, 693, 696-702, 705-736, 743, 760-761, 764, 767-775, 809-817, 822-827, 831-839, 842-861, 864-865, 873-884, 903, 906-909, 913-933, 949-951, 955, 958-977, 991, 994, 1033-1037, 1041-1044, 1047-1055, 1058-1072, 1075-1078, 1085-1104 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_management.py 266 201 24% 45-47, 61-63, 96-128, 167, 213-247, 270-305, 322-324, 351-364, 381-388, 410, 438-456, 476-484, 489-490, 506-508, 614-664, 668, 672, 686, 698, 702-717, 721-722, 743, 748, 771-824, 844-852 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_ops.py 76 60 21% 123-203, 332-416, 467-473 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scale.py 158 90 43% 83, 88, 125, 141-162, 167-176, 180-188, 193, 198, 202, 228-239, 242, 245-246, 249, 252, 257-260, 275-277, 282, 324-335, 340, 344, 348, 351, 355-400, 403-409, 414, 424, 426, 428, 432 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scale_optimizer.py 79 52 34% 61-74, 78, 114-126, 129-136, 139-141, 147-150, 174-184, 211-221, 226-229, 233, 237, 241, 245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scaling_gradient_tape.py 73 53 27% 43, 115-125, 163-181, 190, 200, 238-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/mixed_precision.py 51 30 41% 33-74, 219, 332, 339-362, 382-386, 413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/mixed_precision_global_state.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/ftrl.py 72 53 26% 95-130, 134-138, 141-150, 154-170, 186-202, 218-235, 252-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/gen_training_ops.py 1619 1436 11% 57-77, 83, 115-135, 141, 167-191, 197, 226-248, 254, 282-306, 312, 353-378, 384, 414-434, 440, 490-510, 516, 552-572, 578, 617-637, 643, 662-681, 687, 720-744, 750, 780-800, 806, 835-854, 860, 886-906, 912, 952-972, 978, 1011-1038, 1043-1056, 1086-1113, 1118-1131, 1156-1185, 1190-1206, 1236-1266, 1271-1286, 1313-1343, 1348-1365, 1404-1437, 1442-1459, 1498-1527, 1532-1547, 1575-1601, 1606-1618, 1665-1692, 1697-1712, 1747-1773, 1778-1791, 1829-1856, 1861-1874, 1893-1916, 1921-1933, 1965-1995, 2000-2017, 2049-2078, 2083-2099, 2127-2153, 2158-2171, 2199-2225, 2230-2243, 2269-2296, 2301-2313, 2351-2377, 2382-2395, 2423-2451, 2456-2472, 2500-2530, 2535-2553, 2585-2616, 2621-2638, 2668-2699, 2704-2722, 2769-2800, 2805-2822, 2860-2887, 2892-2908, 2949-2977, 2982-2998, 3034-3067, 3072-3090, 3126-3156, 3161-3179, 3211-3239, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/momentum.py 40 22 45% 80-83, 86-87, 90-98, 101-102, 111-112, 121-122, 131-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/monitored_session.py 481 348 28% 153-188, 192-247, 251, 255, 259, 263, 267, 271, 275, 279, 283, 288-300, 315, 337-427, 512-601, 614, 639-644, 648-657, 660-661, 690-694, 698-707, 710-711, 734-751, 756-758, 774, 819-834, 852-853, 857, 861, 871, 874, 877, 880, 883-887, 893-897, 902-910, 915-929, 939, 950, 1034, 1118-1124, 1132, 1154-1155, 1159, 1163, 1173-1177, 1185, 1188-1197, 1200, 1206-1207, 1230-1231, 1235-1238, 1249-1272, 1276-1295, 1299-1316, 1342-1344, 1349-1351, 1354-1365, 1368-1384, 1412-1414, 1418, 1422-1453, 1458-1481, 1484-1486, 1501-1514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/moving_averages.py 131 103 21% 87-114, 152-178, 220-266, 378-382, 387, 421-473, 485, 509-511, 545-561 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/optimizer.py 403 311 23% 58-61, 76-80, 85-87, 97, 102, 109, 112, 115, 118-132, 139, 142, 146-151, 158, 161, 165-176, 188, 191, 194, 199-213, 327-343, 353, 399-412, 458-519, 523-529, 561-640, 667-735, 755-773, 783, 794-811, 816-843, 848-857, 862-866, 869-875, 883, 894-898, 914, 924, 932, 944, 957, 980-982, 1002, 1032-1038, 1057, 1074, 1090-1094, 1109-1116, 1134-1142, 1156-1163, 1171-1179, 1202-1239, 1245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_adagrad.py 44 25 43% 63-74, 77-82, 85-90, 95-96, 103-104, 111-112, 120-121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_gradient_descent.py 30 13 57% 57-62, 65, 74, 83, 93, 103-107 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/py_checkpoint_reader.py 40 17 58% 31-48, 61, 73-74, 98-99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/quantize_training.py 15 4 73% 