python /home/admin/mtr/script_for_cron.py -j coverage -m 9 -a '' -s coverage -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded root_folder /data_2/data_log/job/2025/February/25022025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_2/data_log/job/2025/February/25022025/coverage/htmlcov new path : /data_2/data_log/job/2025/February/25022025/coverage/ command : coverage3 run /home/admin/workarea/git/Velours/python/tests/python_tests.py --short_python3 `cat ~/.fotonower_pass/bdd.py.pass` cat: /home/admin/.fotonower_pass/bdd.py.pass: Aucun fichier ou dossier de ce type 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 : 10593 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 : 0.3308112621307373 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 Tue Feb 25 17:20:28 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 : 10593 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 2025-02-25 17:20:32.164082: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-25 17:20:32.191064: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-25 17:20:32.192983: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb4e4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-25 17:20:32.193035: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-25 17:20:32.197082: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-25 17:20:32.338853: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1ab6a150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-25 17:20:32.338939: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-25 17:20:32.340531: 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 2025-02-25 17:20:32.340990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:20:32.344036: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:20:32.347209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:20:32.347782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:20:32.350696: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:20:32.352155: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:20:32.358046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:20:32.360031: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:20:32.360152: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:20:32.360905: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-25 17:20:32.360921: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-25 17:20:32.360930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-25 17:20:32.362236: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9562 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 2025-02-25 17:20:33.019985: 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 2025-02-25 17:20:33.020094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:20:33.020126: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:20:33.020149: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:20:33.020170: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:20:33.020196: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:20:33.020221: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:20:33.020242: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:20:33.021876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:20:33.023309: 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 2025-02-25 17:20:33.023370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:20:33.023390: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:20:33.023408: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:20:33.023426: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:20:33.023445: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:20:33.023464: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:20:33.023481: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:20:33.024943: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:20:33.024977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-25 17:20:33.024987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-25 17:20:33.024995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-25 17:20:33.026377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9562 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 : [] 2025-02-25 17:20:43.192107: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:20:43.389085: 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 1898412 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 15 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 : 5304 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 : 0.000614166259765625 nb_pixel_total : 15555 time to create 1 rle with old method : 0.03773784637451172 length of segment : 256 time for calcul the mask position with numpy : 0.0033445358276367188 nb_pixel_total : 146539 time to create 1 rle with old method : 0.39923834800720215 length of segment : 375 time for calcul the mask position with numpy : 0.00039124488830566406 nb_pixel_total : 14255 time to create 1 rle with old method : 0.04441213607788086 length of segment : 151 time for calcul the mask position with numpy : 0.00020313262939453125 nb_pixel_total : 5613 time to create 1 rle with old method : 0.018303871154785156 length of segment : 48 time for calcul the mask position with numpy : 0.000110626220703125 nb_pixel_total : 1824 time to create 1 rle with old method : 0.006109952926635742 length of segment : 39 time spent for convertir_results : 1.5343942642211914 time spend for datou_step_exec : 22.873141288757324 time spend to save output : 5.745887756347656e-05 total time spend for step 1 : 22.873198747634888 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 3291 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 : 0.013076066970825195 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, 0.99548423, [(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, 106), (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, 25), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 25), (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, 24), (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)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,109,76,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,231,24,232,24,270,23,273']), (957285035, 492601069, 445, 28, 582, 19, 424, 0.99165547, [(302, 35, 37), (266, 36, 102), (250, 37, 129), (241, 38, 143), (201, 39, 188), (190, 40, 203), (186, 41, 218), (182, 42, 233), (178, 43, 243), (175, 44, 252), (172, 45, 262), (168, 46, 275), (164, 47, 289), (158, 48, 301), (155, 49, 310), (153, 50, 317), (151, 51, 323), (150, 52, 326), (148, 53, 329), (146, 54, 333), (145, 55, 335), (144, 56, 337), (143, 57, 339), (141, 58, 342), (140, 59, 345), (138, 60, 348), (136, 61, 352), (134, 62, 355), (132, 63, 359), (130, 64, 363), (128, 65, 366), (126, 66, 370), (124, 67, 373), (123, 68, 375), (121, 69, 378), (119, 70, 380), (117, 71, 383), (116, 72, 385), (115, 73, 387), (114, 74, 389), (112, 75, 392), (111, 76, 394), (110, 77, 396), (109, 78, 398), (109, 79, 399), (108, 80, 401), (107, 81, 403), (106, 82, 405), (106, 83, 406), (105, 84, 408), (104, 85, 410), (103, 86, 412), (102, 87, 415), (101, 88, 417), (100, 89, 419), (99, 90, 420), (98, 91, 422), (97, 92, 424), (96, 93, 426), (94, 94, 429), (93, 95, 431), (92, 96, 432), (91, 97, 434), (90, 98, 436), (90, 99, 436), (90, 100, 437), (90, 101, 438), (90, 102, 439), (90, 103, 441), (91, 104, 441), (91, 105, 443), (91, 106, 444), (91, 107, 445), (91, 108, 446), (91, 109, 447), (92, 110, 447), (92, 111, 448), (92, 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523), (45, 175, 523), (45, 176, 522), (44, 177, 523), (44, 178, 523), (44, 179, 523), (43, 180, 524), (43, 181, 524), (43, 182, 524), (42, 183, 524), (42, 184, 524), (41, 185, 525), (41, 186, 525), (40, 187, 525), (40, 188, 525), (39, 189, 526), (38, 190, 526), (38, 191, 526), (37, 192, 527), (37, 193, 526), (36, 194, 527), (36, 195, 527), (35, 196, 527), (35, 197, 527), (35, 198, 527), (34, 199, 528), (34, 200, 527), (34, 201, 527), (34, 202, 526), (33, 203, 527), (33, 204, 526), (33, 205, 525), (33, 206, 525), (33, 207, 524), (33, 208, 523), (33, 209, 522), (32, 210, 523), (32, 211, 522), (32, 212, 521), (32, 213, 520), (32, 214, 519), (32, 215, 518), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 512), (32, 222, 512), (32, 223, 511), (32, 224, 510), (31, 225, 511), (31, 226, 510), (31, 227, 509), (31, 228, 509), (31, 229, 508), (31, 230, 507), (31, 231, 505), (31, 232, 504), (31, 233, 503), (31, 234, 501), (31, 235, 499), (31, 236, 497), (31, 237, 495), (31, 238, 494), (31, 239, 493), (31, 240, 491), (31, 241, 490), (31, 242, 489), (31, 243, 488), (31, 244, 487), (31, 245, 486), (31, 246, 484), (31, 247, 483), (31, 248, 481), (31, 249, 479), (31, 250, 477), (31, 251, 475), (31, 252, 473), (31, 253, 472), (31, 254, 471), (31, 255, 469), (31, 256, 468), (31, 257, 467), (31, 258, 466), (31, 259, 464), (31, 260, 463), (31, 261, 461), (31, 262, 459), (31, 263, 457), (31, 264, 456), (31, 265, 454), (31, 266, 453), (31, 267, 452), (31, 268, 451), (31, 269, 450), (31, 270, 449), (31, 271, 448), (31, 272, 447), (31, 273, 445), (31, 274, 444), (31, 275, 443), (32, 276, 440), (32, 277, 438), (32, 278, 436), (32, 279, 434), (33, 280, 432), (33, 281, 430), (33, 282, 429), (33, 283, 427), (34, 284, 425), (34, 285, 424), (34, 286, 422), (35, 287, 420), (35, 288, 418), (36, 289, 416), (36, 290, 414), (37, 291, 411), (37, 292, 410), (38, 293, 407), (39, 294, 405), (40, 295, 403), (40, 296, 401), (41, 297, 399), (41, 298, 398), (42, 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(160, 360, 232), (162, 361, 229), (163, 362, 228), (165, 363, 225), (167, 364, 223), (168, 365, 222), (170, 366, 219), (172, 367, 217), (174, 368, 215), (176, 369, 212), (178, 370, 210), (180, 371, 208), (181, 372, 206), (183, 373, 204), (184, 374, 203), (186, 375, 200), (188, 376, 198), (190, 377, 196), (192, 378, 193), (195, 379, 190), (197, 380, 188), (200, 381, 184), (202, 382, 182), (204, 383, 180), (206, 384, 178), (208, 385, 175), (209, 386, 174), (211, 387, 172), (212, 388, 170), (214, 389, 168), (216, 390, 165), (219, 391, 161), (222, 392, 158), (226, 393, 153), (229, 394, 149), (233, 395, 144), (238, 396, 138), (243, 397, 132), (247, 398, 126), (251, 399, 121), (254, 400, 117), (258, 401, 112), (261, 402, 107), (265, 403, 102), (270, 404, 95), (275, 405, 89), (285, 406, 77), (297, 407, 62), (306, 408, 50), (321, 409, 22)], 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(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)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1740500428_1898146_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5304 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 : 0.15868854522705078 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 Tue Feb 25 17:20:53 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 : 5304 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-25 17:20:57.004883: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-25 17:20:57.031162: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-25 17:20:57.033128: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb4e8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-25 17:20:57.033191: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-25 17:20:57.037619: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-25 17:20:57.209941: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1c0a76a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-25 17:20:57.210000: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-25 17:20:57.211013: 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 2025-02-25 17:20:57.211470: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:20:57.213501: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:20:57.215741: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:20:57.216081: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:20:57.222089: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:20:57.223504: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:20:57.229019: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:20:57.230462: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:20:57.230567: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:20:57.231354: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-25 17:20:57.231377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-25 17:20:57.231388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-25 17:20:57.232643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4633 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. 2025-02-25 17:20:57.360771: 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 2025-02-25 17:20:57.360925: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:20:57.360948: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:20:57.360970: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:20:57.360990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:20:57.361009: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:20:57.361029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:20:57.361049: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:20:57.362022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:20:57.363198: 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 2025-02-25 17:20:57.363250: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:20:57.363269: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:20:57.363285: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:20:57.363302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:20:57.363317: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:20:57.363334: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:20:57.363351: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:20:57.364293: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:20:57.364329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-25 17:20:57.364339: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-25 17:20:57.364347: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-25 17:20:57.365345: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4633 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 : [] 2025-02-25 17:21:07.666836: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:21:07.877478: 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: (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 1899913 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5085 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 : 10374 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 : 0.0005602836608886719 nb_pixel_total : 16902 time to create 1 rle with old method : 0.05827522277832031 length of segment : 107 time for calcul the mask position with numpy : 0.018784761428833008 nb_pixel_total : 480724 time to create 1 rle with new method : 0.034763336181640625 length of segment : 632 time for calcul the mask position with numpy : 0.0005581378936767578 nb_pixel_total : 36641 time to create 1 rle with old method : 0.08538198471069336 length of segment : 133 time for calcul the mask position with numpy : 0.00012803077697753906 nb_pixel_total : 4794 time to create 1 rle with old method : 0.011865377426147461 length of segment : 51 time spent for convertir_results : 0.4958209991455078 time spend for datou_step_exec : 20.825658321380615 time spend to save output : 5.53131103515625e-05 total time spend for step 1 : 20.825713634490967 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 400 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 : 0.010804414749145508 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, 0.9988372, [(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, 0.9977443, [(711, 22, 21), (926, 22, 46), (608, 23, 146), (894, 23, 103), (598, 24, 233), (851, 24, 157), (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), (477, 56, 607), (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), (422, 100, 668), (419, 101, 671), (417, 102, 673), (414, 103, 676), (410, 104, 680), (406, 105, 684), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (369, 113, 721), (365, 114, 725), (362, 115, 728), (359, 116, 731), (356, 117, 734), (353, 118, 737), (351, 119, 739), (349, 120, 741), (346, 121, 744), (344, 122, 746), (342, 123, 748), (339, 124, 751), (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), (287, 134, 802), (279, 135, 810), (273, 136, 816), (267, 137, 822), (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), (234, 148, 855), (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), (107, 210, 979), (106, 211, 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['450,47,449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,419,25,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46,451,46'])], 'temp/1740500453_1898146_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 : 0.22820425033569336 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 Tue Feb 25 17:21:15 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 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 : 10374 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-25 17:21:19.309980: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-25 17:21:19.335174: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-25 17:21:19.337220: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb4e8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-25 17:21:19.337278: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-25 17:21:19.341103: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-25 17:21:19.599752: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1c5cd5c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-25 17:21:19.599795: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-25 17:21:19.600739: 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 2025-02-25 17:21:19.601560: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:21:19.604888: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:21:19.607271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:21:19.607613: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:21:19.610781: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:21:19.612170: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:21:19.618056: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:21:19.619916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:21:19.620026: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:21:19.621157: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-25 17:21:19.621173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-25 17:21:19.621181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-25 17:21:19.622587: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8714 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. 2025-02-25 17:21:19.734122: 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 2025-02-25 17:21:19.734246: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:21:19.734265: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:21:19.734282: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:21:19.734298: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:21:19.734315: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:21:19.734330: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:21:19.734348: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:21:19.735740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:21:19.737025: 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 2025-02-25 17:21:19.737075: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:21:19.737094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:21:19.737110: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:21:19.737127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:21:19.737143: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:21:19.737159: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:21:19.737175: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:21:19.738585: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:21:19.738619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-25 17:21:19.738628: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-25 17:21:19.738635: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-25 17:21:19.740109: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8714 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 : [] 2025-02-25 17:21:32.882078: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:21:33.047163: 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 1900990 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5083 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 : 10372 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 : 1.7133910655975342 nb_pixel_total : 3693266 time to create 1 rle with new method : 0.46176838874816895 length of segment : 2041 time spent for convertir_results : 3.6649928092956543 time spend for datou_step_exec : 27.055914640426636 time spend to save output : 0.08758068084716797 total time spend for step 1 : 27.143495321273804 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 719 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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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 : 0.01365208625793457 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, 2268, 118, 2241, 0.984999, [(675, 120, 112), (520, 121, 481), (1051, 121, 380), (503, 122, 946), (486, 123, 981), (471, 124, 1014), (456, 125, 1045), (442, 126, 1092), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1222), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (364, 135, 1265), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 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(860, 2121, 278), (863, 2122, 273), (866, 2123, 269), (869, 2124, 264), (872, 2125, 259), (875, 2126, 255), (877, 2127, 251), (880, 2128, 246), (883, 2129, 242), (886, 2130, 237), (889, 2131, 232), (893, 2132, 226), (896, 2133, 221), (899, 2134, 216), (903, 2135, 209), (906, 2136, 204), (909, 2137, 199), (913, 2138, 193), (917, 2139, 186), (920, 2140, 181), (924, 2141, 174), (928, 2142, 166), (932, 2143, 154), (936, 2144, 142), (946, 2145, 124), (956, 2146, 106), (967, 2147, 87), (978, 2148, 67), (989, 2149, 48), (1001, 2150, 27), (1013, 2151, 6)], ['1001,2150,936,2144,788,2097,694,2075,616,2039,371,1987,215,1963,127,1971,54,1824,53,1748,39,1677,39,1454,29,1248,29,892,21,696,27,542,39,458,103,292,210,206,291,179,373,132,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2011,293,2098,420,2148,535,2168,613,2165,833,2128,914,2112,994,2081,1068,2028,1139,2001,1207,1967,1255,1931,1368,1879,1444,1845,1674,1780,1868,1756,1920,1662,2015,1581,2015,1496,2039,1420,2046,1329,2072,1177,2101,1093,2142'])], 'temp/1740500475_1898146_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3689330 proportion of common points : 0.999196976406273 #&_# 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 : 0.7944862842559814 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 Tue Feb 25 17:21:49 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 : 0.011298894882202148 nb_pixel_total : 2953 time to create 1 rle with old method : 0.009863615036010742 time for calcul the mask position with numpy : 0.0018873214721679688 nb_pixel_total : 16114 time to create 1 rle with old method : 0.0426025390625 time for calcul the mask position with numpy : 0.0018489360809326172 nb_pixel_total : 5619 time to create 1 rle with old method : 0.016254663467407227 time for calcul the mask position with numpy : 0.003496885299682617 nb_pixel_total : 83907 time to create 1 rle with old method : 0.24877715110778809 time for calcul the mask position with numpy : 0.0018961429595947266 nb_pixel_total : 16465 time to create 1 rle with old method : 0.03835129737854004 time for calcul the mask position with numpy : 0.0016529560089111328 nb_pixel_total : 7617 time to create 1 rle with old method : 0.01789689064025879 time for calcul the mask position with numpy : 0.0019328594207763672 nb_pixel_total : 38822 time to create 1 rle with old method : 0.0909576416015625 time for calcul the mask position with numpy : 0.001676797866821289 nb_pixel_total : 14771 time to create 1 rle with old method : 0.048224687576293945 time for calcul the mask position with numpy : 0.002384662628173828 nb_pixel_total : 29478 time to create 1 rle with old method : 0.07827162742614746 time for calcul the mask position with numpy : 0.0016489028930664062 nb_pixel_total : 3782 time to create 1 rle with old method : 0.009369850158691406 time for calcul the mask position with numpy : 0.0020627975463867188 nb_pixel_total : 10820 time to create 1 rle with old method : 0.02662062644958496 time for calcul the mask position with numpy : 0.0016717910766601562 nb_pixel_total : 13914 time to create 1 rle with old method : 0.03363776206970215 time for calcul the mask position with numpy : 0.0015285015106201172 nb_pixel_total : 1207 time to create 1 rle with old method : 0.002998828887939453 time for calcul the mask position with numpy : 0.0014853477478027344 nb_pixel_total : 2371 time to create 1 rle with old method : 0.005787372589111328 time for calcul the mask position with numpy : 0.0014812946319580078 nb_pixel_total : 4274 time to create 1 rle with old method : 0.010185480117797852 time for calcul the mask position with numpy : 0.001558542251586914 nb_pixel_total : 9883 time to create 1 rle with old method : 0.023614883422851562 time for calcul the mask position with numpy : 0.0015096664428710938 nb_pixel_total : 1227 time to create 1 rle with old method : 0.003421306610107422 time for calcul the mask position with numpy : 0.001495361328125 nb_pixel_total : 3952 time to create 1 rle with old method : 0.009489774703979492 time for calcul the mask position with numpy : 0.001592874526977539 nb_pixel_total : 6640 time to create 1 rle with old method : 0.015833616256713867 time for calcul the mask position with numpy : 0.0015010833740234375 nb_pixel_total : 693 time to create 1 rle with old method : 0.00177001953125 time for calcul the mask position with numpy : 0.0015513896942138672 nb_pixel_total : 13114 time to create 1 rle with old method : 0.032227277755737305 time for calcul the mask position with numpy : 0.0016350746154785156 nb_pixel_total : 2079 time to create 1 rle with old method : 0.005375862121582031 time for calcul the mask position with numpy : 0.0015828609466552734 nb_pixel_total : 3162 time to create 1 rle with old method : 0.008202314376831055 time for calcul the mask position with numpy : 0.0016286373138427734 nb_pixel_total : 5410 time to create 1 rle with old method : 0.014025211334228516 time for calcul the mask position with numpy : 0.0015904903411865234 nb_pixel_total : 4292 time to create 1 rle with old method : 0.011269330978393555 time for calcul the mask position with numpy : 0.001600503921508789 nb_pixel_total : 5486 time to create 1 rle with old method : 0.019658803939819336 time for calcul the mask position with numpy : 0.002595663070678711 nb_pixel_total : 4139 time to create 1 rle with old method : 0.02239060401916504 time for calcul the mask position with numpy : 0.0015747547149658203 nb_pixel_total : 2771 time to create 1 rle with old method : 0.007024288177490234 time for calcul the mask position with numpy : 0.0015873908996582031 nb_pixel_total : 10568 time to create 1 rle with old method : 0.024864912033081055 time for calcul the mask position with numpy : 0.001560211181640625 nb_pixel_total : 1547 time to create 1 rle with old method : 0.0038127899169921875 time for calcul the mask position with numpy : 0.0014615058898925781 nb_pixel_total : 3533 time to create 1 rle with old method : 0.00886845588684082 time for calcul the mask position with numpy : 0.0015730857849121094 nb_pixel_total : 8615 time to create 1 rle with old method : 0.020632266998291016 time for calcul the mask position with numpy : 0.0016727447509765625 nb_pixel_total : 27346 time to create 1 rle with old method : 0.06433987617492676 time for calcul the mask position with numpy : 0.0018415451049804688 nb_pixel_total : 3302 time to create 1 rle with old method : 0.009240865707397461 time for calcul the mask position with numpy : 0.0015375614166259766 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006239414215087891 time for calcul the mask position with numpy : 0.0015876293182373047 nb_pixel_total : 2732 time to create 1 rle with old method : 0.006735324859619141 time for calcul the mask position with numpy : 0.001504659652709961 nb_pixel_total : 3847 time to create 1 rle with old method : 0.009408712387084961 time for calcul the mask position with numpy : 0.0015172958374023438 nb_pixel_total : 2324 time to create 1 rle with old method : 0.005548954010009766 time for calcul the mask position with numpy : 0.0014872550964355469 nb_pixel_total : 2447 time to create 1 rle with old method : 0.009538650512695312 time for calcul the mask position with numpy : 0.002383708953857422 nb_pixel_total : 2410 time to create 1 rle with old method : 0.010899543762207031 time for calcul the mask position with numpy : 0.0017309188842773438 nb_pixel_total : 1247 time to create 1 rle with old method : 0.004416704177856445 time for calcul the mask position with numpy : 0.0017771720886230469 nb_pixel_total : 2781 time to create 1 rle with old method : 0.007025957107543945 time for calcul the mask position with numpy : 0.0015454292297363281 nb_pixel_total : 11959 time to create 1 rle with old method : 0.028499126434326172 time for calcul the mask position with numpy : 0.0015072822570800781 nb_pixel_total : 595 time to create 1 rle with old method : 0.0015511512756347656 time for calcul the mask position with numpy : 0.001552581787109375 nb_pixel_total : 13159 time to create 1 rle with old method : 0.03176760673522949 time for calcul the mask position with numpy : 0.0014810562133789062 nb_pixel_total : 1234 time to create 1 rle with old method : 0.0032911300659179688 time for calcul the mask position with numpy : 0.0014927387237548828 nb_pixel_total : 4122 time to create 1 rle with old method : 0.011585235595703125 time for calcul the mask position with numpy : 0.0014700889587402344 nb_pixel_total : 1024 time to create 1 rle with old method : 0.01860356330871582 time for calcul the mask position with numpy : 0.0027701854705810547 nb_pixel_total : 8726 time to create 1 rle with old method : 0.021468400955200195 time for calcul the mask position with numpy : 0.0016016960144042969 nb_pixel_total : 1248 time to create 1 rle with old method : 0.0031201839447021484 time for calcul the mask position with numpy : 0.0017304420471191406 nb_pixel_total : 9682 time to create 1 rle with old method : 0.025888919830322266 time for calcul the mask position with numpy : 0.0016701221466064453 nb_pixel_total : 1654 time to create 1 rle with old method : 0.004067897796630859 time for calcul the mask position with numpy : 0.0015354156494140625 nb_pixel_total : 345 time to create 1 rle with old method : 0.0009071826934814453 time for calcul the mask position with numpy : 0.0015714168548583984 nb_pixel_total : 904 time to create 1 rle with old method : 0.0023534297943115234 time for calcul the mask position with numpy : 0.0015420913696289062 nb_pixel_total : 2058 time to create 1 rle with old method : 0.004998207092285156 time for calcul the mask position with numpy : 0.0015232563018798828 nb_pixel_total : 337 time to create 1 rle with old method : 0.0008816719055175781 time for calcul the mask position with numpy : 0.0014650821685791016 nb_pixel_total : 875 time to create 1 rle with old method : 0.0022726058959960938 time for calcul the mask position with numpy : 0.0014812946319580078 nb_pixel_total : 858 time to create 1 rle with old method : 0.0022580623626708984 time for calcul the mask position with numpy : 0.0014841556549072266 nb_pixel_total : 595 time to create 1 rle with old method : 0.0015439987182617188 time for calcul the mask position with numpy : 0.0015101432800292969 nb_pixel_total : 2390 time to create 1 rle with old method : 0.005918979644775391 time for calcul the mask position with numpy : 0.001542806625366211 nb_pixel_total : 887 time to create 1 rle with old method : 0.002246856689453125 time for calcul the mask position with numpy : 0.0015110969543457031 nb_pixel_total : 577 time to create 1 rle with old method : 0.0014562606811523438 time for calcul the mask position with numpy : 0.0015537738800048828 nb_pixel_total : 585 time to create 1 rle with old method : 0.0014948844909667969 time for calcul the mask position with numpy : 0.001497030258178711 nb_pixel_total : 1704 time to create 1 rle with old method : 0.004243135452270508 time for calcul the mask position with numpy : 0.001542806625366211 nb_pixel_total : 1636 time to create 1 rle with old method : 0.004235267639160156 time for calcul the mask position with numpy : 0.0016317367553710938 nb_pixel_total : 12957 time to create 1 rle with old method : 0.03564858436584473 time for calcul the mask position with numpy : 0.0017275810241699219 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0026924610137939453 time for calcul the mask position with numpy : 0.0014996528625488281 nb_pixel_total : 1081 time to create 1 rle with old method : 0.002703428268432617 time for calcul the mask position with numpy : 0.0015408992767333984 nb_pixel_total : 3092 time to create 1 rle with old method : 0.00755000114440918 time for calcul the mask position with numpy : 0.001649618148803711 nb_pixel_total : 16681 time to create 1 rle with old method : 0.04004168510437012 time for calcul the mask position with numpy : 0.001535177230834961 nb_pixel_total : 1514 time to create 1 rle with old method : 0.0036725997924804688 time for calcul the mask position with numpy : 0.0015537738800048828 nb_pixel_total : 1731 time to create 1 rle with old method : 0.0046539306640625 time for calcul the mask position with numpy : 0.0017240047454833984 nb_pixel_total : 18427 time to create 1 rle with old method : 0.05030012130737305 time for calcul the mask position with numpy : 0.001636505126953125 nb_pixel_total : 1044 time to create 1 rle with old method : 0.002904653549194336 time for calcul the mask position with numpy : 0.0016520023345947266 nb_pixel_total : 9204 time to create 1 rle with old method : 0.02327895164489746 time for calcul the mask position with numpy : 0.0015723705291748047 nb_pixel_total : 267 time to create 1 rle with old method : 0.0007441043853759766 time for calcul the mask position with numpy : 0.0015854835510253906 nb_pixel_total : 1333 time to create 1 rle with old method : 0.00411677360534668 time for calcul the mask position with numpy : 0.0016934871673583984 nb_pixel_total : 1009 time to create 1 rle with old method : 0.0030155181884765625 time for calcul the mask position with numpy : 0.0015575885772705078 nb_pixel_total : 969 time to create 1 rle with old method : 0.0025758743286132812 time for calcul the mask position with numpy : 0.0015652179718017578 nb_pixel_total : 844 time to create 1 rle with old method : 0.0025153160095214844 time for calcul the mask position with numpy : 0.001550912857055664 nb_pixel_total : 713 time to create 1 rle with old method : 0.0020525455474853516 time for calcul the mask position with numpy : 0.0016047954559326172 nb_pixel_total : 5082 time to create 1 rle with old method : 0.014465093612670898 time for calcul the mask position with numpy : 0.0017092227935791016 nb_pixel_total : 616 time to create 1 rle