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/05022025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_2/data_log/job/2025/February/05022025/coverage/htmlcov new path : /data_2/data_log/job/2025/February/05022025/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 : 10774 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 : 0.20192813873291016 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 Wed Feb 5 11:20:29 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 mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10774 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 2025-02-05 11:20:32.773890: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-05 11:20:32.781532: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-05 11:20:32.783176: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fe3f8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-05 11:20:32.783214: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-05 11:20:32.785646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-05 11:20:32.899980: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x42053300 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-05 11:20:32.900027: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-05 11:20:32.901460: 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 2025-02-05 11:20:32.901829: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:20:32.904571: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:20:32.907014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:20:32.907529: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:20:32.909952: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:20:32.911161: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:20:32.915735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:20:32.917284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:20:32.917347: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:20:32.918109: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-05 11:20:32.918126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-05 11:20:32.918137: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-05 11:20:32.919536: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9841 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 2025-02-05 11:20:33.614972: 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 2025-02-05 11:20:33.615091: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:20:33.615125: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:20:33.615156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:20:33.615184: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:20:33.615212: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:20:33.615241: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:20:33.615270: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:20:33.617072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:20:33.618825: 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 2025-02-05 11:20:33.618881: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:20:33.618937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:20:33.618970: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:20:33.618998: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:20:33.619047: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:20:33.619077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:20:33.619104: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:20:33.620876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:20:33.620921: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-05 11:20:33.620937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-05 11:20:33.620950: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-05 11:20:33.622811: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9841 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 : [] 2025-02-05 11:20:42.584647: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:20:42.898725: 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 3436928 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4686 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 : 9975 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 : 0.0005359649658203125 nb_pixel_total : 15552 time to create 1 rle with old method : 0.037171363830566406 length of segment : 256 time for calcul the mask position with numpy : 0.0028815269470214844 nb_pixel_total : 145330 time to create 1 rle with old method : 0.38184595108032227 length of segment : 371 time for calcul the mask position with numpy : 0.0002865791320800781 nb_pixel_total : 14255 time to create 1 rle with old method : 0.03465414047241211 length of segment : 151 time for calcul the mask position with numpy : 0.0001556873321533203 nb_pixel_total : 5614 time to create 1 rle with old method : 0.014177560806274414 length of segment : 48 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1826 time to create 1 rle with old method : 0.004492044448852539 length of segment : 40 time spent for convertir_results : 1.3880689144134521 time spend for datou_step_exec : 20.912941455841064 time spend to save output : 5.4836273193359375e-05 total time spend for step 1 : 20.912996292114258 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 3260 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 : 0.012005329132080078 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, 0.99548495, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (126, 30, 28), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,226,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.99237615, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (237, 40, 152), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 310), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), (89, 111, 449), (89, 112, 450), (89, 113, 451), (89, 114, 453), (89, 115, 454), (89, 116, 455), (88, 117, 456), (88, 118, 457), (87, 119, 459), (87, 120, 459), (86, 121, 461), (86, 122, 461), (85, 123, 463), (84, 124, 464), (84, 125, 465), (83, 126, 466), (82, 127, 468), (82, 128, 468), (81, 129, 470), (80, 130, 471), (78, 131, 473), (77, 132, 475), (75, 133, 477), (73, 134, 480), (71, 135, 482), (70, 136, 484), (68, 137, 486), (67, 138, 488), (65, 139, 490), (64, 140, 492), (63, 141, 493), (61, 142, 496), (60, 143, 497), (59, 144, 499), (58, 145, 501), (58, 146, 501), (57, 147, 503), (57, 148, 504), (57, 149, 505), (56, 150, 507), (56, 151, 507), (55, 152, 509), (55, 153, 510), (54, 154, 511), (54, 155, 512), (54, 156, 513), (53, 157, 514), (53, 158, 514), (52, 159, 516), (52, 160, 516), (52, 161, 516), (51, 162, 517), (51, 163, 517), (50, 164, 518), (50, 165, 518), (49, 166, 519), (49, 167, 520), (48, 168, 521), (48, 169, 521), (47, 170, 522), (47, 171, 522), (46, 172, 523), (46, 173, 523), (46, 174, 523), (45, 175, 524), (45, 176, 523), (44, 177, 524), (44, 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['598,172,591,172,576,167,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,501,40,514,32,517,29,531,25,539,25,540,24,560,24,561,25,579,25,580,26,593,26,594,25,625,25,628,24,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8296314, [(291, 3, 129), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 44), 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32, 38), (474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,529,10,532,12,537,12,538,11,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1738750829_3436695_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 9957 ############################### 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 : 0.15590357780456543 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 Wed Feb 5 11:20:51 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 mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 9581 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-05 11:20:55.275455: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-05 11:20:55.284067: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-05 11:20:55.285522: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fe3fc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-05 11:20:55.285545: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-05 11:20:55.288342: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-05 11:20:55.434766: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x433d3140 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-05 11:20:55.434833: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-05 11:20:55.435886: 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 2025-02-05 11:20:55.436246: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:20:55.438647: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:20:55.441117: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:20:55.441588: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:20:55.444311: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:20:55.445765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:20:55.450608: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:20:55.452107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:20:55.452203: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:20:55.452961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-05 11:20:55.452980: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-05 11:20:55.452991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-05 11:20:55.454566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5189 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. 2025-02-05 11:20:55.582757: 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 2025-02-05 11:20:55.582964: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:20:55.583016: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:20:55.583041: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:20:55.583062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:20:55.583083: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:20:55.583103: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:20:55.583125: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:20:55.584242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:20:55.585335: 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 2025-02-05 11:20:55.585394: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:20:55.585413: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:20:55.585429: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:20:55.585445: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:20:55.585462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:20:55.585478: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:20:55.585495: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:20:55.586474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:20:55.586510: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-05 11:20:55.586519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-05 11:20:55.586527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-05 11:20:55.587583: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5189 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 : [] 2025-02-05 11:21:05.160913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:21:05.425381: 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 3438435 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 347 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 : 5636 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 : 0.0005314350128173828 nb_pixel_total : 16902 time to create 1 rle with old method : 0.05074357986450195 length of segment : 107 time for calcul the mask position with numpy : 0.009021759033203125 nb_pixel_total : 480769 time to create 1 rle with new method : 0.02370905876159668 length of segment : 632 time for calcul the mask position with numpy : 0.0005121231079101562 nb_pixel_total : 36560 time to create 1 rle with old method : 0.08620190620422363 length of segment : 133 time for calcul the mask position with numpy : 0.00013136863708496094 nb_pixel_total : 4792 time to create 1 rle with old method : 0.012044429779052734 length of segment : 50 time spent for convertir_results : 0.487713098526001 time spend for datou_step_exec : 20.663636445999146 time spend to save output : 4.8160552978515625e-05 total time spend for step 1 : 20.663684606552124 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 397 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 : 0.013468265533447266 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, 0.998838, [(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, 0.9977361, [(711, 22, 22), (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), (561, 30, 479), (556, 31, 486), (550, 32, 495), (545, 33, 502), (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), (490, 46, 586), (488, 47, 590), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (463, 66, 625), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (454, 72, 636), (453, 73, 637), (452, 74, 638), (451, 75, 639), (449, 76, 641), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (443, 81, 647), (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), (432, 91, 658), (431, 92, 659), (430, 93, 660), (429, 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['471,46,445,46,444,45,433,45,432,44,431,45,426,45,424,41,424,37,423,36,422,31,419,25,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,421,0,422,1,439,1,440,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,472,45'])], 'temp/1738750851_3436695_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 : 0.277332067489624 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 Wed Feb 5 11:21:14 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 mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 5636 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-05 11:21:18.174154: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-05 11:21:18.207332: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-05 11:21:18.208834: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fe404000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-05 11:21:18.208881: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-05 11:21:18.211712: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-05 11:21:18.372905: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x43a30bc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-05 11:21:18.372977: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-05 11:21:18.374062: 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 2025-02-05 11:21:18.374750: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:21:18.378700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:21:18.381695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:21:18.382266: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:21:18.385100: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:21:18.386443: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:21:18.391718: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:21:18.393205: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:21:18.393302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:21:18.394102: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-05 11:21:18.394120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-05 11:21:18.394130: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-05 11:21:18.395502: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5155 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. 2025-02-05 11:21:18.544015: 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 2025-02-05 11:21:18.544230: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:21:18.544259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:21:18.544280: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:21:18.544300: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:21:18.544316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:21:18.544341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:21:18.544360: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:21:18.545471: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:21:18.547129: 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 2025-02-05 11:21:18.547177: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:21:18.547197: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:21:18.547235: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:21:18.547254: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:21:18.547274: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:21:18.547290: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:21:18.547308: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:21:18.548356: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:21:18.548416: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-05 11:21:18.548429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-05 11:21:18.548437: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-05 11:21:18.549618: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5155 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 : [] 2025-02-05 11:21:31.287396: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:21:31.547313: 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 3439205 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 347 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 : 5636 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 : 0.06948685646057129 nb_pixel_total : 3693047 time to create 1 rle with new method : 0.11921238899230957 length of segment : 2042 time spent for convertir_results : 1.3115053176879883 time spend for datou_step_exec : 25.311094045639038 time spend to save output : 3.838539123535156e-05 total time spend for step 1 : 25.311132431030273 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 717 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 : 0.013562202453613281 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, 0.985061, [(675, 120, 112), (520, 121, 481), (1050, 121, 381), (503, 122, 946), (486, 123, 981), (470, 124, 1015), (455, 125, 1046), (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), (363, 135, 1266), (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, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (323, 159, 1389), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (280, 182, 1508), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1681), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 1710), (203, 210, 1712), (201, 211, 1716), (199, 212, 1719), (198, 213, 1721), (196, 214, 1725), (195, 215, 1727), (193, 216, 1730), (192, 217, 1733), (191, 218, 1735), (189, 219, 1738), (188, 220, 1740), (187, 221, 1742), (186, 