45-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner_impl.py 176 128 27% 97-118, 138-165, 175-192, 196, 200, 204, 208, 212, 231, 236, 248-283, 293-298, 327-356, 368-385, 390, 411, 451-480 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/rmsprop.py 70 50 29% 107-118, 121-131, 134-142, 145-161, 173-189, 201-218, 231-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saver.py 480 383 20% 84, 104-124, 144-154, 173-179, 194, 206-207, 254-285, 298-311, 335-361, 379-390, 409-417, 462, 484-557, 574-583, 598-610, 800-843, 846-848, 851, 856-902, 906-914, 926-927, 931-942, 957-973, 981, 992-1008, 1021, 1032, 1045-1049, 1061-1062, 1075-1080, 1139-1217, 1253, 1283-1334, 1346, 1460, 1471-1490, 1496-1514, 1580-1599, 1603-1607, 1626-1634, 1679-1728 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/functional_saver.py 119 48 60% 51, 64-73, 118, 148, 153-154, 165-169, 179-182, 188-191, 202-263, 284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_hook.py 16 4 75% 44, 51, 55, 59 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object.py 34 8 76% 41, 47-51, 55, 78, 101 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object_util.py 161 98 39% 54-56, 63-64, 67-70, 84-85, 90-95, 101, 111, 120, 139, 143, 145-169, 175-183, 190, 196-209, 230-303, 319, 342 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/server_lib.py 200 151 24% 57-95, 146-150, 153-164, 173, 184, 194, 213, 235, 285-313, 318, 324, 327, 330-334, 348-361, 365, 374, 388-392, 407-411, 427-434, 457-464, 473-491, 527-530, 534-538, 542-546, 555-573 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_manager.py 149 120 19% 42-47, 144-155, 189-227, 288-319, 353-383, 411-442, 453-459, 473, 487, 502-512, 529-551, 557-558, 561-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_run_hook.py 41 13 68% 110, 127, 150, 169, 186, 211, 226-228, 240, 245, 255, 263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/slot_creator.py 62 48 23% 55-101, 124-135, 161-173, 190-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/summary_io.py 12 1 92% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/supervisor.py 339 239 29% 308-357, 360-369, 380-390, 405-416, 427-433, 444-455, 464-469, 478-484, 493-500, 509, 518, 530, 539, 548, 557, 561, 570, 579, 588, 597, 606, 615, 624, 628-636, 661-688, 720-745, 772-780, 801-808, 831-847, 859, 869, 879, 883, 898-902, 910-918, 928-931, 999-1023, 1038-1040, 1043-1051, 1066-1073, 1076-1077, 1081-1098, 1112-1114, 1117-1122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/sync_replicas_optimizer.py 150 114 24% 181-205, 223, 248-358, 375-378, 392, 403, 417, 439-458, 462, 476-478, 481-497, 501-513 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/base.py 342 122 64% 71-76, 79-82, 86, 93-95, 98, 112, 126, 143-155, 161, 165, 170-173, 181-183, 187, 211, 233, 241-253, 266, 277-278, 291-307, 320, 323-324, 338-348, 352-354, 364-367, 369, 373, 376, 411, 414, 489-494, 521-526, 544-546, 550, 558, 562, 566, 570, 601, 624, 629, 633, 637-640, 726, 736-737, 754, 780-790, 822, 825, 829-839, 876, 883, 923-925, 933, 1004-1005, 1022-1023 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/data_structures.py 536 236 56% 28-30, 70, 72, 74, 92, 94, 97, 144, 167, 171, 184, 187, 193, 205, 214, 221, 228, 232, 236, 240, 248-252, 257-261, 266, 271, 314, 318, 321, 324, 328, 336, 349-350, 353, 356-366, 369, 372, 375, 381, 387, 390, 444-445, 454-455, 459-462, 465-468, 472, 482, 484-485, 494, 501, 510, 522-523, 532-545, 550, 568-574, 577, 580, 583, 586, 589, 592, 597, 600-601, 604-605, 608, 611-612, 626, 629, 646-648, 654, 657, 660, 666-669, 672-676, 679-683, 689-690, 693, 696, 699, 702, 717, 720, 732, 747, 751-754, 757-760, 770, 777, 785, 806, 808-809, 815, 823-824, 828-832, 845-859, 862-864, 867, 870, 875, 878-879, 882, 892-938, 943, 947-954, 957, 962, 967, 970, 973, 976, 983, 987, 991-998, 1001-1009, 1013, 1048-1055 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/graph_view.py 201 126 37% 78-86, 97-135, 177-179, 192, 211-312, 319-330, 335-356, 379-380, 385-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/layer_utils.py 110 13 88% 33-34, 190, 231, 242, 268, 293-296, 301-304 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/python_state.py 