with old method : 0.0017344951629638672 time for calcul the mask position with numpy : 0.0015895366668701172 nb_pixel_total : 8473 time to create 1 rle with old method : 0.021803617477416992 time for calcul the mask position with numpy : 0.0015957355499267578 nb_pixel_total : 248 time to create 1 rle with old method : 0.0007176399230957031 time for calcul the mask position with numpy : 0.0016031265258789062 nb_pixel_total : 735 time to create 1 rle with old method : 0.002101421356201172 time for calcul the mask position with numpy : 0.001678466796875 nb_pixel_total : 7540 time to create 1 rle with old method : 0.019432544708251953 time for calcul the mask position with numpy : 0.001590728759765625 nb_pixel_total : 1500 time to create 1 rle with old method : 0.00394892692565918 time for calcul the mask position with numpy : 0.0016028881072998047 nb_pixel_total : 1436 time to create 1 rle with old method : 0.0038793087005615234 time for calcul the mask position with numpy : 0.001598358154296875 nb_pixel_total : 1634 time to create 1 rle with old method : 0.004450321197509766 time for calcul the mask position with numpy : 0.0016050338745117188 nb_pixel_total : 1096 time to create 1 rle with old method : 0.0028657913208007812 time for calcul the mask position with numpy : 0.001544952392578125 nb_pixel_total : 297 time to create 1 rle with old method : 0.0008935928344726562 time for calcul the mask position with numpy : 0.0015611648559570312 nb_pixel_total : 915 time to create 1 rle with old method : 0.002697467803955078 time for calcul the mask position with numpy : 0.0015604496002197266 nb_pixel_total : 956 time to create 1 rle with old method : 0.002659320831298828 time for calcul the mask position with numpy : 0.0015728473663330078 nb_pixel_total : 1127 time to create 1 rle with old method : 0.0029697418212890625 time for calcul the mask position with numpy : 0.0015790462493896484 nb_pixel_total : 1322 time to create 1 rle with old method : 0.003625631332397461 time for calcul the mask position with numpy : 0.0016126632690429688 nb_pixel_total : 8502 time to create 1 rle with old method : 0.02183055877685547 time for calcul the mask position with numpy : 0.001596212387084961 nb_pixel_total : 889 time to create 1 rle with old method : 0.002405881881713867 time for calcul the mask position with numpy : 0.0015690326690673828 nb_pixel_total : 2199 time to create 1 rle with old method : 0.005885124206542969 time for calcul the mask position with numpy : 0.0027379989624023438 nb_pixel_total : 11122 time to create 1 rle with old method : 0.034983158111572266 time for calcul the mask position with numpy : 0.0015735626220703125 nb_pixel_total : 2684 time to create 1 rle with old method : 0.007236480712890625 time for calcul the mask position with numpy : 0.002082347869873047 nb_pixel_total : 885 time to create 1 rle with old method : 0.0025954246520996094 time for calcul the mask position with numpy : 0.0015909671783447266 nb_pixel_total : 477 time to create 1 rle with old method : 0.0013513565063476562 time for calcul the mask position with numpy : 0.0015969276428222656 nb_pixel_total : 943 time to create 1 rle with old method : 0.002614259719848633 batch 1 Loaded 104 chid ids of type : 4677 Number RLEs to save : 9636 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 : 0.011290550231933594 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.260117053985596 time spend to save output : 0.011584997177124023 total time spend for step 1 : 13.27170205116272 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1740500509_1898146_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 104 ############################### 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 : 0.3709244728088379 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 Tue Feb 25 17:22:03 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 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/1740500523_1898146_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 0.218s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 6.1658313274383545 time spend to save output : 0.011891841888427734 total time spend for step 1 : 6.177723169326782 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 : 0.012902259826660156 [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 : 0.010691404342651367 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, 0.063849844, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052212115, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012273409, None)], 'temp/1740500523_1898146_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 : 0.15795063972473145 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 Tue Feb 25 17:22:09 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 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 : 0.03254365921020508 time to convert the images to numpy array : 0.014461040496826172 total time to convert the images to numpy array : 0.04729437828063965 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 : 6496 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 : 6496 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.04610133171081543 time used to do the prediction : 0.09165310859680176 save descriptor for thcl : 355 time to traite the descriptors : 0.08193111419677734 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.6135056018829346 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 : 1.9311904907226562e-05 save missing photos in datou_result : time spend for datou_step_exec : 11.467527627944946 time spend to save output : 1.5361919403076172 total time spend for step 1 : 13.003719568252563 step2:argmax Tue Feb 25 17:22:22 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', 0.01771297, 332, '355'), 'temp/1740500529_1898146_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 : 0.009368419647216797 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 : 0.013480901718139648 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 : 0.012609720230102539 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 : 1.0013580322265625e-05 save missing photos in datou_result : time spend for datou_step_exec : 0.0014729499816894531 time spend to save output : 0.03630685806274414 total time spend for step 2 : 0.037779808044433594 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', 0.01771297, 332, '355'), 'temp/1740500529_1898146_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 : 0.47617506980895996 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 Tue Feb 25 17:22:23 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 2025-02-25 17:22:28.516010: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-25 17:22:28.518398: 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 2025-02-25 17:22:28.518595: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:22:28.518670: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:22:28.543674: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:22:28.543913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:22:28.585522: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:22:28.597646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:22:28.654279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:22:28.655760: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:22:28.657043: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-25 17:22:28.695095: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-25 17:22:28.697061: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb258000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-25 17:22:28.697104: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-25 17:22:28.702238: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x21dbe500 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-25 17:22:28.702270: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-25 17:22:28.704650: 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 2025-02-25 17:22:28.704752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:22:28.704775: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-25 17:22:28.704850: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-25 17:22:28.704879: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-25 17:22:28.704914: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-25 17:22:28.704951: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-25 17:22:28.704990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-25 17:22:28.706246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-25 17:22:28.706663: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-25 17:22:28.706715: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-25 17:22:28.706728: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-25 17:22:28.706737: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-25 17:22:28.708436: 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 : 6496 max_wait_temp : 1 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 : [] /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_) 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 desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 14.209354877471924 time used to load_weights : 0.15387725830078125 0it [00:00, ?it/s] 3it [00:00, 1021.01it/s]2025-02-25 17:22:46.034888: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1740500542_1898146_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1740500542_1898146_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1740500542_1898146_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.6616945266723633 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 time to traite the descriptors : 0.028542757034301758 storage_type for insertDescriptorsMulti : 3 To insert : 1171252764 To insert : 1171252784 To insert : 1171252487 time to insert the descriptors : 1.5607919692993164 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252764, 1171252784, 1171252487] Looping around the photos to save general results len do output : 3 /1171252764Didn't retrieve data . /1171252784Didn't retrieve data . /1171252487Didn'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 ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', 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 : 0.012244939804077148 save_final save missing photos in datou_result : time spend for datou_step_exec : 27.598528623580933 time spend to save output : 0.012739419937133789 total time spend for step 1 : 27.611268043518066 step2:argmax Tue Feb 25 17:22:51 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 Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 Inside saveOutput : final : True verbose : False photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.98535734, 4674, '3609'), 'temp/1740500542_1898146_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.9677326, 4674, '3609'), 'temp/1740500542_1898146_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.9263306, 4674, '3609'), 'temp/1740500542_1898146_1171252487_5ebdd6b0a6bb39942a3808ed114806de.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 : 0.009687185287475586 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 : 0.01193380355834961 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 : 0.011943817138671875 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 : 4.5299530029296875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00017595291137695312 time spend to save output : 0.03789377212524414 total time spend for step 2 : 0.038069725036621094 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252764': [(1171252764, 'jrm', 0.98535734, 4674, '3609'), 'temp/1740500542_1898146_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252784': [(1171252784, 'jrm', 0.9677326, 4674, '3609'), 'temp/1740500542_1898146_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9263306, 4674, '3609'), 'temp/1740500542_1898146_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## 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 : 1.2932384014129639 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 Tue Feb 25 17:22:52 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 inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory l 3637 free memory gpu now : 2944 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 : [] 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_1 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_1[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_2[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 : 10.19848895072937 time used to load_weights : 0.1532275676727295 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.095552921295166 (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 : 0.03992009162902832 storage_type for insertDescriptorsMulti : 3 To insert : 1171275314 To insert : 1171275372 To insert : 1171291875 time to insert the descriptors : 1.2425336837768555 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171275314, 1171275372, 1171291875] Looping around the photos to save general results len do output : 3 /1171275314Didn't retrieve data . /1171275372Didn't retrieve data . /1171291875Didn'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, '1171275314', 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, '1171291875', 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 : 0.01302480697631836 save_final save missing photos in datou_result : time spend for datou_step_exec : 22.15275502204895 time spend to save output : 0.013401031494140625 total time spend for step 1 : 22.16615605354309 step2:argmax Tue Feb 25 17:23:14 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 Argmax ! calculate argmax for thcl : 3655 Inside saveOutput : final : True verbose : False photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96526104, 4723, '3655'), 'temp/1740500571_1898146_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.9673552, 4723, '3655'), 'temp/1740500571_1898146_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.9706071, 4723, '3655'), 'temp/1740500571_1898146_1171291875_b62cd9e0d976b143f86fe82d072798c0.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 : 0.009694814682006836 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 : 0.012323141098022461 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 : 0.012337684631347656 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 : 5.245208740234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0001742839813232422 time spend to save output : 0.03907442092895508 total time spend for step 2 : 0.03924870491027832 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171275314': [(1171275314, 'tapis_vide', 0.96526104, 4723, '3655'), 'temp/1740500571_1898146_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9673552, 4723, '3655'), 'temp/1740500571_1898146_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171291875': [(1171291875, 'tapis_vide', 0.9706071, 4723, '3655'), 'temp/1740500571_1898146_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### 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 : 0.21927881240844727 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 Tue Feb 25 17:23:15 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 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/1740500595_1898146 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 2.0575332641601562 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 2.274873733520508 time spend to save output : 3.314018249511719e-05 total time spend for step 1 : 2.274906873703003 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 /1339569285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569286Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569287Didn'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 : 0.01310873031616211 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1339569285: ['917849322', 'temp/1740500594_1898146_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1339569286: ['917849322', 'temp/1740500594_1898146_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1339569287: ['917849322', 'temp/1740500594_1898146_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 : 0.3129267692565918 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 Tue Feb 25 17:23:17 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 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 : 0.0002796649932861328 time to convert the images to numpy array : 1.647589921951294 total time to convert the images to numpy array : 1.6484284400939941 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 : 2944 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 : 2944 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 6.958240985870361 time used to do the prediction : 0.25089430809020996 save descriptor for thcl : 500 time to traite the descriptors : 0.07325267791748047 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.792017936706543 time spend for datou_step_exec : 15.494641780853271 time spend to save output : 4.887580871582031e-05 total time spend for step 1 : 15.494690656661987 step2:argmax Tue Feb 25 17:23:33 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 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 : 0.00026702880859375 time spend to save output : 6.437301635742188e-05 total time spend for step 2 : 0.0003314018249511719 step3:rotate Tue Feb 25 17:23:33 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 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/1740500614_1898146 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 2.0840723514556885 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 : 2.184006452560425 time spend to save output : 3.170967102050781e-05 total time spend for step 3 : 2.1840381622314453 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 /1339569291Didn'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 : 0.36435627937316895 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1339569291: ['917849322', 'temp/1740500597_1898146_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 : 0.5495421886444092 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 Tue Feb 25 17:23:36 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 : 20811475 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740500618_1898146 we have uploaded 4 photos in the portfolio 20811475 time of upload the photos Elapsed time : 3.2110233306884766 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/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1740500615_1898146_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 : 20811477 Result OK ! uploaded one batch 0 Elapsed time : 21.313983917236328 time spend for datou_step_exec : 29.091779232025146 time spend to save output : 2.0265579223632812e-05 total time spend for step 1 : 29.09179949760437 step2:tile Tue Feb 25 17:24:05 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 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/1740500615_1898146_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 : 20811478 with name tile_taggage_varroa feed_id_new_photos : 20811478 filename : temp/1740500615_1898146_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/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.009590864181518555 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/1740500652_1898146 we have uploaded 1 photos in the portfolio 20811478 Importing ! upload mediasElapsed time : 0.8696649074554443 , 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 : 0.9335968494415283 time spend for datou_step_exec : 7.555518627166748 time spend to save output : 2.86102294921875e-05 total time spend for step 2 : 7.55554723739624 step3:rotate Tue Feb 25 17:24:13 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 : 20811479 Needs to change image size ! time for calcul the mask position with numpy : 0.0005152225494384766 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0031783580780029297 .time for calcul the mask position with numpy : 0.0003733634948730469 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0026547908782958984 . 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 : 0.0004715919494628906 nb_pixel_total : 694 time to create 1 rle with old method : 0.0021753311157226562 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038695335388183594 nb_pixel_total : 1162 time to create 1 rle with old method : 0.003392934799194336 . 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 : 0.00046539306640625 nb_pixel_total : 221 time to create 1 rle with old method : 0.0007355213165283203 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003879070281982422 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0034902095794677734 . 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 : 0.0004203319549560547 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004322528839111328 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003867149353027344 nb_pixel_total : 1161 time to create 1 rle with old method : 0.002718210220336914 . 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 : 0.0004680156707763672 nb_pixel_total : 414 time to create 1 rle with old method : 0.001087188720703125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037860870361328125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0027611255645751953 . 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 : 0.00048542022705078125 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0031223297119140625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003886222839355469 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027174949645996094 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003676414489746094 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007719993591308594 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00046706199645996094 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0032999515533447266 .time for calcul the mask position with numpy : 0.0003826618194580078 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002822399139404297 . 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 : 0.00041413307189941406 nb_pixel_total : 694 time to create 1 rle with old method : 0.0017235279083251953 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00040078163146972656 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0028204917907714844 . 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 : 0.0003962516784667969 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006170272827148438 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038242340087890625 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0028231143951416016 . 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 : 143 time to create 1 rle with old method : 0.0004489421844482422 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003955364227294922 nb_pixel_total : 1160 time to create 1 rle with old method : 0.002816438674926758 . 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 : 0.00045037269592285156 nb_pixel_total : 414 time to create 1 rle with old method : 0.0013992786407470703 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003788471221923828 nb_pixel_total : 1159 time to create 1 rle with old method : 0.003446340560913086 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00038743019104003906 nb_pixel_total : 1 time to create 1 rle with old method : 3.814697265625e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.00048279762268066406 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0029015541076660156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038313865661621094 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0027174949645996094 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003609657287597656 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007364749908447266 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003948211669921875 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0032117366790771484 .time for calcul the mask position with numpy : 0.00038552284240722656 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002759695053100586 . 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 : 0.00044083595275878906 nb_pixel_total : 727 time to create 1 rle with old method : 0.0017771720886230469 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003733634948730469 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0028710365295410156 . 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 : 0.0004208087921142578 nb_pixel_total : 250 time to create 1 rle with old method : 0.0007169246673583984 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038504600524902344 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0028197765350341797 . 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 : 0.00039505958557128906 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005373954772949219 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038313865661621094 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0028944015502929688 . 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 : 0.0004050731658935547 nb_pixel_total : 450 time to create 1 rle with old method : 0.00121307373046875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039458274841308594 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0028619766235351562 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003647804260253906 nb_pixel_total : 1 time to create 1 rle with old method : 3.1948089599609375e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.0004222393035888672 nb_pixel_total : 1237 time to create 1 rle with old method : 0.003023862838745117 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037980079650878906 nb_pixel_total : 1158 time to create 1 rle with old method : 0.002651214599609375 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00036835670471191406 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006661415100097656 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00045371055603027344 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003210306167602539 .time for calcul the mask position with numpy : 0.00039196014404296875 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002744913101196289 . 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 : 0.0004150867462158203 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 : 0.0003917217254638672 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0027768611907958984 . 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 : 0.00042176246643066406 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006780624389648438 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00039649009704589844 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0027742385864257812 . 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 : 0.00038623809814453125 nb_pixel_total : 169 time to create 1 rle with old method : 0.0004930496215820312 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004184246063232422 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0028901100158691406 . 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 : 0.0003876686096191406 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011496543884277344 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038933753967285156 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002676725387573242 . 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 : 0.0004782676696777344 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0028619766235351562 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003771781921386719 nb_pixel_total : 1157 time to create 1 rle with old method : 0.02268815040588379 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00037598609924316406 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006504058837890625 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 20811479 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740500655_1898146 we have uploaded 24 photos in the portfolio 20811479 time of upload the photos Elapsed time : 7.915711164474487 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 : 11.44392728805542 time spend to save output : 9.393692016601562e-05 total time spend for step 3 : 11.444021224975586 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, '1339569327'] Looping around the photos to save general results len do output : 24 /1339569332Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569334Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569341Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569355Didn'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, '1339569327', 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 : 0.016408681869506836 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1339569332: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1339569333: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1339569334: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1339569335: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1339569336: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1339569337: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1339569338: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1339569339: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1339569340: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1339569341: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1339569342: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1339569343: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1339569344: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1339569345: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1339569346: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1339569347: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1339569348: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1339569349: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1339569350: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1339569351: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1339569352: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1339569353: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1339569354: ['937852786', 'temp/1740500615_1898146_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1339569355: ['937852786', 'temp/1740500615_1898146_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 : 0.2855663299560547 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 Tue Feb 25 17:24:24 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 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/1740500665_1898146 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 1.1399343013763428 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 : 1.2435801029205322 time spend to save output : 4.601478576660156e-05 total time spend for step 1 : 1.2436261177062988 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 /1339569356 /1339569357 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 : 0.01235818862915039 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1339569356': ['911785586', 'temp/1740500664_1898146_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1339569357': ['911785586', 'temp/1740500664_1898146_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 : 0.22062206268310547 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 Tue Feb 25 17:24:26 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 : 20811480 Result OK ! uploaded one batch 0 Elapsed time : 20.62433123588562 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 20.69259238243103 time spend to save output : 2.7418136596679688e-05 total time spend for step 1 : 20.692619800567627 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 /1339569360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339569381Didn'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 : 0.013265371322631836 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1339569360': ['950103132', 'temp/1740500666_1898146_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1339569362': ['950103132', 'temp/1740500666_1898146_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1339569365': ['950103132', 'temp/1740500666_1898146_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1339569367': ['950103132', 'temp/1740500666_1898146_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1339569372': ['950103132', 'temp/1740500666_1898146_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1339569373': ['950103132', 'temp/1740500666_1898146_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1339569374': ['950103132', 'temp/1740500666_1898146_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1339569381': ['950103132', 'temp/1740500666_1898146_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 : 0.6515357494354248 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 Tue Feb 25 17:24:47 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 : 0.07035350799560547 time spend to save output : 7.677078247070312e-05 total time spend for step 1 : 0.07043027877807617 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 : 0.36808347702026367 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 Tue Feb 25 17:24:48 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 : 0.1236872673034668 time spend to save output : 0.00011301040649414062 total time spend for step 1 : 0.12380027770996094 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, 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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 : 0.004643440246582031 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Tue Feb 25 17:24:48 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 : 0.3155062198638916 time spend to save output : 0.00010585784912109375 total time spend for step 1 : 0.3156120777130127 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 : 0.7316105365753174 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 Tue Feb 25 17:24:49 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/1740500688_1898146_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.33536791801452637 time spend to save output : 8.797645568847656e-05 total time spend for step 1 : 0.33545589447021484 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 BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFBFFBFBFBFFBFBFBFBFBFBFBFBFFwe 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 : 4.277466535568237 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 Tue Feb 25 17:24:54 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 Thcl ! we are using the classfication for only one thcl 1528 time to import caffe and check if the image exist : 0.0007379055023193359 time to convert the images to numpy array : 0.005750894546508789 time to import caffe and check if the image exist : 0.003703594207763672 time to convert the images to numpy array : 0.0546717643737793 time to import caffe and check if the image exist : 0.008635282516479492 time to convert the images to numpy array : 0.05219912528991699 time to import caffe and check if the image exist : 0.012768268585205078 time to convert the images to numpy array : 0.053592681884765625 time to import caffe and check if the image exist : 0.013970136642456055 time to convert the images to numpy array : 0.050617218017578125 time to import caffe and check if the image exist : 0.009697437286376953 time to convert the images to numpy array : 0.059058427810668945 time to import caffe and check if the image exist : 0.01208186149597168 