222, 1744), (185, 223, 1746), (183, 224, 1749), (182, 225, 1751), (181, 226, 1753), (180, 227, 1755), (179, 228, 1757), (178, 229, 1759), (177, 230, 1761), (176, 231, 1762), (176, 232, 1763), (175, 233, 1765), (174, 234, 1767), (173, 235, 1768), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1775), (168, 240, 1777), (167, 241, 1779), (166, 242, 1781), (165, 243, 1783), (164, 244, 1785), (163, 245, 1787), (162, 246, 1789), (161, 247, 1791), (159, 248, 1794), (158, 249, 1796), (157, 250, 1798), (156, 251, 1800), (154, 252, 1803), (153, 253, 1805), (152, 254, 1807), (151, 255, 1809), (149, 256, 1812), (148, 257, 1815), (146, 258, 1818), (145, 259, 1820), (143, 260, 1823), (142, 261, 1826), (140, 262, 1829), (139, 263, 1832), (137, 264, 1835), (135, 265, 1839), (133, 266, 1842), (132, 267, 1845), (130, 268, 1849), (128, 269, 1852), (126, 270, 1856), (125, 271, 1859), (124, 272, 1862), (122, 273, 1865), (121, 274, 1868), (120, 275, 1871), (119, 276, 1873), (118, 277, 1876), (116, 278, 1879), (115, 279, 1881), (114, 280, 1884), (113, 281, 1886), (112, 282, 1888), (111, 283, 1890), (110, 284, 1892), (109, 285, 1895), (108, 286, 1897), (108, 287, 1898), (107, 288, 1900), (106, 289, 1902), (105, 290, 1904), (104, 291, 1906), (103, 292, 1908), (103, 293, 1909), (102, 294, 1910), (101, 295, 1912), (101, 296, 1913), (100, 297, 1915), (99, 298, 1917), (99, 299, 1918), (98, 300, 1919), (97, 301, 1921), (97, 302, 1922), (96, 303, 1924), (95, 304, 1925), (95, 305, 1926), (94, 306, 1928), (94, 307, 1928), (93, 308, 1930), (93, 309, 1930), (93, 310, 1931), (93, 311, 1931), (92, 312, 1933), (92, 313, 1933), (92, 314, 1934), (92, 315, 1934), (91, 316, 1936), (91, 317, 1936), (91, 318, 1937), (91, 319, 1937), (90, 320, 1939), (90, 321, 1939), (90, 322, 1940), (89, 323, 1941), (89, 324, 1942), (89, 325, 1943), (89, 326, 1943), (88, 327, 1945), (88, 328, 1945), (88, 329, 1946), (87, 330, 1948), (87, 331, 1948), (87, 332, 1949), (87, 333, 1949), (86, 334, 1951), (86, 335, 1952), (86, 336, 1952), (85, 337, 1954), (85, 338, 1955), (85, 339, 1955), (85, 340, 1956), (84, 341, 1958), (84, 342, 1959), (84, 343, 1959), (83, 344, 1961), (83, 345, 1962), (83, 346, 1963), (83, 347, 1963), (82, 348, 1965), (82, 349, 1966), (82, 350, 1967), (81, 351, 1969), (81, 352, 1970), (81, 353, 1970), (80, 354, 1972), (80, 355, 1973), (80, 356, 1974), (80, 357, 1975), (79, 358, 1977), (79, 359, 1978), (79, 360, 1979), (78, 361, 1981), (78, 362, 1982), (78, 363, 1983), (77, 364, 1985), (77, 365, 1986), (77, 366, 1987), (77, 367, 1988), (76, 368, 1990), (76, 369, 1991), (76, 370, 1992), (75, 371, 1994), (75, 372, 1995), (75, 373, 1996), (74, 374, 1998), (74, 375, 1999), (74, 376, 2000), (73, 377, 2002), (73, 378, 2003), (73, 379, 2004), (72, 380, 2005), (72, 381, 2006), (72, 382, 2007), (71, 383, 2009), (71, 384, 2009), (71, 385, 2010), (70, 386, 2012), (70, 387, 2012), (70, 388, 2013), (70, 389, 2013), (69, 390, 2015), (69, 391, 2015), (69, 392, 2016), (68, 393, 2017), (68, 394, 2018), (68, 395, 2019), (67, 396, 2020), (67, 397, 2021), (67, 398, 2021), (66, 399, 2023), (66, 400, 2023), (65, 401, 2025), (65, 402, 2025), (65, 403, 2026), (64, 404, 2027), (64, 405, 2028), (64, 406, 2028), (63, 407, 2030), (63, 408, 2030), (63, 409, 2031), (62, 410, 2032), (62, 411, 2033), (61, 412, 2034), (61, 413, 2034), (61, 414, 2035), (60, 415, 2036), (60, 416, 2037), (59, 417, 2038), (59, 418, 2039), (58, 419, 2040), (58, 420, 2041), (58, 421, 2041), (57, 422, 2042), (57, 423, 2043), (56, 424, 2044), (56, 425, 2045), (55, 426, 2046), (55, 427, 2047), (54, 428, 2048), (54, 429, 2048), (53, 430, 2050), (53, 431, 2050), (52, 432, 2052), (52, 433, 2052), (51, 434, 2053), (51, 435, 2054), (50, 436, 2055), (50, 437, 2055), (49, 438, 2057), 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['1001,2150,924,2141,772,2092,691,2074,614,2038,289,1973,194,1963,128,1971,54,1825,39,1677,39,1455,29,1244,27,757,21,696,27,543,39,458,93,308,126,270,210,206,291,179,373,132,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2011,293,2098,420,2148,535,2168,614,2165,834,2128,914,2112,994,2078,1072,2031,1132,1997,1214,1958,1273,1931,1368,1879,1444,1848,1656,1785,1855,1718,1974,1662,2015,1581,2015,1501,2038,1419,2046,1351,2067,1177,2101,1098,2141'])], 'temp/1738750874_3436695_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3690531 proportion of common points : 0.9995222483577287 #&_# TEST SUCCEEDED #&_# : 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 : 0.26777124404907227 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 Wed Feb 5 11:22:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0019867420196533203 nb_pixel_total : 5661 time to create 1 rle with old method : 0.013979196548461914 time for calcul the mask position with numpy : 0.0014908313751220703 nb_pixel_total : 1226 time to create 1 rle with old method : 0.003039836883544922 time for calcul the mask position with numpy : 0.0015311241149902344 nb_pixel_total : 13939 time to create 1 rle with old method : 0.03298616409301758 time for calcul the mask position with numpy : 0.0023374557495117188 nb_pixel_total : 83859 time to create 1 rle with old method : 0.20008039474487305 time for calcul the mask position with numpy : 0.0019023418426513672 nb_pixel_total : 2745 time to create 1 rle with old method : 0.006846427917480469 time for calcul the mask position with numpy : 0.0015726089477539062 nb_pixel_total : 5263 time to create 1 rle with old method : 0.012750387191772461 time for calcul the mask position with numpy : 0.001598358154296875 nb_pixel_total : 4241 time to create 1 rle with old method : 0.010375022888183594 time for calcul the mask position with numpy : 0.0015442371368408203 nb_pixel_total : 3782 time to create 1 rle with old method : 0.009116888046264648 time for calcul the mask position with numpy : 0.001672983169555664 nb_pixel_total : 3331 time to create 1 rle with old method : 0.008346796035766602 time for calcul the mask position with numpy : 0.001628875732421875 nb_pixel_total : 2955 time to create 1 rle with old method : 0.007336854934692383 time for calcul the mask position with numpy : 0.0017511844635009766 nb_pixel_total : 29433 time to create 1 rle with old method : 0.07068824768066406 time for calcul the mask position with numpy : 0.0017294883728027344 nb_pixel_total : 27621 time to create 1 rle with old method : 0.06595802307128906 time for calcul the mask position with numpy : 0.0015921592712402344 nb_pixel_total : 2408 time to create 1 rle with old method : 0.006195068359375 time for calcul the mask position with numpy : 0.0015397071838378906 nb_pixel_total : 6645 time to create 1 rle with old method : 0.016751527786254883 time for calcul the mask position with numpy : 0.0015909671783447266 nb_pixel_total : 4247 time to create 1 rle with old method : 0.010774850845336914 time for calcul the mask position with numpy : 0.001806020736694336 nb_pixel_total : 1022 time to create 1 rle with old method : 0.0025458335876464844 time for calcul the mask position with numpy : 0.0017518997192382812 nb_pixel_total : 10765 time to create 1 rle with old method : 0.026369094848632812 time for calcul the mask position with numpy : 0.001542806625366211 nb_pixel_total : 3951 time to create 1 rle with old method : 0.009871482849121094 time for calcul the mask position with numpy : 0.0015227794647216797 nb_pixel_total : 2369 time to create 1 rle with old method : 0.005982637405395508 time for calcul the mask position with numpy : 0.0016109943389892578 nb_pixel_total : 13089 time to create 1 rle with old method : 0.03345465660095215 time for calcul the mask position with numpy : 0.0015985965728759766 nb_pixel_total : 2334 time to create 1 rle with old method : 0.005784511566162109 time for calcul the mask position with numpy : 0.0015559196472167969 nb_pixel_total : 2079 time to create 1 rle with old method : 0.005124092102050781 time for calcul the mask position with numpy : 0.0015330314636230469 nb_pixel_total : 7619 time to create 1 rle with old method : 0.01846146583557129 time for calcul the mask position with numpy : 0.0015265941619873047 nb_pixel_total : 11932 time to create 1 rle with old method : 0.02942180633544922 time for calcul the mask position with numpy : 0.001535177230834961 nb_pixel_total : 3514 time to create 1 rle with old method : 0.008928060531616211 time for calcul the mask position with numpy : 0.0015347003936767578 nb_pixel_total : 5527 time to create 1 rle with old method : 0.014208793640136719 time for calcul the mask position with numpy : 0.0016632080078125 nb_pixel_total : 9890 time to create 1 rle with old method : 0.02393174171447754 time for calcul the mask position with numpy : 0.001642465591430664 nb_pixel_total : 16216 time to create 1 rle with old method : 0.05130743980407715 time for calcul the mask position with numpy : 0.0016968250274658203 nb_pixel_total : 1636 time to create 1 rle with old method : 0.005601644515991211 time for calcul the mask position with numpy : 0.0019578933715820312 nb_pixel_total : 38978 time to create 1 rle with old method : 0.0957334041595459 time for calcul the mask position with numpy : 0.001600027084350586 nb_pixel_total : 12700 time to create 1 rle with old method : 0.03185439109802246 time for calcul the mask position with numpy : 0.001562356948852539 nb_pixel_total : 8613 time to create 1 rle with old method : 0.020423412322998047 time for calcul the mask position with numpy : 0.001546621322631836 nb_pixel_total : 2382 time to create 1 rle with old method : 0.005989551544189453 time for calcul the mask position with numpy : 0.001523733139038086 nb_pixel_total : 2447 time to create 1 rle with old method : 0.005970954895019531 time for calcul the mask position with numpy : 0.0015206336975097656 nb_pixel_total : 3906 time to create 1 rle with old method : 0.009742021560668945 time for calcul the mask position with numpy : 0.0016324520111083984 nb_pixel_total : 16445 time to create 1 rle with old method : 0.03910517692565918 time for calcul the mask position with numpy : 0.0015113353729248047 nb_pixel_total : 942 time to create 1 rle with old method : 0.002578258514404297 time for calcul the mask position with numpy : 0.0014710426330566406 nb_pixel_total : 2781 time to create 1 rle with old method : 0.006884336471557617 time for calcul the mask position with numpy : 0.0015082359313964844 nb_pixel_total : 14875 time to create 1 rle with old method : 0.03562450408935547 time for calcul the mask position with numpy : 0.0017414093017578125 nb_pixel_total : 1614 time to create 1 rle with old method : 0.0039386749267578125 time for calcul the mask position with numpy : 0.001439809799194336 nb_pixel_total : 342 time to create 1 rle with old method : 0.0009479522705078125 time for calcul the mask position with numpy : 0.0014388561248779297 nb_pixel_total : 4119 time to create 1 rle with old method : 0.01021885871887207 time for calcul the mask position with numpy : 0.0015251636505126953 nb_pixel_total : 10574 time to create 1 rle with old method : 0.02698683738708496 time for calcul the mask position with numpy : 0.0014653205871582031 nb_pixel_total : 1252 time to create 1 rle with old method : 0.0031037330627441406 time for calcul the mask position with numpy : 0.0014567375183105469 nb_pixel_total : 880 time to create 1 rle with old method : 0.0023458003997802734 time for calcul the mask position with numpy : 0.0014667510986328125 nb_pixel_total : 4174 time to create 1 rle with old method : 0.010477304458618164 time for calcul the mask position with numpy : 0.001474618911743164 nb_pixel_total : 596 time to create 1 rle with old method : 0.0015423297882080078 time for calcul the mask position with numpy : 0.0014464855194091797 nb_pixel_total : 917 time to create 1 rle with old method : 0.002607583999633789 time for calcul the mask position with numpy : 0.0014595985412597656 nb_pixel_total : 850 time to create 1 rle with old method : 0.002313375473022461 time for calcul the mask position with numpy : 0.0014607906341552734 nb_pixel_total : 2327 time to create 1 rle with old method : 0.005858421325683594 time for calcul the mask position with numpy : 0.0014448165893554688 nb_pixel_total : 887 time to create 1 rle with old method : 0.0021736621856689453 time for calcul the mask position with numpy : 0.0014429092407226562 nb_pixel_total : 199 time to create 1 rle with old method : 0.0005495548248291016 time for calcul the mask position with numpy : 0.0014586448669433594 nb_pixel_total : 577 time to create 1 rle with old method : 0.0014905929565429688 time for calcul the mask position with numpy : 0.0014393329620361328 nb_pixel_total : 693 time to create 1 rle with old method : 0.0018401145935058594 time for calcul the mask position with numpy : 0.0014600753784179688 nb_pixel_total : 337 time to create 1 rle with old method : 0.0009005069732666016 time for calcul the mask position with numpy : 0.001497507095336914 nb_pixel_total : 2764 time to create 1 rle with old method : 0.0069463253021240234 time for calcul the mask position with numpy : 0.0015149116516113281 nb_pixel_total : 1716 time to create 1 rle with old method : 0.004339933395385742 time for calcul the mask position with numpy : 0.0014736652374267578 nb_pixel_total : 1208 time to create 1 rle with old method : 0.0030782222747802734 time for calcul the mask position with numpy : 0.001558065414428711 nb_pixel_total : 1659 time to create 1 rle with old method : 0.004287242889404297 time for calcul the mask position with numpy : 0.0015311241149902344 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0028069019317626953 time for calcul the mask position with numpy : 0.0017423629760742188 nb_pixel_total : 39058 time to create 1 rle with old method : 0.09292864799499512 time for calcul the mask position with numpy : 0.0014951229095458984 nb_pixel_total : 1066 time to create 1 rle with old method : 0.0025861263275146484 time for calcul the mask position with numpy : 0.0013968944549560547 nb_pixel_total : 3091 time to create 1 rle with old method : 0.007551431655883789 time for calcul the mask position with numpy : 0.0015544891357421875 nb_pixel_total : 8596 time to create 1 rle with old method : 0.020728349685668945 time for calcul the mask position with numpy : 0.0015499591827392578 nb_pixel_total : 9519 time to create 1 rle with old method : 0.022646665573120117 time for calcul the mask position with numpy : 0.0014994144439697266 nb_pixel_total : 1330 time to create 1 rle with old method : 0.0034284591674804688 time for calcul the mask position with numpy : 0.0015215873718261719 nb_pixel_total : 16668 time to create 1 rle with old method : 0.040430307388305664 time for calcul the mask position with numpy : 0.0014607906341552734 nb_pixel_total : 1386 time to create 1 rle with old method : 0.0035331249237060547 time for calcul the mask position with numpy : 0.0014557838439941406 nb_pixel_total : 18463 time to create 1 rle with old method : 0.04301166534423828 time for calcul the mask position with numpy : 0.0014400482177734375 nb_pixel_total : 7538 time to create 1 rle with old method : 0.017920970916748047 time for calcul the mask position with numpy : 0.0015554428100585938 nb_pixel_total : 583 time to create 1 rle with old method : 0.001501321792602539 time for calcul the mask position with numpy : 0.0014309883117675781 nb_pixel_total : 1739 time to create 1 rle with old method : 0.004122495651245117 time for calcul the mask position with numpy : 0.001415252685546875 nb_pixel_total : 9059 time to create 1 rle with old method : 0.021270751953125 time for calcul the mask position with numpy : 0.0014679431915283203 nb_pixel_total : 5050 time to create 1 rle with old method : 0.011944770812988281 time for calcul the mask position with numpy : 0.0013546943664550781 nb_pixel_total : 1513 time to create 1 rle with old method : 0.0036983489990234375 time for calcul the mask position with numpy : 0.0014760494232177734 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006871223449707031 time for calcul the mask position with numpy : 0.0013415813446044922 nb_pixel_total : 969 time to create 1 rle with old method : 0.0024118423461914062 time for calcul the mask position with numpy : 0.0014791488647460938 nb_pixel_total : 8466 time to create 1 rle with old method : 0.020482301712036133 time for calcul the mask position with numpy : 0.0014693737030029297 nb_pixel_total : 616 time to create 1 rle with old method : 0.0016410350799560547 time for calcul the mask position with numpy : 0.0015063285827636719 nb_pixel_total : 3165 time to create 1 rle with old method : 0.007909297943115234 time for calcul the mask position with numpy : 0.0016455650329589844 nb_pixel_total : 1134 time to create 1 rle with old method : 0.002798795700073242 time for calcul the mask position with numpy : 0.001508474349975586 nb_pixel_total : 971 time to create 1 rle with old method : 0.0024662017822265625 time for calcul the mask position with numpy : 0.001598358154296875 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006189346313476562 time for calcul the mask position with numpy : 0.0014500617980957031 nb_pixel_total : 6154 time to create 1 rle with old method : 0.015239715576171875 time for calcul the mask position with numpy : 0.0014688968658447266 nb_pixel_total : 249 time to create 1 rle with old method : 0.0006854534149169922 time for calcul the mask position with numpy : 0.0014386177062988281 nb_pixel_total : 735 time to create 1 rle with old method : 0.0019714832305908203 time for calcul the mask position with numpy : 0.0014462471008300781 nb_pixel_total : 1502 time to create 1 rle with old method : 0.003719806671142578 time for calcul the mask position with numpy : 0.0015308856964111328 nb_pixel_total : 1633 time to create 1 rle with old method : 0.004060268402099609 time for calcul the mask position with numpy : 0.0014507770538330078 nb_pixel_total : 597 time to create 1 rle with old method : 0.0015468597412109375 time for calcul the mask position with numpy : 0.0014348030090332031 nb_pixel_total : 300 time to create 1 rle with old method : 0.0008633136749267578 time for calcul the mask position with numpy : 0.001438140869140625 nb_pixel_total : 1092 time to create 1 rle with old method : 0.002964019775390625 time for calcul the mask position with numpy : 0.0014841556549072266 nb_pixel_total : 1441 time to create 1 rle with old method : 0.003735780715942383 time for calcul the mask position with numpy : 0.0014681816101074219 nb_pixel_total : 1524 time to create 1 rle with old method : 0.0037620067596435547 time for calcul the mask position with numpy : 0.0014634132385253906 nb_pixel_total : 915 time to create 1 rle with old method : 0.002390146255493164 time for calcul the mask position with numpy : 0.0014393329620361328 nb_pixel_total : 2198 time to create 1 rle with old method : 0.005559682846069336 time for calcul the mask position with numpy : 0.0014443397521972656 nb_pixel_total : 887 time to create 1 rle with old method : 0.0022542476654052734 time for calcul the mask position with numpy : 0.0014448165893554688 nb_pixel_total : 902 time to create 1 rle with old method : 0.002275705337524414 time for calcul the mask position with numpy : 0.001445770263671875 nb_pixel_total : 954 time to create 1 rle with old method : 0.003281831741333008 time for calcul the mask position with numpy : 0.00162506103515625 nb_pixel_total : 7403 time to create 1 rle with old method : 0.018447399139404297 time for calcul the mask position with numpy : 0.0014998912811279297 nb_pixel_total : 829 time to create 1 rle with old method : 0.002131223678588867 batch 1 Loaded 100 chid ids of type : 4677 Number RLEs to save : 9226 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 : 0.012663602828979492 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.972216606140137 time spend to save output : 0.013302326202392578 total time spend for step 1 : 10.98551893234253 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1738750927_3436695_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 100 ############################### 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 : 0.17644357681274414 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 Wed Feb 5 11:22:19 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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/1738750939_3436695_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 0.080s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 2.854860305786133 time spend to save output : 0.00021791458129882812 total time spend for step 1 : 2.8550782203674316 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 : 0.012718915939331055 [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 : 0.011199712753295898 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, 0.06384094, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05222583, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271269, None)], 'temp/1738750939_3436695_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 : 0.09247660636901855 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 Wed Feb 5 11:22:22 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 Thcl ! we are using the classfication for only one thcl 355 time to import caffe and check if the image exist : 0.009006977081298828 time to convert the images to numpy array : 0.0009906291961669922 total time to convert the images to numpy array : 0.010381221771240234 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 havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 1388 wait 20 seconds l 3637 free memory gpu now : 1388 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 : 6677 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.013872146606445312 time used to do the prediction : 0.1639401912689209 save descriptor for thcl : 355 time to traite the descriptors : 0.07593035697937012 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.48314619064331055 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 : 9.5367431640625e-06 save missing photos in datou_result : time spend for datou_step_exec : 26.22632598876953 time spend to save output : 1.4903240203857422 total time spend for step 1 : 27.716650009155273 step2:argmax Wed Feb 5 11:22:50 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 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', 0.01771314, 332, '355'), 'temp/1738750942_3436695_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 : 0.007998466491699219 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 : 0.010800600051879883 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 : 0.012145519256591797 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 : 5.9604644775390625e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0002770423889160156 time spend to save output : 0.03136253356933594 total time spend for step 2 : 0.03163957595825195 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', 0.01771314, 332, '355'), 'temp/1738750942_3436695_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 : 0.18309640884399414 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 Wed Feb 5 11:22:50 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 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 2025-02-05 11:22:53.837701: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-05 11:22:53.838498: 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 2025-02-05 11:22:53.839462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:22:53.839522: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:22:53.845423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:22:53.845718: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:22:53.849271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:22:53.850606: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:22:53.857206: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:22:53.858667: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:22:53.859272: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-05 11:22:53.887155: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-05 11:22:53.889159: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fe164000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-05 11:22:53.889219: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-05 11:22:53.894212: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4aadcca0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-05 11:22:53.894280: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-05 11:22:53.895502: 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 2025-02-05 11:22:53.895689: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:22:53.895716: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-05 11:22:53.895834: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-05 11:22:53.895869: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-05 11:22:53.895906: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-05 11:22:53.895946: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-05 11:22:53.895984: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-05 11:22:53.897305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-05 11:22:53.897392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-05 11:22:53.897471: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-05 11:22:53.897486: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-05 11:22:53.897502: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-05 11:22:53.898891: 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 : 6677 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 : 12.107937574386597 time used to load_weights : 0.19134926795959473 0it [00:00, ?it/s] 3it [00:00, 572.50it/s]2025-02-05 11:23:09.164567: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1738750970_3436695_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1738750970_3436695_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1738750970_3436695_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.0381429195404053 (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 : 0.031249046325683594 storage_type for insertDescriptorsMulti : 3 To insert : 1171252784 To insert : 1171252764 To insert : 1171252487 time to insert the descriptors : 0.6632606983184814 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252784, 1171252764, 1171252487] Looping around the photos to save general results len do output : 3 /1171252784Didn't retrieve data . /1171252764Didn'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, '1171252784', None, None, None, None, None, None) ('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, '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 : 0.012398242950439453 save_final save missing photos in datou_result : time spend for datou_step_exec : 22.27185082435608 time spend to save output : 0.012704133987426758 total time spend for step 1 : 22.284554958343506 step2:argmax Wed Feb 5 11:23:12 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 : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.9677602, 4674, '3609'), 'temp/1738750970_3436695_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.9853672, 4674, '3609'), 'temp/1738750970_3436695_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.92622906, 4674, '3609'), 'temp/1738750970_3436695_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 : 0.007764339447021484 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 : 0.012453317642211914 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 : 0.016609907150268555 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 : 2.384185791015625e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00016069412231445312 time spend to save output : 0.04106402397155762 total time spend for step 2 : 0.04122471809387207 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252784': [(1171252784, 'jrm', 0.9677602, 4674, '3609'), 'temp/1738750970_3436695_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853672, 4674, '3609'), 'temp/1738750970_3436695_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.92622906, 4674, '3609'), 'temp/1738750970_3436695_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 : 0.16411375999450684 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 Wed Feb 5 11:23:12 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 : 2904 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 : 10.870665311813354 time used to load_weights : 0.15677499771118164 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.1885535717010498 (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 : 0.040923357009887695 storage_type for insertDescriptorsMulti : 3 To insert : 1171275372 To insert : 1171291875 To insert : 1171275314 time to insert the descriptors : 0.9069540500640869 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171275372, 1171291875, 1171275314] Looping around the photos to save general results len do output : 3 /1171275372Didn't retrieve data . /1171291875Didn't retrieve data . /1171275314Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '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) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.012476444244384766 save_final save missing photos in datou_result : time spend for datou_step_exec : 22.516101360321045 time spend to save output : 0.012897491455078125 total time spend for step 1 : 22.528998851776123 step2:argmax Wed Feb 5 11:23:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 Inside saveOutput : final : True verbose : False photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.9673909, 4723, '3655'), 'temp/1738750992_3436695_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.9706845, 4723, '3655'), 'temp/1738750992_3436695_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96514434, 4723, '3655'), 'temp/1738750992_3436695_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.007952690124511719 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 : 0.010605812072753906 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 : 0.011971235275268555 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 : 6.9141387939453125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00020241737365722656 time spend to save output : 0.035637855529785156 total time spend for step 2 : 0.03584027290344238 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171275372': [(1171275372, 'tapis_vide', 0.9673909, 4723, '3655'), 'temp/1738750992_3436695_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171291875': [(1171291875, 'tapis_vide', 0.9706845, 4723, '3655'), 'temp/1738750992_3436695_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96514434, 4723, '3655'), 'temp/1738750992_3436695_1171275314_6e0a72c8fa00d5e4b018bd689b547133.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 : 0.12671732902526855 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 Wed Feb 5 11:23:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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/1738751016_3436695 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.15975022315979 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 1.3737633228302002 time spend to save output : 3.170967102050781e-05 total time spend for step 1 : 1.3737950325012207 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 /1334893624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893627Didn'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 : 0.012453794479370117 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1334893624: ['917849322', 'temp/1738751015_3436695_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1334893626: ['917849322', 'temp/1738751015_3436695_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1334893627: ['917849322', 'temp/1738751015_3436695_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 : 0.15396761894226074 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 Wed Feb 5 11:23:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 500 time to import caffe and check if the image exist : 0.00026488304138183594 time to convert the images to numpy array : 0.14194798469543457 total time to convert the images to numpy array : 0.1426241397857666 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 : 2904 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 : 2904 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 1.110968828201294 time used to do the prediction : 0.11281943321228027 save descriptor for thcl : 500 time to traite the descriptors : 0.06996607780456543 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.7160882949829102 time spend for datou_step_exec : 7.230386018753052 time spend to save output : 4.100799560546875e-05 total time spend for step 1 : 7.230427026748657 step2:argmax Wed Feb 5 11:23:44 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 : 0.00018525123596191406 time spend to save output : 4.76837158203125e-05 total time spend for step 2 : 0.00023293495178222656 step3:rotate Wed Feb 5 11:23:44 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/1738751025_3436695 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.5636579990386963 Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres 0 photos founded : [] rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres 0 photos founded : [] rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres 0 photos founded : [] time spend for datou_step_exec : 0.663607120513916 time spend to save output : 2.6226043701171875e-05 total time spend for step 3 : 0.6636333465576172 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 /1334893635Didn'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 : 0.014481306076049805 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1334893635: ['917849322', 'temp/1738751017_3436695_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 : 0.1385354995727539 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 Wed Feb 5 11:23:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step 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 : 20253922 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738751028_3436695 we have uploaded 4 photos in the portfolio 20253922 time of upload the photos Elapsed time : 3.2527925968170166 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/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1738751025_3436695_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 : 20253947 Result OK ! uploaded one batch 0 Elapsed time : 21.824517250061035 time spend for datou_step_exec : 29.433349609375 time spend to save output : 2.5272369384765625e-05 total time spend for step 1 : 29.433374881744385 step2:tile Wed Feb 5 11:24:15 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 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/1738751025_3436695_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 : 20253996 with name tile_taggage_varroa feed_id_new_photos : 20253996 filename : temp/1738751025_3436695_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/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.009789466857910156 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/1738751062_3436695 we have uploaded 1 photos in the portfolio 20253996 Importing ! upload mediasElapsed time : 1.014699935913086 , 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 : 1.0806524753570557 time spend for datou_step_exec : 8.02387285232544 time spend to save output : 4.124641418457031e-05 total time spend for step 2 : 8.023914098739624 step3:rotate Wed Feb 5 11:24:23 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 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 : 20253997 Needs to change image size ! time for calcul the mask position with numpy : 0.0006792545318603516 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0042269229888916016 .time for calcul the mask position with numpy : 0.0003685951232910156 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0029935836791992188 . 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 : 0.00041031837463378906 nb_pixel_total : 694 time to create 1 rle with old method : 0.0022301673889160156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037217140197753906 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0034999847412109375 . 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 : 0.00040030479431152344 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 : 0.0003693103790283203 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0035791397094726562 . 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 : 0.0003910064697265625 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004892349243164062 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003604888916015625 nb_pixel_total : 1161 time to create 1 rle with old method : 0.003538846969604492 . 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 : 0.0004031658172607422 nb_pixel_total : 414 time to create 1 rle with old method : 0.0013689994812011719 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037479400634765625 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0035767555236816406 . 