17 1 94% 87 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/tracking.py 131 52 60% 82, 92-94, 98, 102-113, 119-125, 140, 177-178, 185-186, 190-191, 221, 226, 231-234, 237-252, 274-276, 323-324, 330 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py 602 378 37% 69-72, 90-115, 129, 133, 136, 143-145, 147-149, 151, 229, 244, 248-249, 252-254, 272-277, 285, 291-294, 302-308, 324-348, 355-379, 392-418, 433, 465-476, 493, 511-512, 537-607, 616, 621, 626, 631, 636, 640, 651-664, 710-740, 763, 780, 788-806, 810-814, 831-845, 849-850, 864-867, 871, 876, 881, 893, 911-921, 941-945, 959, 963-982, 990, 998, 1002-1014, 1018-1024, 1029, 1036-1037, 1040-1045, 1094-1107, 1125-1140, 1164-1198, 1259, 1263, 1268-1281, 1286-1290, 1333-1335, 1343, 1468-1481, 1485-1490, 1520-1529, 1540-1541, 1568-1601, 1705-1712, 1808-1821, 1825-1830, 1857-1866, 1877-1878, 1902-1933, 2009-2016 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training.py 105 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_ops.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_util.py 95 65 32% 66-68, 88-103, 120-137, 158-162, 172-184, 201-209, 222-239, 243-253 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/warm_starting_util.py 150 123 18% 122-126, 152-156, 173-189, 240-311, 340-372, 396-411, 465-549 /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/user_ops.py 10 1 90% 32 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/all_util.py 36 6 83% 78-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat.py 51 9 82% 60-61, 80, 86, 111, 116, 178-180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat_internal.py 9 3 67% 34-36 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/decorator_utils.py 52 11 79% 26-32, 51, 71, 100, 108, 119, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py 208 81 61% 95, 97, 102-110, 129-131, 188-239, 264, 314-317, 379, 381-382, 390, 439-442, 463-471, 485, 487, 489, 495, 497-500, 553, 565-568, 596-601, 635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py 58 21 64% 69, 81, 100-104, 120-125, 128-131, 156, 170, 181-188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/function_utils.py 59 38 36% 31-32, 36, 51-63, 78-86, 95-101, 106-119, 127-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/is_in_graph_mode.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lazy_loader.py 27 4 85% 50-52, 66-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lock_util.py 43 3 93% 68, 92, 112 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/memory.py 11 4 64% 40-45 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/module_wrapper.py 132 60 55% 37, 44-48, 52, 68-78, 104-105, 108, 133-140, 144-152, 161-162, 170-174, 193-206, 219-227, 230-233, 236, 239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py 303 121 60% 91-93, 100-101, 150, 157-162, 165, 167-171, 178-179, 181, 185, 220-221, 223-224, 231, 256, 332, 335, 379-382, 418-441, 485-486, 489, 496, 508-512, 596, 599, 605, 610, 653-657, 696, 777, 782, 789-804, 809-822, 827-828, 833, 837, 844, 1032-1037, 1189, 1249-1285, 1347-1351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/object_identity.py 114 27 76% 44, 47-48, 51-52, 56, 61, 70, 114, 141, 148, 155, 159, 162-168, 179-181, 190, 199, 202, 205, 232 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/compare.py 87 69 21% 94-118, 139-186, 190, 194-200, 216-255, 274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/serialization.py 29 19 34% 43-76 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_contextlib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_decorator.py 98 16 84% 172-173, 180-181, 188-193, 218, 222, 224, 257, 260, 272, 276 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_export.py 145 47 68% 114, 118, 132, 154, 174, 178, 180, 194-207, 220-228, 241-249, 274, 302, 307, 329-331, 343-344, 388-393 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_inspect.py 138 55 60% 34, 43, 79-90, 95, 118, 126, 131-147, 190-235, 282, 291-293, 298, 327, 342, 347, 362, 377, 382, 397, 402, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_should_use.py 98 67 32% 45-61, 64-68, 72-86, 95, 100-101, 105-107, 113-117, 122-129, 148-161, 179-202, 235 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_stack.py 70 6 91% 37-38, 61, 76, 88, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/all_renames_v2.