time to convert the images to numpy array : 0.05660653114318848 time to import caffe and check if the image exist : 0.009456396102905273 time to convert the images to numpy array : 0.05974721908569336 time to import caffe and check if the image exist : 0.013951301574707031 time to convert the images to numpy array : 0.05342555046081543 time to import caffe and check if the image exist : 0.008597135543823242 time to convert the images to numpy array : 0.0610349178314209 total time to convert the images to numpy array : 0.07271003723144531 list photo_ids error: [] list photo_ids correct : [987515238, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515175, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515248, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515182, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515183, 987515184, 987515185, 987515186, 987515187, 987515188, 987515189, 987515249, 987515250, 987515224, 987515226, 987515227, 987515228, 987515230, 987515201, 987515202, 987515204, 987515205, 987515239, 987515240, 987515241] 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 l 3637 free memory gpu now : 2944 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 : 2944 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'res5b']) time used to do the prepocess of the images : 0.05630826950073242 time used to do the prediction : 0.2704343795776367 save descriptor for thcl : 1528 time to traite the descriptors : 4.358483791351318 storage_type for insertDescriptorsMulti : 1 To insert : 987515238 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515223 To insert : 987515175 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515247 To insert : 987515248 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515187 To insert : 987515188 To insert : 987515189 To insert : 987515249 To insert : 987515250 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515239 To insert : 987515240 To insert : 987515241 time to insert the descriptors : 25.741137981414795 time spend for datou_step_exec : 34.82830786705017 time spend to save output : 8.416175842285156e-05 total time spend for step 1 : 34.828392028808594 step2:argmax Tue Feb 25 17:25:29 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 Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 time spend for datou_step_exec : 0.0010933876037597656 time spend to save output : 1.0728836059570312e-05 total time spend for step 2 : 0.001104116439819336 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515238': [('987515238', 'Carton', 0.9995734, 1927, '1528'), 'temp/1740500689_1898146_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.977496, 1927, '1528'), 'temp/1740500689_1898146_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5294241, 1927, '1528'), 'temp/1740500689_1898146_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.9993691, 1927, '1528'), 'temp/1740500689_1898146_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963819, 1927, '1528'), 'temp/1740500689_1898146_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.99747795, 1927, '1528'), 'temp/1740500689_1898146_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920987, 1927, '1528'), 'temp/1740500689_1898146_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998153, 1927, '1528'), 'temp/1740500689_1898146_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515242': [('987515242', 'Carton', 0.93575025, 1927, '1528'), 'temp/1740500689_1898146_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.87416875, 1927, '1528'), 'temp/1740500689_1898146_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.817528, 1927, '1528'), 'temp/1740500689_1898146_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.86539733, 1927, '1528'), 'temp/1740500689_1898146_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992323, 1927, '1528'), 'temp/1740500689_1898146_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966836, 1927, '1528'), 'temp/1740500689_1898146_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.98118687, 1927, '1528'), 'temp/1740500689_1898146_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.87366575, 1927, '1528'), 'temp/1740500689_1898146_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.99169356, 1927, '1528'), 'temp/1740500689_1898146_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9677866, 1927, '1528'), 'temp/1740500689_1898146_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.9734244, 1927, '1528'), 'temp/1740500689_1898146_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.98693174, 1927, '1528'), 'temp/1740500689_1898146_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.98694104, 1927, '1528'), 'temp/1740500689_1898146_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99390584, 1927, '1528'), 'temp/1740500689_1898146_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515231': [('987515231', 'Carton', 0.99942076, 1927, '1528'), 'temp/1740500689_1898146_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924326, 1927, '1528'), 'temp/1740500689_1898146_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.9833673, 1927, '1528'), 'temp/1740500689_1898146_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.9447678, 1927, '1528'), 'temp/1740500689_1898146_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89165974, 1927, '1528'), 'temp/1740500689_1898146_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5373023, 1927, '1528'), 'temp/1740500689_1898146_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7668076, 1927, '1528'), 'temp/1740500689_1898146_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.99981457, 1927, '1528'), 'temp/1740500689_1898146_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9771353, 1927, '1528'), 'temp/1740500689_1898146_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8581459, 1927, '1528'), 'temp/1740500689_1898146_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9272954, 1927, '1528'), 'temp/1740500689_1898146_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.990026, 1927, '1528'), 'temp/1740500689_1898146_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977729, 1927, '1528'), 'temp/1740500689_1898146_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99242157, 1927, '1528'), 'temp/1740500689_1898146_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515190': [('987515190', 'Carton', 0.9763338, 1927, '1528'), 'temp/1740500689_1898146_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.99991167, 1927, '1528'), 'temp/1740500689_1898146_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993962, 1927, '1528'), 'temp/1740500689_1898146_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.98467296, 1927, '1528'), 'temp/1740500689_1898146_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.98465455, 1927, '1528'), 'temp/1740500689_1898146_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.96620244, 1927, '1528'), 'temp/1740500689_1898146_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.98592526, 1927, '1528'), 'temp/1740500689_1898146_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999213, 1927, '1528'), 'temp/1740500689_1898146_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.99973255, 1927, '1528'), 'temp/1740500689_1898146_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.79768914, 1927, '1528'), 'temp/1740500689_1898146_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847424, 1927, '1528'), 'temp/1740500689_1898146_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.98115796, 1927, '1528'), 'temp/1740500689_1898146_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515188': [('987515188', 'Carton', 0.9956567, 1927, '1528'), 'temp/1740500689_1898146_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.99778855, 1927, '1528'), 'temp/1740500689_1898146_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515249': [('987515249', 'Carton', 0.9812882, 1927, '1528'), 'temp/1740500689_1898146_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9807283, 1927, '1528'), 'temp/1740500689_1898146_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515224': [('987515224', 'Carton', 0.90834266, 1927, '1528'), 'temp/1740500689_1898146_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9869723, 1927, '1528'), 'temp/1740500689_1898146_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.90067244, 1927, '1528'), 'temp/1740500689_1898146_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.52108735, 1927, '1528'), 'temp/1740500689_1898146_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.9994061, 1927, '1528'), 'temp/1740500689_1898146_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515201': [('987515201', 'Carton', 0.99546236, 1927, '1528'), 'temp/1740500689_1898146_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911069, 1927, '1528'), 'temp/1740500689_1898146_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950706, 1927, '1528'), 'temp/1740500689_1898146_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99087244, 1927, '1528'), 'temp/1740500689_1898146_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1740500689_1898146_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.9995209, 1927, '1528'), 'temp/1740500689_1898146_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9820917, 1927, '1528'), 'temp/1740500689_1898146_987515241_073420d938f5f010ffd5b4353c064e09.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 : 0.2608163356781006 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 Tue Feb 25 17:25:29 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 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/1740500729_1898146_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 : 0.043138742446899414 time to do a prediction : 0.3450782299041748 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 : 1.8518719673156738 time spend to save output : 3.981590270996094e-05 total time spend for step 1 : 1.8519117832183838 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, 6.237613290466237e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4238548668176207e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0660058613609635e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.432363596151845e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.916164364956785e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.80352430511266e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012268198770470917), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9672004529857077e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.3728967235570053e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.211222671633095e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3857362546332297e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4704583009006456e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.129176416725386e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.0001578744559083134), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.000444101809989661), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.533414125442505e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3296655652084155e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.6186636457860004e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5246226869057864e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.614658799553581e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.19909087617998e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.650810923427343e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.0003258866781834513), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.00030624913051724434), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8582972188596614e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.93749131844379e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.70317596005043e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 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-1, 304, -1, 0.0004267779877409339), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.57233249512501e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.2058469514595345e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1740091395040508e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8829026885214262e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.00024748919531702995), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.0004700582940131426), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.00033536332193762064), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.00023517337103839964), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010986372217303142), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.600108751328662e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.0001311882951995358), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.000732035783585161)]} ############################### 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 : 0.23299622535705566 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 Tue Feb 25 17:25:31 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 debut step init detect dechets input : temp/1740500731_1898146_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1740500731_1898146_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 : 0.012993812561035156 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.0002474784851074219 time spend to save output : 0.01335597038269043 total time spend for step 1 : 0.013603448867797852 step2:tile Tue Feb 25 17:25:31 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 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/1740500731_1898146_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 : 20811508 with name tile_correct_upm feed_id_new_photos : 20811508 filename : temp/1740500731_1898146_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/1740500731_1898146_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.011375904083251953 About to upload 1 photos upload in portfolio : 20811508 Result OK ! uploaded one batch 0 Elapsed time : 4.981837749481201 upload mediasElapsed time : 4.993308782577515 , 0Saving 0 CHIs. end of tileElapsed time : 5.006155252456665 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1339569414'] Looping around the photos to save general results len do output : 1 /1339569414Didn'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, '1339569414', 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 : 0.012080669403076172 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.637506484985352 time spend to save output : 0.012328863143920898 total time spend for step 2 : 11.649835348129272 step3:detect_points Tue Feb 25 17:25:43 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 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/1740500731_1898146_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 : 0.0431673526763916 time to do a prediction : 15.327415943145752 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 : 16.73876166343689 time spend to save output : 0.057891845703125 total time spend for step 3 : 16.796653509140015 step4:count_percent_refus Tue Feb 25 17:26:00 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/1740500731_1898146_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) list_photo : [987321136] list_photo_correc : [1339569414] 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 : 0.04891705513000488 save missing photos in datou_result : time spend for datou_step_exec : 0.015245199203491211 time spend to save output : 0.04910564422607422 total time spend for step 4 : 0.06435084342956543 step5:brightness Tue Feb 25 17:26:00 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/1740500731_1898146_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 : 0.02397322654724121 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 : 0.009748458862304688 save missing photos in datou_result : time spend for datou_step_exec : 0.11908340454101562 time spend to save output : 0.03852343559265137 total time spend for step 5 : 0.157606840133667 step6:blur_detection Tue Feb 25 17:26:00 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/1740500731_1898146_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 : 0.008963584899902344 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 : 0.008810758590698242 save missing photos in datou_result : time spend for datou_step_exec : 0.17520642280578613 time spend to save output : 0.023018360137939453 total time spend for step 6 : 0.19822478294372559 step7:send_mail_dechet Tue Feb 25 17:26:00 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, '1339569414'] 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, '1339569414', 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 : 0.01641225814819336 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.4629642963409424 time spend to save output : 0.016731977462768555 total time spend for step 7 : 0.47969627380371094 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 : 0.27126145362854004 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 Tue Feb 25 17:26:01 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/1740500761_1898146_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 time spend for datou_step_exec : 0.16115140914916992 time spend to save output : 5.412101745605469e-05 total time spend for step 1 : 0.16120553016662598 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 : 0.014254331588745117 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:split_time_score Tue Feb 25 17:26:01 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 : 0.14728093147277832 time spend to save output : 0.00012731552124023438 total time spend for step 1 : 0.14740824699401855 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(20811533, 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 , BBBFBFBFFFBFwe 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 : 1.126051902770996 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 Tue Feb 25 17:26:02 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 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/1740500761_1898146_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 0.119s for 300 object proposals image_path : temp/1740500761_1898146_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 0.073s for 300 object proposals image_path : temp/1740500761_1898146_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 2.705s for 300 object proposals image_path : temp/1740500761_1898146_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 0.053s for 300 object proposals image_path : temp/1740500761_1898146_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 0.077s for 300 object proposals image_path : temp/1740500761_1898146_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 0.790s for 300 object proposals len de result frcnn : 6 time spend for datou_step_exec : 6.7210729122161865 time spend to save output : 0.00022864341735839844 total time spend for step 1 : 6.721301555633545 step2:crop_condition Tue Feb 25 17:26:09 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 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 : 0.388134241104126 time spend to save output : 7.05718994140625e-05 total time spend for step 2 : 0.38820481300354004 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/1740500761_1898146_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1740500761_1898146_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1740500761_1898146_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1740500761_1898146_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1740500761_1898146_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 : 0.3478381633758545 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 Tue Feb 25 17:26:10 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 inside step blanchir_image https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos:20811534 treat image : temp/1740500769_1898146_990111206_7ca22c7e68dd0a10509c7987af0cf549.png blanchir func Result OK ! time spend for datou_step_exec : 6.9223268032073975 time spend to save output : 8.58306884765625e-06 total time spend for step 1 : 6.922335386276245 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 : 0.011165857315063477 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(990111206, '1339569745', 0, 300, 0, 381, 1, 1, 'blanc')] [(990111206, '1339569745', 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 : 0.20829057693481445 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 Tue Feb 25 17:26:17 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 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/1740500777_1898146_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 : 7.679412841796875 time spend to save output : 5.054473876953125e-05 total time spend for step 1 : 7.6794633865356445 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 : 0.013062000274658203 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, '1339569971', 0, 897, 0, 1431, 1, 1, 'darker')] [(989962950, '1339569971', 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 : 0.13974952697753906 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 Tue Feb 25 17:26:26 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 : 1339570599 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1339570599 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1339570599 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1339570599 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1339570599 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1339570599 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1339570599 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 : 7.7023766040802 time spend to save output : 7.510185241699219e-05 total time spend for step 1 : 7.702451705932617 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 : 0.012675762176513672 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, 1339570599, 0, 1431, 0, 897, 1, 1, 'img_aug')] [(989962950, 1339570599, 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 : 0.021833181381225586 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 Tue Feb 25 17:26:34 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 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 3.814697265625e-06 elapsed_time : order_list_meta_photo_and_scores 0.00025343894958496094 elapsed_time : fill_and_build_computed_from_old_data 0.02365851402282715 elapsed_time : insert_dashboard_record_day_entry 0.024111509323120117 Creating list_photo_total elapsed_time : select_descriptors 39.58305859565735 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 : 0.04095625877380371 photos_removed : len 115 elapsed_time : remove_photo_duplicate 0.12436223030090332 Creating list_photo_total XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX elapsed_time : count_sum_diff_and_build_graph 0.056188106536865234 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 0.01360321044921875 elapsed_time : compute_and_correct_tag_with_moyenne_mobile 3.5762786865234375e-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 : 20811585 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 : 20811586 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 : 20811587 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 : 20811588 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 : 20811589 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 : 20811590 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 : 20811591 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 : 20811592 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 : 20811593 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 : 20811594 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 : 0.005814130015432098 # 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 19.35386300086975 # 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 : 59.27138662338257 time spend to save output : 1.4781951904296875e-05 total time spend for step 1 : 59.27140140533447 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 : 0.23911428451538086 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 , BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFBFBFFFBFFBFwe 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 : 7.219249248504639 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 Tue Feb 25 17:27:41 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 1.2874603271484375e-05 elapsed_time : order_list_meta_photo_and_scores 1.3589859008789062e-05 ???????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.006630420684814453 elapsed_time : insert_dashboard_record_day_entry 0.3991107940673828 ***** 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 : 0.02742171287536621 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 : 0.015497922897338867 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 : 0.009886503219604492 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 : 0.00996255874633789 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 : 0.009850740432739258 elapsed_time : SPLIT_BY_DARK 0.07980918884277344 ***** END SPLIT BY DARK ***** ((1055001085,), (1055008638,), (1055010730,), (1055011086,), (1055012686,)) ***** BEGIN SPLIT TIME ***** False [12, 20, 29, 38, 51] 1633478400.0 `1633508253.0 1633507200.0 `1633508284.0 1633507200.0 `1633508316.0 1633507200.0 `1633508323.0 1633507200.0 `1633508333.0 1633507200.0 `1633508340.0 1633507200.0 `1633508358.0 1633507200.0 `1633508369.0 1633507200.0 `1633508385.0 1633507200.0 `1633508398.0 1633507200.0 `1633508416.0 1633507200.0 `1633508425.0 1633507200.0 `1633508434.0 1633507200.0 `1633508449.0 1633507200.0 `1633508458.0 1633507200.0 `1633508470.0 1633507200.0 `1633508491.0 1633507200.0 `1633508499.0 1633507200.0 `1633508508.0 1633507200.0 `1633508521.0 1633507200.0 `1633508524.0 1633507200.0 `1633508644.0 1633507200.0 `1633508652.0 1633507200.0 `1633508657.0 1633507200.0 `1633508669.0 1633507200.0 `1633508678.0 1633507200.0 `1633508684.0 1633507200.0 `1633508690.0 1633507200.0 `1633508696.0 1633507200.0 `1633508701.0 1633507200.0 `1633508725.0 1633507200.0 `1633508733.0 1633507200.0 `1633508739.0 1633507200.0 `1633508748.0 1633507200.0 `1633508759.0 1633507200.0 `1633508766.0 1633507200.0 `1633508772.0 1633507200.0 `1633508778.0 1633507200.0 `1633508781.0 1633507200.0 `1633508805.0 1633507200.0 `1633508813.0 1633507200.0 `1633508820.0 1633507200.0 `1633508824.0 1633507200.0 `1633508829.0 1633507200.0 `1633508833.0 1633507200.0 `1633508849.0 1633507200.0 `1633508858.0 1633507200.0 `1633508865.0 1633507200.0 `1633508871.0 1633507200.0 `1633508877.0 1633507200.0 `1633508886.0 1633507200.0 `1633508891.0 1633507200.0 `1633524639.0 1633521600.0 `1633524648.0 1633521600.0 `1633524657.0 1633521600.0 `1633524662.0 1633521600.0 `1633524675.0 1633521600.0 `1633524680.0 1633521600.0 `1633524683.0 1633521600.0 `1633524694.0 1633521600.0 `1633524700.0 1633521600.0 `1633524718.0 1633521600.0 `1633524722.0 1633521600.0 `1633524741.0 1633521600.0 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 0.010590314865112305 ***** 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 Qualite : 0.049377889059021136 # 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 Qualite : 0.051267724965616025 # 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 Qualite : 0.06573670331612967 # 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 Qualite : 0.12804353375222421 # 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 Qualite : 0.07355022343618775 # 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 Qualite : 0.10644996740301908 # 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 1.0306415557861328 # 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 : 7.356163501739502 time spend to save output : 0.16115307807922363 total time spend for step 1 : 7.517316579818726 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 [1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679, 1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055, 1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134] 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', '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) ('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', '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', '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', '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) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 64 time used for this insertion : 0.024307966232299805 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 : 0.024279356002807617 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 Tue Feb 25 17:27:48 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 3.337860107421875e-06 elapsed_time : order_list_meta_photo_and_scores 7.867813110351562e-06 ??? elapsed_time : fill_and_build_computed_from_old_data 0.0005807876586914062 elapsed_time : insert_dashboard_record_day_entry 0.02251887321472168 ---------- 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 7.152557373046875e-06 elapsed_time : order_list_meta_photo_and_scores 1.239776611328125e-05 ? elapsed_time : fill_and_build_computed_from_old_data 0.0003204345703125 elapsed_time : insert_dashboard_record_day_entry 0.026748180389404297 ---------- 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 : 2.436885356903076 time spend to save output : 4.649162292480469e-05 total time spend for step 1 : 2.436931848526001 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 : 0.015271425247192383 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 : 0.014341354370117188 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 Tue Feb 25 17:27:51 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 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 3.337860107421875e-06 elapsed_time : order_list_meta_photo_and_scores 1.0251998901367188e-05 ????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0032804012298583984 elapsed_time : insert_dashboard_record_day_entry 0.023738861083984375 Creating list_photo_total elapsed_time : select_descriptors 0.012420892715454102 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 : 0.011336088180541992 photos_removed : len 0 elapsed_time : remove_photo_duplicate 0.036614418029785156 To do, maybe not at the correct place ! .................................force hashtag to JRM elapsed_time : CREATE_PORT_BATCH_BY_HOUR 0.005964994430541992 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 0.025866031646728516 # 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 : 0.16570329666137695 time spend to save output : 3.5762786865234375e-05 total time spend for step 1 : 0.1657390594482422 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 : 0.019324064254760742 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 : 0.21555829048156738 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 Tue Feb 25 17:27:51 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 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 : 0.7806980609893799 time for calcul the mask position with numpy : 0.006665945053100586 nb_pixel_total : 217207 time to create 1 rle with new method : 0.04768729209899902 time for calcul the mask position with numpy : 0.0036356449127197266 nb_pixel_total : 1008 time to create 1 rle with old method : 0.002398967742919922 time for calcul the mask position with numpy : 0.003451108932495117 nb_pixel_total : 751 time to create 1 rle with old method : 0.0017733573913574219 time for calcul the mask position with numpy : 0.0033745765686035156 nb_pixel_total : 722 time to create 1 rle with old method : 0.0016279220581054688 time for calcul the mask position with numpy : 0.003293752670288086 nb_pixel_total : 2949 time to create 1 rle with old method : 0.0066831111907958984 time for calcul the mask position with numpy : 0.0030066967010498047 nb_pixel_total : 497 time to create 1 rle with old method : 0.0012271404266357422 time for calcul the mask position with numpy : 0.0029807090759277344 nb_pixel_total : 1086 time to create 1 rle with old method : 0.01788330078125 time for calcul the mask position with numpy : 0.005558490753173828 nb_pixel_total : 1924 time to create 1 rle with old method : 0.004425048828125 time for calcul the mask position with numpy : 0.003463268280029297 nb_pixel_total : 413 time to create 1 rle with old method : 0.0012640953063964844 time for calcul the mask position with numpy : 0.0036268234252929688 nb_pixel_total : 526 time to create 1 rle with old method : 0.0014431476593017578 create new chi : 0.1260223388671875 time to delete rle : 0.016324758529663086 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 : 0.17323827743530273 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 : 1.2281949520111084 time spend to save output : 0.0001895427703857422 total time spend for step 1 : 1.2283844947814941 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 : 0.34017443656921387 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 Tue Feb 25 17:27:53 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 : 0.02993011474609375 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 : 2.167060613632202 time spend to save output : 0.00011444091796875 total time spend for step 1 : 2.167175054550171 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 : 0.29111695289611816 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 Tue Feb 25 17:27:56 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 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 : 26.080167293548584 time for calcul the mask position with numpy : 0.27281785011291504 nb_pixel_total : 5233657 time to create 1 rle with new method : 0.4945254325866699 time for calcul the mask position with numpy : 0.033495426177978516 nb_pixel_total : 11972 time to create 1 rle with old method : 0.027602434158325195 time for calcul the mask position with numpy : 0.0329594612121582 nb_pixel_total : 15054 time to create 1 rle with old method : 0.03452491760253906 time for calcul the mask position with numpy : 0.03272891044616699 nb_pixel_total : 13954 time to create 1 rle with old method : 0.032030344009399414 time for calcul the mask position with numpy : 0.03315329551696777 nb_pixel_total : 4888 time to create 1 rle with old method : 0.011672496795654297 time for calcul the mask position with numpy : 0.04679298400878906 nb_pixel_total : 1188492 time to create 1 rle with new method : 0.44818639755249023 time for calcul the mask position with numpy : 0.03639054298400879 nb_pixel_total : 184585 time to create 1 rle with new method : 0.4586515426635742 time for calcul the mask position with numpy : 0.03359055519104004 nb_pixel_total : 18620 time to create 1 rle with old method : 0.04308009147644043 time for calcul the mask position with numpy : 0.03567624092102051 nb_pixel_total : 62945 time to create 1 rle with old method : 0.17816638946533203 time for calcul the mask position with numpy : 0.03378891944885254 nb_pixel_total : 9427 time to create 1 rle with old method : 0.02175593376159668 time for calcul the mask position with numpy : 0.033496856689453125 nb_pixel_total : 9081 time to create 1 rle with old method : 0.021031856536865234 time for calcul the mask position with numpy : 0.03356146812438965 nb_pixel_total : 15987 time to create 1 rle with old method : 0.03722262382507324 time for calcul the mask position with numpy : 0.037994384765625 nb_pixel_total : 33276 time