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 : 0.0004215240478515625 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0029754638671875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035762786865234375 nb_pixel_total : 1157 time to create 1 rle with old method : 0.003025054931640625 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003533363342285156 nb_pixel_total : 264 time to create 1 rle with old method : 0.0009615421295166016 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003712177276611328 nb_pixel_total : 1389 time to create 1 rle with old method : 0.004114389419555664 .time for calcul the mask position with numpy : 0.0003597736358642578 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0035462379455566406 . 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 : 0.0003688335418701172 nb_pixel_total : 694 time to create 1 rle with old method : 0.0022830963134765625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003647804260253906 nb_pixel_total : 1162 time to create 1 rle with old method : 0.003537893295288086 . 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 : 0.0003600120544433594 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006542205810546875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003590583801269531 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002892732620239258 . 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 : 0.0003681182861328125 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004935264587402344 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035381317138671875 nb_pixel_total : 1160 time to create 1 rle with old method : 0.0029006004333496094 . 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 : 0.0003724098205566406 nb_pixel_total : 414 time to create 1 rle with old method : 0.001111745834350586 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036334991455078125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002842426300048828 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034737586975097656 nb_pixel_total : 1 time to create 1 rle with old method : 2.3126602172851562e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.0003941059112548828 nb_pixel_total : 1204 time to create 1 rle with old method : 0.002989053726196289 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003600120544433594 nb_pixel_total : 1158 time to create 1 rle with old method : 0.002939462661743164 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034809112548828125 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007624626159667969 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003981590270996094 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0034492015838623047 .time for calcul the mask position with numpy : 0.00035953521728515625 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0028967857360839844 . 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 : 0.0003819465637207031 nb_pixel_total : 727 time to create 1 rle with old method : 0.0018973350524902344 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003597736358642578 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002942323684692383 . 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 : 0.00039005279541015625 nb_pixel_total : 250 time to create 1 rle with old method : 0.0007472038269042969 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036144256591796875 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0029141902923583984 . 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 : 0.0004525184631347656 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005381107330322266 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038433074951171875 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0030062198638916016 . 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 : 0.0003714561462402344 nb_pixel_total : 450 time to create 1 rle with old method : 0.0012574195861816406 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035643577575683594 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0029098987579345703 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003457069396972656 nb_pixel_total : 1 time to create 1 rle with old method : 2.4318695068359375e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.00038623809814453125 nb_pixel_total : 1237 time to create 1 rle with old method : 0.003180980682373047 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003581047058105469 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0028696060180664062 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003490447998046875 nb_pixel_total : 234 time to create 1 rle with old method : 0.0007038116455078125 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003733634948730469 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003499746322631836 .time for calcul the mask position with numpy : 0.0003590583801269531 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0028803348541259766 . 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 : 0.0003752708435058594 nb_pixel_total : 727 time to create 1 rle with old method : 0.0018951892852783203 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003566741943359375 nb_pixel_total : 1162 time to create 1 rle with old method : 0.00286865234375 . 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 : 0.0003726482391357422 nb_pixel_total : 250 time to create 1 rle with old method : 0.0007214546203613281 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038361549377441406 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0028591156005859375 . 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 : 0.0003681182861328125 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005340576171875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003533363342285156 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0029549598693847656 . 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 : 0.0003643035888671875 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011904239654541016 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003833770751953125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002875089645385742 . 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 : 0.00038313865661621094 nb_pixel_total : 1237 time to create 1 rle with old method : 0.003101348876953125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003535747528076172 nb_pixel_total : 1157 time to create 1 rle with old method : 0.019141197204589844 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.000347137451171875 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006928443908691406 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 20253997 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738751065_3436695 we have uploaded 24 photos in the portfolio 20253997 time of upload the photos Elapsed time : 5.799430847167969 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 : 11.040765523910522 time spend to save output : 7.224082946777344e-05 total time spend for step 3 : 11.04083776473999 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, '1334893821'] Looping around the photos to save general results len do output : 24 /1334893938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334893989Didn'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, '1334893821', 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 : 0.014759302139282227 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1334893938: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1334893943: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1334893945: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1334893947: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1334893949: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1334893951: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1334893953: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1334893955: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1334893957: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1334893959: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1334893962: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1334893964: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1334893966: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1334893968: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1334893970: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1334893972: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1334893974: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1334893976: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1334893978: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1334893981: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1334893983: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1334893985: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1334893987: ['937852786', 'temp/1738751025_3436695_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1334893989: ['937852786', 'temp/1738751025_3436695_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 : 0.12080669403076172 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 Wed Feb 5 11:24:34 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 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/1738751074_3436695 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.7297408580780029 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 : 0.8262240886688232 time spend to save output : 5.245208740234375e-05 total time spend for step 1 : 0.8262765407562256 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 /1334894047 /1334894049 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 : 0.01143789291381836 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1334894047': ['911785586', 'temp/1738751074_3436695_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1334894049': ['911785586', 'temp/1738751074_3436695_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 : 0.1293802261352539 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 Wed Feb 5 11:24:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 20254023 Result OK ! uploaded one batch 0 Elapsed time : 22.02655577659607 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 22.09503149986267 time spend to save output : 2.7418136596679688e-05 total time spend for step 1 : 22.095058917999268 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 /1334894127Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334894173Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334894200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334894204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334894206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334894210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334894213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1334894216Didn'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 : 0.014310359954833984 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1334894127': ['950103132', 'temp/1738751075_3436695_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1334894173': ['950103132', 'temp/1738751075_3436695_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1334894200': ['950103132', 'temp/1738751075_3436695_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1334894204': ['950103132', 'temp/1738751075_3436695_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1334894206': ['950103132', 'temp/1738751075_3436695_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1334894210': ['950103132', 'temp/1738751075_3436695_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1334894213': ['950103132', 'temp/1738751075_3436695_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1334894216': ['950103132', 'temp/1738751075_3436695_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 : 0.13355302810668945 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 Wed Feb 5 11:24:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of 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 : 0.05709195137023926 time spend to save output : 7.510185241699219e-05 total time spend for step 1 : 0.05716705322265625 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 : 0.09469890594482422 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 Wed Feb 5 11:24:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of 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 : 0.12056612968444824 time spend to save output : 7.867813110351562e-05 total time spend for step 1 : 0.12064480781555176 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, 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'312,179,311,178,308,179,309,180', '268,269,264,269,259,266,259,262,261,258,261,250,265,245,269,250,270,257,274,260,278,265,275,267,269,268', '414,281,401,281,414,281']), (946711423, 2096875722, 631, 433, 558, 248, 286, 0.44133398, 1947740397, ['492,272,474,272,473,271,468,271,465,269,460,269,460,268,465,266,467,266,468,265,470,265,471,264,475,264,476,263,479,263,480,262,486,262,487,261,491,261,492,260,495,260,496,259,502,259,506,257,510,257,514,255,517,255,518,254,530,253,531,252,535,252,536,251,538,251,539,252,543,252,544,253,547,253,549,251,553,251,555,253,555,267,552,270,550,270,550,269,548,267,547,267,547,267,548,266,547,265,545,266,540,266,539,264,530,264,529,263,524,263,519,266,513,266,510,268,507,268,506,269,499,270,498,271,493,271', '438,279,435,279,435,273,436,272,448,271,449,272,448,274,443,274,440,277,440,278']), (946711423, 492654799, 631, 399, 569, 68, 251, 0.41876298, 1947740399, []), (946711423, 492624020, 631, 420, 552, 244, 293, 0.35962066, 1947740400, ['474,289,453,289,452,288,439,288,437,286,431,286,427,284,423,284,422,283,422,275,427,275,428,273,430,272,435,272,436,271,438,271,442,269,447,269,450,267,454,267,460,264,464,264,467,262,483,261,484,260,488,260,489,259,494,259,495,258,502,258,503,257,505,257,509,255,512,255,516,252,520,252,521,251,526,250,530,248,534,248,535,247,546,247,547,248,549,248,549,250,550,251,550,266,551,267,551,275,550,276,550,278,549,279,549,281,537,282,535,284,528,284,527,285,504,285,503,286,495,286,492,288,488,287,487,288,475,288']), (946711423, 503548896, 631, 301, 540, 339, 403, 0.740756, 3140491551, ['442,401,371,401,371,397,366,390,365,386,356,386,353,384,348,383,319,383,319,378,314,370,310,370,305,368,304,357,305,353,330,353,339,356,378,356,379,357,474,357,475,356,488,356,493,353,501,354,507,352,517,352,522,351,527,346,530,347,533,351,530,355,527,356,515,356,505,362,503,365,497,368,494,372,489,374,492,376,488,378,490,380,495,380,487,382,485,385,476,387,469,392,461,393,456,395,451,399,447,399', '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 : 0.004542350769042969 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Wed Feb 5 11:24:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 0.2935326099395752 time spend to save output : 9.655952453613281e-05 total time spend for step 1 : 0.29362916946411133 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 : 0.16890692710876465 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 Wed Feb 5 11:24:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blur_detection methode: ratio et variance treat image : temp/1738751098_3436695_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.2960033416748047 time spend to save output : 3.886222839355469e-05 total time spend for step 1 : 0.29604220390319824 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 BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFBFBFFFBFBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 1.5659573078155518 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 Wed Feb 5 11:25:00 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 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 : 0.0014410018920898438 time to convert the images to numpy array : 0.006361722946166992 time to import caffe and check if the image exist : 0.005512714385986328 time to convert the images to numpy array : 0.046532630920410156 time to import caffe and check if the image exist : 0.0072705745697021484 time to convert the images to numpy array : 0.04993104934692383 time to import caffe and check if the image exist : 0.011162281036376953 time to convert the images to numpy array : 0.05106639862060547 time to import caffe and check if the image exist : 0.012735366821289062 time to convert the images to numpy array : 0.0483400821685791 time to import caffe and check if the image exist : 0.010232925415039062 time to convert the images to numpy array : 0.05150604248046875 time to import caffe and check if the image exist : 0.020203590393066406 time to convert the images to numpy array : 0.04299187660217285 time to import caffe and check if the image exist : 0.011184453964233398 time to convert the images to numpy array : 0.05453038215637207 time to import caffe and check if the image exist : 0.012064933776855469 time to convert the images to numpy array : 0.052449941635131836 time to import caffe and check if the image exist : 0.018395185470581055 time to convert the images to numpy array : 0.0450291633605957 total time to convert the images to numpy array : 0.06749272346496582 list photo_ids error: [] list photo_ids correct : [987515223, 987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515247, 987515248, 987515249, 987515250, 987515188, 987515189, 987515190, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222, 987515202, 987515204, 987515205, 987515224, 987515226, 987515227, 987515228, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515239, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515237, 987515238, 987515207, 987515208, 987515209, 987515211, 987515212] 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 : 3125 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 : 3125 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'res5b']) time used to do the prepocess of the images : 0.0603330135345459 time used to do the prediction : 0.29788780212402344 save descriptor for thcl : 1528 time to traite the descriptors : 4.327101707458496 storage_type for insertDescriptorsMulti : 1 To insert : 987515223 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515246 To insert : 987515230 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515187 To insert : 987515239 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515237 To insert : 987515238 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515212 time to insert the descriptors : 9.176628828048706 time spend for datou_step_exec : 18.358115911483765 time spend to save output : 5.745887756347656e-05 total time spend for step 1 : 18.358173370361328 step2:argmax Wed Feb 5 11:25:18 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 Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 time spend for datou_step_exec : 0.0010440349578857422 time spend to