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/renames_v2.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/doc_controls.py 49 30 39% 258-261, 276-322 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/baseline.py 124 78 37% 72-79, 83-90, 95-112, 128-154, 187-197, 220-227, 264-283, 381-398, 414-427, 497-506, 516-525, 607-621, 636-650 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees.py 715 607 15% 61-81, 95-99, 111-115, 130-147, 168, 201-275, 280-287, 317-388, 393-414, 443-471, 476-489, 497, 505-570, 597-621, 625-639, 643-654, 674-680, 686, 690, 703-706, 709, 717-724, 730-731, 761-776, 824, 830-836, 841-889, 898, 906-911, 916-920, 926, 938-982, 986-1010, 1017-1052, 1056-1089, 1093, 1147-1407, 1414, 1417, 1440-1479, 1489-1506, 1513-1521, 1531-1551, 1556, 1565-1578, 1601-1615, 1641-1686, 1691-1700, 1739-1751, 1770-1781, 1840-1889, 1894-1903, 2038-2063, 2190-2214, 2315-2343, 2357-2360 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees_utils.py 42 27 36% 33-37, 45-57, 61, 66, 73-82, 87-94 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn.py 224 162 28% 46-48, 73-102, 125-153, 157-158, 174-230, 233-254, 260, 272, 287-344, 347-359, 363-364, 371-379, 431-456, 489-504, 552-579, 737-759, 787-807, 946-962, 986-1003, 1151-1172, 1199-1221 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn_linear_combined.py 187 140 25% 43-44, 64-65, 69-71, 76-82, 141-226, 288-383, 554-585, 615-643, 657-682, 820-843, 864-888, 1048-1077, 1106-1136 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/head.py 497 410 18% 58, 78-89, 155, 166, 193, 230-240, 274, 299-354, 382-443, 448-472, 487-494, 510-520, 536-559, 563-567, 571-573, 579-584, 590-598, 615-617, 622-628, 632-637, 646-650, 658-666, 671-679, 738-748, 767-774, 778, 782, 787-808, 812-825, 829-852, 893-994, 1062-1077, 1096-1101, 1105, 1109, 1113-1195, 1199-1223, 1265-1373, 1435-1440, 1460-1467, 1471, 1475, 1479-1505, 1514-1532, 1571-1652, 1661-1664, 1668-1676, 1704-1715 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/kmeans.py 110 69 37% 46-48, 51-52, 55-62, 82-84, 87-99, 119-127, 137-145, 173-224, 404-415, 424-425, 436-438, 454, 470-475, 479 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py 283 217 23% 128-133, 138-174, 181-239, 243-244, 248-249, 262-270, 283-308, 326-375, 394-448, 470-539, 547-548, 555, 559, 583-619, 652-678, 719-748, 759-767, 920-945, 968-991, 1107-1119, 1158-1171, 1178-1186, 1325-1349, 1371-1396 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sdca_ops.py 291 245 16% 95-104, 114, 123, 132, 200-251, 254, 257, 261, 265, 270-271, 280-298, 305-307, 310-312, 316-318, 322-333, 337-348, 356-364, 368-371, 379-402, 420-433, 440-449, 464-629, 644-670, 679-699, 715-760, 775-781 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sharded_mutable_dense_hashtable.py 139 101 27% 73-99, 106-120, 124, 135-138, 158-165, 184-192, 204-210, 214, 223-229, 232-235, 262-280, 285, 289, 293, 296-298, 301-307, 310-311, 316-335, 339-352, 364-370 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/metric_keys.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/optimizers.py 46 27 41% 78-91, 95-97, 129-146 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/parsing_utils.py 44 25 43% 138-140, 254-257, 288-312, 321-324, 342-346 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/prediction_keys.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/early_stopping.py 179 131 27% 84-104, 153, 210, 268, 326, 346-356, 365-388, 401-430, 446-450, 454-458, 471-479, 482-484, 487-488, 491-498, 505, 508, 511-512, 515-518, 525-529, 540-549, 552-566, 570-571, 577-592 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/estimator.py 729 611 16% 176-203, 209, 213, 217, 228-231, 246-248, 259-261, 270-271, 321-351, 365-376, 390, 445-462, 478-511, 525-543, 596-639, 646, 717-722, 800, 817-889, 926-1010, 1014-1017, 1020-1021, 1027-1039, 1043, 1048-1054, 1058-1072, 1086, 1097-1100, 1123-1137, 1154-1176, 1179-1182, 1197-1213, 1231-1241, 1250-1354, 1361-1385, 1390-1523, 1527-1563, 1567-1575, 1581-1631, 1636-1658, 1661-1664, 1735-1738, 1756, 1760-1763, 1769-1786, 1793-1805, 1822-1849, 1854-1861, 1871-1941, 1945-1947, 1953-1957, 1962-1968, 1985-1998, 2003-2016, 2021-2026, 2031-2040, 2052, 2065-2110, 2123-2134, 2141-2148, 2338-2341, 2366-2385 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export.py 128 88 31% 65-72, 77-95, 99-103, 139-157, 206-215, 238, 271-277, 302-313, 327-343, 348, 370-376, 404-440, 470-474 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export_lib.