to create 1 rle with old method : 0.07809185981750488 time for calcul the mask position with numpy : 0.03360700607299805 nb_pixel_total : 17533 time to create 1 rle with old method : 0.040407657623291016 time for calcul the mask position with numpy : 0.03323864936828613 nb_pixel_total : 4876 time to create 1 rle with old method : 0.01141500473022461 time for calcul the mask position with numpy : 0.03365898132324219 nb_pixel_total : 25226 time to create 1 rle with old method : 0.05809664726257324 time for calcul the mask position with numpy : 0.03387045860290527 nb_pixel_total : 30773 time to create 1 rle with old method : 0.07136678695678711 time for calcul the mask position with numpy : 0.03557467460632324 nb_pixel_total : 65671 time to create 1 rle with old method : 0.23828959465026855 time for calcul the mask position with numpy : 0.034032583236694336 nb_pixel_total : 12230 time to create 1 rle with old method : 0.028383493423461914 time for calcul the mask position with numpy : 0.034026145935058594 nb_pixel_total : 29560 time to create 1 rle with old method : 0.06997036933898926 time for calcul the mask position with numpy : 0.03418850898742676 nb_pixel_total : 14310 time to create 1 rle with old method : 0.03786420822143555 time for calcul the mask position with numpy : 0.03405952453613281 nb_pixel_total : 15117 time to create 1 rle with old method : 0.03533816337585449 time for calcul the mask position with numpy : 0.03663277626037598 nb_pixel_total : 301487 time to create 1 rle with new method : 0.5189709663391113 time for calcul the mask position with numpy : 0.03403353691101074 nb_pixel_total : 29821 time to create 1 rle with old method : 0.06905508041381836 time for calcul the mask position with numpy : 0.033712148666381836 nb_pixel_total : 40299 time to create 1 rle with old method : 0.09522104263305664 time for calcul the mask position with numpy : 0.033991336822509766 nb_pixel_total : 12680 time to create 1 rle with old method : 0.031203031539916992 time for calcul the mask position with numpy : 0.03344321250915527 nb_pixel_total : 9449 time to create 1 rle with old method : 0.02182602882385254 time for calcul the mask position with numpy : 0.03322958946228027 nb_pixel_total : 15168 time to create 1 rle with old method : 0.03519797325134277 time for calcul the mask position with numpy : 0.03280448913574219 nb_pixel_total : 11140 time to create 1 rle with old method : 0.025679349899291992 time for calcul the mask position with numpy : 0.03328967094421387 nb_pixel_total : 29065 time to create 1 rle with old method : 0.0681920051574707 time for calcul the mask position with numpy : 0.03334665298461914 nb_pixel_total : 22774 time to create 1 rle with old method : 0.05788850784301758 time for calcul the mask position with numpy : 0.0328516960144043 nb_pixel_total : 13880 time to create 1 rle with old method : 0.03275489807128906 time for calcul the mask position with numpy : 0.03622007369995117 nb_pixel_total : 155366 time to create 1 rle with new method : 0.6054112911224365 time for calcul the mask position with numpy : 0.03445935249328613 nb_pixel_total : 63941 time to create 1 rle with old method : 0.14296531677246094 time for calcul the mask position with numpy : 0.03127026557922363 nb_pixel_total : 7836 time to create 1 rle with old method : 0.017652511596679688 time for calcul the mask position with numpy : 0.0324857234954834 nb_pixel_total : 7460 time to create 1 rle with old method : 0.016906261444091797 time for calcul the mask position with numpy : 0.03450918197631836 nb_pixel_total : 44600 time to create 1 rle with old method : 0.10533499717712402 time for calcul the mask position with numpy : 0.033052921295166016 nb_pixel_total : 11879 time to create 1 rle with old method : 0.027508974075317383 time for calcul the mask position with numpy : 0.03370809555053711 nb_pixel_total : 44195 time to create 1 rle with old method : 0.1009666919708252 time for calcul the mask position with numpy : 0.032531023025512695 nb_pixel_total : 23652 time to create 1 rle with old method : 0.05272412300109863 time for calcul the mask position with numpy : 0.03306317329406738 nb_pixel_total : 30006 time to create 1 rle with old method : 0.06822586059570312 time for calcul the mask position with numpy : 0.03276658058166504 nb_pixel_total : 15880 time to create 1 rle with old method : 0.03672003746032715 time for calcul the mask position with numpy : 0.032928466796875 nb_pixel_total : 29845 time to create 1 rle with old method : 0.067352294921875 time for calcul the mask position with numpy : 0.03370094299316406 nb_pixel_total : 144263 time to create 1 rle with old method : 0.32171130180358887 create new chi : 6.887840747833252 time to delete rle : 0.5360844135284424 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 : 2.738704204559326 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 : 36.49243474006653 time spend to save output : 0.0001926422119140625 total time spend for step 1 : 36.49262738227844 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 : 0.19580936431884766 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 Tue Feb 25 17:28:33 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 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/1740500915_1898146 we have uploaded 4 photos in the portfolio 3287159 time of upload the photos Elapsed time : 1.7054903507232666 time spend for datou_step_exec : 3.76155686378479 time spend to save output : 7.128715515136719e-05 total time spend for step 1 : 3.7616281509399414 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 /1339571895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339571896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339571897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1339571898Didn'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 : 0.013510704040527344 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1339571895: ['1006293201', 'temp/1006293201_random_deformation_0.png', []], 1339571896: ['1006293201', 'temp/1006293201_random_deformation_1.png', []], 1339571897: ['1006293201', 'temp/1006293201_random_deformation_2.png', []], 1339571898: ['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 : 0.3373889923095703 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 Tue Feb 25 17:28:37 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 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/1740500917_1898146_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 : 20811668 with name results_test_tile feed_id_new_photos : 20811668 filename : temp/1740500917_1898146_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/1740500917_1898146_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 : 0.33798956871032715 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/1740500924_1898146 we have uploaded 24 photos in the portfolio 20811668 Importing ! upload mediasElapsed time : 9.075885772705078 , 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 : 9.340161800384521 time spend for datou_step_exec : 15.466230392456055 time spend to save output : 4.3392181396484375e-05 total time spend for step 1 : 15.466273784637451 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1339571991': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg'], '1339571992': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg'], '1339571993': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg'], '1339571994': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg'], '1339571995': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg'], '1339571996': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg'], '1339571997': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg'], '1339571998': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg'], '1339571999': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg'], '1339572000': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg'], '1339572001': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg'], '1339572002': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg'], '1339572003': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg'], '1339572004': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg'], '1339572005': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg'], '1339572006': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg'], '1339572007': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg'], '1339572009': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg'], '1339572011': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg'], '1339572012': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg'], '1339572013': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg'], '1339572014': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg'], '1339572015': ['temp/1740500917_1898146_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg'], '1339572016': ['temp/1740500917_1898146_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 : 0.34884071350097656 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 Tue Feb 25 17:28:53 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 : 20811669 time for calcul the mask position with numpy : 0.010268449783325195 nb_pixel_total : 110633 time to create 1 rle with old method : 0.2518014907836914 .time for calcul the mask position with numpy : 0.008699178695678711 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03705787658691406 .time for calcul the mask position with numpy : 0.009862422943115234 nb_pixel_total : 5286 time to create 1 rle with old method : 0.014036417007446289 .time for calcul the mask position with numpy : 0.010043621063232422 nb_pixel_total : 1633 time to create 1 rle with old method : 0.003611326217651367 .time for calcul the mask position with numpy : 0.010871171951293945 nb_pixel_total : 105533 time to create 1 rle with old method : 0.27086710929870605 .time for calcul the mask position with numpy : 0.012595415115356445 nb_pixel_total : 4393 time to create 1 rle with old method : 0.009547948837280273 .time for calcul the mask position with numpy : 0.009638309478759766 nb_pixel_total : 632 time to create 1 rle with old method : 0.0014841556549072266 .time for calcul the mask position with numpy : 0.010303974151611328 nb_pixel_total : 62627 time to create 1 rle with old method : 0.14035749435424805 .time for calcul the mask position with numpy : 0.01006007194519043 nb_pixel_total : 33681 time to create 1 rle with old method : 0.08121776580810547 .time for calcul the mask position with numpy : 0.009310007095336914 nb_pixel_total : 37724 time to create 1 rle with old method : 0.0880124568939209 .time for calcul the mask position with numpy : 0.009545087814331055 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11956286430358887 .time for calcul the mask position with numpy : 0.05546712875366211 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.19135618209838867 .time for calcul the mask position with numpy : 0.011322021484375 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005625247955322266 .time for calcul the mask position with numpy : 0.009509086608886719 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005120038986206055 .time for calcul the mask position with numpy : 0.009873390197753906 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007074832916259766 .time for calcul the mask position with numpy : 0.009450197219848633 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0036821365356445312 .Needs to change image size ! time for calcul the mask position with numpy : 0.010887384414672852 nb_pixel_total : 110633 time to create 1 rle with old method : 0.2476487159729004 .time for calcul the mask position with numpy : 0.00949716567993164 nb_pixel_total : 15826 time to create 1 rle with old method : 0.04023241996765137 .time for calcul the mask position with numpy : 0.010794639587402344 nb_pixel_total : 5286 time to create 1 rle with old method : 0.014423131942749023 .time for calcul the mask position with numpy : 0.011527538299560547 nb_pixel_total : 1633 time to create 1 rle with old method : 0.006562232971191406 .time for calcul the mask position with numpy : 0.009375810623168945 nb_pixel_total : 105533 time to create 1 rle with old method : 0.24624848365783691 .time for calcul the mask position with numpy : 0.00863504409790039 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010173559188842773 .time for calcul the mask position with numpy : 0.009508848190307617 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015935897827148438 .time for calcul the mask position with numpy : 0.009980440139770508 nb_pixel_total : 62627 time to create 1 rle with old method : 0.14803123474121094 .time for calcul the mask position with numpy : 0.00954747200012207 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07891535758972168 .time for calcul the mask position with numpy : 0.009161233901977539 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08838582038879395 .time for calcul the mask position with numpy : 0.009873628616333008 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11597371101379395 .time for calcul the mask position with numpy : 0.040671586990356445 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.2243659496307373 .time for calcul the mask position with numpy : 0.009718179702758789 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005468130111694336 .time for calcul the mask position with numpy : 0.009323835372924805 nb_pixel_total : 2256 time to create 1 rle with old method : 0.004953145980834961 .time for calcul the mask position with numpy : 0.009496688842773438 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0068590641021728516 .time for calcul the mask position with numpy : 0.00953817367553711 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0036602020263671875 .time for calcul the mask position with numpy : 0.009882688522338867 nb_pixel_total : 110633 time to create 1 rle with old method : 0.24008774757385254 .time for calcul the mask position with numpy : 0.008715629577636719 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03585934638977051 .time for calcul the mask position with numpy : 0.00905299186706543 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012845993041992188 .time for calcul the mask position with numpy : 0.009073019027709961 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0039081573486328125 .time for calcul the mask position with numpy : 0.00956106185913086 nb_pixel_total : 105533 time to create 1 rle with old method : 0.23782944679260254 .time for calcul the mask position with numpy : 0.009099960327148438 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010479927062988281 .time for calcul the mask position with numpy : 0.008831501007080078 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015497207641601562 .time for calcul the mask position with numpy : 0.009702444076538086 nb_pixel_total : 62627 time to create 1 rle with old method : 0.14977025985717773 .time for calcul the mask position with numpy : 0.009373903274536133 nb_pixel_total : 33681 time to create 1 rle with old method : 0.08053827285766602 .time for calcul the mask position with numpy : 0.008853435516357422 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08285140991210938 .time for calcul the mask position with numpy : 0.009113550186157227 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11066937446594238 .time for calcul the mask position with numpy : 0.060337066650390625 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.14283514022827148 .time for calcul the mask position with numpy : 0.00881648063659668 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005481243133544922 .time for calcul the mask position with numpy : 0.008906126022338867 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005184650421142578 .time for calcul the mask position with numpy : 0.008651256561279297 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0069425106048583984 .time for calcul the mask position with numpy : 0.00851297378540039 nb_pixel_total : 1662 time to create 1 rle with old method : 0.003931522369384766 .Needs to change image size ! time for calcul the mask position with numpy : 0.010030984878540039 nb_pixel_total : 110633 time to create 1 rle with old method : 0.2509734630584717 .time for calcul the mask position with numpy : 0.008991241455078125 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03525996208190918 .time for calcul the mask position with numpy : 0.008875370025634766 nb_pixel_total : 5286 time to create 1 rle with old method : 0.011365890502929688 .time for calcul the mask position with numpy : 0.008629798889160156 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0038709640502929688 .time for calcul the mask position with numpy : 0.009022712707519531 nb_pixel_total : 105533 time to create 1 rle with old method : 0.23637127876281738 .time for calcul the mask position with numpy : 0.00898599624633789 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010156869888305664 .time for calcul the mask position with numpy : 0.008444786071777344 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015091896057128906 .time for calcul the mask position with numpy : 0.00940704345703125 nb_pixel_total : 62627 time to create 1 rle with old method : 0.14054489135742188 .time for calcul the mask position with numpy : 0.008629560470581055 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07302618026733398 .time for calcul the mask position with numpy : 0.008842229843139648 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08407402038574219 .time for calcul the mask position with numpy : 0.008923530578613281 nb_pixel_total : 48775 time to create 1 rle with old method : 0.10775613784790039 .time for calcul the mask position with numpy : 0.04065513610839844 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.2532997131347656 .time for calcul the mask position with numpy : 0.008497476577758789 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005490541458129883 .time for calcul the mask position with numpy : 0.009030342102050781 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005308628082275391 .time for calcul the mask position with numpy : 0.008337736129760742 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007180452346801758 .time for calcul the mask position with numpy : 0.008653402328491211 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0038869380950927734 . About to upload 4 photos upload in portfolio : 20811669 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740500950_1898146 we have uploaded 4 photos in the portfolio 20811669 time of upload the photos Elapsed time : 2.7583537101745605 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 : 21.83493733406067 time spend to save output : 0.00011348724365234375 total time spend for step 1 : 21.83505082130432 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1339572024: ['1003369118', 'temp/1740500933_1898146_1003369118_58171420504d0b5f05a1233b6c515509_658263370.jpg', [, , , , , , , , , , , , , , , ]], 1339572025: ['1003369118', 'temp/1740500933_1898146_1003369118_58171420504d0b5f05a1233b6c515509_6582633790.jpg', [, , , , , , , , , , , , , , , ]], 1339572026: ['1003369118', 'temp/1740500933_1898146_1003369118_58171420504d0b5f05a1233b6c515509_65826337180.jpg', [, , , , , , , , , , , , , , , ]], 1339572027: ['1003369118', 'temp/1740500933_1898146_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 : 1.3422539234161377 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 Tue Feb 25 17:29:16 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 : 0.3451986312866211 time spend to save output : 7.43865966796875e-05 total time spend for step 1 : 0.3452730178833008 step2:rle_unique_nms_with_priority Tue Feb 25 17:29:17 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 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 : 2.046076536178589 create new chi : 4.863739013671875e-05 time to delete rle : 0.03252696990966797 save time : 6.794929504394531e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.3965084552764893 create new chi : 4.696846008300781e-05 time to delete rle : 0.012614011764526367 save time : 9.5367431640625e-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 : 4.6861817836761475 time spend to save output : 0.00010323524475097656 total time spend for step 2 : 4.686285018920898 step3:ventilate_hashtags_in_portfolio Tue Feb 25 17:29:21 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 : 0.48033666610717773 time spend to save output : 6.008148193359375e-05 total time spend for step 3 : 0.48039674758911133 step4:final Tue Feb 25 17:29:22 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 : 0.09598350524902344 time spend to save output : 3.933906555175781e-05 total time spend for step 4 : 0.0960228443145752 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 : 0.012367725372314453 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 , BBBBBFBFBFBFBFBFBFBFBFFFBFFFwe 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 : 1.3811233043670654 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 Tue Feb 25 17:29:23 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 : 10.287760972976685 time spend to save output : 0.0008819103240966797 total time spend for step 1 : 10.288642883300781 step2:thcl Tue Feb 25 17:29:34 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 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 : 0.0006551742553710938 time spend to save output : 3.123283386230469e-05 total time spend for step 2 : 0.0006864070892333984 step3:argmax Tue Feb 25 17:29:34 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 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 : 5.888938903808594e-05 time spend to save output : 2.1696090698242188e-05 total time spend for step 3 : 8.058547973632812e-05 step4:merge_mask_and_thcl Tue Feb 25 17:29:34 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 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 : 0.00013709068298339844 time spend to save output : 1.1920928955078125e-05 total time spend for step 4 : 0.00014901161193847656 step5:rle_unique_nms_with_priority Tue Feb 25 17:29:34 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 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 : 2.0701394081115723 create new chi : 0.006895542144775391 time to delete rle : 0.40607380867004395 save time : 1.1205673217773438e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.1576027870178223 create new chi : 0.006175994873046875 time to delete rle : 0.7262041568756104 save time : 1.621246337890625e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.1847927570343018 create new chi : 0.0068666934967041016 time to delete rle : 0.5380604267120361 save time : 7.62939453125e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.1757192611694336 create new chi : 5.1975250244140625e-05 time to delete rle : 0.3850243091583252 save time : 8.58306884765625e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.6752617359161377 create new chi : 3.5762786865234375e-05 time to delete rle : 0.3900876045227051 save time : 9.298324584960938e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 0.7699198722839355 create new chi : 0.007161617279052734 time to delete rle : 0.4363574981689453 save time : 1.9073486328125e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 0.6766607761383057 create new chi : 3.1948089599609375e-05 time to delete rle : 0.3909294605255127 save time : 8.58306884765625e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.2874913215637207 create new chi : 5.698204040527344e-05 time to delete rle : 0.4475736618041992 save time : 4.1484832763671875e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.8486974239349365 create new chi : 3.147125244140625e-05 time to delete rle : 0.431286096572876 save time : 1.3828277587890625e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 1.2641189098358154 create new chi : 0.0068700313568115234 time to delete rle : 0.46550750732421875 save time : 7.62939453125e-06 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 1.3481154441833496 create new chi : 0.006980180740356445 time to delete rle : 0.4311246871948242 save time : 1.6927719116210938e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 0.925142765045166 create new chi : 2.4557113647460938e-05 time to delete rle : 0.370851993560791 save time : 9.5367431640625e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 0.7430522441864014 create new chi : 0.0068132877349853516 time to delete rle : 0.5956768989562988 save time : 7.3909759521484375e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.5385100841522217 create new chi : 2.6226043701171875e-05 time to delete rle : 0.46930885314941406 save time : 8.58306884765625e-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), 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), 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), 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)} End step rle-unique-nms time spend for datou_step_exec : 27.153666973114014 time spend to save output : 0.00012183189392089844 total time spend for step 5 : 27.153788805007935 step6:ventilate_hashtags_in_portfolio Tue Feb 25 17:30:01 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 ('papier','refus')) 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 ('papier','refus')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 0.16420745849609375 time spend to save output : 8.153915405273438e-05 total time spend for step 6 : 0.16428899765014648 step7:final Tue Feb 25 17:30:01 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 : 0.019605159759521484 time spend to save output : 4.315376281738281e-05 total time spend for step 7 : 0.019648313522338867 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',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',)} new output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',)} [1008921601, 1008921600, 1008922095, 1008922073, 1008922072, 1008921657, 1008921656, 1008921602, 1008922130, 1008922101, 1008922097, 1008922003, 1008922002, 1008921786] Looping around the photos to save general results len do output : 14 /1008921601.Didn't retrieve data . /1008921600.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 . /1008922130.Didn't retrieve data . /1008922101.Didn't retrieve data . /1008922097.Didn't retrieve data . /1008922003.Didn't retrieve data . /1008922002.Didn't retrieve data . /1008921786.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', '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) ('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', '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) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 42 time used for this insertion : 0.017000913619995117 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',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',)} {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('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 : 0.013935089111328125 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:ventilate_hashtags_in_portfolio Tue Feb 25 17:30:01 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 : 0.06964659690856934 time spend to save output : 0.00011396408081054688 total time spend for step 1 : 0.06976056098937988 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 : 0.015347480773925781 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 : 0.16872620582580566 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 Tue Feb 25 17:30:01 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 : 0.008752107620239258 nb_pixel_total : 110633 time to create 1 rle with old method : 0.2511861324310303 time for calcul the mask position with numpy : 0.007315874099731445 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03531289100646973 time for calcul the mask position with numpy : 0.0070188045501708984 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012912511825561523 time for calcul the mask position with numpy : 0.007062673568725586 nb_pixel_total : 1633 time to create 1 rle with old method : 0.003887653350830078 time for calcul the mask position with numpy : 0.007572174072265625 nb_pixel_total : 105533 time to create 1 rle with old method : 0.260758638381958 time for calcul the mask position with numpy : 0.007930517196655273 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010865449905395508 time for calcul the mask position with numpy : 0.00774073600769043 nb_pixel_total : 632 time to create 1 rle with old method : 0.0016703605651855469 time for calcul the mask position with numpy : 0.008144378662109375 nb_pixel_total : 62627 time to create 1 rle with old method : 0.1445310115814209 time for calcul the mask position with numpy : 0.007793903350830078 nb_pixel_total : 33681 time to create 1 rle with old method : 0.08220410346984863 time for calcul the mask position with numpy : 0.009535551071166992 nb_pixel_total : 37724 time to create 1 rle with old method : 0.09597373008728027 time for calcul the mask position with numpy : 0.008227109909057617 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11582803726196289 time for calcul the mask position with numpy : 0.04613900184631348 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.17917203903198242 time for calcul the mask position with numpy : 0.007035493850708008 nb_pixel_total : 2310 time to create 1 rle with old method : 0.005411863327026367 time for calcul the mask position with numpy : 0.007250547409057617 nb_pixel_total : 2256 time to create 1 rle with old method : 0.00568699836730957 time for calcul the mask position with numpy : 0.007444620132446289 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0078122615814208984 time for calcul the mask position with numpy : 0.007794380187988281 nb_pixel_total : 1662 time to create 1 rle with old method : 0.004155874252319336 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 : 1.8628606796264648 time spend to save output : 0.00010633468627929688 total time spend for step 1 : 1.8629670143127441 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1003369118: 'temp/1740501001_1898146_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 : 0.014095306396484375 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 Tue Feb 25 17:30:03 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 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 6.67572021484375e-06 elapsed_time : order_list_meta_photo_and_scores 1.0013580322265625e-05 ??????? elapsed_time : fill_and_build_computed_from_old_data 0.0008776187896728516 elapsed_time : insert_dashboard_record_day_entry 0.029418230056762695 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 ***** False 1629158400.0 `1629186488.0 1629186300.0 `1629186488.0 1629186300.0 `1629186512.0 1629186300.0 `1629186512.0 1629186300.0 `1629189733.0 1629189600.0 `1629189733.0 1629189600.0 `1629189736.0 1629189600.0 list printed: [[0, 1, 2, 3], [4, 5, 6]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.005330801010131836 ***** 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 Qualite : 0.5219450480102605 # 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 Qualite : 0.1657989516453152 # 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 0.9656155109405518 # DISPLAY ALL COLLECTED DATA : {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} time spend for datou_step_exec : 1.0505695343017578 time spend to save output : 9.298324584960938e-05 total time spend for step 1 : 1.0506625175476074 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 : 0.016605854034423828 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 : 1.0693914890289307 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 Tue Feb 25 17:30:08 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 Begin step datou_step_copy_crop batch 1 Loaded 0 chid ids of type : 0 time spend for datou_step_exec : 0.005445241928100586 time spend to save output : 2.6941299438476562e-05 total time spend for step 1 : 0.0054721832275390625 step2:consolidate_hashtags_from_manual_portfolio Tue Feb 25 17:30:08 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 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 : 3.024174213409424 time spend to save output : 0.00012540817260742188 total time spend for step 2 : 3.0242996215820312 step3:rle_unique_nms_with_priority Tue Feb 25 17:30:11 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 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 : 19.77768301963806 create new chi : 5.698204040527344e-05 time to delete rle : 0.3154134750366211 save time : 3.9577484130859375e-05 nb_obj : 0 nb_hashtags : 3 time to prepare the origin masks : 20.77083706855774 create new chi : 5.0067901611328125e-05 time to delete rle : 0.35817790031433105 save time : 7.200241088867188e-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 : 41.48262071609497 time spend to save output : 0.0004100799560546875 total time spend for step 3 : 41.483030796051025 step4:ventilate_hashtags_in_portfolio Tue Feb 25 17:30:53 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 : 0.1886892318725586 time spend to save output : 0.0001285076141357422 total time spend for step 4 : 0.18881773948669434 step5:final Tue Feb 25 17:30:53 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 : 0.023682594299316406 time spend to save output : 6.628036499023438e-05 total time spend for step 5 : 0.02374887466430664 step6:blur_detection Tue Feb 25 17:30:53 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 : 0.00524139404296875 time spend to save output : 4.76837158203125e-05 total time spend for step 6 : 0.0052890777587890625 step7:brightness Tue Feb 25 17:30:53 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 : 0.004835605621337891 time spend to save output : 4.76837158203125e-05 total time spend for step 7 : 0.004883289337158203 step8:send_mail_cod Tue Feb 25 17:30:53 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_25-02-2025_17_30_53.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 .imagette46734941740501053 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 .imagette46734961740501055 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 .imagette46734971740501060 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 .imagette46734981740501064 4673500 change filename to text .change filename to text .imagette46735001740501067 4673501 change filename to text .imagette46735011740501067 4673502 imagette46735021740501067 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 .imagette46735031740501067 4673504 imagette46735041740501070 4673505 imagette46735051740501070 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 .imagette46735061740501070 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 .imagette46735071740501071 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 .imagette46735081740501073 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_25-02-2025_17_30_53.pdf results_COD_P4709558_25-02-2025_17_30_53.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_25-02-2025_17_30_53.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_25-02-2025_17_30_53.