save output : 2.7179718017578125e-05 total time spend for step 2 : 0.0010712146759033203 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515223': [('987515223', 'Carton', 0.9920705, 1927, '1528'), 'temp/1738751098_3436695_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.999813, 1927, '1528'), 'temp/1738751098_3436695_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.99981433, 1927, '1528'), 'temp/1738751098_3436695_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9772563, 1927, '1528'), 'temp/1738751098_3436695_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.85760486, 1927, '1528'), 'temp/1738751098_3436695_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9269979, 1927, '1528'), 'temp/1738751098_3436695_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.9900331, 1927, '1528'), 'temp/1738751098_3436695_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977816, 1927, '1528'), 'temp/1738751098_3436695_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515247': [('987515247', 'Carton', 0.9996692, 1927, '1528'), 'temp/1738751098_3436695_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.9813048, 1927, '1528'), 'temp/1738751098_3436695_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.98130035, 1927, '1528'), 'temp/1738751098_3436695_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9808287, 1927, '1528'), 'temp/1738751098_3436695_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515188': [('987515188', 'Carton', 0.99567825, 1927, '1528'), 'temp/1738751098_3436695_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.99778974, 1927, '1528'), 'temp/1738751098_3436695_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9763451, 1927, '1528'), 'temp/1738751098_3436695_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515240': [('987515240', 'Carton', 0.99951947, 1927, '1528'), 'temp/1738751098_3436695_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.98213124, 1927, '1528'), 'temp/1738751098_3436695_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9357733, 1927, '1528'), 'temp/1738751098_3436695_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.87390447, 1927, '1528'), 'temp/1738751098_3436695_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81748194, 1927, '1528'), 'temp/1738751098_3436695_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.86642563, 1927, '1528'), 'temp/1738751098_3436695_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992317, 1927, '1528'), 'temp/1738751098_3436695_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515230': [('987515230', 'Carton', 0.99940646, 1927, '1528'), 'temp/1738751098_3436695_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.9994216, 1927, '1528'), 'temp/1738751098_3436695_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992442, 1927, '1528'), 'temp/1738751098_3436695_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.9834546, 1927, '1528'), 'temp/1738751098_3436695_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.9449728, 1927, '1528'), 'temp/1738751098_3436695_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89199126, 1927, '1528'), 'temp/1738751098_3436695_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.53648764, 1927, '1528'), 'temp/1738751098_3436695_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515213': [('987515213', 'Carton', 0.98688644, 1927, '1528'), 'temp/1738751098_3436695_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99393624, 1927, '1528'), 'temp/1738751098_3436695_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.97750425, 1927, '1528'), 'temp/1738751098_3436695_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5292692, 1927, '1528'), 'temp/1738751098_3436695_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.9993697, 1927, '1528'), 'temp/1738751098_3436695_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963768, 1927, '1528'), 'temp/1738751098_3436695_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.99746823, 1927, '1528'), 'temp/1738751098_3436695_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515202': [('987515202', 'Carton', 0.9911175, 1927, '1528'), 'temp/1738751098_3436695_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950747, 1927, '1528'), 'temp/1738751098_3436695_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908442, 1927, '1528'), 'temp/1738751098_3436695_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.9085875, 1927, '1528'), 'temp/1738751098_3436695_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.98695576, 1927, '1528'), 'temp/1738751098_3436695_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9001862, 1927, '1528'), 'temp/1738751098_3436695_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.52144355, 1927, '1528'), 'temp/1738751098_3436695_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515182': [('987515182', 'Carton', 0.99244577, 1927, '1528'), 'temp/1738751098_3436695_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1738751098_3436695_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.99973184, 1927, '1528'), 'temp/1738751098_3436695_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.797918, 1927, '1528'), 'temp/1738751098_3436695_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9848321, 1927, '1528'), 'temp/1738751098_3436695_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.9811288, 1927, '1528'), 'temp/1738751098_3436695_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515239': [('987515239', 'Carton', 0.99978334, 1927, '1528'), 'temp/1738751098_3436695_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999112, 1927, '1528'), 'temp/1738751098_3436695_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993969, 1927, '1528'), 'temp/1738751098_3436695_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9845487, 1927, '1528'), 'temp/1738751098_3436695_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.9846103, 1927, '1528'), 'temp/1738751098_3436695_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.9661706, 1927, '1528'), 'temp/1738751098_3436695_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.98592806, 1927, '1528'), 'temp/1738751098_3436695_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99546033, 1927, '1528'), 'temp/1738751098_3436695_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515237': [('987515237', 'Carton', 0.77007616, 1927, '1528'), 'temp/1738751098_3436695_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.9995734, 1927, '1528'), 'temp/1738751098_3436695_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.87361723, 1927, '1528'), 'temp/1738751098_3436695_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.9917209, 1927, '1528'), 'temp/1738751098_3436695_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9677972, 1927, '1528'), 'temp/1738751098_3436695_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.973552, 1927, '1528'), 'temp/1738751098_3436695_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.98691875, 1927, '1528'), 'temp/1738751098_3436695_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.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 : 0.08287429809570312 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 Wed Feb 5 11:25:18 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 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/1738751118_3436695_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 : 0.04423332214355469 time to do a prediction : 0.23505640029907227 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 : 1.51241135597229 time spend to save output : 5.364418029785156e-05 total time spend for step 1 : 1.512465000152588 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, 6.2537535913675946e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.443457068623811e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.065077270823167e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.4445448565966217e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9228411929361755e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.716283754329197e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.0001236924435943365), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.9482473109965213e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.374064322907543e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.216856564984937e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.38308365649209e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4705673265780206e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1244089364481624e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015774233906995505), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004446927341632545), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.596028106287122e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3291013374328031e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.619374415895436e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5307213036285248e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.610948288544023e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.262316219363129e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.685427746968344e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.00032774187275208533), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.000305362103972584), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.855447408161126e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.90926151239546e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7047002731706016e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.799369783839211e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.343888445466291e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.7019707229337655e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.552424343273742e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.777063158049714e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.098136509535834e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6448003634650377e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.9625811091827927e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4371632914844668e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.881804776843637e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.285143389395671e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.309694178227801e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1705893232137896e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.827725322480546e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.5521899260638747e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.9619309316331055e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.7502358079800615e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.320024800108513e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.295988664031029e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.153710218204651e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.925827331840992e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2385528862068895e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.5718487702542916e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.385866875760257e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.000154119057697244), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.00025907056988216937), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.0001885093515738845), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.00021525051852222532), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.00016432195843663067), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.544825515040429e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.7365437088301405e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.913013253826648e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.00012142213381594047), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.00019627984147518873), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.00018758447549771518), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.0001237576361745596), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.00027198492898605764), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.6028174343318824e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.05594119001762e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 7.024210390227381e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.0008730966947041452), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.0026473766192793846), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.003348147263750434), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.031513892114162445), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.05582983419299126), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.00012441219587344676), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020940434478688985), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.0003694008628372103), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.006834307685494423), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.01587667502462864), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.00941768940538168), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.009789064526557922), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.022184552624821663), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.021971184760332108), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.1930619329214096), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.09657709300518036), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.12340344488620758), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.53409343957901), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.4609564244747162), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.7709310054779053), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8662412166595459), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8501167893409729), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.9842931628227234), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9847691059112549), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919478297233582), (987515173, 1982, 'Carton', 240, -1, 208, -1, 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336, -1, 0.991256594657898), (987515173, 1982, 'Carton', 208, -1, 336, -1, 0.9869384765625), (987515173, 1982, 'Carton', 240, -1, 336, -1, 0.9083011746406555), (987515173, 1982, 'Carton', 272, -1, 336, -1, 0.9452854990959167), (987515173, 1982, 'Carton', 304, -1, 336, -1, 0.9365131855010986), (987515173, 1982, 'Carton', 336, -1, 336, -1, 0.9808309674263), (987515173, 1982, 'Kraft', 112, -1, 112, -1, 1.999768572957805e-09), (987515173, 1982, 'Kraft', 144, -1, 112, -1, 1.706562358094743e-08), (987515173, 1982, 'Kraft', 176, -1, 112, -1, 9.650586889620172e-07), (987515173, 1982, 'Kraft', 208, -1, 112, -1, 3.127942909486592e-05), (987515173, 1982, 'Kraft', 240, -1, 112, -1, 4.446115417522378e-05), (987515173, 1982, 'Kraft', 272, -1, 112, -1, 0.00020339190086815506), (987515173, 1982, 'Kraft', 304, -1, 112, -1, 0.0010847329394891858), (987515173, 1982, 'Kraft', 336, -1, 112, -1, 0.0008296783198602498), (987515173, 1982, 'Kraft', 112, -1, 144, -1, 2.6379073460702784e-05), (987515173, 1982, 'Kraft', 144, -1, 144, -1, 7.031516815914074e-06), (987515173, 1982, 'Kraft', 176, -1, 144, -1, 3.6206088225299027e-06), (987515173, 1982, 'Kraft', 208, -1, 144, -1, 3.555038711056113e-05), (987515173, 1982, 'Kraft', 240, -1, 144, -1, 6.692465831292793e-05), (987515173, 1982, 'Kraft', 272, -1, 144, -1, 8.674102718941867e-05), (987515173, 1982, 'Kraft', 304, -1, 144, -1, 0.00012231581786181778), (987515173, 1982, 'Kraft', 336, -1, 144, -1, 0.00011262364569120109), (987515173, 1982, 'Kraft', 112, -1, 176, -1, 0.000500846712384373), (987515173, 1982, 'Kraft', 144, -1, 176, -1, 0.00012302985123824328), (987515173, 1982, 'Kraft', 176, -1, 176, -1, 9.10988383111544e-05), (987515173, 1982, 'Kraft', 208, -1, 176, -1, 5.1675884606083855e-05), (987515173, 1982, 'Kraft', 240, -1, 176, -1, 0.00011517179518705234), (987515173, 1982, 'Kraft', 272, -1, 176, -1, 0.0004348758957348764), (987515173, 1982, 'Kraft', 304, -1, 176, -1, 0.0009247721754945815), (987515173, 1982, 'Kraft', 336, -1, 176, -1, 0.0014269091188907623), (987515173, 1982, 'Kraft', 112, -1, 208, -1, 6.892659439472482e-05), (987515173, 1982, 'Kraft', 144, -1, 208, -1, 1.8478698621038347e-05), (987515173, 1982, 'Kraft', 176, -1, 208, -1, 2.5876399377011694e-05), (987515173, 1982, 'Kraft', 208, -1, 208, -1, 3.5385077353566885e-05), (987515173, 1982, 'Kraft', 240, -1, 208, -1, 3.74509472749196e-05), (987515173, 1982, 'Kraft', 272, -1, 208, -1, 8.667356451041996e-05), (987515173, 1982, 'Kraft', 304, -1, 208, -1, 0.00012369516480248421), (987515173, 1982, 'Kraft', 336, -1, 208, -1, 0.0003913922409992665), (987515173, 1982, 'Kraft', 112, -1, 240, -1, 0.00030697500915266573), (987515173, 1982, 'Kraft', 144, -1, 240, -1, 4.163396806688979e-05), (987515173, 1982, 'Kraft', 176, -1, 240, -1, 1.2248481652932242e-05), (987515173, 1982, 'Kraft', 208, -1, 240, -1, 7.3480832725181244e-06), (987515173, 1982, 'Kraft', 240, -1, 240, -1, 2.3013888494460844e-05), (987515173, 1982, 'Kraft', 272, -1, 240, -1, 5.817677083541639e-05), (987515173, 1982, 'Kraft', 304, -1, 240, -1, 6.575201405212283e-05), (987515173, 1982, 'Kraft', 336, -1, 240, -1, 0.00018699707288760692), (987515173, 1982, 'Kraft', 112, -1, 272, -1, 0.0014602185692638159), (987515173, 1982, 'Kraft', 144, -1, 272, -1, 0.0006904684705659747), (987515173, 1982, 'Kraft', 176, -1, 272, -1, 0.000273705372819677), (987515173, 1982, 'Kraft', 208, -1, 272, -1, 4.3389616621425375e-05), (987515173, 1982, 'Kraft', 240, -1, 272, -1, 3.34509059030097e-05), (987515173, 1982, 'Kraft', 272, -1, 272, -1, 8.538163092453033e-05), (987515173, 1982, 'Kraft', 304, -1, 272, -1, 0.00011204127076780424), (987515173, 1982, 'Kraft', 336, -1, 272, -1, 0.0004243566654622555), (987515173, 1982, 'Kraft', 112, -1, 304, -1, 0.0010118086356669664), (987515173, 1982, 'Kraft', 144, -1, 304, -1, 0.000903374922927469), (987515173, 1982, 'Kraft', 176, -1, 304, -1, 0.0006184100639075041), (987515173, 1982, 'Kraft', 208, -1, 304, -1, 0.0010802133474498987), (987515173, 1982, 'Kraft', 240, -1, 304, -1, 0.0017862040549516678), (987515173, 1982, 'Kraft', 272, -1, 304, -1, 0.00470611872151494), (987515173, 1982, 'Kraft', 304, -1, 304, -1, 0.004794317297637463), (987515173, 1982, 'Kraft', 336, -1, 304, -1, 0.012559576891362667), (987515173, 1982, 'Kraft', 112, -1, 336, -1, 0.002179016126319766), (987515173, 1982, 