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export_output.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/exporter.py 151 106 30% 41, 62, 97-100, 104, 108-117, 136-145, 150-160, 255-271, 277, 281-307, 319-337, 350-365, 400, 406, 410-416, 457-462, 467, 471-477, 489-507 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/extenders.py 38 25 34% 82-94, 104-107, 113-123 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/gc.py 63 46 27% 87-95, 110-127, 140-147, 161-166, 179-184, 207-217 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/base_head.py 290 222 23% 110, 122, 133, 163, 178, 192, 224, 227, 281-295, 347, 406-469, 498-582, 587-624, 639-648, 652-654, 660-661, 666-667, 673-677, 695-705, 726-759, 763-765, 771-773, 779-784, 790, 798-817, 821-824, 844-855, 896-918, 925-934 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py 205 164 20% 151-193, 198, 203, 208, 229-232, 245-250, 254-259, 263-275, 284-300, 318-362, 366-392, 421-427, 430-433, 443-487, 540-597 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/head_utils.py 30 17 43% 55-66, 70-77, 92-94, 101-102 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_class_head.py 151 113 25% 143-168, 173, 178, 183, 204-207, 220-225, 229-242, 246-262, 271-287, 305-345, 349-359, 368-385, 436-492 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_head.py 182 149 18% 34-35, 40-45, 178-200, 205, 210, 215-220, 250-282, 286-306, 315-342, 346-353, 357-367, 376-395, 454-512, 528-548 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_label_head.py 217 181 17% 161-243, 248, 253, 258, 277-280, 284-315, 322-339, 348-364, 381-399, 403-427, 436-480, 532-587 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/regression_head.py 124 85 31% 143-159, 164, 169, 174, 177-182, 186-203, 212-228, 242-254, 258-269, 279-297, 351-402, 483-484, 493, 568-570, 577 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/basic_session_run_hooks.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/hooks.py 92 67 27% 103-120, 124-141, 148-176, 179-201, 205-207, 211, 237-244, 247-249, 253, 256-266, 275-277 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/session_run_hook.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/numpy_io.py 70 56 20% 50-53, 70-86, 141-224 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/pandas_io.py 61 46 25% 32-36, 50-52, 91-158 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py 208 177 15% 34-38, 51-60, 78-96, 126-145, 158-169, 172-181, 197-212, 215-230, 243-255, 258-271, 285-297, 300-330, 376-504 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/usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tools/analytics.py 8 2 75% 28, 37 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/_tpu_estimator_embedding.py 227 180 21% 58, 62, 66-68, 73-94, 103-122, 137-174, 270-315, 337-358, 362-363, 368, 372-412, 415-418, 424-429, 434-490, 495-505, 510-519, 524-541, 553-557 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/error_handling.py 65 44 32% 56-58, 73-105, 115-117, 122-125, 136-154 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/iteration_count_estimator.py 64 46 28% 68, 72-81, 84, 87, 90, 107-111, 122-123, 132-150, 169-201 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_config.py 99 60 39% 141-189, 227-265, 272, 276, 280, 284, 288, 291-297, 304-309 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12079-12080, 12094-12110, 12121-12184, 12228-12477, 12482-12483, 12488-12495, 12500-12664, 12669-12674, 12679, 12686 /usr/local/lib/python3.8/dist-packages/tornado/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/tornado/concurrent.py 80 50 38% 53, 60-65, 68, 117-135, 154-171, 184-185, 204-207, 229-231, 238, 245, 261-264 /usr/local/lib/python3.8/dist-packages/tornado/escape.py 