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_25-02-2025_17_30_53.pdf','pdf','','0.48','0.009511382621534484') time spend for datou_step_exec : 24.97238302230835 time spend to save output : 7.43865966796875e-05 total time spend for step 8 : 24.97245740890503 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 : 0.17460083961486816 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 Tue Feb 25 17:31:18 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 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 : 2208326713 , rotate : 160 (820, 820) (320, 421, 443, 578) (135, 101, 3) (135, 101) (820, 820, 3) time for calcul the mask position with numpy : 0.0013897418975830078 nb_pixel_total : 6827 time to create 1 rle with old method : 0.018056631088256836 batch 1 Loaded 0 chid ids of type : 0 time for calcul the mask position with numpy : 0.0016260147094726562 nb_pixel_total : 6827 time to create 1 rle with old method : 0.015644073486328125 begin to treate photo :1057314768 add chi : 2208326717 , rotate : 107 (799, 799) (300, 688, 468, 764) (296, 388, 3) (296, 388) (799, 799, 3) time for calcul the mask position with numpy : 0.0018918514251708984 nb_pixel_total : 80916 time to create 1 rle with old method : 0.1761775016784668 batch 1 Loaded 2 chid ids of type : 4021 ++time for calcul the mask position with numpy : 0.002277374267578125 nb_pixel_total : 80840 time to create 1 rle with old method : 0.20291543006896973 time for calcul the mask position with numpy : 0.00485992431640625 nb_pixel_total : 80916 time to create 1 rle with old method : 0.17468643188476562 time for calcul the mask position with numpy : 0.009948968887329102 nb_pixel_total : 4118 time to create 1 rle with old method : 0.009615898132324219 begin to treate photo :1057314766 add chi : 2208326718 , rotate : 85 (693, 693) (2, 98, 69, 418) (349, 96, 3) (349, 96) (693, 693, 3) time for calcul the mask position with numpy : 0.0011718273162841797 nb_pixel_total : 8871 time to create 1 rle with old method : 0.01992058753967285 batch 1 Loaded 3 chid ids of type : 4021 +++time for calcul the mask position with numpy : 0.00298309326171875 nb_pixel_total : 65325 time to create 1 rle with old method : 0.14679765701293945 time for calcul the mask position with numpy : 0.002372264862060547 nb_pixel_total : 98215 time to create 1 rle with old method : 0.22059106826782227 time for calcul the mask position with numpy : 0.0015001296997070312 nb_pixel_total : 6807 time to create 1 rle with old method : 0.014632940292358398 time for calcul the mask position with numpy : 0.0015692710876464844 nb_pixel_total : 8871 time to create 1 rle with old method : 0.01904773712158203 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740501081_1898146 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 1 chid ids of type : 4086 Number RLEs to save : 139 TO DO : save crop sub photo not yet done ! we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740501081_1898146 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 3 chid ids of type : 4086 Number RLEs to save : 728 TO DO : save crop sub photo not yet done ! we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740501082_1898146 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 4 chid ids of type : 4086 Number RLEs to save : 1518 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 4.285614013671875 time spend to save output : 0.00010633468627929688 total time spend for step 1 : 4.285720348358154 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: 0.009521007537841797 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 : 0.014625072479248047 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Tue Feb 25 17:31:23 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 : 0.00035452842712402344 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 : 3.7543301582336426 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 : 4.949828147888184 time spend to save output : 8.0108642578125e-05 total time spend for step 1 : 4.949908256530762 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 : 0.01659250259399414 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 : 0.014983177185058594 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Tue Feb 25 17:31:28 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. 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 : 1740501096.5985336 done ! 1740501097.6381328 {'files': [{'name': 'pca_model.pkl', 'size': 103314, 'last_modified': '2025-02-25T16:31:37.202150', '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 : 8.747474908828735 time spend to save output : 3.3855438232421875e-05 total time spend for step 1 : 8.747508764266968 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 : 0.016446828842163086 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 : 0.014717817306518555 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Tue Feb 25 17:31:37 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 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 : 2025-02-25 17:31:34 create time in s3 : 2025-02-25 16:31:37 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 : 0.0004329681396484375 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 : 4.556926727294922 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 : 8.23906421661377 time spend to save output : 7.700920104980469e-05 total time spend for step 1 : 8.23914122581482 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 : 0.015268802642822266 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 : 0.31431007385253906 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 Tue Feb 25 17:31:46 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 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 : 0.13797259330749512 time spend to save output : 0.00010395050048828125 total time spend for step 1 : 0.1380765438079834 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 [1105701500, 1105701516] Looping around the photos to save general results len do output : 3 /1105703688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1105703689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1105703686Didn'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', '1105701500', None, None, None, None, None, None) ('3990', None, None, None, None, None, None, None, None) ('3990', '6135916', '1105701516', 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 : 0.014246940612792969 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1105703688: [1105701500, 'temp/1740501106_1898146_1105701500_b57a1caec2d74ede6814095fdd28cb27_polygon_blur_2436373819_1.jpg', (108, 300, 16, 138)], 1105703689: [1105701500, 'temp/1740501106_1898146_1105701500_b57a1caec2d74ede6814095fdd28cb27_polygon_blur_2436374262_1.jpg', (47, 300, 91, 247)], 1105703686: [1105701516, 'temp/1740501106_1898146_1105701516_047b0ce16fe5e308d8512c83125c4058_polygon_blur_2436374092_1.jpg', (25, 175, 137, 235)]} 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 : 0.4813675880432129 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 Tue Feb 25 17:31:46 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 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 : 0.06363916397094727 time spend to save output : 3.910064697265625e-05 total time spend for step 1 : 0.06367826461791992 step2:consolidate_hashtags_from_manual_portfolio Tue Feb 25 17:31:47 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 : 1.787322998046875 time spend to save output : 6.365776062011719e-05 total time spend for step 2 : 1.7873866558074951 step3:rle_unique_nms_with_priority Tue Feb 25 17:31:48 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 : 0.03654193878173828 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 : 0.034789085388183594 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 : 0.8990883827209473 time spend to save output : 6.008148193359375e-05 total time spend for step 3 : 0.8991484642028809 step4:ventilate_hashtags_in_portfolio Tue Feb 25 17:31:49 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 : 0.43517589569091797 time spend to save output : 7.128715515136719e-05 total time spend for step 4 : 0.43524718284606934 step5:final Tue Feb 25 17:31:50 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 : 0.021526575088500977 time spend to save output : 2.956390380859375e-05 total time spend for step 5 : 0.02155613899230957 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1114046377: ('0.3133414848207032',), 1114046597: ('0.3133414848207032',)} new output for save of step final : {1114046377: ('0.3133414848207032',), 1114046597: ('0.3133414848207032',)} [1114046377, 1114046597] Looping around the photos to save general results len do output : 2 /1114046377.Didn't retrieve data . /1114046597.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', '1114046377', None, None, None, None, None, None) ('4492', None, None, None, None, None, None, None, None) ('4492', '6549724', '1114046597', 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 : 0.012871980667114258 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 5 output : {1114046377: ('0.3133414848207032',), 1114046597: ('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 : 1.2271089553833008 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 Tue Feb 25 17:31:51 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 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 : 0.052150726318359375 time spend to save output : 5.841255187988281e-05 total time spend for step 1 : 0.05220913887023926 step2:consolidate_hashtags_from_manual_portfolio Tue Feb 25 17:31:51 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 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 : 1.547654151916504 time spend to save output : 3.743171691894531e-05 total time spend for step 2 : 1.5476915836334229 step3:rle_unique_nms_with_priority Tue Feb 25 17:31:53 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 : 0.03635215759277344 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 : 0.034157752990722656 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 : 0.6419515609741211 time spend to save output : 6.4849853515625e-05 total time spend for step 3 : 0.6420164108276367 step4:ventilate_hashtags_in_portfolio Tue Feb 25 17:31:53 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 : 0.8978052139282227 time spend to save output : 9.036064147949219e-05 total time spend for step 4 : 0.8978955745697021 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 : 0.01470041275024414 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 SUCCEEDED #&_#_#&_# #&_#_#&_# 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 : 1109585436 FHTTP Error 404: Not Found can't download the photo : 1109585109 FHTTP Error 404: Not Found can't download the photo : 1109585120 FHTTP Error 404: Not Found can't download the photo : 1109585121 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 : [1109585436, 1109585109, 1109585120, 1109585121] ############################### 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 : 0.00016379356384277344 nb_pixel_total : 6 time to create 1 rle with old method : 4.935264587402344e-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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_) /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/February/25022025/python_test3/output_tests_python-1732.html new path : /proc/1898146/ /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:1950: 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:1951: 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:1957: 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:2141: 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:2142: 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:2148: 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, 143, 147, 152, 156, 160, 164, 168, 172, 176, 181, 185, 189, 193, 197, 201, 205, 209, 213, 217, 221, 225, 230, 249-295, 322-343, 364-391 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/_IntegerGMP.py 407 329 19% 98, 104, 110-118, 134-137, 155-191, 196-212, 215, 218, 222, 226, 248-276, 292-308, 312-314, 317-319, 322-324, 327, 330, 333, 336, 339, 343, 347-356, 359-368, 371-380, 383-392, 395-407, 411-446, 449-450, 453-455, 461-473, 476-491, 494-509, 512-528, 531-542, 546-552, 555-561, 564-575, 578-588, 591-597, 600-605, 611-617, 622, 625, 630-632, 636, 639, 644-653, 658-675, 680-684, 693-708, 711-713, 719-728, 734-738, 744-750, 755-762 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/PublicKey/RSA.py 325 267 18% 96-105, 109, 113, 117-119, 123-125, 129-131, 135-137, 141, 145, 148-150, 153-174, 179, 182, 185, 193, 196-202, 205, 209-210, 213-218, 221-225, 304-368, 376, 379, 382, 385, 388, 391, 394, 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/home/admin/.local/lib/python3.8/site-packages/Crypto/Util/asn1.py 316 251 21% 34-39, 47-49, 52, 55-56, 59, 62-68, 71, 107-141, 147-153, 160-163, 171-180, 187-197, 210-220, 225-246, 289-291, 297-306, 319, 325-341, 400-404, 409, 412, 415, 418, 421, 424, 427, 430-431, 434-435, 446-447, 460, 470-478, 501-507, 512-543, 589, 598, 643-644, 650-664, 680, 686-698, 743-749, 756-757, 772, 778-787, 827-836, 839, 842, 845, 856-869, 887, 892-919, 928-939 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/number.py 223 199 11% 40-47, 53-59, 72-81, 93-98, 111-114, 120-123, 128-136, 147-157, 173-206, 246-339, 359-374, 407-447, 464-483, 489-490, 492-493 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/py3compat.py 82 59 28% 66-110, 114, 116, 118-121, 123, 125-134, 136, 138, 147, 150, 153, 166-171 /home/admin/.local/lib/python3.8/site-packages/Crypto/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/BmpImagePlugin.py 218 179 18% 76-264, 276-284, 291-355, 366, 384, 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/home/admin/.local/lib/python3.8/site-packages/PIL/_version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/apiclient/__init__.py 10 2 80% 7-10 /home/admin/.local/lib/python3.8/site-packages/av/__init__.py 21 1 95% 12 /home/admin/.local/lib/python3.8/site-packages/av/about.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/av/audio/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/codec/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/container/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/av/deprecation.py 36 17 53% 33-34, 37-42, 45-55, 58-68, 77-81 /home/admin/.local/lib/python3.8/site-packages/av/filter/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/sidedata/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/av/video/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/boto3/__init__.py 31 17 45% 35, 61-70, 80-84, 93, 102, 109 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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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/home/admin/.local/lib/python3.8/site-packages/cffi/error.py 19 8 58% 8-15 /home/admin/.local/lib/python3.8/site-packages/cffi/lock.py 10 6 40% 4-7, 11-12 /home/admin/.local/lib/python3.8/site-packages/cffi/model.py 389 160 59% 16, 21, 30-45, 51, 54, 66, 79, 99, 166, 168, 170, 172, 182-183, 186, 189, 196-197, 200, 215, 219, 232, 249-253, 258, 269, 288-290, 304, 314, 318, 359-362, 365-376, 382-394, 400, 405-408, 414, 422-425, 430-462, 467, 471, 495-499, 502-505, 508-509, 512-514, 520-557, 562, 569-572, 584-587, 598-599, 610, 613, 616-617 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/api.py 195 181 7% 62-497, 515, 543-544 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/assets/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/cd.py 189 164 13% 24-50, 63-71, 80-91, 100-112, 120-129, 138-164, 175-244, 253-283, 291-311, 319-338, 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% /home/admin/.local/lib/python3.8/site-packages/colorama/ansi.py 74 8 89% 16, 19, 22, 38, 40, 42, 44, 46 /home/admin/.local/lib/python3.8/site-packages/colorama/ansitowin32.py 131 100 24% 13, 25-26, 29, 35, 38, 41, 44-53, 57-61, 75-104, 114, 117-158, 161-167, 171-174, 183-190, 194-196, 200-202, 206-220, 224-242, 246-257 /home/admin/.local/lib/python3.8/site-packages/colorama/initialise.py 48 32 33% 19-20, 25-48, 52-55, 60-64, 68-71, 75-80 /home/admin/.local/lib/python3.8/site-packages/colorama/win32.py 78 68 13% 11, 17-152 /home/admin/.local/lib/python3.8/site-packages/colorama/winterm.py 119 90 24% 25-34, 37, 40-42, 45-47, 50-58, 61-69, 72-75, 78-83, 86-91, 94-101, 104-109, 115-141, 147-166, 169 /home/admin/.local/lib/python3.8/site-packages/cryptography/__about__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/__init__.py 7 1 86% 18 /home/admin/.local/lib/python3.8/site-packages/cryptography/exceptions.py 37 5 86% 11, 33-34, 61-62 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/_oid.py 122 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/aead.py 143 127 11% 10-19, 28-46, 50-62, 70-85, 97-137, 141-145, 149-158, 162-166, 170-175, 179-187, 199-245, 257-310 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/backend.py 1215 945 22% 189, 200, 219, 229-231, 234-244, 250-264, 269-276, 279-285, 294, 299, 304, 308, 323-327, 334-336, 339-342, 346-349, 357-368, 372, 458, 468-482, 506-514, 519-535, 542, 553-582, 592-602, 605-608, 611-614, 633-638, 644-649, 671-721, 729-780, 783-786, 798-814, 817-820, 823-844, 849-857, 862-863, 868-871, 876-892, 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/home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/cmac.py 46 35 24% 16-17, 27-59, 62-63, 66-73, 76-82, 85-87 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/dh.py 173 143 17% 12, 16-30, 34-35, 40-41, 44-55, 62, 69-104, 108-111, 116-119, 123, 126-141, 154-187, 190-192, 198-212, 215, 223-241, 253-256, 260, 263-278, 288, 295-315 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/dsa.py 119 87 27% 17, 23-35, 46-52, 57-58, 61-68, 75, 82-91, 95, 98-110, 123-139, 142-147, 155, 169-170, 177-185, 189, 192-204, 214-218, 225, 235-236 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ec.py 145 111 23% 20, 26-27, 34-60, 70, 76-81, 87-90, 99-108, 117-122, 127-134, 138, 142, 147-162, 165-179, 182-184, 195, 209-214, 219-226, 230, 234, 237-255, 258-279, 286-302, 312-317 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ed25519.py 70 54 23% 17, 22-23, 30-44, 49-56, 59-77, 82-83, 86-94, 97-117, 125-143, 148-155 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ed448.py 72 54 25% 15, 23-24, 31-45, 50-57, 60-78, 83-84, 87-95, 98-118, 126-144, 149-156 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/hashes.py 45 12 73% 11, 29, 47-53, 65, 79-86 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/hmac.py 46 34 26% 15, 26-48, 52, 55-62, 67-69, 72-79, 82-84 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/poly1305.py 34 23 32% 15, 20-47, 50-54, 57-62, 65-67 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/rsa.py 257 136 47% 37, 46-59, 68-95, 107-162, 172, 180-201, 226-227, 233-234, 242-243, 250-261, 288-289, 306-322, 332-358, 383-384, 395-396, 436, 439-444, 447-451, 494, 520-532, 536, 539, 542-549, 559, 570-571, 581-586 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/utils.py 34 22 35% 11, 15-41, 53, 56 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/x448.py 53 37 30% 15, 22-23, 30-44, 49-56, 61-62, 65-73, 76-79, 87-105, 110-117 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/_conditional.py 74 34 54% 9, 25, 32, 38, 46, 52, 58, 65, 72, 79, 92, 98, 106, 118, 126, 132, 146, 155, 175, 181, 189, 199, 205, 211, 218, 227, 239, 247, 253, 257, 261, 265, 271, 275 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/binding.py 84 16 81% 25-28, 42, 88-99, 110-111, 161, 185, 200 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_asymmetric.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_cipheralgorithm.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_serialization.py 79 35 56% 37-42, 64-67, 83-87, 90-99, 109-116, 126-133, 141-144, 163-168 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/dh.py 112 47 58% 17-19, 24-39, 42-45, 50-54, 58, 62, 66, 71-80, 83-86, 92-96, 100, 104, 109-118, 121-124, 130-134, 138, 142 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/dsa.py 127 57 55% 128-139, 143, 147, 151, 154-158, 161-164, 167, 175-184, 188, 192, 195-199, 202-205, 211, 219-227, 231, 235, 238-242, 245-248, 256-258, 264-266, 270-278, 282-288 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ec.py 216 56 74% 171-184, 315, 321, 327-329, 337-348, 353-361, 364-368, 372, 376, 380, 383-386, 394, 397, 407-417, 422-426, 430, 434, 437-440, 446, 477-480 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ed25519.py 39 14 64% 18-26, 43, 57-65, 69-77, 101 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ed448.py 37 14 62% 15-23, 40, 54-61, 65-73, 103 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/padding.py 46 20 57% 44-57, 69-74, 85-88, 95-101 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/rsa.py 181 91 50% 129-132, 136-143, 156-190, 194-201, 208-214, 221, 229, 237, 254-288, 310, 316, 331, 335, 339, 343, 347, 351, 363-367, 372-375, 386, 402, 416-420, 423, 426-429, 432 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/home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/base.py 21 10 52% 22-24, 32-34, 40-42, 62-64, 70-72 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/pkcs12.py 81 49 40% 44-49, 53, 57, 60-63, 69, 72, 84-110, 114, 118, 122, 125-128, 135, 138-141, 153-155, 163-165, 181-225 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/ssh.py 723 580 20% 39-49, 105-120, 125-130, 138, 143-144, 149-150, 160-165, 170-172, 177-179, 184-187, 192-195, 200-205, 216-218, 222, 226, 230, 234-239, 243, 247, 251-255, 259-261, 275-277, 283-286, 292-308, 314-316, 322-331, 347-351, 357-362, 368-378, 384-391, 397-398, 401-403, 426-432, 438-442, 448-454, 460-464, 470-474, 491-492, 498-502, 508-516, 522-525, 531-541, 556-560, 577-653, 662-730, 773-792, 796, 801, 805, 809, 813, 817, 821, 825, 829, 833, 836-839, 842, 849-873, 882-888, 895-983, 989, 993-1005, 1011-1025, 1030-1045, 1074-1083, 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/home/admin/.local/lib/python3.8/site-packages/cryptography/x509/extensions.py 989 542 45% 52-70, 75, 78, 81, 88-89, 94-95, 105, 116, 121-125, 130-141, 148, 155-158, 161-164, 167, 170, 174, 177, 189-214, 225-226, 236, 243, 251-254, 262-266, 272, 278, 282, 285, 292, 298, 302, 306, 309, 312-315, 318, 321, 330-337, 342, 345-348, 351, 354, 363-370, 375, 378-381, 384, 387, 394-401, 404, 410-413, 419, 423, 427, 434-448, 452, 456, 459, 464-467, 470, 473, 480-483, 487, 490-493, 496, 499, 502, 511-520, 527, 530-533, 536, 539, 548-557, 564, 567-570, 573, 576, 587-636, 639, 646-649, 657-671, 675, 679, 683, 687, 746-768, 771, 778-781, 787, 793, 797, 800, 807-814, 819, 822-825, 828, 831, 842-857, 860, 866-869, 875-882, 886, 892, 901-909, 912, 918-921, 927, 931, 935, 944-949, 952, 958-961, 967, 971, 975, 982-988, 993, 996-999, 1002, 1005, 1012-1015, 1018, 1021, 1024, 1031-1034, 1037, 1040, 1043, 1050-1060, 1065, 1068-1071, 1074, 1077, 1098-1104, 1107, 1110-1113, 1116, 1120, 1123, 1141-1155, 1159, 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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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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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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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/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 /home/admin/.local/lib/python3.8/site-packages/google/auth/metrics.py 48 20 58% 46, 54, 62, 70, 80, 89, 98, 106, 114, 120, 126, 132-135, 149-154 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/__init__.py 20 4 80% 55, 60, 65, 103 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/_http_client.py 42 24 43% 36-38, 42, 46, 50, 80-113 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/_mtls_helper.py 134 107 20% 61-65, 81-88, 109-114, 134-144, 148-186, 190-193, 197-207, 211-222, 241-276, 310-337, 360-365, 401-407 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/mtls.py 35 29 17% 27-39, 53-67, 89-112 /home/admin/.local/lib/python3.8/site-packages/google/auth/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/oauth2/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/google/oauth2/_client.py 126 100 21% 57-69, 85-110, 123-133, 174-217, 259-271, 297-319, 344-367, 396-419, 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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191, 198-199, 215, 230, 261, 279, 293, 309, 327, 335, 343, 350, 360, 364, 368, 389, 393, 397-403, 406-413, 419-421 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/message_factory.py 56 39 30% 49, 78-85, 100-121, 137-157, 176-185 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/pyext/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/pyext/cpp_message.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/reflection.py 13 5 62% 75-78, 95 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/symbol_database.py 44 21 52% 95, 108, 118-120, 130, 148, 169-186 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/text_encoding.py 32 13 59% 73-80, 96-107 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/text_format.py 722 458 37% 79-89, 92, 95, 101, 104, 107, 110, 171-192, 198-202, 226-239, 257-263, 281-287, 302-312, 375-391, 395-407, 410-418, 426-457, 461-512, 516-541, 545-548, 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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, 1624, 1637, 1640, 1643-1645, 1690-1698, 1712, 1726, 1738, 1760-1768, 1787, 1815-1818, 1837-1838, 1913-1923, 1948-1951, 1973, 1998, 2018, 2046-2050, 2076, 2102-2104, 2147-2149, 2163-2165, 2191-2216, 2232-2238, 2262-2277, 2302-2308, 2334-2340, 2353, 2368, 2381, 2384, 2387, 2392-2397, 2401-2414, 2418-2442 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/normalization.py 574 394 31% 14, 16-19, 29-32, 37-46, 64-65, 77-87, 98-99, 111-121, 132, 134-138, 149, 153, 171-187, 203-230, 245, 247-250, 256-257, 260-261, 264-301, 319-384, 433-516, 527-530, 532-565, 570-575, 583, 597-626, 638-669, 675-679, 681-686, 690-700, 703-713, 720-762, 773-861, 898-1040, 1193-1202, 1223-1252, 1262, 1265, 1273-1281, 1286-1288 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/polys.py 731 611 16% 49-70, 104-142, 157, 169, 181, 197, 213, 231-233, 245-248, 263-264, 279-280, 310-311, 338, 369-380, 402-405, 435-438, 459, 480, 511-536, 543, 575-650, 676-678, 721-723, 818-902, 928-957, 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, 2665-2666, 2731-2746, 2771, 2774-2789, 2792, 2795-2796, 2870-2890, 2895-2906, 2909-2934, 2937-2977, 2980, 2983, 3072-3093, 3103-3114, 3117-3177, 3181-3188, 3191, 3194, 3201 /home/admin/.local/lib/python3.8/site-packages/imgaug/random.py 378 202 47% 67, 172, 185, 202-203, 224, 241, 256, 272, 293, 304-305, 318, 336, 348-352, 384, 396-398, 438, 455, 476, 489, 496, 500, 507, 512, 517, 521, 526, 530, 534, 538, 542, 546, 550, 554, 559, 563, 567, 571, 575, 580, 585, 590, 594, 599, 604, 608, 613, 617, 621, 633-643, 654-665, 676-686, 691, 695, 700, 705, 709, 714, 719, 738, 756, 770, 783-785, 798, 811-814, 831, 880-883, 891, 895, 940, 961, 970, 975-978, 985-992, 996-1003, 1022, 1032, 1065-1066, 1080-1082, 1087, 1091, 1154-1156, 1163-1166, 1170-1173, 1196-1198, 1219, 1237-1240, 1260, 1284-1286, 1314-1319, 1324-1325, 1346-1348, 1356, 1373-1376, 1384, 1405-1407, 1411-1431, 1435-1444, 1466, 1477-1478, 1515-1519, 1521, 1550-1561, 1586-1587, 1590-1592, 1595-1596 /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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254-257, 285-293, 341-343, 364-371, 415-425, 464, 469-472, 475, 479-483, 486-495, 509-518, 527, 531, 537, 561, 563, 573, 587, 598-606, 610, 615, 620, 643-647, 654-658, 665-666, 719 /home/admin/.local/lib/python3.8/site-packages/matplotlib/collections.py 835 494 41% 182, 187, 195, 205, 208, 216, 219, 232, 257, 263, 266, 277-280, 300, 305, 328-330, 332-334, 346, 358-359, 362, 365-366, 384-391, 394, 397, 400-405, 434, 443-471, 494, 535, 545-553, 612-617, 635, 638, 649, 652, 677, 680-684, 722-723, 727, 731, 749, 758, 764, 769-775, 777-779, 799, 822, 825, 847-853, 869, 873-887, 890, 894, 901, 906-925, 943, 958-959, 1069-1144, 1170-1173, 1189-1215, 1221-1226, 1238-1244, 1264-1271, 1315-1320, 1323, 1326, 1330-1337, 1408-1412, 1415-1421, 1434-1448, 1451, 1454, 1457, 1460, 1473, 1478, 1541-1549, 1555-1556, 1560-1571, 1575-1580, 1585, 1591, 1598-1603, 1613-1618, 1622, 1626-1635, 1639, 1643-1652, 1656, 1659, 1663, 1680-1683, 1711-1718, 1723-1760, 1764-1765, 1807-1821, 1824-1826, 1836-1846, 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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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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, 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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, 2157-2158, 2164, 2171-2176, 2181-2189, 2198, 2209, 2220-2225, 2234-2239, 2244, 2248, 2284-2292, 2296-2303, 2308, 2315, 2321-2334, 2340-2341, 2344-2429, 2433-2441, 2444-2462, 2489-2502, 2506-2509, 2514-2515, 2518-2604, 2608-2620, 2659-2664, 2669-2676, 2679-2685, 2690-2732, 2752-2753, 2757-2759, 2763, 2767, 2771-2844, 2847-2880, 2895-2896, 2916, 2920-2957, 2960 /home/admin/.local/lib/python3.8/site-packages/matplotlib/transforms.py 1162 372 68% 127-129, 146-155, 219, 238-244, 251, 336, 341, 391, 397-398, 404-405, 411, 421-431, 437-438, 444-445, 451, 462-472, 512, 517, 530-531, 545, 547, 551, 563-566, 574-577, 589-593, 604, 612-617, 635-636, 643-647, 653, 666-670, 764, 774-782, 828, 832, 837, 840, 878, 908-909, 928-929, 952, 980-981, 1000-1004, 1054-1055, 1067, 1095, 1098, 1125, 1129, 1140-1145, 1149, 1153, 1183-1192, 1198-1204, 1207-1212, 1219-1222, 1226-1228, 1235-1238, 1242-1244, 1251-1254, 1258-1260, 1267-1270, 1274-1276, 1401, 1415, 1452, 1458-1462, 1467, 1473, 1502-1503, 1508, 1536, 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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 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tritools.py 77 65 16% 29-30, 44-47, 79-115, 165-190, 220-238, 260-263 /home/admin/.local/lib/python3.8/site-packages/matplotlib/units.py 61 10 84% 66, 117, 122, 132, 150-156, 176 /home/admin/.local/lib/python3.8/site-packages/matplotlib/widgets.py 1888 1585 16% 43-45, 49-51, 55, 59, 63, 76, 80, 91, 107, 133-135, 144-145, 149-150, 194-215, 218-221, 224-228, 231-241, 249, 253, 265-301, 305-315, 326, 330-331, 430-503, 507-527, 531-552, 556-561, 571-586, 603, 703-803, 814-824, 828-835, 839-846, 850, 854-865, 869-906, 910-920, 930, 940, 950-969, 986, 990-991, 1053-1107, 1111-1118, 1121-1143, 1156-1159, 1173-1176, 1190-1196, 1214-1246, 1256-1260, 1268, 1277, 1281, 1287-1305, 1311-1335, 1383-1420, 1424, 1435-1458, 1461-1465, 1468-1501, 1504-1511, 1515-1532, 1536-1548, 1551-1563, 1566, 1569-1575, 1583, 1592, 1596, 1659-1721, 1725-1731, 1734-1753, 1766-1769, 1783-1791, 1797, 1801-1808, 1816-1841, 1849, 1853, 1859-1870, 1888-1919, 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/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, 753, 758-763, 778-781, 784-787, 790, 795-796, 806-852, 933-934, 1015-1046, 1127-1128, 1135, 1165-1206, 1231-1252, 1289-1290, 1300, 1329-1330, 1353-1354, 1380-1401, 1437-1447, 1455-1473, 1583-1594, 1639-1646, 1681-1709, 1717-1724, 1730-1738, 1743-1749, 1797-1803, 1808-1814, 1844-1850, 1878-1918, 1935-1939, 1956-1961, 2009-2011, 2014-2019, 2022-2027, 2105-2112, 2115-2124, 2127-2149, 2171, 2201, 2205-2234, 2237-2242, 2246-2293, 2300-2305, 2310-2342, 2386-2392, 2403-2409, 2414-2431, 2439-2460, 2465-2476, 2481-2500, 2509-2520 /home/admin/.local/lib/python3.8/site-packages/numpy/version.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/oauth2client/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/oauth2client/_helpers.py 98 60 39% 119-133, 139-140, 156-159, 174-179, 194-202, 222-227, 243-246, 250-255, 272-274, 278, 302-307, 323-328, 333-334, 339-341 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_openssl_crypt.py 40 38 5% 18-135 /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/oauth2client/transport.py 89 66 26% 39, 42, 45, 58, 73, 86, 101-107, 123-134, 150-201, 217-251, 279-280 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/__init__.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/cupy.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/dispatch.py 55 33 40% 37-44, 64-69, 79-88, 97-106, 132, 139, 145 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/jax.py 13 8 38% 17-27 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/object_arrays.py 24 20 17% 33-60 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/tensorflow.py 63 50 21% 17-34, 41-56, 65-76, 83-94, 103-106, 113, 120-122, 126-128 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/theano.py 25 18 28% 16-24, 30-42, 47-53 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/torch.py 61 47 23% 22-28, 34, 42-45, 51-95, 100-105, 127-128 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/blas.py 77 72 6% 55-120, 168-243 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/contract.py 315 279 11% 30-44, 48-71, 75-87, 196-330, 337-353, 358, 365-366, 373-374, 470-507, 511, 518-527, 536-600, 611-626, 634-647, 657-667, 674-678, 681-686, 691-693, 710-719, 735-771, 774-778, 781-787, 797, 861-882 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/helpers.py 70 58 17% 42-50, 76-79, 125-134, 168-173, 228-283 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/parser.py 124 106 15% 32, 46, 64-66, 77-84, 97-99, 113-119, 137-138, 154, 183-186, 199-206, 212-243, 272-356 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/path_random.py 165 131 21% 18-25, 87-101, 107, 111, 116-139, 144-155, 158-160, 163, 166-204, 208-209, 247-283, 289-304, 310-319, 355-359, 367-370, 376-378, 384-385 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/paths.py 440 368 16% 51-56, 60, 71-77, 88-97, 130-138, 146-149, 183-236, 243, 247, 257, 267, 274, 312-319, 323, 352-448, 452-453, 462-472, 476-482, 486-496, 502-505, 514-615, 657-661, 700-719, 749-767, 778, 786-794, 809-816, 825-832, 842, 853-865, 903-916, 955-1053, 1057-1058, 1076-1077, 1092-1095, 1117, 1125-1129 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/sharing.py 96 58 40% 26, 32, 36, 40-43, 68-74, 81, 88-90, 98-103, 112-119, 130-139, 150-168, 185-190, 196-201 /home/admin/.local/lib/python3.8/site-packages/packaging/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/packaging/_structures.py 36 16 56% 8, 11, 14, 17, 20, 23, 26, 29, 37, 40, 43, 46, 49, 52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/packaging/version.py 163 65 60% 69, 76, 81-84, 87-90, 93-96, 99-102, 105-108, 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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 /home/admin/.local/lib/python3.8/site-packages/pandas/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/extensions/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/indexers/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/interchange/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/types/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/arrays/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/compat/__init__.py 33 14 58% 41-44, 56, 68, 80, 92, 104, 118, 131, 148-154 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/_constants.