'Kraft', 144, -1, 336, -1, 0.005711999721825123), (987515173, 1982, 'Kraft', 176, -1, 336, -1, 0.000820145767647773), (987515173, 1982, 'Kraft', 208, -1, 336, -1, 0.0012651708675548434), (987515173, 1982, 'Kraft', 240, -1, 336, -1, 0.007805165834724903), (987515173, 1982, 'Kraft', 272, -1, 336, -1, 0.012551764957606792), (987515173, 1982, 'Kraft', 304, -1, 336, -1, 0.017962489277124405), (987515173, 1982, 'Kraft', 336, -1, 336, -1, 0.0077572716400027275), (987515173, 1982, 'Lointain_Papier_Magazine', 112, -1, 112, -1, 1.4926945790527668e-10), (987515173, 1982, 'Lointain_Papier_Magazine', 144, -1, 112, -1, 8.220395919522616e-09), (987515173, 1982, 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(987515173, 1982, 'autre_refus', 112, -1, 208, -1, 8.874581544660032e-05), (987515173, 1982, 'autre_refus', 144, -1, 208, -1, 0.00018549419473856688), (987515173, 1982, 'autre_refus', 176, -1, 208, -1, 0.00031927667441777885), (987515173, 1982, 'autre_refus', 208, -1, 208, -1, 0.00035717099672183394), (987515173, 1982, 'autre_refus', 240, -1, 208, -1, 0.00019904173677787185), (987515173, 1982, 'autre_refus', 272, -1, 208, -1, 0.00028721222770400345), (987515173, 1982, 'autre_refus', 304, -1, 208, -1, 0.00020244983898010105), (987515173, 1982, 'autre_refus', 336, -1, 208, -1, 0.00024391883926000446), (987515173, 1982, 'autre_refus', 112, -1, 240, -1, 0.000233861937886104), (987515173, 1982, 'autre_refus', 144, -1, 240, -1, 0.00010854891297640279), (987515173, 1982, 'autre_refus', 176, -1, 240, -1, 6.488907820312306e-05), (987515173, 1982, 'autre_refus', 208, -1, 240, -1, 2.52698428084841e-05), (987515173, 1982, 'autre_refus', 240, -1, 240, -1, 7.296034164028242e-05), (987515173, 1982, 'autre_refus', 272, -1, 240, -1, 0.0001397058367729187), (987515173, 1982, 'autre_refus', 304, -1, 240, -1, 8.950028131948784e-05), (987515173, 1982, 'autre_refus', 336, -1, 240, -1, 8.176553092198446e-05), (987515173, 1982, 'autre_refus', 112, -1, 272, -1, 0.0002686330699361861), (987515173, 1982, 'autre_refus', 144, -1, 272, -1, 0.00011163864110130817), (987515173, 1982, 'autre_refus', 176, -1, 272, -1, 0.0001247913605766371), (987515173, 1982, 'autre_refus', 208, -1, 272, -1, 5.1118731789756566e-05), (987515173, 1982, 'autre_refus', 240, -1, 272, -1, 2.915779077738989e-05), (987515173, 1982, 'autre_refus', 272, -1, 272, -1, 4.317512139095925e-05), (987515173, 1982, 'autre_refus', 304, -1, 272, -1, 6.830896745668724e-05), (987515173, 1982, 'autre_refus', 336, -1, 272, -1, 0.00014221204037312418), (987515173, 1982, 'autre_refus', 112, -1, 304, -1, 0.00011836835619760677), (987515173, 1982, 'autre_refus', 144, -1, 304, -1, 0.00021751580061390996), (987515173, 1982, 'autre_refus', 176, -1, 304, -1, 0.0004385433276183903), (987515173, 1982, 'autre_refus', 208, -1, 304, -1, 0.0004268559569027275), (987515173, 1982, 'autre_refus', 240, -1, 304, -1, 6.587868847418576e-05), (987515173, 1982, 'autre_refus', 272, -1, 304, -1, 3.1739375117467716e-05), (987515173, 1982, 'autre_refus', 304, -1, 304, -1, 1.1808988347183913e-05), (987515173, 1982, 'autre_refus', 336, -1, 304, -1, 1.8825141523848288e-05), (987515173, 1982, 'autre_refus', 112, -1, 336, -1, 0.00024713538005016744), (987515173, 1982, 'autre_refus', 144, -1, 336, -1, 0.00047018102486617863), (987515173, 1982, 'autre_refus', 176, -1, 336, -1, 0.0003349797334522009), (987515173, 1982, 'autre_refus', 208, -1, 336, -1, 0.00023819139460101724), (987515173, 1982, 'autre_refus', 240, -1, 336, -1, 0.00010660880798241124), (987515173, 1982, 'autre_refus', 272, -1, 336, -1, 9.587183740222827e-05), (987515173, 1982, 'autre_refus', 304, -1, 336, -1, 0.00013130570005159825), (987515173, 1982, 'autre_refus', 336, -1, 336, -1, 0.0007333921384997666)]} ############################### 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 : 0.2113637924194336 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 Wed Feb 5 11:25:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec debut step init detect dechets input : temp/1738751120_3436695_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1738751120_3436695_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 : 0.0773627758026123 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.0003533363342285156 time spend to save output : 0.0779571533203125 total time spend for step 1 : 0.07831048965454102 step2:tile Wed Feb 5 11:25:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We 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/1738751120_3436695_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 : 20254037 with name tile_correct_upm feed_id_new_photos : 20254037 filename : temp/1738751120_3436695_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/1738751120_3436695_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.010684728622436523 About to upload 1 photos upload in portfolio : 20254037 Result OK ! uploaded one batch 0 Elapsed time : 4.891938209533691 upload mediasElapsed time : 4.902732610702515 , 0Saving 0 CHIs. end of tileElapsed time : 4.915597677230835 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1334894387'] Looping around the photos to save general results len do output : 1 /1334894387Didn'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, '1334894387', 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 : 0.01174020767211914 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.638372421264648 time spend to save output : 0.01205134391784668 total time spend for step 2 : 11.650423765182495 step3:detect_points Wed Feb 5 11:25:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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/1738751120_3436695_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 : 0.03942084312438965 time to do a prediction : 16.084348917007446 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 : 17.288366556167603 time spend to save output : 0.07558822631835938 total time spend for step 3 : 17.363954782485962 step4:count_percent_refus Wed Feb 5 11:25:49 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 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/1738751120_3436695_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) list_photo : [987321136] list_photo_correc : [1334894387] 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 : 0.04773092269897461 save missing photos in datou_result : time spend for datou_step_exec : 0.01969599723815918 time spend to save output : 0.04797172546386719 total time spend for step 4 : 0.06766772270202637 step5:brightness Wed Feb 5 11:25:49 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 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/1738751120_3436695_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 : 0.007764339447021484 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 : 0.008031129837036133 save missing photos in datou_result : time spend for datou_step_exec : 0.09996557235717773 time spend to save output : 0.01998758316040039 total time spend for step 5 : 0.11995315551757812 step6:blur_detection Wed Feb 5 11:25:50 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 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/1738751120_3436695_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 : 0.010091543197631836 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 : 0.009474754333496094 save missing photos in datou_result : time spend for datou_step_exec : 0.15003085136413574 time spend to save output : 0.023939847946166992 total time spend for step 6 : 0.17397069931030273 step7:send_mail_dechet Wed Feb 5 11:25:50 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 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, '1334894387'] 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, '1334894387', 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 : 0.01379084587097168 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.38863062858581543 time spend to save output : 0.0142059326171875 total time spend for step 7 : 0.40283656120300293 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 : 0.12724614143371582 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 Wed Feb 5 11:25:50 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 inside step blanche_jaune_detection treat image : temp/1738751150_3436695_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 time spend for datou_step_exec : 0.18153810501098633 time spend to save output : 7.557868957519531e-05 total time spend for step 1 : 0.18161368370056152 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 : 0.014651775360107422 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:split_time_score Wed Feb 5 11:25:51 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 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 : 0.15123462677001953 time spend to save output : 0.00011229515075683594 total time spend for step 1 : 0.15134692192077637 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(20254055, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]} résultat du premier test BROCA : True True ############################### TEST crop_conditional ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1335 frcnn is not linked in the step_by_step architecture ! WARNING : step 1336 crop_condition is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] origin We have 1 , BBBFBFBFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 time to download the photos : 0.2599966526031494 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 Wed Feb 5 11:25:51 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 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 : [] WARNING: Logging before InitGoogleLogging() is written to STDERR F0205 11:25:53.449129 3436695 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) /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/PIL/BmpImagePlugin.py 218 181 17% 52, 56, 76-264, 276-284, 291-355, 366, 384, 388-449 /home/admin/.local/lib/python3.8/site-packages/PIL/ExifTags.py 340 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/GifImagePlugin.py 585 527 10% 55, 71-74, 77-80, 84-108, 112-120, 124-139, 142-155, 158-410, 413-430, 433-456, 459, 480-491, 506-543, 547-564, 568-574, 578-649, 653, 658-670, 674-680, 684-746, 756-793, 812-844, 849-854, 865-872, 882, 886-905, 914-967, 971-983, 1002-1015, 1036-1048 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpGradientFile.py 68 53 22% 32-43, 47, 51, 55, 59, 70-98, 105-137 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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 389 251 35% 16, 21, 30-45, 48, 51, 54, 57-63, 66, 69, 75, 79, 82, 92, 99, 166, 168, 170, 172, 175, 182-183, 186, 189, 196-197, 200, 208-220, 232, 236, 243-254, 258, 269, 273-274, 288-290, 303-306, 311, 314, 317-322, 332-333, 336-337, 340-341, 351-356, 359-362, 365-376, 382-394, 397-401, 404-464, 467, 470-471, 474-477, 495-499, 502-505, 508-509, 512-514, 520-557, 561-566, 569-572, 582-587, 590-610, 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/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/utils.py 75 45 40% 29-30, 34-37, 41, 47-50, 59-61, 66-67, 70-74, 77, 80-84, 87, 97-104, 108-119, 126, 129 /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 /home/admin/.local/lib/python3.8/site-packages/dateutil/_common.py 25 15 40% 14-17, 20-25, 28, 34, 37-41 /home/admin/.local/lib/python3.8/site-packages/dateutil/_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/__init__.py 33 4 88% 31-32, 47-48 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/_parser.py 812 687 15% 63-75, 91-184, 187, 190-194, 197, 201, 206, 211, 216, 222-223, 226-231, 234, 238, 320, 323-327, 330-334, 337-340, 343-346, 349, 352, 355-358, 367-378, 382-391, 396-400, 404, 408, 412, 415-426, 429-454, 461-472, 475-565, 636-659, 707-873, 877-1004, 1007-1038, 1042-1054, 1057, 1070-1090, 1093-1097, 1103-1109, 1116-1127, 1135-1139, 1142-1152, 1160-1175, 1178-1215, 1218-1240, 1243-1248, 1257-1264, 1365-1368, 1383, 1386-1388, 1391-1579, 1586, 1598-1601, 1604-1605 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/isoparser.py 185 150 19% 26-37, 51-55, 134-146, 159-163, 176-179, 199, 207-210, 213-251, 254-295, 316-328, 331-381, 384-412 /home/admin/.local/lib/python3.8/site-packages/dateutil/relativedelta.py 241 206 15% 112-229, 232-262, 266, 270, 273-280, 294-308, 318-402, 405, 408, 411-413, 440, 458, 476, 496-501, 521-531, 548, 568, 571-576, 581-592, 597 /home/admin/.local/lib/python3.8/site-packages/dateutil/rrule.py 979 862 12% 26-27, 72, 87-89, 96-103, 106-111, 114-122, 125-147, 150-169, 172-180, 186-189, 195-210, 216-228, 249-269, 276-302, 434-698, 707-760, 765-774, 777-1030, 1062-1077, 1103-1109, 1119-1121, 1125-1251, 1254, 1257-1261, 1265-1276, 1279-1282, 1285-1292, 1295-1300, 1303, 1317-1323, 1326-1333, 1338, 1341, 1344, 1347, 1350-1354, 1360, 1366, 1374, 1381, 1384-1413, 1475, 1478, 1493, 1497-1504, 1507, 1513-1533, 1542-1561, 1566-1613, 1625-1725, 1732 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_common.py 161 124 23% 20-28, 55-129, 139-144, 169-177, 196-202, 205, 222-242, 258-264, 290, 293-300, 303-310, 314-317, 321-350, 366-372, 375-393, 396-405, 409, 414, 417 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_factories.py 49 21 57% 22, 34-52, 64-79 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/tz.py 803 644 20% 75, 78, 82, 98, 106, 109-112, 118, 121, 144-152, 155, 158, 162, 166, 180, 183-186, 191, 194, 206-216, 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71 53 25% 18, 40, 59-69, 79, 90-92, 107-116, 130-154, 168-191 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/point.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/publicKey.py 48 32 33% 11-12, 15-23, 26-32, 35, 39, 43-76, 80-97 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/signature.py 35 23 34% 10-12, 15-18, 21, 25-40, 44-45 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/base.py 8 2 75% 8, 12 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/binary.py 15 5 67% 15, 26, 36, 48-49 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/compatibility.py 24 13 46% 13, 19, 22-39 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/der.py 149 110 26% 27-28, 32-43, 47-53, 57, 61, 65, 69-74, 78-89, 93-111, 115-121, 125-131, 135-144, 148-152, 156-164, 168-180, 184-194, 198-207, 211-227, 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/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, 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81-82, 88, 91, 102, 129-149, 178-181, 200-210, 252-264, 285-293, 341-343, 364-371, 396-403, 415-425, 458-495, 509-518, 527, 531, 537, 555-563, 573, 583-587, 591, 595-620, 636, 643-647, 654-658, 665-666, 718-724 /home/admin/.local/lib/python3.8/site-packages/matplotlib/collections.py 835 666 20% 156-202, 205, 208, 211, 215-221, 232, 251-300, 305, 310-341, 345-419, 431, 434, 443-471, 484-485, 494, 529-531, 535, 545-553, 558, 562, 574-582, 610-623, 635, 638, 649, 652, 676-690, 700-703, 707, 722-723, 727, 730-734, 748-751, 754, 757-760, 764, 767-783, 798-801, 815-817, 822, 825, 841-859, 868-897, 901, 906-925, 943, 957-967, 971-972, 994-997, 1000-1001, 1004, 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, 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1118-1124, 1132, 1140-1165 /home/admin/.local/lib/python3.8/site-packages/matplotlib/figure.py 1041 867 17% 69-70, 83-84, 88, 92, 96-98, 102-103, 107, 110, 116-118, 152-154, 161-178, 187-214, 218-239, 262-282, 286, 304-308, 314, 357-394, 401-404, 411-414, 421-425, 429, 433, 441, 451, 457, 467, 477, 489-490, 516-527, 615-641, 744-770, 774-783, 904-919, 926-957, 969-992, 1009, 1128-1150, 1188-1200, 1277-1315, 1346-1357, 1400-1418, 1460-1478, 1501-1502, 1543-1545, 1587-1614, 1639-1641, 1645-1647, 1659-1660, 1680-1693, 1705-1729, 1732-1737, 1766-1808, 1812-1827, 1831-1837, 1949-2148, 2151-2154, 2219-2252, 2256, 2260, 2266, 2276-2277, 2280, 2292-2308, 2316, 2332, 2335, 2350, 2358-2373, 2399, 2402, 2505-2597, 2600-2601, 2610-2620, 2652-2684, 2689, 2698-2700, 2736-2744, 2758, 2763-2766, 2769, 2780-2787, 2793, 2814-2819, 2827, 2851-2856, 2889-2890, 2909-2923, 2933, 3007-3024, 3056-3066, 3087, 3091, 3095, 3099, 3109-3110, 3127, 3144, 3148-3153, 3161-3185, 3192-3194, 3200, 3203-3220, 3223-3247, 3253, 3366-3378, 3429-3474, 3484-3494, 3507-3509, 3539-3549, 3600-3629 /home/admin/.local/lib/python3.8/site-packages/matplotlib/font_manager.py 563 423 25% 135-136, 177, 190-191, 207-212, 217-244, 250-258, 269-291, 295-301, 305-307, 347-456, 474-524, 594-608, 622-631, 634-642, 645, 648, 658, 664, 670, 676, 685, 693, 699, 705, 715, 725-729, 739-742, 752-755, 768-781, 794-807, 820-837, 844, 853-857, 865, 885-892, 896, 907-920, 938, 958-962, 991-1024, 1037-1047, 1053, 1060, 1067, 1073, 1077-1079, 1094-1111, 1123-1128, 1136-1139, 1149-1157, 1171-1175, 1188-1199, 1257-1265, 1269, 1316-1359, 1365-1444, 1454-1458, 1463-1464, 1490, 1515-1523, 1539-1540, 1545-1548 /home/admin/.local/lib/python3.8/site-packages/matplotlib/gridspec.py 277 216 22% 48-56, 59-63, 79, 83, 97-99, 108-113, 121, 130-135, 143, 167-205, 213-226, 230-263, 273-316, 371-379, 400-410, 425-434, 443, 467-474, 501-505, 511-521, 529, 553-555, 558, 570-605, 612, 616, 619, 629-630, 635-636, 641-645, 648, 651, 654, 657, 663-673, 679-683, 691, 697, 739 /home/admin/.local/lib/python3.8/site-packages/matplotlib/hatch.py 143 103 28% 16-17, 20-28, 33-34, 37-45, 50-55, 58-64, 69-75, 78-84, 91-97, 102-121, 126-129, 136-137, 144-145, 153-154, 162-168, 183-189, 205-225 /home/admin/.local/lib/python3.8/site-packages/matplotlib/image.py 760 661 13% 83-110, 123-157, 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, 1530-1564, 1619-1689, 1708-1724, 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 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend.py 470 385 18% 69-74, 77-80, 83-90, 93-94, 343, 416-657, 666-671, 677-680, 684, 687, 694-706, 711-737, 764, 769, 774, 778-779, 797-806, 816-906, 921-941, 945, 949, 953, 957, 963, 977-979, 983, 1001-1012, 1016, 1020-1022, 1026, 1030, 1040-1041, 1047-1050, 1072-1093, 1110, 1121-1158, 1161-1164, 1189-1198, 1202, 1209-1238, 1243-1250, 1298-1348 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend_handler.py 343 255 26% 41-43, 79-82, 85, 89-93, 98-102, 125-139, 164, 189-192, 195-206, 231-236, 249-273, 290-312, 343-350, 355-359, 369, 375-384, 389-396, 404-407, 410-415, 420-428, 440-443, 447-464, 468-473, 477, 487-502, 510, 521, 538-545, 551-629, 659-664, 670-712, 719-720, 748-773, 782-807, 813-817 /home/admin/.local/lib/python3.8/site-packages/matplotlib/lines.py 679 562 17% 36-60, 64-69, 78-106, 118-201, 262-271, 310-414, 440-484, 492, 506-508, 518, 537-538, 596-597, 605, 616-618, 622-624, 627-635, 