144 91 37% 54, 61, 75, 83, 88, 102-103, 108, 115, 137-144, 159-165, 173, 178, 183, 192-196, 204, 209, 214, 225-227, 245-256, 309-375, 379-390 /usr/local/lib/python3.8/dist-packages/tornado/gen.py 298 227 24% 97, 127-138, 142-153, 189-241, 254, 279-282, 343-359, 363-367, 375-380, 383-386, 392-397, 400, 403-406, 457, 481-523, 539-544, 586-622, 639-643, 660, 663, 706-714, 720-768, 771-791, 796-802, 808-811, 831-842 /usr/local/lib/python3.8/dist-packages/tornado/ioloop.py 272 176 35% 59-61, 68, 71, 170-179, 201, 215, 231, 236, 241, 264-279, 298, 309-313, 320, 324, 333-341, 368, 374, 380, 399, 408, 417, 425, 438-445, 458, 490-532, 546, 580-587, 602, 620, 629, 644, 654, 663, 680-696, 713-726, 733, 742-763, 767, 787-789, 803-809, 821-825, 835, 838, 871-877, 884-887, 891-894, 901, 904-911, 914-916, 919-946 /usr/local/lib/python3.8/dist-packages/tornado/locks.py 158 107 32% 27, 43-44, 48-51, 115-116, 119-122, 130-142, 146-154, 158, 202-203, 206, 213, 220-225, 232, 240-258, 271, 274, 282, 382-386, 389-395, 399-412, 422-440, 443, 451, 454, 462, 475-476, 480-482, 523, 526, 536, 545-548, 551, 559, 562, 570 /usr/local/lib/python3.8/dist-packages/tornado/log.py 111 87 22% 39-40, 44-45, 56-71, 75-78, 138-161, 164-207, 216-255, 267-336 /usr/local/lib/python3.8/dist-packages/tornado/platform/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py 156 116 26% 36, 45-75, 78-90, 95-104, 107-123, 126-135, 138-139, 142-151, 154, 162, 169, 172-184, 189-194, 202, 205, 224, 229, 253-261, 264-266, 269-275, 278-280, 292, 308, 314, 338-346 /usr/local/lib/python3.8/dist-packages/tornado/queues.py 147 96 35% 40, 62-70, 75, 78, 154-166, 171, 175, 178, 181-184, 199-207, 214-223, 246-252, 260-270, 284-288, 296, 299, 303, 306, 309, 314-316, 320-324, 327, 330, 333-342, 371, 374, 377, 404, 407, 410 /usr/local/lib/python3.8/dist-packages/tornado/util.py 177 116 34% 37-40, 51-63, 81-84, 87, 101, 114, 120, 128, 149-157, 161-165, 177-185, 198-203, 210-213, 228, 270-287, 299, 305, 308, 328-334, 340-350, 355-356, 361-363, 375-380, 383-395, 404-407, 421-430, 436, 448-452, 458, 462-465, 470-472 /usr/local/lib/python3.8/dist-packages/traitlets/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/_version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/config/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/config/application.py 366 257 30% 54, 70, 74, 86-93, 113-117, 151-157, 177-181, 197-199, 209-229, 243-247, 273-282, 287-289, 297, 305-306, 310-332, 336-345, 348-359, 363-378, 385-406, 413, 419-421, 429-434, 438, 443-453, 469-498, 503-541, 550-589, 594, 599-609, 623-642, 646-650, 653-654, 662-664, 708-711 /usr/local/lib/python3.8/dist-packages/traitlets/config/configurable.py 190 142 25% 63-93, 102, 119-129, 134-168, 180-186, 196-200, 211-218, 227-250, 255, 260-286, 294-328, 342-343, 363-367, 373-379, 411-421 /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py 417 299 28% 53-55, 80, 83, 87, 91-93, 99-104, 108, 115-131, 138-147, 172-176, 180, 184-196, 206-216, 220-226, 232, 235, 238, 241-250, 260-266, 270-271, 277, 280-281, 285, 288-289, 292-297, 321-322, 339-344, 347, 356-357, 378-381, 385, 401-408, 411-412, 415-423, 426-427, 436-439, 452-458, 462-473, 477-489, 507-517, 521-527, 587-592, 596-597, 602-610, 636-686, 714-726, 738-748, 751-754, 757-758, 761, 766-768, 772-773, 782-807, 812-833, 846-857 /usr/local/lib/python3.8/dist-packages/traitlets/log.py 10 7 30% 18-27 /usr/local/lib/python3.8/dist-packages/traitlets/traitlets.py 1265 803 37% 52, 99-102, 110-118, 126-146, 153-156, 163-166, 173-178, 203-211, 217, 219, 221, 235-236, 245-252, 271-277, 281-285, 288-291, 294-297, 300-302, 325-332, 336-340, 343-346, 350-351, 406, 439-457, 473-475, 480-484, 499-514, 519-524, 527-543, 556, 559-574, 582-585, 588-594, 597-606, 609-612, 618-625, 632-637, 644-649, 660, 667, 681-686, 690-693, 697-706, 709-712, 729-732, 785, 788, 806-819, 848, 851, 894, 907, 914, 924, 933, 953-959, 965-977, 983-986, 992-1008, 1022-1028, 1031-1045, 1057-1065, 1076-1131, 1134, 1143-1176, 1179-1189, 1192-1198, 1228-1235, 1263-1265, 1284-1286, 1291-1297, 1319-1327, 1331-1334, 1338-1343, 1352, 1377-1387, 1395-1396, 1401, 1405, 1421-1437, 1441-1450, 1458-1459, 