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/compat/_optional.py 49 29 41% 71-88, 145, 149-173 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/compressors.py 27 12 56% 16-17, 30-41, 54, 69 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/numpy/__init__.py 18 2 89% 19, 25 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/numpy/function.py 161 57 65% 68-86, 99-103, 113-115, 125-127, 158-164, 175, 180, 192-201, 221-226, 325-330, 345-351, 365-371, 388-391 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/pickle_compat.py 93 66 29% 27-57, 147-149, 157-174, 181-190, 195-196, 209-220, 233-234, 244-249 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/pyarrow.py 17 6 65% 17-22 /home/admin/.local/lib/python3.8/site-packages/pandas/core/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/_numba/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/_numba/executor.py 18 10 44% 41-59 /home/admin/.local/lib/python3.8/site-packages/pandas/core/accessor.py 84 27 68% 28, 34, 44-46, 55, 58, 61, 96, 99, 112, 224-230, 306-319, 324-326, 331-333, 338-340 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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, 189, 199, 210, 226-227, 275-284, 310-327, 338, 356, 380-384, 391, 404, 410-412, 416, 420, 472, 478, 482, 486, 492, 509, 517 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/cast.py 756 615 19% 102-103, 134, 151, 172-179, 194-210, 222-236, 245-251, 256, 261, 269-317, 324, 331, 350-422, 437-445, 475-492, 512-527, 532, 537, 545-551, 553, 556, 609-611, 635-640, 649-652, 658, 662-739, 760-764, 779-781, 795-873, 888, 926-944, 972-978, 990-995, 1000-1007, 1051-1168, 1199, 1227-1254, 1273-1294, 1326-1348, 1369-1385, 1407-1410, 1416, 1421, 1426, 1447, 1454, 1460-1465, 1469, 1471, 1477-1479, 1487-1509, 1531-1560, 1566-1570, 1636-1707, 1723-1747, 1767-1910, 1919-1921 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/common.py 323 197 39% 82-89, 105-109, 128-139, 186, 231-234, 265-272, 306, 341-348, 383, 413-419, 451-457, 489-495, 537-548, 579-587, 589, 595-598, 646, 760, 809, 864, 900-906, 944-953, 987, 1025, 1060-1070, 1112, 1115-1124, 1165, 1202, 1286, 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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/core/window/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/common.py 81 72 11% 18-146, 150-161, 166-168 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/doc.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/ewm.py 227 170 25% 18-19, 70-94, 118-121, 347-394, 409, 415, 445, 492, 517-546, 569-600, 621-630, 651-663, 700-735, 771-812, 825-830, 843-847, 868-892, 898, 901, 904, 912, 921, 924, 970-1012 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/expanding.py 74 23 69% 17-18, 125, 137, 171, 186, 209, 239, 266, 293, 320, 347, 406, 466, 508, 526, 566, 602, 676, 719, 789, 812-816 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/numba_.py 146 129 12% 49-75, 111-173, 208-236, 244-257, 293-349 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/online.py 52 43 17% 32-86, 91-99, 102-114, 117-118 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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, 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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, 666-668, 673-678, 684-687, 690-697, 702-721, 725-732, 738-751, 761-765, 769, 772, 775, 779-789, 795-799, 809-825, 834-843, 846-890, 893-895, 906-915, 918-956, 961-996, 999-1005, 1026, 1044-1058, 1094-1109, 1131-1135, 1159-1194, 1205-1210, 1221-1244, 1292-1330, 1349-1360, 1363-1364, 1367-1442, 1447-1456, 1465-1512, 1520-1611, 1614, 1619-1625, 1636-1638, 1642-1653, 1658-1684, 1721-1754, 1759-1779, 1783-1788, 1796-1803, 1812-1817, 1824-1833, 1839-1846, 1857-1859, 1862-1865, 1879-1912, 1921-1946, 1954-1971, 1979-1983, 1994-2019, 2023-2026, 2060-2061, 2083-2123, 2181, 2202-2224, 2238-2240 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/info.py 356 179 50% 28, 315, 342-346, 353-355, 412, 416-427, 438, 451-452, 456, 468, 480, 485, 490, 494-495, 505-511, 524-525, 535-545, 549, 553, 557-559, 570-571, 581-585, 615-619, 624, 629, 634, 639, 642-644, 647-650, 656-667, 692-695, 701-707, 710-713, 730, 735, 740, 745, 750, 754, 758, 762, 766-769, 783, 786-791, 795-797, 806, 811, 816, 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, 136-138, 143-146, 156-158, 173-182, 235-248, 272-307, 389-462, 467-476, 557-579, 582, 587-589, 593, 596, 599, 602, 606-610, 619-624, 627, 630-631, 634, 642, 666-674, 679, 685-686, 699-731, 737-739, 746-748, 766-770, 785-791, 839-866, 894-898, 935-938, 983-1059, 1113-1116, 1154-1188, 1249-1259, 1309-1373, 1406-1413, 1426-1430, 1470-1489, 1493-1505, 1509-1515, 1545-1574, 1584-1613, 1619-1620, 1625-1630, 1643-1721, 1746-1783, 1786-1788, 1792-1806, 1811-1827, 1867-1894, 1898-1910, 1913-1914, 1918-1939, 1977-2001, 2006, 2010, 2014-2016, 2019-2022, 2031, 2037, 2042-2045, 2053-2092, 2096, 2100, 2104, 2109, 2114, 2117, 2125-2130, 2133, 2136-2141, 2146-2160, 2164-2167, 2176-2201, 2205-2207, 2211, 2215-2223, 2230-2231, 2239, 2254-2257, 2260, 2296-2310, 2314, 2318, 2321-2326, 2335, 2341-2348, 2352, 2359-2384, 2388, 2393-2403, 2407, 2411, 2415, 2419, 2424, 2428-2435, 2455-2537, 2541-2544, 2553-2555, 2559, 2563, 2567, 2571, 2605-2611, 2615, 2620-2627, 2631, 2635-2642, 2646-2647, 2650-2651, 2655, 2659, 2663, 2667, 2671, 2675, 2679, 2690, 2694, 2698, 2702-2704, 2714-2718, 2727, 2732, 2743-2747, 2759, 2765, 2768-2811, 2817-2823, 2830, 2834-2835, 2839-2842, 2846, 2850-2880, 2885-2892, 2897-2917, 2920-2942, 2947-2963, 2970-3003, 3008-3012, 3021-3102, 3113-3116, 3125-3128, 3132-3135, 3145-3168, 3178-3203, 3207-3229, 3282-3288, 3292, 3296-3306, 3314-3317, 3321-3347, 3355, 3364-3372, 3377, 3382, 3386, 3391, 3395, 3399, 3403, 3408, 3412, 3417, 3427-3436, 3441, 3445, 3449-3450, 3461, 3472-3474, 3478-3487, 3491-3499, 3503-3506, 3517-3528, 3536-3607, 3639-3689, 3712-3727, 3732, 3739-3770, 3804-4002, 4015-4070, 4075-4125, 4136-4156, 4166-4182, 4196-4225, 4247, 4255, 4280-4321, 4327-4375, 4398-4424, 4428-4482, 4495, 4500-4502, 4512-4581, 4594, 4598, 4602-4605, 4614-4629, 4640-4646, 4660, 4664, 4668-4674, 4679-4709, 4712, 4725, 4728-4737, 4746-4754, 4760-4777, 4785-4786, 4793, 4798, 4814-4836, 4840-4900, 4906-4925, 4938-4998, 5017-5029, 5051-5067, 5071-5075, 5079-5082, 5090-5092, 5109-5117, 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, 2016-2023, 2035-2044, 2047, 2050-2052, 2055-2071, 2074-2076, 2079-2082, 2090-2133, 2136-2167, 2182, 2186-2194, 2197-2214, 2228-2245, 2266-2293, 2296-2299, 2353-2379, 2382-2385, 2388, 2391-2392, 2402-2411, 2444-2445 /home/admin/.local/lib/python3.8/site-packages/pandas/io/stata.py 1590 1368 14% 84, 280-397, 412-505, 577-664, 682-692, 697-736, 752-788, 811-819, 897-900, 912, 924, 927, 930, 933, 941-953, 973-1051, 1134-1169, 1175-1176, 1182-1207, 1211-1212, 1220-1221, 1231-1239, 1245-1248, 1251, 1254, 1257, 1260, 1263, 1266, 1269, 1272, 1275, 1278, 1284-1293, 1297-1348, 1354-1377, 1381-1382, 1386-1395, 1399-1405, 1408-1420, 1423-1426, 1429-1438, 1441-1450, 1453-1462, 1465-1537, 1541-1553, 1556-1558, 1562-1579, 1582-1636, 1639-1667, 1670-1671, 1686-1688, 1702-1847, 1851-1894, 1897-1904, 1907-1936, 1948-2013, 2020-2021, 2028-2029, 2039-2040, 2050-2053, 2072-2090, 2094-2099, 2106-2108, 2115-2133, 2137-2147, 2167-2183, 2205-2228, 2336-2357, 2363, 2369, 2378-2402, 2409-2444, 2452-2461, 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, 1632-1634, 1638-1640, 1647-1659, 1666-1675, 1679-1696, 1736-1783, 1808-1859, 1864, 1877-1907, 1918, 1981-1984, 2000, 2013, 2025, 2060-2072, 2111-2121, 2155, 2176-2203, 2218, 2237-2259, 2272, 2290, 2304, 2317, 2327-2337, 2406, 2409 /home/admin/.local/lib/python3.8/site-packages/psutil/_common.py 442 251 43% 29-30, 33-34, 39, 131-133, 144-145, 157, 161-162, 278-279, 282-283, 295-304, 307, 320-332, 340-350, 360-367, 377-384, 412, 447-457, 462, 466-469, 481-488, 496-503, 509-517, 524-545, 552-553, 565-566, 576-590, 605-606, 623-628, 634-639, 645-678, 682-690, 694-695, 703-704, 721-727, 739-747, 757-760, 770-780, 785-798, 803-832, 836-842, 846 /home/admin/.local/lib/python3.8/site-packages/psutil/_compat.py 243 215 12% 27, 30-41, 57-119, 132-272, 278-324, 330-345 /home/admin/.local/lib/python3.8/site-packages/psutil/_pslinux.py 1130 874 23% 56, 113, 121-124, 217-232, 239-245, 258-264, 298-305, 310-313, 344-371, 390-492, 498-546, 592-616, 622-652, 657-672, 683-727, 778-797, 800-813, 832-868, 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/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, 1184-1192, 1196-1209, 1215-1242, 1249-1276, 1280-1298, 1303-1332, 1336-1416, 1420-1424, 1434-1438, 1442-1545, 1550-1571, 1584-1593, 1598-1630, 1639-1642, 1655-1687, 1692-1697, 1702-1781, 1788-1791, 1805-1839, 1844-1874, 1878-1883, 1887-1890, 1896-1999, 2003-2058, 2062-2065, 2240-2249, 2397-2406, 2631-2640, 2799-2820, 2996-3017, 3179-3188, 3372-3381, 3577-3608, 3708-3724, 3743-3862 /home/admin/.local/lib/python3.8/site-packages/seaborn/cm.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/crayons.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/xkcd_rgb.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/distributions.py 923 859 7% 109, 118, 124-126, 132, 140-155, 164-176, 180-186, 192-200, 204-210, 215-268, 284-329, 353-713, 728-873, 889-1031, 1050-1215, 1221-1279, 1285-1322, 1328-1358, 1391-1456, 1618-1754, 1922-1956, 2044-2082, 2147-2302, 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, 619-621, 624-638, 643-651, 666, 672, 683-728, 774-780, 790-857, 861-899, 906-919, 939-949, 974-986, 990-1004, 1033-1055, 1058-1060, 1065-1085, 1100-1150, 1153-1207, 1214-1235, 1402-1408 /home/admin/.local/lib/python3.8/site-packages/seaborn/miscplot.py 27 20 26% 20-30, 35-48 /home/admin/.local/lib/python3.8/site-packages/seaborn/palettes.py 231 199 14% 64-67, 71-72, 76-77, 81-90, 145-226, 287-297, 359-371, 431-454, 459-467, 543-548, 624-629, 702-709, 729-735, 761-762, 789-790, 905-942, 948-977, 1021-1038 /home/admin/.local/lib/python3.8/site-packages/seaborn/rcmod.py 113 83 27% 113-117, 122, 127, 132-135, 174-298, 330-331, 378-443, 483-484, 489-491, 494, 497-501, 548-556 /home/admin/.local/lib/python3.8/site-packages/seaborn/regression.py 319 271 15% 13-14, 36-56, 60-66, 69, 86-136, 141-153, 158-188, 193-229, 233-248, 252-265, 269-290, 294-296, 300-317, 321-331, 335-340, 345-376, 385-408, 413-425, 576-636, 826-839, 1071-1096 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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, 552-554, 557, 583-584, 587, 599, 619-621, 639-640, 662-665, 697-702, 724, 750, 761, 767, 784, 800, 816, 840-857 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_affinity_propagation.py 142 119 16% 22-32, 119-254, 370-377, 385, 388, 409-434, 450-463, 485 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_agglomerative.py 337 242 28% 42-80, 90-131, 221, 228, 240-241, 245-342, 423-603, 608-609, 613-614, 618-619, 658, 838, 842, 848, 852, 857, 864-866, 874-880, 888-889, 907, 910, 920-924, 946, 1077-1082, 1098-1105, 1109 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_bicluster.py 178 145 19% 35-47, 58-69, 74-83, 93-100, 103-105, 119-126, 133-164, 167-179, 291, 301-315, 444-454, 457-481, 486-520, 532-540, 544-546 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_birch.py 231 194 16% 27-37, 50-89, 140-152, 155-164, 171-175, 179-242, 281-290, 293-297, 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, 413-450, 478-488, 494-535, 539-578, 583-605, 616-649, 684-695, 729, 975, 986-987, 1027-1028, 1031-1069, 1096-1097, 1100, 1103, 1129, 1141-1166, 1192-1193, 1201-1225, 1252, 1270-1276, 1290, 1294-1363, 1582, 1593 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_theil_sen.py 113 89 21% 57-74, 112-128, 147, 178-193, 298-306, 309-343, 359-400 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_isomap.py 54 38 30% 131-141, 144-169, 190-193, 211-212, 229-230, 254-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_locally_linear.py 225 201 11% 49-73, 108-114, 159-189, 292-521, 644-655, 658-665, 688-689, 705-706, 725-734 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_mds.py 111 89 20% 71-132, 234-276, 379-387, 390, 398, 418-419, 439-461 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_spectral_embedding.py 179 148 17% 52-72, 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, 861-862, 880-886, 910-911, 967-978, 1004-1005, 1033-1041, 1067-1075, 1101-1108, 1136-1142, 1180-1191, 1241-1251, 1296-1298, 1342, 1347, 1354-1373, 1379-1405, 1422-1434, 1443-1470, 1594-1635, 1747-1790, 1845, 1937-1954 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/__init__.py 32 1 97% 37 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py 342 251 27% 96-116, 127-136, 141-142, 160-184, 244-265, 268, 274-306, 310-314, 380-386, 390-406, 421-429, 433, 437, 450, 473-489, 510-511, 514-523, 539-540, 556-557, 573-574, 590-591, 607-608, 624-625, 631-639, 643-644, 704, 708-721, 747-892, 896-968, 1278-1284, 1288, 1608-1611, 1619 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_split.py 467 353 24% 78-83, 92-95, 99, 106, 155-161, 183-185, 235, 238-245, 262-264, 273-298, 324-333, 354, 428, 432-444, 499, 502-536, 562, 636, 640-690, 693-695, 731-732, 831-834, 859-887, 934-944, 968-971, 997, 1057, 1060-1075, 1099-1102, 1128, 1156-1169, 1195-1202, 1226-1229, 1232, 1284, 1340, 1350-1354, 1386-1388, 1413, 1416, 1484-1489, 1492-1503, 1575-1580, 1583-1594, 1626, 1691-1696, 1699-1757, 1793-1794, 1803-1864, 1906-1909, 1933-1937, 1941-1945, 1966, 1972, 1993, 2017-2018, 2058-2073, 2168-2199, 2211-2241, 2250-2252 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py 380 335 12% 231-279, 288-304, 309-313, 438-446, 543-657, 666-709, 838-892, 937-964, 980-1021, 1039-1045, 1165-1182, 1189-1197, 1202-1209, 1353-1417, 1444-1476, 1483-1518, 1625-1645, 1671 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/__init__.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_base.py 443 394 11% 61-66, 86-111, 135-139, 160-197, 223-248, 274-296, 307-315, 318-358, 361-525, 529, 538, 547, 581-594, 649-765, 816-846, 855, 889-901, 978-1083, 1139-1170 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_classification.py 158 136 14% 153-159, 179, 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% 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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/tensorflow_hub/module_attachment_pb2.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_def_pb2.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_impl.py 17 6 65% 46, 50-52, 57, 62 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_spec.py 39 20 49% 40, 74-78, 83, 88, 109, 129, 162-171, 185, 200 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/module_v2.py 20 5 75% 91, 96, 99-103 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/native_module.py 440 365 17% 50-54, 58-82, 124, 127-131, 134-136, 139-151, 212-233, 262-278, 325-328, 348-354, 359, 362-363, 366-367, 370-371, 374-380, 383, 386-387, 402-411, 428-451, 454-468, 483-532, 536-632, 636-643, 648, 653-654, 669-670, 676-688, 693-707, 719, 727-732, 750-759, 776-816, 821-826, 831-835, 841, 854-863, 921-922, 945-947, 951-959, 963-967, 983-1017, 1036-1089, 1094-1111, 1116-1128, 1133-1153, 1158-1173, 1177-1178 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/registry.py 21 5 76% 22-23, 41, 52-54 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/resolver.py 199 100 50% 132-135, 145-156, 164-174, 178, 190-199, 204, 209, 219-227, 249-252, 257-258, 268-273, 278-283, 302-335, 380-426, 436-437, 442-445, 462, 476, 485, 488-490, 506-510, 514, 518-521 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/saved_model_lib.py 205 160 22% 53, 60, 66-75, 79, 86-91, 107-119, 123-126, 133-138, 148, 155-161, 180-192, 197-198, 203-208, 223-240, 263-321, 359, 363-372, 375, 379-382, 386, 390, 394, 405-411, 415-423, 426-429, 432-437, 440-443, 451-458, 463-467 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/saved_model_module.py 16 9 44% 46-49, 86-93 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tensor_info.py 134 107 20% 35-41, 45, 50-56, 61-67, 75, 80, 85, 89, 93-97, 102-115, 132, 139-144, 161-185, 202-206, 223-243, 262-281, 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, 744, 768, 772, 789, 829-833, 896, 949-954 /home/admin/.local/lib/python3.8/site-packages/torch/_classes.py 25 11 56% 6-7, 10-13, 22-24, 28, 48 /home/admin/.local/lib/python3.8/site-packages/torch/_jit_internal.py 591 404 32% 35, 41-43, 59-111, 141-161, 168-174, 227-241, 247-258, 275-289, 297-311, 331-381, 391-404, 414-423, 426-427, 435, 550-556, 564, 567, 639-660, 668-674, 681-684, 688-689, 693, 696, 700-704, 707-710, 738-739, 748-752, 760, 763-766, 780, 783, 832, 840-855, 859-865, 870-875, 880-885, 890-893, 898-910, 913-919, 926-932, 935-940, 943, 990, 993, 998-1001, 1005, 1011, 1016, 1027-1028, 1035, 1042, 1049-1051, 1055-1070, 1075-1080, 1084-1087, 1091-1096, 1099-1101, 1104-1110, 1118, 1122, 1126-1133, 1137-1138, 1148-1206, 1210-1229, 1234-1235, 1238-1257, 1267-1270 /home/admin/.local/lib/python3.8/site-packages/torch/_linalg_utils.py 49 35 29% 13-19, 26-29, 38-42, 50-52, 58-59, 65, 71, 77, 83-88, 94-101, 106 /home/admin/.local/lib/python3.8/site-packages/torch/_lobpcg.py 424 387 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523-525, 529-540, 550, 558, 561, 564-568, 575-577 /home/admin/.local/lib/python3.8/site-packages/torch/_utils_internal.py 24 6 75% 15, 18, 27, 31-33 /home/admin/.local/lib/python3.8/site-packages/torch/_vmap_internals.py 85 67 21% 15-21, 24-26, 31-35, 41-85, 92-108, 116-124, 128-132, 138-144, 250-257, 265-278 /home/admin/.local/lib/python3.8/site-packages/torch/amp/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/amp/autocast_mode.py 100 89 11% 9-14, 180-222, 225-248, 251-271, 274-276 /home/admin/.local/lib/python3.8/site-packages/torch/ao/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/dynamic/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/dynamic/linear.py 77 54 30% 20-36, 39, 42, 47, 50, 53-54, 58-73, 78, 81, 84, 88-89, 97-136 /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/linear.py 128 92 28% 11-22, 25, 30-36, 42, 45, 48-50, 54-61, 66-67, 71-73, 76, 87-105, 109, 112, 117, 120, 123-125, 129-141, 146, 149, 152, 156-157, 167-207 /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/utils.py 25 12 52% 4, 21-26, 29, 32-34, 38 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/__init__.py 14 2 86% 20-21 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fake_quantize.py 172 100 42% 22, 25, 28, 31, 48-53, 57, 61, 65, 69, 73, 77, 118-152, 156, 159-178, 182, 192-194, 200-224, 235-241, 246, 250, 280-285, 289, 293, 309, 445-451, 461-462, 472-473, 483-484, 494-495 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fuse_modules.py 61 47 23% 16-20, 24-30, 45-67, 70-82, 85-95, 151, 162 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fuser_method_mappings.py 96 68 29% 26-45, 63-91, 108-119, 135-141, 149, 175-181, 190, 195, 200-201, 244-252, 260-270 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/observer.py 535 396 26% 26-29, 32, 35, 38-40, 81-82, 102-103, 107, 111, 172-202, 216-223, 250-253, 272-327, 331, 422-439, 446-455, 460, 464, 469-470, 528-529, 540-554, 597-615, 620, 623-648, 652, 655, 667-703, 724, 737-738, 781-791, 794-817, 862-877, 890-893, 900-943, 953-1006, 1017-1032, 1045-1068, 1071-1127, 1131-1147, 1150-1154, 1166-1187, 1220-1226, 1229, 1233, 1253-1260, 1263, 1267, 1284-1285, 1288-1289, 1293, 1297, 1315-1317, 1320, 1324, 1340, 1343, 1347, 1351-1356, 1360, 1368-1374, 1382-1393, 1402-1422 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/qconfig.py 141 96 32% 66, 89-93, 197-213, 270-315, 339, 364-372, 375-383, 390-405, 427-451, 458-482, 489-495, 498 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quant_type.py 9 2 78% 13-19 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantization_mappings.py 83 38 54% 177-181, 186, 191, 196-199, 204, 213-222, 230-237, 242, 249-252, 257, 262, 268-274, 280-289, 293, 299-302, 311, 314 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantize.py 251 219 13% 32, 56-72, 92-96, 101, 106, 109-116, 135-203, 206, 224-229, 262-285, 290-306, 316-322, 341-350, 382-435, 452-463, 478-485, 516-524, 542-564, 577-614, 624-631 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantize_jit.py 82 64 22% 8-9, 12-13, 20, 29, 38-45, 48-63, 66-67, 70-71, 75-94, 97-98, 101-102, 107-119, 171-172, 212-213 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/stubs.py 30 17 43% 13-15, 18, 30-32, 35, 54-59, 62-64 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/utils.py 147 113 23% 100-103, 106-108, 111, 115, 123, 126-151, 165-170, 173-175, 178-180, 186, 193-195, 202, 208, 214, 220, 226-228, 239-243, 246-264, 271-295, 304-348, 355-359, 367-372 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/__init__.py 88 60 32% 32-74, 78-82, 150-173, 238-276, 295, 299, 308, 324-326 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/anomaly_mode.py 20 8 60% 74-75, 80, 83, 102-103, 106, 109 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/forward_ad.py 42 25 40% 21-26, 37-43, 68-75, 103-111, 147, 150, 153 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/function.py 191 119 38% 58, 104-109, 144, 147, 182, 226, 235-239, 246-253, 257, 308-309, 315, 340, 366, 391, 398-434, 449-450, 456-457, 462-475, 485-487, 493-511, 523-537, 558-563, 566-571, 574-576, 581-585, 588-589, 593-594, 597, 600, 603, 606 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/functional.py 329 308 6% 9-14, 19-36, 44-53, 63-75, 81-87, 92-103, 109-135, 142-159, 168-203, 266-293, 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1295-1306, 1310, 1386-1396 /home/admin/.local/lib/python3.8/site-packages/torch/fx/graph_module.py 355 283 20% 40-52, 57-59, 62-64, 70-71, 76-80, 84-89, 93-96, 103-105, 112-113, 119-124, 136-178, 183-205, 210-224, 228-229, 243-260, 263-277, 301-309, 336-379, 392, 401-404, 418-468, 493-510, 531-553, 570-612, 620-622, 631-656, 661-667, 670-679, 689-693, 699-701, 704, 707-708, 711-713 /home/admin/.local/lib/python3.8/site-packages/torch/fx/immutable_collections.py 27 5 81% 14, 37, 40, 43, 46 /home/admin/.local/lib/python3.8/site-packages/torch/fx/interpreter.py 144 96 33% 67-89, 108-133, 149-152, 173-185, 203-204, 221-224, 242-246, 266-269, 287, 301-307, 321-325, 338-343, 387-399, 415-417, 433-434, 439-441, 446, 454-459 /home/admin/.local/lib/python3.8/site-packages/torch/fx/node.py 246 180 27% 12, 36-43, 54-62, 66-71, 74-92, 153-200, 211, 222, 237-244, 255, 258-259, 271, 282, 294, 305, 319, 332-334, 347-349, 359, 363, 369-380, 383-385, 394-407, 441-461, 483-503, 515-533, 562-569, 582-589, 597-598, 605-616 /home/admin/.local/lib/python3.8/site-packages/torch/fx/operator_schemas.py 221 176 20% 14, 30, 34, 43-45, 61, 64-84, 88-120, 138-154, 158-182, 186-230, 259-328, 353-367, 392-408 /home/admin/.local/lib/python3.8/site-packages/torch/fx/proxy.py 223 134 40% 40-43, 47, 63-84, 94-105, 115-151, 160, 169, 184, 191-192, 196, 234-238, 241, 246, 249, 252-260, 263-284, 288, 291, 297-319, 327-330, 336-338, 341, 352-355, 358, 362, 365, 368, 372, 375, 378, 384-386, 396-397 /home/admin/.local/lib/python3.8/site-packages/torch/fx/subgraph_rewriter.py 152 133 12% 20-31, 41-42, 49-86, 90-132, 250-428 /home/admin/.local/lib/python3.8/site-packages/torch/hub.py 304 250 18% 20-55, 83-90, 94-98, 103, 108-110, 122-140, 144-145, 150-169, 176-241, 244-290, 294-295, 299-304, 308-322, 336-341, 352, 394-407, 446-458, 530-541, 563-573, 591-633, 640-643, 647-660, 699-731 /home/admin/.local/lib/python3.8/site-packages/torch/jit/__init__.py 52 16 69% 75, 130, 194, 215-217, 220, 223, 230-235, 242, 248, 253 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_async.py 10 2 80% 84, 96 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_builtins.py 42 1 98% 163 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_check.py 87 75 14% 63-82, 85-112, 125-132, 140-181, 195-240 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_decomposition_utils.py 6 3 50% 5-8 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_freeze.py 29 23 21% 101-121, 167-173, 207-220 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_fuser.py 73 61 16% 12-17, 30-63, 69-74, 77, 80-107, 140 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_ir_utils.py 14 7 50% 6-8, 11-12, 15, 18 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_monkeytype_config.py 102 75 26% 15-18, 24-32, 38-53, 61-66, 69, 73-149, 155, 163, 179-183 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_recursive.py 501 443 12% 39-47, 50-52, 55-66, 70-79, 82-93, 102-107, 118, 127-330, 347-362, 368-375, 378-384, 399-414, 423-435, 453-458, 469-595, 602-608, 611-612, 616-638, 643-651, 654-657, 660-669, 672-685, 693-740, 747-776, 784-797, 809-824, 827-845, 851, 857-867, 870-877, 886-910 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_script.py 595 404 32% 62, 71-72, 151, 155, 161-162, 182, 185, 188, 191, 194, 197, 200-205, 208, 211-213, 218-225, 228-229, 232, 244-248, 254, 272, 279-280, 292-318, 326, 334-350, 355, 358, 366-379, 427-434, 437-443, 446-452, 457-461, 464, 467-470, 475, 496, 501-503, 506-523, 526-545, 548, 559-561, 592-598, 614-620, 624-633, 642-665, 673, 682, 691, 705-706, 714, 728, 731, 734, 737, 740, 743, 747-749, 760-761, 764-785, 788-814, 817, 820, 827-832, 835, 838, 841, 844, 849-854, 860-865, 870-874, 943, 959-969, 972-1002, 1006-1007, 1022, 1036, 1239-1351, 1359-1361, 1370-1389, 1394-1416, 1420-1422, 1430-1457, 1461-1466, 1473-1475, 1480-1483, 1486, 1489-1503, 1508-1509, 1512-1531, 1536, 1539, 1542, 1545-1570, 1573, 1577-1578 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_serialization.py 70 57 19% 78-84, 150-169, 173-184, 188-194, 198-209, 251-261, 285-291 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_state.py 63 35 44% 27-37, 47, 51, 66-67, 71-74, 78, 82-83, 98-102, 105, 108-114, 118-119 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_trace.py 453 383 15% 34-54, 61-72, 84-92, 95-140, 144-160, 173-185, 222-277, 281-283, 287, 292-305, 321-535, 554-559, 563-577, 581-584, 734-830, 922-1005, 1014, 1023-1087, 1090, 1093-1095, 1098-1100, 1103, 1106, 1119, 1129-1130, 1171-1176 /home/admin/.local/lib/python3.8/site-packages/torch/jit/annotations.py 254 213 16% 34-37, 54-56, 59-63, 66-91, 97, 101-109, 113-127, 132-144, 154-170, 175-232, 245-250, 255-270, 276-287, 290-301, 306-398, 402-405 /home/admin/.local/lib/python3.8/site-packages/torch/jit/frontend.py 612 469 23% 30-31, 42, 100-105, 108, 118-125, 133-134, 138-139, 153-166, 170-183, 189-216, 234-265, 270-281, 285-288, 292-294, 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310-322, 326, 329, 333, 336, 340, 343-365 /home/admin/.local/lib/python3.8/site-packages/torch/multiprocessing/spawn.py 98 71 28% 16-19, 22, 36, 50-52, 55, 66-76, 81-83, 89, 105-160, 165-166, 179-199, 235-240 /home/admin/.local/lib/python3.8/site-packages/torch/nn/__init__.py 20 13 35% 32-47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/_reduction.py 31 26 16% 8-20, 28-43, 47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/common_types.py 25 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/functional.py 1164 929 20% 13, 461-482, 492-503, 562-587, 597-608, 657-671, 682-696, 743-757, 768-782, 829-843, 854-868, 885-912, 926-948, 962-980, 994-1012, 1027-1037, 1052-1061, 1076-1080, 1084-1088, 1116-1121, 1125-1129, 1157-1162, 1166-1170, 1211-1214, 1228-1231, 1248-1252, 1260-1264, 1282-1302, 1320-1346, 1364-1385, 1408-1414, 1422-1428, 1453, 1455, 1489-1493, 1503-1509, 1529-1535, 1544-1550, 1573-1579, 1600-1606, 1628-1634, 1668-1676, 1733-1735, 1745-1747, 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/home/admin/.local/lib/python3.8/site-packages/torch/nn/init.py 193 102 47% 24-54, 102, 104, 106, 109, 114, 116-119, 136, 154, 176, 190-192, 233-238, 256-281, 287, 323-327, 350-353, 360, 396, 405-406, 444-451, 469-495, 514-526, 535-537 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/modules/__init__.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/modules/fused.py 57 26 54% 13-16, 22-25, 31-34, 40-43, 49-52, 58-61, 67-70, 76-79, 85-88, 94-97, 104-107, 113-116, 123-125 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/conv_fused.py 250 143 43% 44-66, 69, 72-79, 82, 85-87, 90-92, 95-114, 118, 121, 129-133, 158-185, 197-218, 221-258, 296-300, 342, 349, 353, 376-382, 385, 390, 428-432, 474, 481, 485, 508-514, 517, 522, 568-572, 637, 654, 658, 690-704, 707, 713, 716-719, 722-725 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/linear_fused.py 84 65 23% 37-57, 60, 63-65, 68, 71-73, 76-78, 81-118, 126-130, 139-155, 158-167 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/linear_relu.py 20 9 55% 32, 35, 39, 42-47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/modules/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/modules/linear_relu.py 22 10 55% 27, 30-39, 42, 46, 50 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/bn_relu.py 36 14 61% 21, 26-28, 33, 38, 42, 57, 62-64, 69, 74, 78 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/conv_relu.py 74 40 46% 28, 36-43, 47, 51-55, 59-61, 78, 86-92, 96, 100-104, 108-110, 127-128, 136-142, 146, 150-160, 164-166 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/linear_relu.py 17 5 71% 25, 28, 32, 36, 40 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/__init__.py 23 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/_functions.py 137 119 13% 10-103, 107-172, 178-230, 234-265, 270-271, 275 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/activation.py 397 222 44% 47-50, 54, 57-58, 101-102, 157-160, 163, 166-167, 216-227, 230, 233-234, 262, 265-266, 290, 325-326, 329, 354, 387-388, 391, 394-395, 423-424, 427, 430-431, 468-469, 472, 509-511, 514, 517-518, 553-555, 558, 561-562, 603-604, 607, 610-611, 637-638, 641, 644, 684, 719-720, 723, 726, 767-769, 772, 775-776, 799, 833-835, 838, 841, 874-875, 878, 881, 945-983, 986-999, 1003-1006, 1060-1164, 1220-1223, 1226, 1229, 1252, 1275, 1311-1312, 1315-1317, 1320, 1323, 1367-1368, 1371-1373, 1376, 1379, 1405-1406, 1438-1439, 1442-1444, 1447, 1450 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/adaptive.py 114 92 19% 122-161, 164-167, 170-241, 247-259, 278-279, 296-312 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py 197 143 27% 37-63, 66-71, 74-77, 80, 83, 98-107, 129-130, 135-168, 190-209, 213-214, 217-227, 297-298, 332-333, 406-407, 439-440, 512-513, 545-546, 661-665, 668-669, 674-675, 681-748, 795-819 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/channelshuffle.py 13 4 69% 45-46, 49, 52 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/container.py 345 202 41% 19-24, 76, 80, 85-86, 93-98, 102-105, 108-109, 112-117, 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, 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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 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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 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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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42-45, 49-51, 55-57, 61-63, 70, 75, 79-80, 84-85, 89-90, 94-95, 99-100, 104-105, 109-110 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/dataframes.py 198 106 46% 33-34, 37-38, 51, 54, 57-62, 65-67, 70, 73, 77-81, 84-88, 91-95, 100-103, 108, 111, 116, 119, 129-133, 136, 139, 143-146, 154-156, 159, 162, 171-174, 177, 180, 189-191, 194, 197, 205-207, 210, 213, 221-223, 226, 229, 237-239, 242, 245-246, 250-255, 261-263, 272, 276, 279, 282-285, 294-297, 300, 303, 306-308, 316-317 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/datapipes.py 100 70 30% 21, 24-26, 32, 35-37, 43-44, 47-54, 60, 63-78, 84-85, 88-106, 112-114, 117-120, 123-130 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/structures.py 12 7 42% 13-15, 18-21 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/datapipe.py 179 100 44% 21-22, 116-123, 127, 132, 135-141, 152-154, 157-162, 166-168, 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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 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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, 103-110, 120-143, 146-148, 151-159, 169-190, 193-202, 209-212, 215-220, 225, 236-255, 258-260, 270-273, 276, 286-291, 294-299, 310-320, 323-325, 339-349, 359-370, 374-400, 403-409, 449-460, 464-511, 747-798, 824-826, 878-880 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/googlenet.py 95 59 38% 27-28, 31-34, 37, 42-43, 46-47, 53-54, 58-71, 77-81, 84-94, 104-106, 178-207 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/inception.py 130 81 38% 28-29, 32-35, 38, 44-45, 48-49, 55-56, 59-60, 66-67, 70-71, 77-78, 81-82, 88-91, 94-112, 115-116, 122, 132-147, 150-160, 170-172, 250-276 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenet.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv2.py 62 33 47% 26-27, 30-33, 36-38, 49-51, 54-57, 60-64, 136-152 /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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273-288, 300-308, 396-398, 432-434, 468-470 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/vision_transformer.py 223 155 30% 46-52, 64-75, 98-108, 111-119, 136-152, 155-157, 178-266, 269-287, 291-305, 318-337, 618-620, 651-653, 684-686, 717-719, 749-751, 784-838 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/__init__.