645-651, 654, 657-699, 708-714, 718-720, 724-726, 732-878, 882, 890, 898, 906, 914, 922, 930, 938-948, 956, 959-965, 973, 981, 989, 997, 1006-1010, 1019-1023, 1027-1029, 1035-1037, 1047-1049, 1059-1061, 1089-1096, 1116-1119, 1130-1134, 1167-1179, 1192-1193, 1196-1207, 1217, 1227, 1237, 1248-1252, 1263-1266, 1276-1287, 1297-1308, 1329-1332, 1336-1355, 1368-1371, 1384-1387, 1395, 1403, 1416-1419, 1432-1435, 1443, 1451, 1462, 1472-1481, 1484-1521, 1525-1526, 1566-1575, 1590, 1594-1599 /home/admin/.local/lib/python3.8/site-packages/matplotlib/markers.py 427 328 23% 253-272, 278-291, 294, 297, 300, 312-316, 319, 322, 325, 340-367, 376, 383-386, 395, 402-405, 408, 412-413, 424-429, 445-458, 474-480, 483, 486-488, 491, 494, 497-515, 523-541, 544, 547-556, 559, 562-573, 584-611, 614, 617, 620, 623, 626-641, 644-655, 658-659, 662-682, 685-704, 707-728, 731-754, 757-775, 780-783, 786-787, 792-795, 798-801, 806-809, 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250, 254, 261, 265, 272, 279, 287-289, 308-319, 328-345, 348, 351, 394-417, 441-464, 477-483, 495, 537-546, 587-590, 599-601, 619-642, 651, 662, 671-682, 704-725, 735-738, 749-762, 772-788, 796, 807-810, 834-878, 889-922, 942-1001, 1019, 1029-1030, 1043-1045, 1077-1083 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/__init__.py 28 8 71% 75, 95, 104-110 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/geo.py 273 183 33% 24, 27-28, 33-34, 41-57, 61-106, 111-114, 119-120, 123, 126, 129-130, 133, 136, 139, 142, 145-146, 152, 159-162, 170-172, 179-181, 187-188, 195, 205, 213, 216, 219, 222, 236-237, 240, 244-245, 256-267, 271, 278, 282, 285-288, 291, 302-309, 313, 319-323, 327, 330-333, 336, 347-377, 381, 387-393, 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 860 526 39% 119-120, 135-157, 163-167, 175, 180-182, 186-193, 204-209, 231-356, 360-375, 383-384, 397, 445-446, 476, 512-516, 552-556, 576-584, 589, 594, 599-601, 609, 614, 619, 658-686, 803-869, 882-890, 902-906, 911, 916, 921-923, 940, 945, 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3267, 3278, 3289, 3300, 3311, 3322 /home/admin/.local/lib/python3.8/site-packages/matplotlib/quiver.py 390 338 13% 291-314, 318, 322-345, 348, 357-362, 365, 373-374, 377-385, 407-437, 441-443, 477-506, 516-527, 530-536, 540-544, 549-571, 575-577, 592-596, 599-605, 608-663, 670-723, 897-941, 967-973, 1024-1117, 1122-1162, 1173-1180 /home/admin/.local/lib/python3.8/site-packages/matplotlib/rcsetup.py 414 127 69% 68-69, 75-82, 99, 110, 127, 135-136, 159, 169-170, 185, 188-189, 218, 230-234, 238, 260, 282, 288, 290, 292, 294, 296-300, 304, 344-347, 354, 366-367, 381-384, 395-398, 411, 415, 427-428, 438, 457-483, 506-524, 534, 537-541, 549-552, 560, 568, 583-589, 683, 686, 689-692, 695-698, 705, 716-718, 738-739, 746, 750, 758, 761, 783, 786-792 /home/admin/.local/lib/python3.8/site-packages/matplotlib/scale.py 274 155 43% 69, 76, 86, 105-113, 120, 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, 708-709, 726 /home/admin/.local/lib/python3.8/site-packages/matplotlib/spines.py 315 261 17% 33, 54-86, 90-99, 103-109, 113-114, 126-131, 136-140, 153-197, 200, 203-206, 216-219, 223-225, 230-282, 287-290, 313-325, 329-330, 334-386, 408-419, 423, 429-442, 448-451, 456-460, 476-477, 491, 494-505, 508-512, 539, 543, 546, 549, 552-555, 559-574, 578, 582, 585, 588 /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 335 272 19% 94-103, 108-110, 113-114, 118, 122-123, 127, 131-138, 142-149, 153-164, 170, 176-177, 188-189, 202, 206-218, 222-228, 298-321, 342-345, 351-361, 365, 384, 388-389, 392, 401-415, 423-427, 431-444, 448, 452-457, 464-488, 500-508, 512-516, 520-521, 525-539, 543-545, 568-570, 574-577, 584-635, 650, 737-830 /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 812 676 17% 41-49, 67-90, 97, 105, 130, 165-183, 201-219, 223-233, 236-239, 246-268, 274-275, 278-281, 292-313, 317-321, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/ticker.py 1228 996 19% 165-167, 170, 173, 176, 179, 182, 186, 193, 196-197, 213, 217-218, 225, 233, 236, 245, 255, 261, 269, 283-284, 294-297, 300, 303, 316-317, 325, 328, 331, 346, 354, 365, 374, 428-439, 452, 481-486, 498, 509-512, 520, 531, 544-564, 572-578, 588, 620-622, 626-650, 654-666, 672-694, 698-703, 706-740, 748-776, 780-809, 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, 1644-1649, 1665, 1673, 1685-1686, 1690-1693, 1697-1698, 1701, 1717-1719, 1723-1724, 1727, 1739-1747, 1756, 1767, 1791-1795, 1800, 1804, 1808-1811, 1815-1816, 1819-1830, 1835-1850, 1860, 1864-1865, 1869-1870, 1873-1879, 1886-1896, 1900-1907, 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/home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/art3d.py 488 390 20% 28-31, 36-39, 62-73, 97-98, 102, 116-119, 129-130, 144-146, 150-159, 164, 179-180, 208-209, 224-229, 248-254, 265, 269-273, 289-290, 296-300, 306-314, 320-329, 337-344, 354-355, 361-362, 368-377, 382-384, 404-405, 421-422, 426, 429-434, 455-456, 472-473, 476-481, 486-489, 494-496, 501-506, 529-531, 534, 546-547, 551-552, 570-580, 583-592, 595-602, 605, 611-613, 636-640, 643-645, 649-650, 668-695, 698-700, 703-705, 708, 720-721, 724-754, 758-768, 771-778, 781, 787-789, 809-816, 871-896, 914-916, 920-928, 944-947, 953-955, 960-966, 970-971, 977-1040, 1044-1045, 1049-1050, 1054-1065, 1070-1073, 1078-1081, 1097-1101, 1111-1118, 1127-1132, 1141-1146, 1173-1189, 1198-1227 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py 1305 1135 13% 122-180, 183-184, 187-188, 195, 200-207, 211-213, 217, 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534-576 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/proj3d.py 101 81 20% 20-29, 39-48, 58-68, 95-107, 125-130, 148-150, 154-162, 167-173, 177-181, 185-192, 199-206, 210, 217-218, 230-231, 235, 239-240, 244-249 /home/admin/.local/lib/python3.8/site-packages/numpy/__config__.py 30 16 47% 12-16, 27-28, 69-78 /home/admin/.local/lib/python3.8/site-packages/numpy/__init__.py 142 52 63% 124, 128-132, 279-313, 317-325, 351-358, 368-374, 378-391, 410-417 /home/admin/.local/lib/python3.8/site-packages/numpy/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/_globals.py 19 2 89% 26, 85 /home/admin/.local/lib/python3.8/site-packages/numpy/_pytesttester.py 51 43 16% 38-44, 128-201 /home/admin/.local/lib/python3.8/site-packages/numpy/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/compat/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/compat/_inspect.py 67 17 75% 75, 86, 107, 122-123, 126-129, 136, 182-191 /home/admin/.local/lib/python3.8/site-packages/numpy/compat/py3k.py 59 25 58% 39-41, 44-46, 49-51, 54, 57, 60, 65, 68-71, 74-77, 85, 103, 106, 109, 134-135 /home/admin/.local/lib/python3.8/site-packages/numpy/core/__init__.py 85 16 81% 23-48, 62-68, 125-126, 135, 142, 148-151 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs.py 261 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs_scalars.py 48 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_asarray.py 34 26 24% 19, 94-135 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype.py 157 137 13% 27-28, 35-42, 46-49, 60, 65, 95-100, 104-156, 163-175, 180-186, 191-230, 245-253, 257-279, 286-296, 300-301, 308-318, 324-342 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype_ctypes.py 54 36 33% 33, 37-68, 84-93, 106, 108, 110, 112, 116 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_exceptions.py 98 57 42% 11-14, 35, 42-44, 47, 58-59, 62, 75-78, 85-86, 90-91, 103-104, 108-109, 128-138, 145-146, 150-153, 160-190, 193-194 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_internal.py 430 327 24% 16-17, 24, 27-51, 57-76, 89-133, 141, 158-203, 207, 209, 211, 213, 215, 220, 222-223, 227, 230-233, 242, 246, 251-265, 282-284, 291-293, 300-302, 320, 332, 343, 352, 361-363, 370-372, 379-381, 388-392, 400-416, 431-434, 457-466, 490-495, 561-562, 565-567, 570-573, 576-585, 589, 592, 596-598, 601-742, 746-757, 761-779, 782-785, 789-791, 794, 798-803, 810-811, 830, 834, 852, 871, 877-878, 907, 909 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_methods.py 155 118 24% 40, 44, 48, 52, 58, 62-64, 68-86, 93-99, 102-104, 108-123, 126-159, 163-193, 197-258, 262-272, 275, 282-287, 290 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_string_helpers.py 15 5 67% 68-69, 97-100 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_type_aliases.py 122 13 89% 47-53, 108, 224-230 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_ufunc_config.py 87 23 74% 192-203, 217, 302-310, 356, 432, 437 /home/admin/.local/lib/python3.8/site-packages/numpy/core/arrayprint.py 550 435 21% 29-30, 66-98, 252-265, 295, 325-330, 340-350, 355-359, 362, 365, 372-414, 420-452, 472, 490-511, 520, 673-698, 702-712, 719-740, 751-858, 861-865, 872-894, 898-978, 981-1001, 1081-1087, 1169-1178, 1186-1191, 1194, 1201, 1204, 1212-1224, 1230-1237, 1242-1253, 1257, 1260-1263, 1270-1284, 1287-1289, 1292, 1300, 1305, 1308-1310, 1322, 1330-1336, 1339-1346, 1355, 1360, 1362, 1390-1398, 1411-1423, 1430-1470, 1475, 1523, 1530, 1540, 1551, 1556, 1595, 1658-1664 /home/admin/.local/lib/python3.8/site-packages/numpy/core/defchararray.py 438 243 45% 57-61, 68, 79-84, 92-94, 98, 124, 150, 177, 203, 229, 255, 259, 283, 307-311, 315, 339-344, 349, 376, 415-416, 420, 451-456, 461, 507, 511, 556, 592, 597, 640, 645, 680, 716, 745, 772, 798, 824, 851, 878, 904, 931, 935, 960, 966, 996-1001, 1036-1037, 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334-337 /home/admin/.local/lib/python3.8/site-packages/numpy/core/multiarray.py 104 23 78% 145, 244-245, 338, 492-495, 610, 661, 822, 880, 957, 1018, 1068, 1148, 1206, 1290, 1365, 1406, 1460, 1554, 1622, 1690 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numeric.py 495 346 30% 73, 138-142, 146, 202, 215, 280-282, 286, 337-345, 354, 416-418, 422, 485-496, 564, 568, 614-618, 622, 663, 667, 741, 745, 837-844, 848, 934-936, 940, 1072-1133, 1137, 1211-1238, 1242, 1317-1332, 1379-1391, 1395, 1447-1466, 1471, 1475, 1592-1673, 1764-1779, 1783, 1842-1846, 1855, 2011-2051, 2093-2108, 2116-2123, 2127, 2164, 2176, 2249-2250, 2254, 2337-2378, 2382, 2439-2453, 2457, 2496-2505 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numerictypes.py 150 71 53% 173-181, 219-227, 270-281, 355, 420, 436, 500-506, 566-572, 576-588, 651-672 /home/admin/.local/lib/python3.8/site-packages/numpy/core/overrides.py 58 9 84% 102, 107, 175-181 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/home/admin/.local/lib/python3.8/site-packages/numpy/fft/_pocketfft.py 164 120 27% 50-75, 79-88, 93-102, 111-114, 119, 211-216, 312-317, 405-410, 509-514, 607-612, 674-679, 683-698, 702-708, 712, 815, 918, 1014, 1107, 1200-1205, 1257, 1362-1367, 1424 /home/admin/.local/lib/python3.8/site-packages/numpy/fft/helper.py 46 33 28% 16, 64-73, 111-120, 160-169, 216-221 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/__init__.py 39 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_datasource.py 177 139 21% 59-66, 104-128, 146-147, 150-151, 192-193, 248-254, 258-261, 267-268, 274-278, 288-291, 295-300, 306-315, 325-342, 357-373, 399-415, 421-429, 463-485, 521-533, 578-579, 582, 586-591, 595, 618, 652, 683, 700-704 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_iotools.py 352 300 15% 30-35, 42-46, 53-57, 81-84, 120-131, 168, 172-197, 201-206, 209-216, 219-224, 227, 288-310, 339-380, 383, 413-419, 505, 524, 529, 539-541, 568-582, 587-596, 601-669, 672-675, 678-700, 703, 707-723, 746-751, 754-763, 796-820, 861-898 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_version.py 75 61 19% 56-76, 80-97, 101-112, 115-134, 137, 140, 143, 146, 149, 152, 155 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraypad.py 218 200 8% 29-30, 55, 81-83, 109-126, 146-151, 175-183, 208-227, 257-293, 321-378, 401-451, 482-518, 522, 736-876 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py 197 166 16% 34, 82-122, 127-130, 135, 270-317, 325-359, 364, 430-463, 467, 500-510, 514, 584-631, 635, 732-733, 738, 775, 779, 819-824 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arrayterator.py 71 58 18% 85-90, 93, 101-125, 132-134, 161-162, 172, 177-219 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/format.py 266 234 12% 192-194, 212-216, 230-235, 238-245, 270-281, 304-336, 352-364, 371-389, 396-413, 430-440, 452, 468, 499, 532, 552-567, 576-625, 663-696, 731-789, 842-890, 902-918 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/function_base.py 1153 979 15% 56, 114-143, 147, 231-241, 277, 377-419, 486-490, 494-497, 588-618, 622-623, 665-719, 723, 804, 810-811, 989-1157, 1161, 1249-1294, 1298, 1406-1439, 1443, 1485-1496, 1500, 1569-1596, 1600, 1627-1637, 1641, 1679-1694, 1698, 1750, 1754, 1794-1798, 1833-1840, 1866-1869, 1887-1905, 1926-1934, 1939, 1945-1948, 2113, 2118, 2120, 2122, 2131, 2140-2163, 2168-2232, 2236-2253, 2257-2316, 2321, 2448-2543, 2548, 2679-2701, 2796-2801, 2905-2910, 3009-3014, 3109-3114, 3182-3190, 3194, 3198, 3202, 3256-3262, 3388-3392, 3396, 3475-3477, 3481, 3508-3510, 3539-3565, 3570, 3655-3660, 3666-3716, 3721, 3863-3867, 3873, 3976-3979, 3986-3992, 3997-4004, 4009-4015, 4020-4122, 4126, 4215-4240, 4244, 4353-4375, 4379, 4447-4560, 4564, 4656-4755, 4759, 4811-4817, 4821, 4915-4932 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/histograms.py 287 254 11% 29, 49-50, 72-73, 96-97, 118-119, 146-161, 182-196, 224-226, 263-270, 285-301, 309-331, 342-357, 382-451, 460, 467, 668-670, 675, 791-929, 934-940, 1014-1129 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/index_tricks.py 258 185 28% 32, 93-107, 149-207, 325-420, 423, 593, 607, 610, 657-661, 665, 678-681, 695-696, 758-761, 775, 891-909, 977-978, 982, 1006-1013 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/mixins.py 59 12 80% 10-13, 19-21, 29-31, 39, 54 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/nanfunctions.py 279 219 22% 61-66, 96-110, 135-139, 164-180, 209-221, 225, 313-336, 340, 428-451, 455, 494-500, 504, 544-550, 554, 647-648, 652, 717-718, 722, 787-788, 792, 854-855, 859, 937-957, 965-974, 984-1000, 1010-1020, 1025, 1113-1124, 1129, 1245-1249, 1255, 1358-1362, 1371-1381, 1391-1405, 1413-1418, 1424, 1518-1567, 1572, 1670-1676 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/npyio.py 854 780 9% 34-37, 83, 86-89, 97, 108-112, 189-202, 205, 208, 215-221, 224, 228, 231, 242-260, 269-273, 277-281, 396-450, 455, 519-529, 534-535, 618, 622-623, 689, 695-726, 732-758, 768, 903-1188, 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 438 345 21% 41, 138-165, 169, 229-261, 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, 1211, 1219-1243, 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 263 196 25% 31-49, 53, 161-170, 174, 251-260, 264, 359-414, 418, 487-505, 509, 588-602, 648, 660, 716-723, 727-732, 736, 766-792, 796, 866-874, 878, 937-942, 989-991, 1034-1036, 1043-1048, 1055-1060, 1064, 1136-1164, 1168, 1238-1260 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py 91 70 23% 21-22, 26-35, 97-114, 119, 301-335, 340-359, 363, 411, 420-428, 470-471, 475, 536-544 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/twodim_base.py 177 114 36% 34-40, 44, 95-98, 151-154, 158, 211, 216, 220, 231, 289-303, 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 90 35% 70-77, 81, 112-114, 118, 157-160, 164, 200-203, 207, 240-244, 300, 336-341, 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 454 415 9% 38-46, 50-51, 67-69, 76-127, 134-141, 188-194, 221, 260-277, 329-379, 391-407, 415-431, 452-482, 537-628, 672-677, 737-812, 835-947, 950-956, 1003-1004, 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 678 574 15% 88, 91, 94, 97, 100, 103-105, 108-110, 113, 126, 129, 133, 137-157, 165-174, 177-185, 188-190, 194-196, 200-203, 206-208, 212, 230, 235, 286-306, 310, 378-395, 399, 456-467, 473, 538-546, 550, 618-666, 756-764, 770, 890-982, 1059-1077, 1081, 1159-1176, 1179-1181, 1314-1333, 1453-1473, 1479, 1617-1674, 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 1774 26% 102-113, 122, 124, 204-212, 217-222, 270-277, 282-291, 342, 393, 414-425, 439-469, 532-534, 543-547, 575-579, 627-636, 646-663, 708-714, 768-776, 779, 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, 1466-1469, 1534-1537, 1544-1547, 1621-1636, 1682-1686, 1726-1753, 1788-1809, 1814-1817, 1924-1943, 1969, 1995, 2021, 2047, 2073, 2101-2103, 2139-2143, 2179-2183, 2244-2251, 2323-2331, 2361-2373, 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, 2855-2882, 2887, 2889, 2895-2896, 2900-2909, 2915-2932, 2938, 2943, 2956, 3012, 3030-3042, 3047, 3054-3058, 3062, 3065, 3074-3121, 3180-3183, 3190-3194, 3204-3210, 3224-3337, 3347-3402, 3411-3419, 3427-3431, 3438-3500, 3512, 3516, 3532-3535, 3539, 3554-3555, 3570-3571, 3576, 3591-3594, 3599, 3629-3630, 3635, 3652, 3660, 3664-3665, 3697-3706, 3710-3725, 3776-3809, 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, 4585-4591, 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, 6422, 6462-6469, 6472, 6475, 6478, 6481-6485, 6491-6500, 6505, 6510, 6524, 6527, 6530, 6536-6542, 6559, 6611-6616, 6640-6647, 6651-6679, 6683-6695, 6698-6705, 6710-6717, 6723-6729, 6766-6777, 6812-6813, 6833-6865, 6872-6882, 6898-6908, 6923, 6967-6983, 6998-7001, 7016-7022, 7037-7043, 7059-7062, 7083-7101, 7136-7139, 7154-7158, 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, 7951-7952, 8000-8002, 8011, 8017, 8082, 8116-8127, 8186 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/extras.py 560 468 16% 48, 101-102, 152-154, 207-209, 259, 262, 272-280, 290-293, 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 238 178 25% 15-33, 69, 116-165, 168-187, 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/version.py 9 0 100% /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 67 59% 69, 76, 81-84, 87-90, 93-96, 99-102, 105-108, 198, 228, 236-261, 272-273, 289-290, 305-306, 317, 328, 339-342, 355, 371-380, 397, 408, 419, 428, 439, 450, 461, 470, 472, 474, 476, 482-484, 497, 527, 533, 547, 560 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/home/admin/.local/lib/python3.8/site-packages/pygit2/credentials.py 56 18 68% 52, 56, 60, 63, 74-75, 79, 83, 86, 113-116, 120, 124, 127, 132, 138 /home/admin/.local/lib/python3.8/site-packages/pygit2/errors.py 26 20 23% 34-65, 70 /home/admin/.local/lib/python3.8/site-packages/pygit2/ffi.