1476-1486, 1507, 1510-1524, 1561, 1566, 1574-1586, 1591, 1596, 1600-1601, 1604-1607, 1610-1614, 1655, 1660, 1664, 1666, 1674-1677, 1680-1688, 1691-1692, 1695-1696, 1699-1701, 1705, 1717-1718, 1746, 1752-1755, 1788-1790, 1793-1803, 1806-1809, 1812-1818, 1839-1853, 1869-1871, 1878-1882, 1886-1953, 1973-1977, 1984-1988, 1998-2002, 2009-2012, 2024-2026, 2033-2036, 2046-2054, 2061-2064, 2075-2082, 2087-2091, 2096-2101, 2111-2113, 2120-2123, 2136-2138, 2142-2145, 2155-2163, 2217, 2221, 2226, 2231-2233, 2236-2244, 2247-2257, 2265-2267, 2312-2314, 2317-2321, 2324-2326, 2416, 2421, 2429, 2433-2449, 2458-2461, 2501, 2504-2507, 2511-2515, 2517, 2524-2526, 2529-2533, 2536-2556, 2560, 2562-2563, 2567-2572, 2585-2591, 2602-2605, 2638-2645, 2649-2655, 2659-2663, 2666-2683, 2687-2690 /usr/local/lib/python3.8/dist-packages/traitlets/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/utils/bunch.py 12 8 33% 12-15, 18, 22-24 /usr/local/lib/python3.8/dist-packages/traitlets/utils/getargspec.py 64 58 9% 22-86 /usr/local/lib/python3.8/dist-packages/traitlets/utils/importstring.py 17 13 24% 27-42 /usr/local/lib/python3.8/dist-packages/traitlets/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/wcwidth/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/table_wide.py 1 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/table_zero.py 1 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/wcwidth.py 37 28 24% 116-129, 183-196, 212-220 /usr/local/lib/python3.8/dist-packages/wrapt/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/wrapt/decorators.py 186 91 51% 11-23, 40-41, 55-56, 60, 64, 68, 72, 76, 86, 91, 95, 99-102, 105-106, 112, 117-120, 123, 138, 142, 146, 149-150, 154, 158, 162-163, 165, 205, 208-212, 253-279, 292-294, 322, 343-390, 411, 444-445, 450-451, 454, 464-514 /usr/local/lib/python3.8/dist-packages/wrapt/importer.py 102 75 26% 12, 37-45, 52-98, 103-109, 112-119, 128-135, 145-148, 153, 156-159, 164, 172-221, 227-230 /usr/local/lib/python3.8/dist-packages/wrapt/wrappers.py 472 304 36% 11, 32, 36, 40, 44, 51, 60, 78-87, 91, 95, 99, 103, 107, 111, 114, 117, 121, 124, 130, 134, 138, 141, 144, 147, 150, 153, 156, 159, 162, 165, 168-190, 196-199, 202-216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300, 303-304, 307-308, 311-312, 315-316, 319-320, 323-324, 327-328, 331-332, 335-336, 339-340, 343-344, 347-348, 351-352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 406, 409, 412, 415, 418, 421, 424, 427, 431, 437, 442-453, 456-461, 471-477, 505-533, 542-566, 578-624, 704-719, 727-728, 733-771, 774, 777-780, 791-794, 797-798, 801, 804, 807-811, 819-828, 831, 834-836, 839-858, 870-880, 899-928, 936-947 /usr/local/lib/python3.8/dist-packages/zmq/__init__.py 41 16 61% 23-24, 32-37, 54-60, 64-67 /usr/local/lib/python3.8/dist-packages/zmq/_future.py 323 280 13% 28-112, 129-144, 149, 152-161, 165-168, 176, 187, 196-199, 208-212, 216-241, 249-278, 282-289, 299, 308-315, 319-350, 354-406, 410-445, 448-483, 488-493, 503-509, 513-515, 519-521, 528, 532-533, 540-543 /usr/local/lib/python3.8/dist-packages/zmq/asyncio/__init__.py 48 20 58% 18-19, 28, 34-37, 41-43, 53, 60, 75-76, 83-87, 93 /usr/local/lib/python3.8/dist-packages/zmq/backend/__init__.py 26 15 42% 14-17, 22, 28-40 /usr/local/lib/python3.8/dist-packages/zmq/backend/cython/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/zmq/backend/select.py 15 7 53% 29-35 /usr/local/lib/python3.8/dist-packages/zmq/error.py 79 44 44% 37-50, 58, 61, 90-91, 101-102, 107-108, 120, 123-124, 132-144, 157-162, 165, 168, 183-184 /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:720: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_origin_portfolio_ids = [int(item) for item in options.list_origin_portfolio_ids.split(",")] if options.list_origin_portfolio_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:721: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_photo_ids = [int(item) for item in options.list_photo_ids.split(",")] if options.list_photo_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:722: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval = [int(item) for item in options.interval_rotation.split(",")] if