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_box_convert.py 28 22 21% 17-25, 39-47, 61-63, 77-81 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_register_onnx_ops.py 34 16 53% 16-21, 25-44, 57-60 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_utils.py 59 49 17% 13-15, 19-25, 29-38, 45-69, 74-77, 82-84, 92-106 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/boxes.py 162 123 24% 73, 90, 106-112, 128-133, 148-165, 189-216, 232-235, 241-252, 269-273, 292-304, 320-337, 356-362, 367-381, 398-415 /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 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/_presets.py 119 91 24% 24-26, 29, 32, 48-53, 56-62, 65-72, 75, 93-98, 101-118, 121-128, 131, 149-153, 156-162, 165-171, 174, 184-199, 202, 205 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/autoaugment.py 207 167 19% 16-90, 127-131, 136-221, 224, 249-253, 262-281, 284, 314-319, 322, 347-365, 368-377, 402-405, 408, 433-453, 456-463, 497-507, 510-531, 535, 539, 543, 552-601, 604-615 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional.py 505 409 19% 39-47, 71-76, 88-93, 105-110, 115, 120, 135-174, 193-209, 234-239, 259, 262-271, 275, 279, 283, 287-289, 292, 295-307, 310-315, 318-323, 327, 332, 355-360, 417-421, 424, 428, 432, 476-481, 501-506, 523-547, 581-585, 600-605, 623-633, 668-688, 703-708, 730-753, 779-797, 814-819, 836-841, 858-863, 897-902, 930-935, 961-995, 1042-1081, 1132-1214, 1232-1237, 1259-1264, 1283-1288, 1317-1355, 1370-1375, 1391-1399, 1414-1419, 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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 928 711 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, 775-796, 801, 812-814, 837, 839, 842, 860, 862, 882-943, 957-1805 /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 1753 1005 43% 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, 1191-1192, 1196-1198, 1201-1281, 1326-1334, 1348, 1351-1353, 1365-1376, 1379-1397, 1401-1450, 1454-1500, 1505-1545, 1550-1556, 1564, 1567, 1570-1571, 1574-1577, 1580, 1583, 1586-1588, 1596, 1605, 1613, 1621, 1635, 1657, 1662-1672, 1675-1678, 1682-1735, 1739-1742, 1747-1785, 1791, 1795-1804, 1811, 1820, 1828-1865, 1874-1875, 1878-1894, 1903-1919, 1924-1941, 1945-1957, 1966-1969, 1975-1985, 1993-1996, 2002-2006, 2010, 2016, 2021-2024, 2032-2034, 2037-2041, 2045-2068, 2108, 2119, 2126-2129, 2148-2149, 2154-2203, 2206-2241, 2258-2264, 2267-2277, 2280-2282, 2313, 2316-2328, 2331-2332, 2334-2369, 2371, 2376, 2422, 2428, 2432, 2440, 2442, 2448, 2451, 2453, 2455, 2461, 2463, 2465, 2467, 2469, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2505, 2511, 2515, 2519, 2521, 2525, 2528, 2534, 2536, 2538, 2540, 2542, 2550, 2552, 2555, 2559, 2562, 2564, 2570, 2572, 2574, 2577, 2584, 2586, 2588, 2590, 2592, 2594, 2596, 2598, 2603, 2605, 2607, 2609, 2611, 2617, 2619, 2621, 2635, 2638, 2640, 2642, 2644, 2648, 2650, 2656, 2658, 2660, 2663-2667, 2701-2703, 2718-2720, 2736, 2742-2744, 2746, 2751, 2760-2762, 2771-2774, 2783-2787, 2803, 2808, 2811, 2819-2833, 2837-2843, 2855-2873 /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 808 37% 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, 1106-1116, 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 325 253 22% 9-77, 82-130, 135-351, 370-372, 375-376, 395, 413-426, 430, 435, 439-443, 473 /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 1061 486 54% 52, 61-65, 72-77, 83-95, 99, 115-119, 141-142, 149-153, 161, 163, 179, 197-198, 228, 234-235, 256, 259-264, 273-279, 327-328, 356-363, 369, 371-449, 454-458, 481-482, 497-503, 506, 511-513, 538, 541-542, 547, 555, 568, 600-601, 604-605, 610-612, 615, 638-647, 653-655, 665, 675-681, 684-685, 689-694, 700-701, 704-705, 714-721, 724-732, 735-736, 752-757, 763-797, 802-815, 821-823, 843-845, 850-857, 874-924, 932-1006, 1011-1081, 1122-1124, 1128-1129, 1189, 1210-1231, 1243, 1249-1255, 2536-2538, 2541-2542, 2545-2552, 2562, 2569-2581, 2584, 2588-2592, 2599-2606, 2667-2669, 2762-2763, 2777-2778, 2789-2791, 2798-2808, 2816, 2848, 2859 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py 1402 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, 1779, 1812-1815, 1854-1856, 1929-1930, 1933-1934, 1939, 1948, 1954, 1959-1960, 1980-1985, 1989-2177 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py 1963 1252 36% 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, 2132, 2165-2168, 2213-2215, 2222-2236, 2239-2248, 2253-2254, 2257-2266, 2269-2290, 2292-2293, 2357-2369, 2373-2418, 2440-2441, 2456, 2458, 2460, 2462, 2468, 2476, 2479, 2495, 2506, 2510, 2515-2619, 2626-2814, 3022-3033, 3038, 3041-3042, 3047-3049, 3052, 3076-3078, 3087, 3098-3110, 3121-3122, 3135, 3165, 3181, 3186-3193, 3452-3526, 3530-3569 /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 298 134 55% 16-17, 21, 26, 40, 47-48, 70, 97-98, 106, 125-126, 143-155, 179-183, 194-196, 198-201, 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 506 157 69% 48-49, 54, 73, 77, 85, 96, 114, 138-139, 145-146, 155, 237, 261, 267, 270, 283-292, 295, 298, 322, 325, 328, 331-342, 352-356, 367, 380-385, 412, 442, 444, 486, 498, 501, 513, 561-566, 570, 579, 595-599, 604-610, 615-621, 626-642, 645, 648, 651-667, 670-673, 676-679, 682-685, 688-695, 698-732 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/lib_split_time_score.py 1938 923 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, 1267-1342, 1345-1363, 1368-1405, 1410-1434, 1439-1458, 1686-1691, 1694-1712, 1759-1763, 1812, 1835-1855, 1939-1940, 1990, 2019, 2041-2096, 2166, 2169-2173, 2185, 2208, 2221, 2242-2243, 2265-2269, 2351, 2417, 2422, 2448-2452, 2538-2540, 2575-2576, 2607-2608, 2622-2624, 2641-2668, 2750-2751, 2768, 2815-2817, 2834-2836, 2870-2871, 2879-2892, 2907-2908, 2915, 2937-2938, 2945, 2953-2957, 2963-2967, 2977, 2990-2994, 3005, 3049-3100, 3108-3116, 3143-3145, 3164, 3170-3171, 3180-3182, 3184, 3211-3213, 3219, 3231-3232, 3240-3261, 3268, 3314, 3320-3389, 3398-3456, 3487-3488, 3534-3536, 3570, 3625, 3638, 3671-3716, 3748-3754, 3822-3823, 3848, 3854-3855, 3861, 3874-3875, 3889, 3896, 3911-3929, 3939-3957, 3964, 3968-4002 /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 51 69% 42, 44-52, 86, 99, 110-111, 115, 128-131, 139, 173-181, 186-231, 246, 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 /home/admin/workarea/git/Velours/python/mtr/utils/upload_batch.py 58 19 67% 37-39, 46-47, 55-58, 68-78 /home/admin/workarea/git/Velours/python/mtr/utils/utils_timer.py 11 3 73% 13-15 /home/admin/workarea/git/Velours/python/prod/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/prod/caffe_vision.py 1390 1154 17% 47, 68-72, 77-81, 110-112, 118-121, 131-132, 143, 149, 156-170, 173-175, 181, 184, 189, 192, 213-214, 220-274, 285, 313-315, 321, 326, 333-337, 340-344, 347-351, 360-367, 381, 394-395, 400, 410-416, 419-421, 431-573, 577-592, 595-602, 606-759, 763-782, 787-810, 814-877, 883-889, 894-901, 907-926, 934-947, 954-958, 964-981, 986-1001, 1008-1028, 1039, 1052, 1074-1076, 1082, 1085, 1095, 1099, 1103, 1116-1121, 1126-1333, 1401-2359 /home/admin/workarea/git/Velours/python/prod/cod/main_cod.py 559 276 51% 16-24, 27-43, 46, 49-50, 54, 60-90, 95-96, 107-108, 129, 134-137, 147-150, 156, 165-167, 169-171, 176-178, 184, 198, 206-209, 215, 219-220, 258-259, 269, 316-336, 342-359, 364, 370-372, 375-378, 383-416, 440-450, 471, 483-505, 524-525, 527-529, 533-538, 541-542, 569, 580, 596-597, 607, 619-625, 630-647, 656-657, 666-667, 670-671, 696-801 /home/admin/workarea/git/Velours/python/prod/memo/SLA_RUBBIA.py 546 200 63% 13-14, 115-116, 125-135, 149-164, 185-188, 196-223, 227, 229-230, 234-238, 257-258, 267-268, 270-273, 276, 287-295, 308-310, 317-320, 363, 383-427, 444-450, 452-453, 460-470, 484, 492, 505, 515-517, 525-527, 536, 540, 543-547, 549-550, 553, 561, 568-573, 620-622, 625-628, 643-646, 661-662, 666-667, 671-672, 675, 681-683, 693-695, 719, 725, 727-728, 734, 745, 749, 760-761, 774 /home/admin/workarea/git/Velours/python/prod/memo/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/prod/memo/example_of_unwanted_materials.py 90 58 36% 13-15, 43-62, 86-100, 104-126, 130-163, 167-170, 174-179 /home/admin/workarea/git/Velours/python/prod/memo/lib_sla.py 333 75 77% 19, 49, 68, 75, 79, 99-115, 132, 135, 185, 194-195, 230-231, 234, 240, 249-250, 257-258, 261-262, 280-282, 286-288, 293, 306-308, 368, 376-381, 385-399, 419, 453, 487, 504, 521-525, 544-547, 550-554, 585, 596-601 /home/admin/workarea/git/Velours/python/prod/memo/memo.py 833 286 66% 46, 52, 76, 82, 110, 159, 230-231, 236-238, 245-247, 249-252, 255-257, 260, 285-286, 304, 307, 310-311, 316-318, 321-329, 336-337, 342-343, 349-350, 358-361, 371-372, 407, 433, 441, 443, 455-535, 591-593, 607-610, 613-617, 679-714, 762-764, 778-781, 848-850, 864-874, 915-917, 924-925, 957-963, 986, 1022-1027, 1041, 1060, 1073, 1079, 1082, 1088, 1094, 1099, 1104, 1109, 1111-1112, 1118-1172, 1198-1249, 1254-1261, 1292-1293, 1315-1319, 1322-1336 /home/admin/workarea/git/Velours/python/prod/non_supervised_algorithm.py 131 59 55% 40, 58, 72-73, 155, 160, 165-166, 175-264 /home/admin/workarea/git/Velours/python/prod/vision_faster_rcnn.py 244 171 30% 13-16, 61-89, 98, 133-137, 145-172, 178-219, 223-360, 378-380, 398, 401, 415-458 /home/admin/workarea/git/Velours/python/tests/__init__.py 0 0 100% /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 636 67% 37, 43-45, 51-52, 58-60, 66-70, 101-104, 108, 122-126, 157-159, 186-188, 194-197, 203-205, 213-215, 221-224, 230-232, 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, 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435 199 54% 52-85, 88-89, 121, 128, 146-149, 157, 165, 177, 185, 196, 207, 215, 223, 231, 239, 247, 268, 276, 284, 292, 302, 310, 318, 328, 344, 355, 372, 388, 396, 404, 412, 420, 428, 440, 449, 456-458, 465-470, 473, 477-480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524-544, 553-561, 565, 569, 573, 577, 581, 586, 590-610, 614-615, 619-620, 629-638, 647-656, 660, 664, 668, 672-675, 679-682, 700-705, 708, 712, 720, 728-733, 737-742, 746, 750, 754, 758-760, 766, 776-778, 782-784, 799-803, 815, 819, 823, 827, 836-845, 854-864, 868, 872, 876-879, 883-886 /usr/lib/python3/dist-packages/keystoneclient/auth/__init__.py 4 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/base.py 82 44 46% 46-48, 63-67, 86-93, 126, 159-164, 186, 215, 230, 245, 257, 267, 285-297, 315-318, 328-329, 344-347, 366-374 /usr/lib/python3/dist-packages/keystoneclient/auth/cli.py 29 21 28% 43-65, 87-95 /usr/lib/python3/dist-packages/keystoneclient/auth/conf.py 28 14 50% 41, 57, 83-93, 123-132 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41-42, 46, 50, 53-59, 67-75, 83-91 /usr/lib/python3/dist-packages/keystoneclient/v2_0/services.py 15 6 60% 25, 35, 39, 43-46, 50 /usr/lib/python3/dist-packages/keystoneclient/v2_0/tenants.py 76 54 29% 37, 40, 44-58, 61, 66, 71, 80-82, 85, 89-98, 110-130, 135-149, 153, 157, 161, 167 /usr/lib/python3/dist-packages/keystoneclient/v2_0/tokens.py 58 36 38% 24, 28, 32, 36, 44-69, 72, 75, 85, 94-96, 108-115, 124-125 /usr/lib/python3/dist-packages/keystoneclient/v2_0/users.py 51 32 37% 27, 30, 33, 42-43, 46, 55-57, 61-63, 68-70, 75-78, 87-91, 97-102, 106, 113-126, 130 /usr/lib/python3/dist-packages/keystoneclient/v3/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/access_rules.py 29 14 52% 58-61, 73-76, 87-90, 104-107, 111, 116 /usr/lib/python3/dist-packages/keystoneclient/v3/application_credentials.py 49 32 35% 72-98, 121-124, 136-139, 150-153, 166-169, 173 /usr/lib/python3/dist-packages/keystoneclient/v3/auth.py 22 10 55% 42-48, 60-66 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69, 82, 96, 109 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/mappings.py 22 8 64% 36-38, 76, 89, 99, 136, 149 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/projects.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/protocols.py 31 16 48% 38-49, 52-54, 72, 90, 105, 123, 141 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/saml.py 16 9 44% 37-40, 56-59, 62-79 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/service_providers.py 22 8 64% 38-40, 52, 65, 75, 89, 102 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/access_tokens.py 20 9 55% 23-24, 38-51 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/consumers.py 17 4 76% 38, 43, 47, 53 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/core.py 20 5 75% 28-29, 62-65 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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, 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140-141, 149, 156, 159, 167, 170, 178, 181, 188-189, 192, 211, 237-248, 262-265, 269-278, 309, 339, 355-357, 375-388, 392-395, 400-402, 414-423, 541, 545, 548, 559, 574, 584-585, 589-592, 603-604, 612-613, 623-632, 635, 638, 648-676, 688-697, 713-729, 738-741, 753-763, 776-779, 797-806, 809, 869-870, 873, 876, 879, 930-934, 938-949, 966-968, 972-973, 977-988, 994-1006, 1026, 1047, 1086, 1104, 1134-1136, 1152, 1173, 1199-1200, 1229, 1233-1238, 1253, 1291-1295, 1299-1312, 1346-1351, 1354, 1358-1360, 1395-1406, 1409, 1414-1416, 1461-1469, 1478, 1482, 1497-1502, 1509-1510, 1513-1517, 1521, 1524, 1529-1530, 1533, 1548-1552, 1555, 1558-1559, 1562-1566, 1570-1581, 1584, 1587, 1596-1617, 1640-1647, 1672-1697, 1707-1710, 1717, 1724, 1737-1747, 1758-1788, 1802-1807, 1831-1858, 1861-1863, 1878-1879, 1882-1886, 1894-1910, 1913-1914, 1917-1918, 1921-1922, 1979-1999, 2003, 2031-2034, 2039-2049, 2059, 2066-2071, 2120-2141, 2145-2148, 2151-2187, 2196-2201, 2205, 2209, 2213-2215, 2219, 2223-2224, 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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, 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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, 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736-754, 763, 778 /usr/lib/python3/dist-packages/paramiko/transport.py 1294 636 51% 121-122, 401-406, 409-427, 457-458, 557-576, 588-589, 598, 626-632, 672-674, 683-686, 690, 734-756, 770, 787-791, 819-832, 838-843, 859, 921, 935, 947, 994, 1008-1011, 1013-1014, 1028-1031, 1035, 1039-1042, 1077-1096, 1107-1110, 1122, 1135-1140, 1155-1166, 1180-1183, 1202-1220, 1232-1245, 1307-1363, 1399-1403, 1414, 1428-1430, 1441-1443, 1466-1471, 1520-1556, 1595, 1599, 1604, 1650-1658, 1668-1681, 1699-1707, 1724-1730, 1743-1745, 1767, 1778, 1793, 1809-1812, 1815-1835, 1841-1842, 1848, 1854-1855, 1875-1878, 1882-1884, 1910, 1912, 1933-1934, 1940-1946, 1951, 1964-1971, 1975-1982, 1985-1990, 2020-2038, 2049-2050, 2068, 2092, 2095-2096, 2098-2099, 2101-2102, 2109-2112, 2125, 2133-2147, 2155-2170, 2172-2217, 2227, 2241, 2253, 2256-2259, 2264, 2266, 2279, 2283-2284, 2302-2317, 2354, 2371, 2442, 2451, 2459, 2466-2472, 2482, 2487, 2493-2499, 2519, 2529-2532, 2543, 2551-2557, 2580-2582, 2609-2610, 2627, 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577-608 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/__about__.py 10 0 100% /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/__init__.py 3 0 100% /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/_compat.py 12 1 92% 17 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/_structures.py 41 17 59% 10, 13, 16, 19, 22, 25, 28, 31, 42, 45, 48, 51, 54, 57, 60, 63, 66 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/markers.py 130 73 44% 47, 50, 53, 56, 62, 68, 74, 142-145, 149-168, 184-197, 204-211, 215-238, 242-246, 250-257, 275-280, 283, 286, 297-301 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/requirements.py 72 17 76% 91-92, 98-102, 110-124, 127 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/specifiers.py 306 190 38% 83-93, 96-102, 109, 112, 115-123, 126-134, 137, 140-142, 146, 150, 154, 158, 161, 165-180, 183-211, 243-245, 248, 251, 254, 257, 260, 263, 269-271, 397-410, 416-446, 450, 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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 /usr/lib/python3/dist-packages/pkg_resources/extern/__init__.py 36 5 86% 21, 32, 54-57 /usr/lib/python3/dist-packages/pkg_resources/py2_warn.py 6 0 100% /usr/lib/python3/dist-packages/pkg_resources/py31compat.py 12 5 58% 9-13 /usr/lib/python3/dist-packages/rfc3986/__init__.py 16 0 100% /usr/lib/python3/dist-packages/rfc3986/_mixin.py 112 86 23% 28-51, 54, 59-63, 68-72, 77-81, 91, 113-123, 139-147, 165-168, 182-185, 199-202, 216-219, 229, 247-299, 308-319, 341-353 /usr/lib/python3/dist-packages/rfc3986/abnf_regexp.py 63 0 100% /usr/lib/python3/dist-packages/rfc3986/api.py 15 6 60% 38, 52, 77, 92-93, 106 /usr/lib/python3/dist-packages/rfc3986/compat.py 23 10 57% 20-21, 25-26, 45-47, 52-54 /usr/lib/python3/dist-packages/rfc3986/exceptions.py 45 24 47% 18, 28, 37, 52-59, 71-81, 89-95, 103-110 /usr/lib/python3/dist-packages/rfc3986/iri.py 50 34 32% 49-57, 61-73, 76, 86-89, 111-142 /usr/lib/python3/dist-packages/rfc3986/misc.py 31 5 84% 116-121 /usr/lib/python3/dist-packages/rfc3986/normalizers.py 79 63 20% 24, 29-37, 42, 47, 52-67, 72-76, 81-83, 88-90, 101-105, 114-139, 144-167 /usr/lib/python3/dist-packages/rfc3986/parseresult.py 166 124 25% 34-46, 50, 55, 60, 65, 81-92, 98-112, 134-139, 152, 159-172, 176-181, 193-198, 208-220, 227-244, 267-273, 287, 294-314, 327-337, 342-364, 368-385 /usr/lib/python3/dist-packages/rfc3986/uri.py 30 17 43% 88-96, 102-115, 128, 144-147 /usr/lib/python3/dist-packages/rfc3986/validators.py 129 99 23% 60-73, 87-89, 103-105, 119-123, 135-136, 148-149, 165-174, 190-199, 220-240, 245-251, 256-258, 265-271, 284-289, 306-309, 324-329, 344, 359, 374, 389, 396, 411-430, 435-450 /usr/lib/python3/dist-packages/secretstorage/__init__.py 21 18 14% 17-53 /usr/lib/python3/dist-packages/secretstorage/collection.py 104 99 5% 22-201 /usr/lib/python3/dist-packages/secretstorage/defines.py 11 0 100% /usr/lib/python3/dist-packages/secretstorage/dhcrypto.py 28 22 21% 18-59 /usr/lib/python3/dist-packages/secretstorage/exceptions.py 5 0 100% /usr/lib/python3/dist-packages/secretstorage/item.py 73 68 7% 17-145 /usr/lib/python3/dist-packages/secretstorage/util.py 112 107 4% 15-180 /usr/lib/python3/dist-packages/simplejson/__init__.py 80 55 31% 120-122, 126-130, 248-279, 372-385, 457, 519-535, 539-562, 577 /usr/lib/python3/dist-packages/simplejson/compat.py 29 16 45% 5-18, 24, 26 /usr/lib/python3/dist-packages/simplejson/decoder.py 225 166 26% 14-15, 26-27, 60-133, 145-234, 237-270, 369, 373, 390, 392, 397, 399 /usr/lib/python3/dist-packages/simplejson/encoder.py 394 341 13% 13-14, 42-62, 69-103, 244, 247, 249, 251, 272, 284-302, 314-380, 400-404, 407-417, 441-722 /usr/lib/python3/dist-packages/simplejson/errors.py 29 23 21% 7-12, 16-23, 41-50, 53 /usr/lib/python3/dist-packages/simplejson/raw_json.py 3 1 67% 9 /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, 125-126, 136, 145-152, 160, 174-180, 187-191, 203, 222-229, 238-243, 247-251, 260-263, 267-287, 308-350, 355-405, 409-433, 440-445, 450-462 /usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py 68 51 25% 26-28, 31, 40-42, 46-55, 67-73, 83-92, 102-105, 108-122, 133-138, 152-173 /usr/local/lib/python3.8/dist-packages/IPython/core/autocall.py 16 5 69% 40, 48, 57, 69-70 /usr/local/lib/python3.8/dist-packages/IPython/core/builtin_trap.py 48 32 33% 26-34, 40-44, 47-51, 55-63, 67-70, 75-77, 82-86 /usr/local/lib/python3.8/dist-packages/IPython/core/compilerop.py 44 23 48% 59-63, 74-93, 101, 107, 113, 131-136, 141-150, 157-160 /usr/local/lib/python3.8/dist-packages/IPython/core/completer.py 817 691 15% 151-152, 161, 207-209, 225-230, 235-241, 268-281, 287-290, 302-322, 336-341, 344, 373-383, 386, 399, 404, 438-450, 479-504, 508, 543-544, 549, 554-557, 562-563, 617-631, 640-651, 660-679, 696-733, 738-743, 767-812, 840-843, 873-879, 885, 901-917, 926-944, 966-970, 991, 1001-1004, 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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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263-291, 294 /usr/local/lib/python3.8/dist-packages/IPython/core/oinspect.py 489 415 15% 50, 86-88, 96-112, 124-132, 154-191, 196, 209-215, 227-231, 238-266, 277-285, 303-319, 338-352, 361-366, 373-376, 380, 384-386, 390-394, 401-415, 453-475, 481-490, 495-512, 531-550, 555-567, 586-656, 680-683, 688-692, 717-905, 915-922, 950-988, 997-1031 /usr/local/lib/python3.8/dist-packages/IPython/core/page.py 174 147 16% 37-43, 51, 62-78, 86-123, 152-234, 248-260, 267-280, 288-302, 311-318, 323-336, 339-343 /usr/local/lib/python3.8/dist-packages/IPython/core/payload.py 19 11 42% 39-49, 52, 55 /usr/local/lib/python3.8/dist-packages/IPython/core/prefilter.py 286 178 38% 67, 121-125, 133-135, 145, 150, 154-156, 160-161, 169-171, 181, 186, 190-192, 196-197, 205-208, 215, 219-221, 225-232, 236, 240, 252-253, 257-262, 266-269, 283-312, 325-337, 355-358, 362, 365, 383-386, 390, 393, 404-407, 415-419, 428-433, 447-451, 464-475, 487-490, 506-524, 540-543, 558-567, 570, 577-580, 590-597, 607-667, 682 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/usr/local/lib/python3.8/dist-packages/IPython/lib/clipboard.py 41 33 20% 17-33, 38-44, 53-67 /usr/local/lib/python3.8/dist-packages/IPython/lib/display.py 254 202 20% 99-115, 119-128, 133-151, 155-173, 178-194, 198-200, 204-210, 213-219, 223-231, 234-237, 240-243, 261-264, 268-276, 308-310, 314-319, 327-328, 343-344, 381-387, 390-391, 399-405, 410, 471-488, 512-544, 550-566, 575-579, 584-592, 597-605, 627-628, 631-639, 642, 645-649, 652-654 /usr/local/lib/python3.8/dist-packages/IPython/lib/pretty.py 500 393 21% 108-111, 116-117, 127-134, 140-144, 151-155, 162-166, 171-175, 186-197, 200-207, 210-214, 218-229, 237-248, 254-261, 271-276, 280-286, 290-295, 299-302, 309-321, 343-354, 358-399, 409-417, 423, 429-430, 433-435, 438-439, 445-450, 453-461, 467-469, 475-477, 480-483, 486-494, 497-500, 508-538, 547-559, 568-586, 596-608, 614-623, 628-648, 659-678, 684-690, 695-703, 708-718, 724-725, 760-761, 778-782, 803-811, 814-819, 822-827, 831-836, 844-856 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280-284, 292-303, 308-323, 331-334, 338-341, 345-348, 351-360, 367-374, 380 /usr/local/lib/python3.8/dist-packages/IPython/terminal/magics.py 89 67 25% 20-35, 41, 46-57, 60-63, 68-78, 83-84, 129-138, 171-195, 199-203 /usr/local/lib/python3.8/dist-packages/IPython/terminal/prompts.py 59 37 37% 15, 18-26, 30, 38, 41-43, 48-49, 54, 62, 67, 72, 75, 80-97, 100-107 /usr/local/lib/python3.8/dist-packages/IPython/terminal/pt_inputhooks/__init__.py 25 15 40% 23, 27, 30, 35-50 /usr/local/lib/python3.8/dist-packages/IPython/terminal/ptutils.py 86 64 26% 38-54, 58-61, 67-70, 74-77, 80-92, 99-133, 140-144, 155-168 /usr/local/lib/python3.8/dist-packages/IPython/terminal/shortcuts.py 140 111 21% 27-28, 34-95, 99-104, 109-152, 161, 170, 174-176, 180-184, 188-191, 194, 200, 203, 215-226, 238-249, 253-254, 258-274 /usr/local/lib/python3.8/dist-packages/IPython/testing/__init__.py 9 5 44% 33-37 /usr/local/lib/python3.8/dist-packages/IPython/testing/skipdoctest.py 3 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/PyColorize.py 110 82 25% 143, 186-196, 200-205, 217-281, 286-325, 330 /usr/local/lib/python3.8/dist-packages/IPython/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/_process_common.py 68 57 16% 34-40, 69-111, 130-133, 151, 171-175, 190-212 /usr/local/lib/python3.8/dist-packages/IPython/utils/_process_posix.py 82 57 30% 37-39, 62-67, 94-97, 115-118, 133-203, 214-224 /usr/local/lib/python3.8/dist-packages/IPython/utils/_sysinfo.py 1 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/capture.py 103 70 32% 18-21, 24-25, 29-35, 38, 41, 44, 47, 50, 53, 56, 59, 76-80, 83, 88-90, 95-97, 110, 114-119, 131-134, 137-163, 166-170 /usr/local/lib/python3.8/dist-packages/IPython/utils/colorable.py 7 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/coloransi.py 67 12 82% 93-94, 122-124, 149, 156, 161, 172-173, 179-180 /usr/local/lib/python3.8/dist-packages/IPython/utils/contexts.py 26 16 38% 37-38, 42-53, 56-60, 70 /usr/local/lib/python3.8/dist-packages/IPython/utils/data.py 6 3 50% 22-23, 28 /usr/local/lib/python3.8/dist-packages/IPython/utils/decorators.py 14 10 29% 38-49 /usr/local/lib/python3.8/dist-packages/IPython/utils/dir2.py 34 28 18% 16-20, 38-51, 64-84 /usr/local/lib/python3.8/dist-packages/IPython/utils/encoding.py 23 7 70% 30, 53-58, 64-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/frame.py 17 11 35% 50-53, 66-67, 81-82, 91-94 /usr/local/lib/python3.8/dist-packages/IPython/utils/generics.py 9 2 78% 12, 30 /usr/local/lib/python3.8/dist-packages/IPython/utils/importstring.py 12 10 17% 27-39 /usr/local/lib/python3.8/dist-packages/IPython/utils/io.py 130 76 42% 28-31, 41-42, 47-49, 52-64, 68-73, 84, 122-132, 136-139, 143-145, 149-150, 153-154, 170-188, 207-211, 216-218, 223-227, 232-236, 246-248 /usr/local/lib/python3.8/dist-packages/IPython/utils/ipstruct.py 76 56 26% 85-88, 111-123, 146-151, 165-166, 180-182, 196-198, 212-215, 223-229, 232, 245, 263, 271, 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, 81-82, 97-99, 119, 123, 128-129, 134, 152-165 /usr/local/lib/python3.8/dist-packages/IPython/utils/syspathcontext.py 32 22 31% 24, 27-31, 34-40, 46, 49-53, 56-62 /usr/local/lib/python3.8/dist-packages/IPython/utils/tempdir.py 23 12 48% 24-26, 29-30, 35, 38, 51-53, 56-57 /usr/local/lib/python3.8/dist-packages/IPython/utils/terminal.py 59 37 37% 27-33, 53, 58, 62, 68-69, 73, 81-105, 110-112, 117-119, 123-125, 129 /usr/local/lib/python3.8/dist-packages/IPython/utils/text.py 255 192 25% 22, 43-47, 52-56, 61, 66-70, 101, 106-110, 115-119, 124-128, 148-165, 186-201, 215-230, 274-285, 306-309, 329-334, 342-346, 354-356, 397-409, 417-425, 460-473, 484, 509-510, 542-569, 593-610, 618-624, 629-635, 644-647, 702-707, 732-739, 763-770 /usr/local/lib/python3.8/dist-packages/IPython/utils/timing.py 35 24 31% 33, 42, 51-52, 58-67, 83-96, 106, 115 /usr/local/lib/python3.8/dist-packages/IPython/utils/wildcard.py 51 31 39% 44-52, 56, 61-73, 78-85, 92-111 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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, 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/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 0 100% /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 77 28% 16-17, 22-24, 28-31, 55-59, 84-90, 114-118, 145-156, 171-180, 194-195, 212-219, 236-242, 246-251, 281, 285-289, 292-298, 301-308, 311-317, 320-335 /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 4 94% 72-73, 106, 162 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/image_data_generator.py 213 111 48% 17-18, 301, 308-310, 316, 327-331, 335-342, 347-349, 354-356, 361-363, 421, 649-666, 708, 712, 714, 717-720, 725-728, 734-739, 761, 764, 771-778, 783-790, 795, 804, 814, 819, 877, 882, 885, 888, 902-903, 933-988 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/iterator.py 146 51 65% 50, 54, 59, 74, 78-95, 101, 104, 112-116, 127, 175, 180-183, 188-193, 201, 205-209, 243-250, 253, 255-257, 263-268, 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 54 55% 16-18, 47, 71-76, 107-109, 111, 118-119, 121-122, 125-127, 132, 153-154, 179, 217, 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 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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, 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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% 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438-447, 456-465, 499-501, 515-528, 536-549, 552-555, 562, 573-590, 602, 608, 614, 622-654, 663-683, 692-760, 771-772, 777-780, 790, 802, 805-808, 818-819, 827-828, 835-836 /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 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137-139, 173-185 /usr/local/lib/python3.8/dist-packages/pywt/data/_wavelab_signals.py 180 175 3% 69-259 /usr/local/lib/python3.8/dist-packages/pywt/version.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/rsa/__init__.py 10 2 80% 34-36 /usr/local/lib/python3.8/dist-packages/rsa/common.py 57 46 19% 22-25, 50-53, 76-78, 99-102, 112-127, 139-144, 167-179, 183-185 /usr/local/lib/python3.8/dist-packages/rsa/core.py 20 16 20% 23-26, 32-42, 48-53 /usr/local/lib/python3.8/dist-packages/rsa/key.py 242 170 30% 55-56, 110-116, 124-128, 140-146, 163, 177, 207, 210, 214, 218, 221-227, 230, 233, 256-260, 269-277, 291-292, 301-302, 320-321, 332-341, 371-379, 382, 385, 389, 393, 396-402, 412, 415, 418-422, 434-438, 450-453, 477-511, 520-548, 563-564, 573-574, 610-654, 670-684, 697, 721-728, 760-781, 790-803 /usr/local/lib/python3.8/dist-packages/rsa/pem.py 50 42 16% 29-32, 39-78, 97-105, 120-132 /usr/local/lib/python3.8/dist-packages/rsa/pkcs1.py 124 99 20% 40, 95-120, 145-154, 182-189, 243-264, 284-297, 318-319, 337-357, 371-376, 388-397, 411-425, 436-440, 447-458 /usr/local/lib/python3.8/dist-packages/rsa/prime.py 63 52 17% 34-36, 53-62, 82-115, 130-141, 160-167, 182-183, 187-198 /usr/local/lib/python3.8/dist-packages/rsa/randnum.py 30 22 27% 32-43, 50-57, 67-70, 81-96 /usr/local/lib/python3.8/dist-packages/rsa/transform.py 14 9 36% 34, 58-66, 70-72 /usr/local/lib/python3.8/dist-packages/scipy/__config__.py 28 14 50% 12-13, 24-25, 28-37 /usr/local/lib/python3.8/dist-packages/scipy/__init__.py 62 9 85% 65, 126-128, 132-136, 141-142 /usr/local/lib/python3.8/dist-packages/scipy/_distributor_init.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/_ccallback.py 98 71 28% 15-23, 87-88, 91, 95, 99, 103, 106, 125-131, 135-159, 168-180, 184-202, 207, 216-222, 227 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_numpy_compat.py 275 254 8% 18-50, 58-87, 92-102, 109-197, 203-289, 294-568, 573-781 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_testutils.py 77 62 19% 31-73, 81-82, 89-104, 108-119, 126-145 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_threadsafety.py 33 9 73% 33-37, 40-41, 45-46 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_uarray/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/_uarray/_backend.py 93 47 49% 48-57, 61-74, 96-99, 194, 213, 275, 306, 338-340, 343, 346, 363, 379-392, 404-417 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_util.py 166 107 36% 19-24, 42-60, 90-100, 111-126, 134-136, 164, 167-170, 186-196, 238-241, 244, 249, 251-252, 320-347, 367-389, 393, 396-397, 400-401, 404-405, 408-409, 412-414, 418-422 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_version.py 76 33 57% 59, 72, 82-87, 91-95, 101-112, 116, 124-132, 140, 143, 146, 155 /usr/local/lib/python3.8/dist-packages/scipy/_lib/decorator.py 252 115 54% 51-68, 77-78, 102, 114, 121-122, 124, 126-127, 140, 146, 150, 162-163, 175, 181, 192-195, 244, 253, 259-261, 275-276, 282, 290-292, 294, 297, 310-319, 328-424 /usr/local/lib/python3.8/dist-packages/scipy/_lib/deprecation.py 15 4 73% 11-14, 18-20 /usr/local/lib/python3.8/dist-packages/scipy/_lib/doccer.py 97 21 78% 53, 116-125, 140, 145, 165, 171, 200, 250, 268-274 /usr/local/lib/python3.8/dist-packages/scipy/_lib/six.py 177 112 37% 43-65, 75-76, 87-92, 106-114, 119-121, 127, 134-142, 155, 160, 165, 170, 173, 176-177, 185-192, 202-204, 210-270, 277 /usr/local/lib/python3.8/dist-packages/scipy/_lib/uarray.py 13 5 62% 17-20, 24-25 /usr/local/lib/python3.8/dist-packages/scipy/cluster/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/cluster/hierarchy.py 780 667 14% 155, 162-167, 177-178, 195-200, 278, 360, 442, 527, 629, 731, 1040, 1045-1046, 1049, 1052-1053, 1058, 1061, 1068, 1072, 1075, 1113, 1115, 1118, 1121, 1130, 1133-1136, 1139-1142, 1145-1148, 1164, 1178, 1191, 1204, 1216, 1250-1278, 1299-1310, 1357-1391, 1452-1488, 1529-1551, 1555-1559, 1566, 1675-1700, 1758-1774, 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/usr/local/lib/python3.8/dist-packages/scipy/fft/_basic.py 64 23 64% 9-13, 154, 252, 339, 434, 510, 570, 668, 764, 857, 947, 1040, 1083, 1183, 1226, 1333, 1375, 1463, 1506 /usr/local/lib/python3.8/dist-packages/scipy/fft/_helper.py 10 2 80% 62, 101 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/basic.py 133 87 35% 16-30, 44-58, 72-90, 103, 110, 117, 124, 131, 138, 146-161, 172-186, 198-223, 234-251 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/helper.py 106 84 21% 28-38, 43-77, 86-95, 102-106, 111-133, 137-141, 147-153, 158-170, 190-195, 210 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/realtransforms.py 64 42 34% 19-46, 71-99 /usr/local/lib/python3.8/dist-packages/scipy/fft/_realtransforms.py 28 8 71% 63, 121, 179, 237, 387, 451, 568, 618 /usr/local/lib/python3.8/dist-packages/scipy/integrate/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_bvp.py 378 352 7% 29-57, 75-116, 124-142, 152-158, 243-276, 310-317, 322-347, 418-502, 506, 513, 556-577, 590-602, 632, 642-710, 1001-1159 /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, 1545-1672, 1742-1750, 1771-1894, 1901-1910, 1914, 1918-1945, 1949-2053, 2056-2059, 2132-2138, 2158-2171, 2234-2239, 2253-2315, 2352-2417, 2426-2430, 2550-2570, 2599-2712, 2716-2728, 2903-2985, 2993-2994, 2998, 3069-3166, 3170-3242, 3246 /usr/local/lib/python3.8/dist-packages/scipy/optimize/slsqp.py 190 178 6% 57-66, 181-212, 237-475, 483-531 /usr/local/lib/python3.8/dist-packages/scipy/optimize/tnc.py 102 76 25% 243-277, 342-413, 420-441 /usr/local/lib/python3.8/dist-packages/scipy/optimize/zeros.py 471 417 11% 54-62, 65-68, 73-81, 86-93, 266-365, 377-469, 546-554, 643-651, 773-781, 878-886, 896-899, 904-908, 919-927, 932-937, 949-972, 980-992, 1002, 1013-1037, 1048-1064, 1067-1077, 1081-1085, 1089, 1092, 1096-1123, 1127-1132, 1139-1213, 1218-1244, 1353-1375 /usr/local/lib/python3.8/dist-packages/scipy/signal/__init__.py 44 2 95% 330, 343 /usr/local/lib/python3.8/dist-packages/scipy/signal/_arraytools.py 49 41 16% 43-46, 54, 92-106, 143-155, 196-209, 237-243 /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, 1421-1515, 1539-1561, 1592-1632, 1691-1704, 1765-1787, 1851-1876, 1938-1965, 2017-2046, 2155-2175, 2294-2386, 2393-2398, 2457-2474, 2520-2534, 2583-2600, 2652-2679, 2731-2759, 2866, 2983, 3094, 3218, 3380, 3385, 3389, 3421-3445, 3525-3615, 3693-3753, 3833-3915, 3993-4054, 4067-4074, 4091-4112, 4129-4154, 4161-4163, 4167-4179, 4201-4263, 4285-4288, 4318-4332, 4340-4357, 4371-4396, 4404-4435, 4449-4463, 4540-4568, 4648, 4728, 4764-4803 /usr/local/lib/python3.8/dist-packages/scipy/signal/fir_filter_design.py 272 250 8% 26-32, 79-85, 127-128, 250-261, 389-482, 593-684, 836-855, 968-1068, 1082-1090, 1215-1265 /usr/local/lib/python3.8/dist-packages/scipy/signal/lti_conversion.py 160 142 11% 76-114, 118-121, 125-126, 130-133, 137-139, 143-148, 176-195, 256-284, 304, 334, 405-504 /usr/local/lib/python3.8/dist-packages/scipy/signal/ltisys.py 919 757 18% 54-58, 66-70, 75, 79-82, 87, 92, 103-106, 117-120, 131-134, 206-221, 229, 236, 243, 250, 274, 284, 295, 383-398, 406-409, 414, 418, 425, 432, 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, 1077-1079, 1089-1098, 1110-1113, 1116, 1123-1125, 1135-1138, 1143-1153, 1156-1187, 1201-1207, 1212-1242, 1248-1269, 1273-1290 /usr/local/lib/python3.8/dist-packages/scipy/sparse/construct.py 232 206 11% 62, 138-188, 218, 252-273, 311-355, 384-398, 406-431, 465, 499, 545-623, 668-677, 750-793, 842 /usr/local/lib/python3.8/dist-packages/scipy/sparse/coo.py 294 251 15% 129-198, 201-236, 242-263, 271-291, 294-300, 306-317, 323-330, 352-372, 394-414, 417-420, 425-443, 448-454, 459-475, 480-513, 521-525, 533-537, 541-554, 561-564, 571-579, 583-586, 589-593, 619 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csc.py 87 58 33% 111-119, 125-126, 129-132, 137-155, 164-180, 188-194, 200-206, 209, 212-214, 217-219, 222, 225, 228, 235, 261 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_laplacian.py 51 44 14% 69-81, 85, 89-111, 115-128 /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, 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/usr/local/lib/python3.8/dist-packages/scipy/stats/distributions.py 9 0 100% /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, 1096-1114, 1144-1165, 1214-1229, 1258-1282, 1322-1343, 1372-1410, 1465-1468, 1500, 1535-1543, 1556-1561, 1579-1586, 1604-1610, 1646-1690, 1718-1736, 1786, 1825-1831, 1876-1878, 1927-1929, 1967-1974, 2030-2071, 2101-2135, 2176-2177, 2205-2222, 2262-2283, 2343-2351, 2367-2381, 2411-2430, 2460-2490, 2521-2526, 2628-2654, 2664-2668, 2714-2720, 2736-2749, 2798-2801, 2824-2837, 2867-2888, 2931-2977 /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats_extras.py 159 141 11% 62-103, 128-129, 156-190, 235-241, 260-286, 316-320, 346-371, 400-404, 429-444, 463-477 /usr/local/lib/python3.8/dist-packages/scipy/stats/stats.py 1635 1486 9% 172, 217-227, 231-245, 249-274, 328-339, 391-406, 459-503, 531-549, 594-599, 648-654, 706-717, 768-779, 828, 877-883, 950-970, 974-1021, 1064-1072, 1149-1175, 1259-1288, 1357-1374, 1437-1463, 1520-1560, 1625-1637, 1681-1693, 1738-1740, 1805-1820, 1825-1864, 1934-1954, 2008-2036, 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/usr/local/lib/python3.8/dist-packages/skimage/_shared/_warnings.py 54 42 22% 46-70, 109-145 /usr/local/lib/python3.8/dist-packages/skimage/_shared/fft.py 9 4 56% 12-15 /usr/local/lib/python3.8/dist-packages/skimage/_shared/utils.py 123 45 63% 63-66, 99, 104-114, 154-163, 179, 235-236, 242-243, 251-253, 270-278, 298-304, 333-334, 362, 365, 369 /usr/local/lib/python3.8/dist-packages/skimage/_shared/version_requirements.py 60 51 15% 6, 10-12, 48-62, 67-69, 95-117, 141-155, 177-179 /usr/local/lib/python3.8/dist-packages/skimage/color/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/skimage/color/adapt_rgb.py 25 11 56% 19, 37-40, 58-62, 77-79 /usr/local/lib/python3.8/dist-packages/skimage/color/colorconv.py 355 254 28% 79-82, 124-140, 147-153, 188-212, 249-294, 331-348, 486-490, 596-598, 638-643, 681-685, 717, 750, 793-811, 816-818, 840-855, 880-920, 925-927, 974-993, 1034-1056, 1092, 1128, 1176-1213, 1252-1280, 1311, 1336, 1371, 1407, 1459-1462, 1516-1521, 1559-1563, 1571-1573, 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/usr/local/lib/python3.8/dist-packages/skimage/draw/_random_shapes.py 119 107 10% 40-62, 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 /usr/local/lib/python3.8/dist-packages/skimage/feature/_canny.py 108 100 7% 45-50, 157-297 /usr/local/lib/python3.8/dist-packages/skimage/feature/_daisy.py 97 90 7% 97-222 /usr/local/lib/python3.8/dist-packages/skimage/feature/_hog.py 82 77 6% 6-19, 34-43, 142-295 /usr/local/lib/python3.8/dist-packages/skimage/feature/_orb_descriptor_positions.py 6 0 100% /usr/local/lib/python3.8/dist-packages/skimage/feature/blob.py 148 130 12% 38-52, 79-81, 111-141, 168-185, 192-210, 312-373, 468-533, 618-644 /usr/local/lib/python3.8/dist-packages/skimage/feature/brief.py 41 33 20% 119-130, 143-185 /usr/local/lib/python3.8/dist-packages/skimage/feature/censure.py 100 81 19% 31-76, 80-90, 94-102, 106-107, 201-216, 242-296 /usr/local/lib/python3.8/dist-packages/skimage/feature/corner.py 200 168 16% 41-44, 95-104, 159-173, 191-197, 236-242, 246-248, 285, 317-325, 394-397, 433-445, 513-525, 583-588, 657-672, 733-737, 801-916, 981-1024, 1071, 1138 /usr/local/lib/python3.8/dist-packages/skimage/feature/haar.py 58 44 24% 21-33, 77-84, 195-219, 290-321 /usr/local/lib/python3.8/dist-packages/skimage/feature/match.py 36 33 8% 53-97 /usr/local/lib/python3.8/dist-packages/skimage/feature/orb.py 132 113 14% 120-131, 134-135, 140-161, 172-210, 213-222, 244-276, 290-348 /usr/local/lib/python3.8/dist-packages/skimage/feature/peak.py 133 124 7% 12-20, 28-40, 48-53, 162-254, 285-357 /usr/local/lib/python3.8/dist-packages/skimage/feature/template.py 59 53 10% 9-17, 22-28, 113-179 /usr/local/lib/python3.8/dist-packages/skimage/feature/texture.py 115 104 10% 106-155, 217-278, 326-337, 381-383, 444-493 /usr/local/lib/python3.8/dist-packages/skimage/feature/util.py 64 52 19% 10, 21, 27, 40, 77-135, 141-143, 166-174 /usr/local/lib/python3.8/dist-packages/skimage/filters/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/skimage/filters/_gabor.py 28 21 25% 10-12, 76-95, 170-177 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/call_trees.py 97 10 90% 102-106, 136, 198-207, 211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/conditional_expressions.py 12 1 92% 29 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/continue_statements.py 91 46 49% 34, 62-74, 79-93, 97-115, 124-128, 131-136, 149-154, 157-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/control_flow.py 208 86 59% 126-128, 144-155, 160-167, 184-195, 200-203, 236, 343-353, 357-378, 381-396, 399-432, 447-510 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/directives.py 92 39 58% 67-87, 94-101, 104-115, 139-140, 153-154, 156-157, 164-168, 171, 174 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/function_scopes.py 57 8 86% 70-88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/lists.py 100 72 28% 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 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/converter.py 125 24 81% 131, 136-138, 171, 187, 310-335, 340 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/function_wrappers.py 59 20 66% 55, 60-61, 66-68, 74, 76, 83, 85, 90-101, 107-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/naming.py 64 19 70% 62, 72-73, 79-88, 98-99, 110-113, 117-118 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/unsupported_features_checker.py 25 9 64% 35, 40-43, 46-49, 54, 57 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py 290 117 60% 55, 78-120, 146-147, 189, 206, 212-214, 218, 263-267, 280-287, 305, 308-309, 319-333, 343, 355, 362, 374, 413, 421-422, 425-426, 448, 450, 452, 459-464, 467-468, 481-482, 488-489, 499, 511, 514-522, 526-528, 532-534, 541-580, 588-590, 655-665, 732-734, 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, 735-751, 765-769, 772, 775-776, 779-781, 785-804, 808-811, 815-825, 830-846, 851-856, 861-886, 923, 932-966, 991-999, 1005-1030 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/data_structures.py 150 118 21% 45-54, 59-104, 109-163, 168, 189-199, 204-215, 220, 226-227, 257-269, 274-286, 291-295, 321-332, 337, 342-345, 351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/exceptions.py 24 14 42% 50-59, 77-80, 85-86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/logical.py 43 16 63% 29, 35, 47, 54, 64-67, 73, 78, 83-85, 90, 95, 100 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/py_builtins.py 253 176 30% 62, 68-84, 92-99, 120-161, 165-169, 173, 177-180, 184, 188-190, 195-197, 201, 205-207, 211-217, 221-223, 227-233, 237, 241, 247-273, 278, 284-294, 298, 303-319, 324-326, 336-343, 347-351, 355-361, 365, 369, 373-375, 379, 383, 387-389, 393, 397, 401-403, 407, 411, 415-417, 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, 418, 420-422, 455-456, 468-471, 507-511, 515-526, 566, 571-576, 580-586, 662, 665, 671, 695-700, 714-728, 758, 761, 770, 773, 779, 782, 785, 794, 797, 822-841, 844-871, 874, 877, 884, 886, 906-912, 928-931 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/error_utils.py 80 60 25% 84-123, 144-145, 148, 165-175, 179-209, 212-219, 222-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/errors.