options.interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:723: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval = [float(item) for item in options.interval_resize.split(",")] if options.interval_resize is not "" else None /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:750: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi = [float(item) for item in options.mother_crop_portfolio_multi.split(",")] if options.mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1952: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1958: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2143: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2149: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py:84: SyntaxWarning: "is not" with a literal. Did you mean "!="? if (default_value.shape.ndims is not 0 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py:85: SyntaxWarning: "is not" with a literal. Did you mean "!="? and default_value.shape.ndims is not 1): /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py:285: SyntaxWarning: "is" with a literal. Did you mean "=="? minval_is_zero = minval is 0 # pylint: disable=literal-comparison /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py:286: SyntaxWarning: "is" with a literal. Did you mean "=="? maxval_is_one = maxval is 1 # pylint: disable=literal-comparison /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py:795: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(key) is 1: /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py:804: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(key) is 1: /usr/local/lib/python3.8/dist-packages/zmq/eventloop/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/zmq/eventloop/ioloop.py 64 35 45% 23-26, 42-45, 49-52, 55-60, 64-67, 75, 89-91, 103-106, 114-117, 129-130, 135 /usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py 246 181 26% 44, 49, 53-54, 60-61, 114-138, 142, 146, 150, 154, 182-189, 200-203, 241-243, 255-258, 265, 271-279, 285-287, 295-299, 306-307, 311, 344-393, 397, 401-419, 423, 427, 430, 435-444, 449-469, 473-486, 491-505, 508-509, 513-522, 526-527, 531-532, 536-542, 546 /usr/local/lib/python3.8/dist-packages/zmq/sugar/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/zmq/sugar/attrsettr.py 29 21 28% 15-32, 36, 40-48, 52 /usr/local/lib/python3.8/dist-packages/zmq/sugar/constants.py 53 5 91% 34, 47, 104, 107-108 /usr/local/lib/python3.8/dist-packages/zmq/sugar/context.py 125 78 38% 27, 43-49, 53-54, 57, 60, 64, 77-79, 89-94, 119-132, 139-145, 152-154, 157-163, 180-192, 196, 210-222, 229, 236, 240-243, 247-253, 257-266 /usr/local/lib/python3.8/dist-packages/zmq/sugar/frame.py 29 12 59% 12-14, 51, 61-62, 66-67, 77-78, 82-83 /usr/local/lib/python3.8/dist-packages/zmq/sugar/poll.py 62 48 23% 22-23, 26, 44-57, 61, 71-75, 95-99, 126-152 /usr/local/lib/python3.8/dist-packages/zmq/sugar/socket.py 229 160 30% 32, 39-40, 59-63, 66-67, 75, 78, 86, 99-101, 104-106, 114-117, 124-132, 142-155, 166, 175-177, 186-188, 207-209, 229-231, 261-283, 290-300, 313-328, 390-400, 431-447, 475-481, 499, 519-520, 548-549, 566-568, 592-593, 610-611, 631-632, 646-651, 673-674, 697-704, 725-746, 754-755 /usr/local/lib/python3.8/dist-packages/zmq/sugar/stopwatch.py 15 11 27% 12-21, 25, 29-30 /usr/local/lib/python3.8/dist-packages/zmq/sugar/tracker.py 56 37 34% 54-63, 68-74, 96-118 /usr/local/lib/python3.8/dist-packages/zmq/sugar/version.py 20 7 65% 17-18, 26-29, 36, 41 /usr/local/lib/python3.8/dist-packages/zmq/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/zmq/utils/constant_names.py 16 1 94% 549 /usr/local/lib/python3.8/dist-packages/zmq/utils/jsonapi.py 21 11 48% 26-27, 37-45, 53-56 /usr/local/lib/python3.8/dist-packages/zmq/utils/strtypes.py 23 13 43% 18-20, 24-29, 33-38 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ TOTAL 811164 561804 31% ret : 0 command : coverage3 html -i --omit=/usr/local/lib/python3.8/dist-packages/*,/home/admin/.local/lib/python3.8/site-packages/*,/usr/lib/python3/dist-packages/* -d htmlcov ret : 0 command : coverage3 report -i -m ret : 0 348.86user 143.07system 17:07.78elapsed 47%CPU (0avgtext+0avgdata 6533452maxresident)k 11812376inputs+989680outputs (252286major+13182613minor)pagefaults 0swaps