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/inspect_utils.py 144 65 55% 57, 64, 72-79, 89, 91, 171-173, 195-237, 246, 261-262, 292-293, 299, 304-336, 349 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/loader.py 34 2 94% 80, 88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/origin_info.py 114 17 85% 77, 81-85, 117, 139-155, 180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/parser.py 116 28 76% 63-67, 75-78, 81, 93, 149-178, 197-198, 213, 239, 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 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py 121 9 93% 78, 191-194, 204-206, 209-211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py 166 26 84% 56, 78, 109, 159-174, 249, 279-292, 295-296 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/templates.py 147 28 81% 69-70, 86-87, 90-93, 98-99, 158-167, 175, 179, 190-195, 260, 283, 289-292 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/transformer.py 134 27 80% 131, 258-259, 264-265, 322, 329, 382-395, 398-406, 415-418, 421, 443 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/ag_logging.py 47 19 60% 37, 87-88, 111, 117, 126-128, 132-135, 140-142, 146-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/compat_util.py 14 3 79% 31, 37-38 /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, 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/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 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/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, 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/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, 2672, 2675, 2682, 2686, 2691, 2695, 2708-2712, 2715, 2724-2728, 2733, 2739, 2751-2756 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distributed_file_utils.py 33 21 36% 57-58, 62, 66-68, 73-86, 91-104 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribution_strategy_context.py 102 23 77% 51, 151, 176, 256-266, 275-280, 302-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/estimator_training.py 176 152 14% 42-46, 51-55, 61-86, 94-124, 130-177, 184-200, 216-283, 297-336, 346-384 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/input_lib.py 673 488 27% 83-91, 124-131, 148-150, 155, 159, 162, 165-169, 172, 175, 180-216, 231-240, 245-256, 263-289, 292, 295-298, 301, 305-377, 388-391, 400, 405, 410, 415, 420, 424-427, 432, 444-450, 454, 459, 463, 469-481, 495-508, 513-521, 524, 527, 536, 546-571, 576, 580, 589-590, 593, 597-621, 650-705, 709-729, 734, 746-747, 763, 770-773, 785, 791-794, 797-803, 806-810, 830-845, 849-873, 879-885, 893-899, 906-909, 912-920, 923-927, 953-975, 1007-1019, 1025-1065, 1082-1086, 1089, 1093-1095, 1110-1112, 1130-1156, 1165-1167, 1171, 1174, 1178-1181, 1184, 1187, 1197, 1223-1239, 1244-1249, 1254, 1258, 1271, 1283, 1295, 1305-1306, 1319-1323, 1327, 1331, 1335, 1342-1344, 1348-1350, 1354-1357, 1361-1364, 1368, 1374-1388, 1396-1415, 1422-1431, 1440-1456, 1467-1478, 1498-1500, 1515, 1519-1521, 1543-1561, 1567, 1571-1579, 1595-1608 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/input_ops.py 41 27 34% 46-53, 58-61, 65, 79-101 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/mirrored_strategy.py 526 409 22% 65-74, 78-80, 113-203, 220-236, 241-252, 265-281, 285, 305-325, 329-332, 336-341, 426-429, 439-442, 451-476, 481-488, 492-505, 510-545, 553-569, 573-607, 613, 616, 626-633, 638, 645, 649-657, 664-669, 674-725, 733-739, 742-769, 777-788, 791-793, 796, 799-810, 818, 824-835, 838-846, 850-853, 856-858, 861, 865, 869, 873, 877, 881, 885, 889, 893, 896-898, 910, 914, 922-975, 978-1001, 1006-1010, 1016-1020, 1024-1026, 1030-1032, 1048-1086, 1090-1092 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/multi_worker_util.py 74 57 23% 40-46, 72-93, 120-134, 150-168, 173-188, 211-227, 243, 256, 261, 266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/numpy_dataset.py 45 29 36% 34-73, 79-91, 98 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/one_device_strategy.py 155 82 47% 80, 107, 146, 164, 182, 215, 231, 242, 251-256, 259-268, 271, 277, 284, 289, 293-294, 299, 303, 313, 318-357, 360-362, 365-366, 371, 374-380, 384, 387, 390, 394, 398, 402, 406, 409-410, 414, 418, 422, 426, 432, 436, 443-444, 449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/parameter_server_strategy.py 285 204 28% 111-120, 131-136, 151-158, 165-168, 188-255, 270-305, 311, 314, 321, 332-344, 349, 353-367, 376, 385-390, 393, 398-456, 460, 464-471, 476-481, 489-491, 498-515, 518-529, 533-539, 542-544, 547-552, 557, 580-591, 594-612, 616, 620, 624, 628, 632, 635, 640, 644, 648, 652, 664 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/reduce_util.py 18 5 72% 42-51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/shared_variable_creator.py 37 28 24% 29-35, 63-97 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/summary_op_util.py 15 7 53% 40-48 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/tpu_strategy.py 450 334 26% 65-76, 81-85, 101-105, 151-161, 168-174, 199-204, 210, 266-270, 282-349, 359-374, 378-381, 384, 388, 398-405, 412, 417, 424-432, 439-444, 452-539, 544-545, 550-565, 569-577, 582-611, 615, 623, 627-668, 675-712, 715-735, 738-740, 743-745, 748, 751-770, 774-777, 782-792, 796-798, 802, 806, 810, 814, 818, 822, 825, 828-834, 841-843, 846-851, 863, 866-867, 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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84-139, 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, 137, 140-146, 149-151, 154 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/common_shapes.py 35 26 26% 38-70, 84-86, 102-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/composite_tensor.py 26 9 65% 54, 71, 83-88, 107-112 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/composite_tensor_utils.py 57 41 28% 35, 43-47, 53-90, 97-110, 134-159 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/config.py 108 44 59% 37, 51, 64, 77, 91, 105, 121, 163, 178, 193, 209-219, 251-262, 275, 295-300, 406, 443, 472, 500, 541, 626, 632 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/constant_op.py 118 57 52% 39-46, 51-57, 62-65, 86-89, 93-94, 160, 273-292, 311-315, 337, 343-344, 347-350, 368-378 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/convert_to_constants.py 300 263 12% 56-70, 91-135, 147, 159, 179-185, 202-223, 248-302, 315-320, 330-335, 347-352, 366-374, 393-412, 447-650, 678-680, 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 72 66% 37-44, 82-84, 105, 110, 115, 119-163, 227, 246, 281, 316, 334, 349, 365, 382, 401, 419, 438, 454, 468, 485, 515, 520-524, 528-533, 545-546, 549-560 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/framework_lib.py 40 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/func_graph.py 504 152 70% 89-90, 95, 103-104, 115, 122, 214, 245, 251, 263, 293-319, 349-359, 390-391, 396, 419, 447, 452, 456, 468, 483, 508-528, 572, 581-582, 590, 626, 631-643, 662, 680-682, 686-690, 699, 704-705, 710-724, 728, 753, 769-772, 782, 786-793, 865, 873, 881, 891-893, 928, 933-934, 938-939, 966-970, 1004-1008, 1039-1045, 1064-1065, 1070, 1105-1113, 1169, 1173-1182, 1196, 1205, 1213-1216, 1226-1246, 1249, 1261 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/function.py 527 443 16% 123-128, 132-184, 198, 201-212, 270-304, 310-311, 316-329, 333-334, 338-340, 345, 350, 355, 360-361, 370-371, 375-376, 380-477, 489-493, 510-540, 544-560, 563-579, 617-625, 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/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 639 339 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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1298-1299, 1322-1325, 1346, 1360, 1368-1369, 1378-1379, 1390, 1400-1405, 1409-1410, 1433-1497, 1522-1525, 1553, 1559, 1565-1573, 1576-1582, 1587, 1591, 1603-1606, 1612, 1678, 1741-1744, 1787, 1802, 1818-1821, 1827, 1833-1847, 1852-1857, 1863-1877, 1883-1893, 1912-2056, 2069-2157 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/node.py 51 9 82% 76, 120-122, 158-159, 176-177, 182-184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/partial_batch_padding_handler.py 62 46 26% 34-36, 40-53, 58-62, 66-87, 91-111 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/sequential.py 196 102 48% 126, 167-169, 172, 190-199, 204, 215, 226-227, 241-255, 262-266, 270-299, 302-305, 311-312, 329-335, 359-363, 366-383, 387-403, 407-409, 413, 417-422 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py 459 256 44% 65-72, 86, 171, 196-197, 246-248, 347, 398-410, 450, 472, 480-494, 498, 527-544, 566-582, 785-881, 906-915, 936-952, 1039-1098, 1143, 1146-1151, 1273, 1290-1291, 1340-1359, 1400-1417, 1434-1439, 1464-1465, 1497-1500, 1525-1526, 1542, 1544, 1548-1549, 1559, 1568, 1572, 1576, 1581-1584, 1600, 1624-1627, 1634-1635, 1641, 1645, 1649-1660, 1663-1669, 1677-1688, 1691, 1701, 1721-1729, 1737, 1739, 1744, 1750-1760, 1785-1814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_arrays.py 259 222 14% 42-43, 126-458, 462-467, 471-477, 482-484, 501-535, 539-542, 546-550, 555-557, 564-583, 621-649, 678-687, 705-708 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_distributed.py 322 282 12% 46-47, 52-56, 73-120, 164-290, 315-420, 444-574, 599-672, 698-720, 737-754, 766-779, 786, 789, 793, 798 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_eager.py 119 98 18% 37-39, 55-82, 114-219, 250-283, 308-322, 349-366 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_generator.py 239 198 17% 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 29 69% 27-28, 80-87, 152-154, 230-238, 299-307 /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 363 275 24% 72-73, 76-79, 82-83, 114, 135, 140, 167-189, 207-210, 216-221, 251-257, 263-296, 301-305, 314-347, 356, 360-382, 386, 388, 392, 397-402, 415-426, 441-474, 490-493, 518-540, 553-675, 678, 691-692, 717-739, 754-756, 766, 774, 779-781, 792, 797 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/io_utils.py 84 52 38% 32-33, 84-99, 102, 105-134, 143, 152, 161, 170, 182-185, 201-209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/layer_utils.py 202 85 58% 50, 55, 62-69, 79-90, 136, 152-162, 205-208, 222-223, 228, 262, 287-294, 310-326, 345-357, 382-396, 400-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/losses_utils.py 58 39 33% 48-49, 54-55, 61-67, 91-112, 117-121, 137-148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/metrics_utils.py 216 159 26% 75-93, 119-149, 157-166, 170-173, 179-182, 199-204, 227-234, 296-456, 472-475, 495-539 /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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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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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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4936-4979, 4985-5012, 5032-5073, 5079-5100, 5125-5180, 5186-5212, 5230-5271, 5277-5297, 5312-5346, 5352-5364, 5384-5432, 5438-5463, 5493-5537, 5543-5567, 5598-5650, 5656-5682, 5699-5743, 5749-5770, 5788-5829, 5835-5855, 5870-5897, 5903-5915, 5936-5964, 5970-5981, 6000-6041, 6047-6067, 6089-6114, 6120-6135, 6149-6182, 6188-6203, 6223-6251, 6257-6268, 6281-6307, 6313-6322, 6393-6436, 6442-6465, 6478-6504, 6510-6519, 6541-6587, 6593-6618 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_debug_ops.py 422 389 8% 50-90, 96-116, 144-185, 191-211, 240-290, 296-322, 358-415, 421-451, 479-529, 535-561, 627-696, 702-739, 815-858, 864-883 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_decode_proto_ops.py 80 64 20% 102-165, 171-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_encode_proto_ops.py 59 44 25% 81-123, 129-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_experimental_dataset_ops.py 3926 3668 7% 36-78, 84-105, 134-176, 182-202, 231-280, 286-311, 327-368, 374-395, 417-459, 465-488, 509-578, 584-620, 636-687, 693-719, 735-761, 767-776, 792-818, 824-833, 852-872, 877-885, 908-951, 957-979, 999-1047, 1053-1080, 1096-1140, 1146-1167, 1196-1246, 1252-1278, 1294-1338, 1344-1365, 1387-1430, 1436-1460, 1476-1529, 1535-1562, 1578-1606, 1612-1622, 1641-1664, 1669-1678, 1701-1746, 1752-1774, 1794-1844, 1850-1877, 1918-1987, 1993-2023, 2048-2110, 2116-2144, 2159-2202, 2208-2228, 2241-2267, 2273-2283, 2298-2339, 2345-2365, 2381-2424, 2430-2451, 2490-2547, 2553-2582, 2601-2661, 2667-2696, 2709-2735, 2741-2751, 2768-2813, 2819-2840, 2855-2898, 2904-2924, 2957-3012, 3018-3045, 3087-3169, 3175-3223, 3240-3284, 3290-3311, 3331-3372, 3378-3399, 3423-3472, 3478-3503, 3522-3575, 3581-3608, 3626-3673, 3679-3702, 3718-3760, 3766-3787, 3811-3858, 3864-3887, 3906-3948, 3954-3976, 3990-4026, 4032-4047, 4060-4087, 4093-4103, 4129-4177, 4183-4205, 4222-4266, 4272-4293, 4315-4366, 4372-4394, 4409-4450, 4456-4476, 4491-4532, 4538-4558, 4599-4665, 4671-4700, 4725-4785, 4791-4818, 4833-4874, 4880-4899, 4912-4938, 4944-4953, 4981-5021, 5027-5046, 5062-5103, 5109-5129, 5162-5221, 5227-5257, 5296-5353, 5359-5387, 5400-5426, 5432-5441, 5458-5502, 5508-5529, 5544-5585, 5591-5611, 5668-5719, 5725-5752, 5797-5908, 5914-5983, 6033-6146, 6152-6222, 6239-6281, 6287-6308, 6339-6379, 6385-6405, 6429-6478, 6484-6508, 6536-6577, 6583-6605, 6625-6683, 6689-6719, 6737-6782, 6788-6811, 6827-6869, 6875-6895, 6919-6965, 6971-6993, 7030-7157, 7163-7233, 7252-7294, 7300-7321, 7335-7369, 7375-7389, 7403-7438, 7444-7459, 7473-7494, 7499-7507, 7520-7546, 7552-7562, 7588-7634, 7640-7661, 7678-7720, 7726-7746, 7768-7819, 7825-7846, 7861-7902, 7908-7927, 7942-7983, 7989-8008 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_functional_ops.py 540 492 9% 58-106, 112-138, 157-186, 192-202, 226-252, 258-269, 297-344, 350-373, 395-441, 447-471, 489-522, 528-542, 565-613, 619-643, 674-721, 727-750, 781-823, 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 492 436 11% 48-63, 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, 103-136, 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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 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/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, 1005-1034, 1106-1144, 1147-1149, 1152-1155, 1159-1161, 1165, 1169-1174, 1191-1215, 1223-1226, 1311-1347, 1408-1446, 1520-1537, 1588-1599, 1645-1667, 1673-1694, 1698, 1709-1711, 1729-1737, 1756-1762, 1781-1789, 1801-1806, 1810, 1820-1826, 1829-1833, 1903-1930, 1936-1950, 1954, 1965-1967, 1986-1993, 2012-2020, 2039, 2057-2066, 2084, 2096-2102, 2106, 2116-2122, 2125-2129 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/loss_reduction.py 16 1 94% 68 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/losses.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/losses_impl.py 237 180 24% 61, 71-72, 86-87, 112-129, 134-135, 174-203, 245-255, 296-311, 348-361, 414-437, 480-493, 548-591, 633-643, 690-707, 756-778, 805-830, 873-887 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/util.py 88 59 33% 58-121, 139-145, 158-175, 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, 1065-1068, 1073-1077, 1083-1084, 1090-1093, 1099-1102, 1108-1116, 1122-1132, 1138-1148, 1154-1160, 1166-1173, 1180, 1185-1189, 1197-1225, 1231-1259, 1265-1300, 1306-1315, 1324-1340, 1350, 1356-1366, 1371, 1377-1393, 1404-1420, 1431-1488, 1492-1498, 1503-1535, 1541, 1547, 1553-1587, 1605-1608, 1614-1633, 1638-1649, 1654-1665, 1671-1698, 1705-1746, 1755, 1760, 1765, 1771, 1777-1797, 1803-1835, 1845-1850, 1857-1858, 1864-1865, 1871-1878, 1884, 1890, 1899-1908, 1913-1915, 1920-1923, 1931-1940, 1945-1974, 1980-1988 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/math_ops.py 1023 622 39% 138-140, 171-173, 187-189, 224-226, 264-268, 276, 292-293, 296, 299, 302, 327-332, 381, 389, 399, 410, 436, 461-471, 477-478, 505-506, 541-552, 580-591, 619-625, 653-658, 693-698, 725-729, 775-792, 814-825, 844, 863, 882, 901, 920, 939, 958, 985-997, 1000-1002, 1008-1010, 1015-1016, 1054-1070, 1075-1090, 1107-1118, 1151, 1177, 1195-1198, 1215-1223, 1259-1260, 1273-1276, 1285-1288, 1354, 1399, 1445, 1481, 1486-1494, 1499-1505, 1564-1592, 1597-1598, 1609-1627, 1632, 1637-1640, 1692-1697, 1741, 1750-1751, 1792, 1864-1873, 1928-1934, 2001-2006, 2061-2062, 2110-2114, 2157-2160, 2191-2192, 2236-2241, 2281-2286, 2322-2323, 2367-2372, 2419, 2428-2429, 2480-2485, 2525-2526, 2579-2584, 2624-2625, 2680-2685, 2726-2745, 2787-2789, 2912-2983, 3075-3083, 3096-3106, 3113-3123, 3142-3147, 3166-3182, 3223-3239, 3282-3310, 3317, 3366-3368, 3387-3389, 3447-3464, 3497, 3565-3567, 3618-3620, 3673-3675, 3709-3720, 3734-3748, 3765-3778, 3819-3824, 3868-3873, 3941-3949, 4017, 4056-4064, 4102, 4132-4140, 4174, 4232-4361, 4416-4431, 4460-4463, 4499-4500, 4517-4518, 4532-4533, 4560, 4597, 4643, 4669-4670, 4694 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/metrics.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/metrics_impl.py 796 681 14% 76, 111-161, 180-209, 226-228, 255-277, 282-311, 358-393, 446-457, 508-621, 625-626, 720-859, 912-919, 972-994, 1045-1099, 1154-1202, 1257-1270, 1324-1331, 1382-1417, 1465-1471, 1502-1518, 1556-1569, 1612-1626, 1665-1678, 1721-1735, 1774-1787, 1830-1844, 1883-1896, 1939-1953, 2006-2048, 2103-2129, 2180-2222, 2226-2232, 2246-2265, 2286-2288, 2323-2334, 2375-2385, 2417-2429, 2470-2480, 2558-2565, 2630-2657, 2710-2735, 2788-2806, 2868-2908, 2930-2962, 2985-3000, 3035-3089, 3142-3182, 3199-3220, 3236, 3306-3322, 3360-3372, 3413-3423, 3481-3512, 3526, 3613-3620, 3690-3750 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nccl_ops.py 85 57 33% 47, 64-72, 93, 110, 127, 146, 163-170, 183-186, 202-207, 212-236, 241-251, 257-260, 264-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn_grad.py 344 217 37% 43, 71, 105, 127, 148-149, 171-172, 194-195, 216, 227, 238, 250, 266, 300-302, 319-320, 342-346, 364-384, 406-407, 413, 418-419, 426-427, 434, 439-440, 446-448, 453-455, 461, 466, 471, 480-484, 489, 494-495, 510-511, 522-536, 548-562, 568-582, 608, 630, 644-648, 656, 667, 678, 690-692, 704-705, 717, 733-735, 751, 782, 804, 830-833, 861-908, 913, 918, 923, 954-1002, 1020-1040, 1045, 1050-1051, 1065, 1081-1105, 1126-1138 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn_impl.py 390 293 25% 86-109, 161-186, 240, 299-315, 382-383, 419-439, 471-477, 495-500, 520-536, 584-589, 616-617, 642-646, 661-667, 690-708, 763-794, 852, 920-957, 1021, 1061-1088, 1116, 1137-1149, 1187-1214, 1250, 1271-1328, 1347, 1415-1421, 1498-1545, 1594-1597, 1641, 1660-1663, 1726-1848, 1941, 2046-2063, 2142, 2240-2258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn_ops.py 1086 861 21% 62-81, 130-142, 169-214, 220, 231, 296-299, 317-319, 468-482, 512-588, 593-635, 638, 646-658, 686-717, 739-760, 887-890, 909, 935-1001, 1030-1075, 1085, 1095-1106, 1200-1274, 1371, 1523, 1545-1566, 1637-1664, 1721, 1783-1817, 1910, 1997-2006, 2071-2072, 2133-2136, 2202-2206, 2274-2283, 2343-2448, 2460-2462, 2481-2482, 2549-2551, 2614-2623, 2700-2714, 2729, 2769-2774, 2785-2794, 2821-2824, 2853-2856, 2861, 2882-2884, 2910-2916, 2921-2943, 2968-3022, 3062-3065, 3090-3092, 3119-3122, 3147-3149, 3154-3158, 3218, 3277-3338, 3405-3413, 3472-3539, 3590, 3621-3647, 3680-3691, 3722-3730, 3763-3781, 3806-3814, 3848-3874, 3910-3923, 3954-3972, 3996-4004, 4039-4047, 4110-4116, 4137-4144, 4160-4172, 4179-4190, 4198-4208, 4214-4217, 4235-4240, 4259-4264, 4269-4289, 4330-4343, 4419-4464, 4494, 4524, 4608, 4685-4693, 4758, 4814-4820, 4828-4838, 4888-4890, 4954-4959, 4999-5000, 5005 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/numerics.py 39 21 46% 46-49, 64-69, 99-122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/op_selector.py 193 169 12% 26-29, 34-40, 54-59, 72-77, 96-115, 126-131, 148-161, 174-175, 188-194, 213-226, 230-234, 267-308, 319-321, 325, 339-363, 393-419 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/optional_grad.py 12 2 83% 27, 33 /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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/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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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/segment_id_ops.py 50 34 32% 55-71, 98-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_grad.py 32 18 44% 29-32, 58-71, 99-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py 147 77 48% 86-96, 136-153, 187-197, 276, 278-280, 292, 298-299, 340-341, 371-386, 415-416, 443-444, 449-451, 459-465, 544-558, 598, 637-643 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py 686 328 52% 65-70, 76-81, 97-102, 126-143, 151, 161-163, 184-191, 254-255, 270, 291-300, 318-320, 447-452, 464-468, 471, 474, 477, 480-491, 506, 519, 522-524, 527-530, 535-538, 548, 554, 559, 564-566, 576, 581, 589, 593, 597-599, 602-604, 628, 641-643, 665-669, 673-688, 692-698, 713-743, 747-749, 765, 786-792, 810-816, 851, 855, 878-880, 898-900, 919-921, 940-942, 960-962, 980-982, 1000-1002, 1050-1052, 1101, 1150, 1199, 1206-1207, 1222, 1225, 1228, 1231, 1236, 1240, 1245, 1251, 1257, 1262, 1267, 1272, 1277, 1415-1421, 1512, 1517, 1527, 1531, 1534-1536, 1549-1550, 1571-1576, 1586, 1595-1632, 1639-1642, 1649, 1655, 1657, 1671-1731, 1790-1791, 1800-1807, 1846, 1853-1856, 1865-1868, 1871, 1874, 1877-1881, 1884-1885, 1889-1890, 1894-1895, 1899-1900, 1904-1905, 1909-1910, 1914-1915, 1919-1920, 1924-1925, 1929-1930, 1934-1935, 1939-1940, 1943-1944, 1947-1948, 1954, 1963, 1967-1973, 1980-1987, 1992, 1997-1999, 2050-2061, 2076, 2079, 2082-2083 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resources.py 38 19 50% 53-57, 62, 67, 86-104, 118-120 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn.py 455 407 11% 55-66, 82-93, 110-123, 127-130, 135-142, 163, 230-315, 330-357, 449-514, 638-716, 760-936, 1105-1259, 1330-1444, 1482-1538, 1594-1635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell_impl.py 509 368 28% 63-68, 86-101, 127-164, 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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1128-1145, 1180-1199, 1225-1255, 1262-1273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/template.py 245 168 31% 154, 212-225, 236-239, 289-314, 317-378, 381-393, 398, 403, 408, 413-419, 424, 429-433, 439-441, 446-450, 455-459, 464, 469, 474, 482, 489-494, 497, 501-515, 519-522, 528, 531, 535, 583-596, 599-655, 661-681, 687-689, 695-697, 703-705, 711-713, 719 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/tensor_array_grad.py 93 49 47% 70-80, 102-111, 129-137, 159-168, 184-192, 214-224, 240-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/tensor_array_ops.py 537 380 29% 101-167, 171, 175, 179, 183, 195-199, 214-220, 224-225, 233-250, 254-262, 266-279, 283-290, 294-307, 311-319, 324-326, 332-347, 352-370, 374-377, 383, 432-473, 477, 481, 485, 491, 503-507, 511-512, 516, 520-527, 531-543, 547-558, 562-568, 572-582, 587-595, 600-610, 615-633, 637-640, 644, 688-717, 722, 726, 731, 735, 739, 742, 749-778, 793-828, 832-834, 837-841, 845-852, 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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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/usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/preempted_hook.py 46 27 41% 40, 43-45, 48, 61-72, 75-77, 80-93 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/session_support.py 205 148 28% 45, 50, 71-74, 79-91, 95, 104-106, 111-124, 128-140, 144, 150-160, 165-175, 211-222, 226-247, 253-259, 262-265, 268, 271, 277-284, 293-298, 304-306, 325-333, 338-360, 364-381, 384-406, 417, 420-424, 435, 438, 449, 452 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer.py 795 657 17% 113-144, 158-179, 197-200, 217-231, 237-249, 274, 280-281, 293-295, 300, 314, 333, 340-346, 350-354, 361-373, 378-380, 386, 393-399, 402, 418-443, 448-455, 460-462, 479-509, 514-520, 524-531, 534, 538-552, 556, 571-585, 596-599, 612-745, 762-857, 871-909, 926-983, 989-1022, 1043-1055, 1060-1080, 1093-1122, 1125, 1140-1194, 1209-1213, 1219-1233, 1237-1252, 1256-1266, 1279-1283, 1294-1399, 1402, 1406, 1435-1600, 1630-1659, 1680-1701 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_flags.py 250 163 35% 103-152, 155, 160-170, 174-183, 186-194, 199-211, 214, 219-229, 245-259, 263-290, 299-315, 319, 335-340, 353-363, 376-385, 401-418, 423-431, 436-444, 449-454, 468 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_pb2.py 28 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_report.py 242 183 24% 68-119, 126-130, 137-139, 143-160, 166-183, 193-199, 202, 205-206, 213-214, 217, 220, 223, 243-277, 281-284, 289-296, 300-304, 310-314, 319-335, 340-343, 348-361, 366-382, 387-396, 402-408 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/topology.py 90 60 33% 29-32, 37-40, 75-94, 98-129, 133-140, 145, 150, 164, 169, 182, 195, 199, 204, 211, 216, 220-228 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu.py 696 574 18% 90-93, 116-119, 145-147, 160-162, 175, 180-190, 196-198, 202-207, 216-221, 232-233, 235, 271-286, 303-350, 353-359, 363-399, 402-414, 420-463, 466-471, 474-481, 484, 488-508, 512-587, 591-604, 607-609, 617, 622-624, 627, 638-639, 642-645, 650-653, 658, 726-780, 886, 898-903, 926-1028, 1093-1354, 1373-1415, 1430-1461, 1532-1611, 1683, 1743, 1797, 1819-1831, 1843-1845, 1848, 1852-1858, 1861-1864, 1867, 1871, 1895-1896, 1936-1963, 1984-2001 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_embedding.py 574 454 21% 106-126, 158-162, 197, 202, 223-229, 263-268, 316-322, 381-399, 458-482, 522, 691-781, 790, 801, 810, 822, 833, 837, 841, 845, 849, 853-908, 945-1012, 1033-1034, 1045-1112, 1116-1118, 1135-1163, 1175-1197, 1215-1237, 1246-1248, 1255-1267, 1273-1274, 1280-1281, 1291, 1294, 1297, 1301, 1308-1309, 1312, 1315, 1319-1377, 1384-1386, 1389-1397, 1401, 1406-1477, 1484-1486, 1489-1497, 1503, 1508-1580, 1587, 1591, 1595-1643, 1648-1657, 1662, 1667-1671, 1676-1697, 1703-1721, 1732-1758 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_embedding_gradient.py 53 37 30% 45-50, 71-97, 118-126, 142-179 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_feed.py 285 232 19% 52-83, 98-104, 120-121, 165-198, 206-209, 214, 219, 237-251, 258, 276-295, 300, 312, 331-337, 347, 360-362, 378-382, 404-432, 445-458, 482-496, 529-546, 596-604, 618, 621, 679-722, 757-765, 782-794, 834-882, 897-920, 934-935 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_function.py 28 11 61% 35, 38, 48-54, 58, 66-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_optimizer.py 64 43 33% 56-70, 86-107, 144-161, 184-192, 206, 220, 224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_sharding.py 91 68 25% 36-38, 41-44, 48-51, 60-62, 67, 82-91, 98, 114-120, 132-135, 161-188, 204-216, 239-252 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_strategy_util.py 95 73 23% 53-127, 146-206 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_system_metadata.py 101 75 26% 56-149, 154-166, 174-176, 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 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_state_pb2.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_utils.py 161 125 22% 63-67, 82-85, 98-104, 125-135, 181-201, 286-291, 297-378, 384-386, 406-427, 447-459, 463, 469-476 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/coordinator.py 141 104 26% 142-159, 178-185, 201-244, 251-255, 263, 296-299, 311, 319-320, 353-395, 401, 405-407, 444-457, 478-481, 484-496, 500, 504, 508-509 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/device_setter.py 64 45 30% 53-54, 66-68, 93-98, 111-133, 202-231 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/evaluation.py 106 74 30% 47-61, 74-78, 92-94, 97, 100, 105-109, 112, 116-130, 145-149, 153, 156, 160-169, 225-277 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/__init__.py 0 0 100% /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, 3244-3259, 3288-3316, 3321-3335, 3375-3402, 3407-3423, 3452-3474, 3480, 3509-3534, 3540, 3571-3595, 3601, 3632-3658, 3664, 3714-3737, 3743, 3782-3804, 3810, 3852-3874, 3880, 3917-3943, 3949, 3982-4003, 4009, 4038-4061, 4067, 4109-4131, 4137 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/gradient_descent.py 28 10 64% 51-53, 56, 63, 69, 73-77, 80-81 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/input.py 387 284 27% 74-75, 103-114, 174-202, 256-267, 315-317, 363-376, 383, 399-401, 404-415, 418, 421, 425-430, 434, 438, 442, 446-449, 453-463, 467-475, 509-577, 582-597, 602-627, 631-634, 638-642, 647-656, 660-667, 671-678, 698-709, 714-718, 723-735, 740-752, 756, 765-791, 804-832, 840-876, 885-919, 1009, 1066, 1177, 1234, 1335, 1399, 1498, 1562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/learning_rate_decay.py 87 58 33% 97-103, 150-179, 268-280, 356-368, 444-451, 507-514, 579-591, 664-676, 757-771 /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 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py 68 56 18% 57-73, 87-122, 150-174, 177, 182 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/mode_keys.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/model_fn.py 176 139 21% 152-164, 180-184, 213-221, 237-242, 283-295, 316-331, 353-377, 405-408, 424-430, 459-507, 512-516, 534-538, 542-543, 548-550, 577-598, 607-620 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/run_config.py 315 208 34% 95-113, 118-121, 126-130, 135-139, 145-149, 154-199, 211-226, 236-248, 254-328, 337-341, 536-586, 596-597, 609-627, 635-689, 697-724, 728, 738, 742, 746, 750, 754, 758, 762, 802, 806, 810, 814, 818, 822, 826, 830, 834, 838, 842, 846, 850, 855, 860, 865, 870, 907, 933-946, 957-975 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tools/__init__.py 0 0 100% /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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/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:1950: 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:1951: 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:1957: 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:2141: 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:2142: 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:2148: 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 810185 560481 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 : 0 339.36user 129.15system 13:31.62elapsed 57%CPU (0avgtext+0avgdata 5527312maxresident)k 9367720inputs+994632outputs (34008major+12978918minor)pagefaults 0swaps