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/pygit2/index.py 226 171 24% 44-50, 54-59, 63, 66, 69, 72-77, 80-94, 97, 110-111, 115-116, 119-120, 129-144, 157-167, 172-173, 178-180, 188-190, 202-212, 232-249, 272-295, 322-331, 338-343, 348, 353, 356, 359-360, 365-369, 377-384, 388-396, 402, 405-417, 420-421, 424, 430-434, 437, 440, 443-457, 460 /home/admin/.local/lib/python3.8/site-packages/pygit2/packbuilder.py 40 23 42% 37-43, 47, 50, 53, 57-59, 62-64, 67-69, 72, 75-77, 81 /home/admin/.local/lib/python3.8/site-packages/pygit2/refspec.py 37 17 54% 37-38, 43, 48, 53, 58, 63, 69, 74, 77-84, 90, 96 /home/admin/.local/lib/python3.8/site-packages/pygit2/remote.py 155 112 28% 41-60, 69-71, 74, 80, 86, 92, 97-101, 106-107, 122-130, 138-164, 169-171, 177, 181-182, 188-192, 198-202, 220-224, 241-249, 252-265, 268-275, 284-296, 306-319, 326-327, 332-333, 338-339, 345-346, 352-353 /home/admin/.local/lib/python3.8/site-packages/pygit2/repository.py 552 422 24% 77, 85, 105-117, 121, 139-163, 169-174, 184-198, 204-205, 208-211, 214, 217, 223-224, 237-241, 250-254, 281-291, 308-316, 324-346, 353-354, 361-362, 369-373, 410-429, 444-453, 459-479, 543-568, 575, 615-636, 644-648, 690-698, 704-729, 743-762, 818-838, 894-919, 983-1039, 1074-1091, 1095-1104, 1128-1129, 1141, 1148-1149, 1187-1224, 1249-1262, 1288-1303, 1310-1316, 1326-1327, 1347-1363, 1372-1375, 1383-1393, 1396-1399, 1402-1404, 1407, 1410, 1413-1416, 1423-1424, 1427, 1439-1442, 1445-1446, 1449, 1452, 1455, 1459, 1462, 1491, 1493, 1497, 1501-1506 /home/admin/.local/lib/python3.8/site-packages/pygit2/settings.py 74 25 66% 40, 43, 65-71, 81, 86, 90, 98, 102, 110, 120, 128, 138, 148, 153, 158, 163, 168, 173, 178 /home/admin/.local/lib/python3.8/site-packages/pygit2/submodule.py 37 19 49% 35-40, 43, 47-51, 56-57, 62-63, 68-69, 74-75, 80-81 /home/admin/.local/lib/python3.8/site-packages/pygit2/utils.py 60 46 23% 33-36, 40-49, 53-62, 66-70, 85-102, 105, 108, 119-121, 124, 127-132 /home/admin/.local/lib/python3.8/site-packages/python_http_client/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/python_http_client/client.py 106 76 28% 11-15, 29-31, 38, 45, 52, 59-62, 92-99, 109, 118-136, 145, 154-155, 174-184, 196, 207-290, 293, 296 /home/admin/.local/lib/python3.8/site-packages/python_http_client/exceptions.py 46 16 65% 8-17, 20, 30, 93-97 /home/admin/.local/lib/python3.8/site-packages/pytz/__init__.py 198 125 37% 56-75, 87-108, 113-124, 167-190, 195, 204-206, 226-228, 231, 234, 237, 240, 244-246, 250-254, 257, 260, 295, 307, 347, 350-366, 379-390, 403-406, 409, 412, 415, 418, 421, 425-427, 431-435, 491-502, 509-512, 516 /home/admin/.local/lib/python3.8/site-packages/pytz/exceptions.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pytz/lazy.py 100 59 41% 4-8, 21-28, 31-38, 41-48, 51-58, 61-68, 87, 98-106, 142, 151-160 /home/admin/.local/lib/python3.8/site-packages/pytz/tzfile.py 76 66 13% 21, 25-123, 126-133 /home/admin/.local/lib/python3.8/site-packages/pytz/tzinfo.py 178 126 29% 7-8, 34-41, 49-58, 66, 76, 87-89, 97, 105, 113, 117-119, 144-148, 151, 156, 183-194, 198-204, 251-259, 320-397, 422-428, 461-467, 499-505, 508-517, 524, 542-580 /home/admin/.local/lib/python3.8/site-packages/requests/__init__.py 68 27 60% 49-50, 54-55, 64, 80-86, 91-100, 108-109, 123-124, 127-136 /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 10 52% 30-35, 45-50 /home/admin/.local/lib/python3.8/site-packages/requests/adapters.py 194 147 24% 61, 74, 93, 97, 142-155, 158, 163-169, 188-192, 211-235, 249-290, 304-329, 340-358, 366-368, 384-397, 411, 426-432, 453-538 /home/admin/.local/lib/python3.8/site-packages/requests/api.py 19 10 47% 58-59, 73, 85, 99-100, 115, 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 239 176 26% 19-20, 36-38, 41, 44, 47, 52-58, 70, 73, 76, 80, 85, 88, 92, 96, 100, 115, 118, 121, 131-137, 146-148, 156-167, 201-204, 212-223, 231-232, 240, 248-249, 257, 265-266, 275, 279-283, 287-291, 299-304, 313-319, 322-325, 334, 341, 347, 350-356, 360-364, 378-384, 398-413, 417-420, 424-426, 430-433, 437, 441-452, 461-489, 495-504, 530-539, 549-561 /home/admin/.local/lib/python3.8/site-packages/requests/exceptions.py 37 8 78% 19-24, 41-42 /home/admin/.local/lib/python3.8/site-packages/requests/hooks.py 14 11 21% 16, 24-33 /home/admin/.local/lib/python3.8/site-packages/requests/models.py 455 368 19% 89-104, 115-134, 146-203, 210-216, 223-227, 273-291, 294, 298-311, 337-350, 367-378, 381, 384-392, 396-398, 402-408, 417-482, 487-493, 502-571, 575-587, 593-609, 622-629, 636-638, 660-704, 707, 710, 715-718, 721-726, 729, 739, 749, 753, 764-768, 775, 780, 788, 793, 812-851, 863-885, 891-904, 920-942, 952-975, 981-992, 997-1021, 1029-1034 /home/admin/.local/lib/python3.8/site-packages/requests/packages.py 17 4 76% 5-10 /home/admin/.local/lib/python3.8/site-packages/requests/sessions.py 268 219 18% 56, 67-88, 97-103, 115-125, 129-157, 173-281, 288-301, 315-332, 338-354, 396-451, 454, 457, 469-500, 563-591, 601-602, 612-613, 623-624, 637, 649, 661, 671, 680-749, 758-780, 788-794, 798-799, 806-810, 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 19 51% 41-44, 49, 52, 55, 58, 61, 65, 68-73, 77, 80, 91, 96, 99 /home/admin/.local/lib/python3.8/site-packages/requests/utils.py 485 411 15% 76-121, 127-130, 134-196, 202-253, 258-260, 268-297, 303-310, 331-337, 357-366, 393-398, 424-433, 445-459, 469-474, 485, 493-506, 521-535, 545-560, 566-577, 582-587, 602-626, 641-656, 667-678, 689-693, 703-704, 711-715, 724-739, 750-761, 772-821, 830-833, 842-859, 873-886, 902, 920-946, 962-984, 993-1013, 1022-1029, 1038-1040, 1044-1056, 1068-1076, 1083-1094 /home/admin/.local/lib/python3.8/site-packages/sendgrid/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/base_interface.py 22 15 32% 23-30, 38-46, 49, 59-62 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/endpoints/__init__.py 0 0 100% /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 18 40% 19-27, 36, 48, 56, 65-66, 75-81 /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 43 46% 41-60, 68, 77-80, 92, 108, 120, 137, 145, 154, 162, 171, 179, 189, 198-213, 222-228 /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 470 334 29% 50-80, 88, 100-102, 112-114, 122-124, 133, 150-190, 198, 208, 213, 229-241, 258-276, 280, 296-308, 324-330, 335, 356-368, 388-394, 406, 415-433, 441, 445, 454-458, 466-490, 494, 503-507, 515-535, 543, 547, 557-561, 569-592, 601, 612-629, 633, 642-653, 662, 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/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 11 52% 13-18, 26, 35, 43, 53, 60, 69 /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 21 22% 18-28, 42-55, 66-69 /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 3 57% 55-58 /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/zipp.py 123 73 41% 28, 47-50, 62, 73-75, 78-79, 82, 89-92, 100-111, 121-124, 127-130, 223-224, 232-242, 246, 250, 254, 258, 262, 265-266, 269-270, 273, 276, 279, 282, 285, 288-291, 294, 297, 300-301, 307-312 /home/admin/mtr/.credentials/credentials.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/__init__.py 1 0 100% /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/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/admin_queries.py 457 392 14% 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, 317-323, 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, 473-485, 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 291 256 12% 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, 368-397, 402-429, 436-450, 455-485, 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 1135 23% 44, 57, 69, 87, 95-97, 100-104, 117-135, 149-153, 162-170, 186-295, 303-308, 311-316, 319-326, 329-348, 351-359, 362-369, 378-402, 406-409, 420-461, 472-492, 503-523, 526-531, 534-541, 551-572, 576-597, 608, 620, 626-627, 630, 646, 651, 658-659, 662-663, 682, 689, 696, 712, 719, 722, 726, 743-783, 801, 808-811, 831, 851-923, 934-1044, 1083, 1090, 1123-1124, 1127-1129, 1135-1138, 1141-1146, 1150-1153, 1158, 1164, 1179, 1191-1195, 1204-1208, 1217, 1228-1232, 1250-1310, 1337, 1339, 1342-1349, 1366-1374, 1384-1396, 1400-1407, 1410-1416, 1419-1427, 1432-1439, 1443-1456, 1460-1473, 1476-1483, 1486-1493, 1498-1519, 1524-1534, 1541, 1548-1554, 1556, 1568-1573, 1585-1590, 1602-1607, 1613-1617, 1624-1630, 1635-1649, 1655-1663, 1668-1687, 1691-1710, 1714-1729, 1733-1753, 1757-1775, 1781-1803, 1808-1818, 1821-1829, 1835-1852, 1858-1868, 1873-1908, 1912-1920, 1923-1927, 1930-1952, 1957-1970, 1975-1983, 1986-1994, 2001-2026, 2029-2069, 2075-2083, 2087-2095, 2100-2109, 2112-2121, 2124-2129, 2137, 2144-2212, 2257-2281, 2286-2309, 2313-2331, 2334-2342, 2352-2377, 2385-2386, 2394, 2413-2444, 2449-2477, 2481-2487, 2491-2510, 2514-2523, 2527-2531, 2535-2547, 2551-2578, 2582-2609, 2612-2650, 2654-2690, 2693-2708, 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 213 183 14% 18-19, 23-24, 29-128, 143, 146-147, 150, 154, 160-161, 169, 181-232, 237-350 /home/admin/workarea/git/Velours/python/mtr/database_queries/descriptor_queries.py 354 327 8% 22-79, 82-103, 106-145, 160-264, 270-301, 304-321, 328-352, 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 98 34% 12-13, 33-34, 36-37, 40-46, 49, 54-61, 70-80, 83-95, 103-114, 117-133, 137-140, 147-160, 163-167, 182 /home/admin/workarea/git/Velours/python/mtr/database_queries/graph_nodes_queries.py 77 64 17% 22-34, 38-54, 59-130 /home/admin/workarea/git/Velours/python/mtr/database_queries/hashtag_queries.py 158 110 30% 46-50, 64-65, 72, 80-91, 94-110, 113-125, 128-133, 136-142, 145-155, 158-165, 168-183, 186-193, 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 84 20% 30-71, 74-81, 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 558 469 16% 12, 50-75, 81-101, 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, 674-687, 694-721, 724-742, 749-802, 805-826, 832-849, 852-864, 868-922, 928-948, 968, 975-986, 989-1010 /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py 511 407 20% 39, 41, 44, 47, 56-72, 75-94, 97-114, 118-124, 127-136, 140-158, 163-180, 184-199, 202-212, 215-222, 225-235, 240-255, 258-263, 266-271, 274-284, 287-299, 302-311, 315-327, 332-341, 344-354, 357-365, 369-375, 378-383, 386-390, 393-397, 400-410, 415-468, 473-497, 513-519, 522-526, 535-541, 548-571, 576-584, 587-594, 598-608, 615-616, 620-622, 630, 634-638, 642-662, 675, 684, 717-748, 750-759, 764-801 /home/admin/workarea/git/Velours/python/mtr/datou/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py 1753 1190 32% 43-44, 75-101, 108-109, 112-113, 117-118, 153, 158, 172-174, 188-189, 199, 206-209, 212-213, 227-229, 241-243, 246-247, 277, 302-306, 311, 328-332, 335, 344-406, 434, 462-463, 481-495, 509-516, 522-573, 580-624, 652-653, 660-663, 674-677, 691, 696, 700-702, 721, 729-730, 738-739, 773-776, 788-797, 807-810, 818, 821, 823-825, 829-853, 863-870, 874, 877, 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, 1561, 1564, 1567, 1570-1571, 1574-1577, 1580, 1583, 1586-1588, 1596, 1603-1609, 1612-1617, 1621, 1634-1653, 1656-1659, 1662-1672, 1675-1678, 1682-1735, 1739-1742, 1747-1785, 1790-1791, 1794-1806, 1809-1813, 1819-1822, 1827-1871, 1874-1875, 1878-1894, 1903-1919, 1924-1941, 1945-1957, 1966-1969, 1975-1985, 1993-1996, 2002-2006, 2009-2012, 2015-2018, 2021-2024, 2027-2034, 2037-2041, 2045-2068, 2086-2110, 2113-2122, 2125-2151, 2154-2203, 2206-2241, 2258-2264, 2267-2277, 2280-2282, 2299, 2313, 2316-2328, 2331-2332, 2334-2369, 2371, 2375-2381, 2422, 2426, 2428, 2430, 2432, 2434, 2436, 2438, 2440, 2442, 2444, 2446, 2448, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2511, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2528, 2530, 2532, 2534, 2536, 2538, 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2555, 2559, 2562, 2564, 2566, 2568, 2570, 2572, 2574, 2577, 2580, 2582, 2584, 2586, 2588, 2590, 2592, 2594, 2596, 2598, 2600, 2603, 2605, 2607, 2609, 2611, 2617, 2619, 2621, 2623, 2625, 2630-2667, 2682, 2688, 2701-2703, 2718-2720, 2731, 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 478 208 56% 16-23, 35-37, 46, 51-62, 73-84, 101-122, 135-149, 178, 194, 200, 212, 215, 222-246, 252-290, 302-321, 335, 358-382, 385, 389, 392, 396, 399-402, 412, 417-418, 439-440, 456, 469-470, 495, 499-500, 506, 512-513, 522-523, 570, 577-580, 589, 616, 635-652, 660, 665, 675-679, 684-685, 687-691, 694, 723-743, 747-771 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_data_increase.py 204 197 3% 7-121, 125-162, 167-218, 221-294, 297-339 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_save.py 1287 1137 12% 10-15, 18-183, 191-369, 374-436, 445-486, 489-553, 559-633, 638-744, 747-761, 764-779, 783-809, 813-864, 873-902, 907-936, 943-1043, 1047-1078, 1086-1087, 1089-1091, 1095, 1098, 1102, 1106-1116, 1138-1140, 1155-1157, 1165, 1172-1175, 1194-1213, 1223-1253, 1257-1279, 1295-1332, 1338-1357, 1362-1387, 1393-1457, 1472-1500, 1503-1519, 1523-1534, 1538-1619, 1625, 1631-1632, 1654-1655, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1683, 1685, 1687, 1689-1690, 1692, 1694, 1696, 1698, 1700, 1703, 1705, 1708, 1710, 1712, 1714, 1716, 1719, 1721, 1725-1726, 1730-1739, 1743-1745, 1749-1769, 1775-1783, 1786-1818, 1821-1836, 1840-1875 /home/admin/workarea/git/Velours/python/mtr/datou/datou_local_cache_db.py 157 135 14% 11-32, 35-36, 40-56, 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 325 254 22% 9-77, 82-130, 135-351, 370-372, 375-376, 395, 413-426, 430, 435, 439-443, 449, 473 /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 372 364 2% 15-244, 249-267, 271-372, 376-558 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_post_processing.py 1061 868 18% 28-121, 138, 141-142, 149-153, 161, 163, 179, 187-189, 197-198, 209-215, 226-293, 310-739, 744-867, 874-924, 932-1006, 1011-1081, 1099-1269, 2533, 2536-2538, 2541-2542, 2545-2552, 2562, 2569-2581, 2584, 2588-2592, 2599-2606, 2621-2638, 2655-2659, 2667-2669, 2709-2735, 2754-2767, 2770-2783, 2789-2791, 2798-2808, 2816, 2832, 2848, 2859 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py 1402 1368 2% 34-89, 92-196, 200-500, 506, 510-696, 703-838, 843-885, 889-968, 975-1356, 1362-1459, 1463-1503, 1508-1551, 1555-1630, 1634-1715, 1719-1733, 1739-1892, 1895-1898, 1905-1986, 1989-2177 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py 1963 1930 2% 35-308, 313-424, 427-556, 560-628, 632-779, 783-1028, 1032-1077, 1081-1187, 1196-1272, 1279-1466, 1470-1499, 1503-1579, 1586-1674, 1678-1855, 1859-2027, 2031-2078, 2088-2369, 2373-2418, 2422-2481, 2485-2511, 2515-2619, 2626-2814, 2995-3193, 3452-3526, 3530-3569 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_send_or_copy.py 554 540 3% 19-195, 200-268, 273-332, 336-379, 383-488, 493-623, 628-790, 795-841 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_sort.py 193 188 3% 12-115, 119-171, 178-183, 189-287, 291-305 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_util.py 298 281 6% 6-55, 62-134, 143-155, 162-209, 213-219, 224-231, 236-315, 319-324, 327-333, 337-398, 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/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 322 307 5% 23-46, 52-92, 96-119, 123-184, 187-213, 218-335, 338-384, 389-412, 415-433, 436-461 /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 88 33% 40, 44, 47-50, 52, 54, 59, 61, 65-68, 91-134, 137-150, 162, 164-167, 170-194 /home/admin/workarea/git/Velours/python/mtr/ses_mailer.py 55 44 20% 16, 20-44, 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/image_utils.py 328 255 22% 21-28, 37-52, 72-85, 88, 91-113, 118-122, 129, 138-162, 166, 174, 181-191, 194-236, 239, 242-253, 256, 259, 262, 265-298, 301-314, 343, 346-354, 363-365, 368-381, 385-397, 401-441, 446-465, 470-473, 476-484 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/simple_image_editor.py 2091 1975 6% 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, 1993-2006, 2014-2389, 2395-2421, 2431-2465, 2479-2741, 2752-2799, 2804-2840, 2846-2881, 2894, 2900, 2906, 2912, 2918, 2924, 2929-2966, 2973-3014, 3020-3044, 3052-3129, 3140-3156, 3164-3189, 3200-3304, 3336-3465, 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/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/swift_upload_manager.py 151 131 13% 15-29, 32-48, 51-54, 57-79, 92-99, 103-111, 118-127, 130, 133-142, 145-156, 160-174, 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/upload_batch.py 58 53 9% 21-95 /home/admin/workarea/git/Velours/python/mtr/utils/utils_timer.py 11 8 27% 12-20 /home/admin/workarea/git/Velours/python/tests/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/tests/cod_main_test.py 75 67 11% 8-59, 69-124, 128, 131, 134, 138 /home/admin/workarea/git/Velours/python/tests/datou_test.py 1923 1825 5% 24-72, 83-130, 140-232, 236-277, 280-366, 378-416, 428-472, 482-518, 523-588, 594-681, 692-752, 762-837, 851-889, 900-938, 949-1000, 1011-1073, 1084-1176, 1269-1339, 1350-1811, 1822-1879, 1889-1949, 1959-2067, 2081-2133, 2144-2215, 2230-2332, 2343-2401, 2411-2461, 2472-2558, 2563-2681, 2693-2748, 2759-2809, 2819-2869, 2881-2935, 2960, 2966-2968, 2981-2983, 2985-2987, 2996, 3010-3056, 3066-3121, 3131-3186, 3196-3217, 3227-3270, 3280-3328, 3338-3371, 3381-3436, 3446-3492, 3503-3542, 3553-3592, 3674-3676, 3682-3684, 3688, 3700-3701, 3704 /home/admin/workarea/git/Velours/python/tests/python_tests.py 221 61 72% 41, 94-95, 99, 102, 105, 107, 112, 122, 124, 128, 130, 132, 134, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 172, 188, 195-202, 208, 222-225, 245, 248-254, 259, 271-272, 275-278, 288-289, 370, 377 /usr/lib/python3/dist-packages/babel/__init__.py 3 0 100% /usr/lib/python3/dist-packages/babel/_compat.py 51 26 49% 34-56, 63-67, 77-80 /usr/lib/python3/dist-packages/babel/core.py 329 213 35% 27, 70-77, 102-105, 157-168, 192-193, 216-219, 261-331, 334-337, 343, 346, 349-355, 358, 363-365, 378-393, 418-421, 432-435, 446-449, 468, 482, 494, 506, 515, 531, 542, 554, 566, 580, 592, 604, 615-618, 626, 632, 641, 650, 659, 673, 687, 704, 720, 731, 740, 749, 759, 773, 787, 801, 814, 836, 851, 867, 884, 896, 907, 918, 932, 962-977, 1026-1040, 1083-1115, 1131-1133 /usr/lib/python3/dist-packages/babel/localedata.py 112 86 23% 34-39, 49-55, 66, 96-123, 138-156, 167, 170, 181-189, 198-201, 204, 207, 210-221, 224, 227, 230 /usr/lib/python3/dist-packages/babel/plural.py 280 181 35% 43-73, 112-119, 122-123, 137-139, 149-150, 158, 161, 164-166, 184-189, 211-229, 242-252, 272, 292, 306-315, 334-349, 353, 358-359, 363, 367, 371, 375, 413-421, 425-432, 435-438, 441-444, 447-461, 464-471, 474-478, 481-484, 487-495, 498, 520-521, 538, 550-553, 567-583, 598-603, 620, 623-629 /usr/lib/python3/dist-packages/certifi/__init__.py 2 0 100% /usr/lib/python3/dist-packages/certifi/core.py 5 0 100% /usr/lib/python3/dist-packages/chardet/__init__.py 11 7 36% 31-39 /usr/lib/python3/dist-packages/chardet/big5freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/big5prober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/chardistribution.py 117 83 29% 49-59, 65-68, 72-82, 88-98, 103, 110, 115-118, 125-129, 134-137, 144-148, 153-156, 163-167, 172-175, 182-189, 194-197, 204-214, 219-222, 229-233 /usr/lib/python3/dist-packages/chardet/charsetgroupprober.py 72 61 15% 34-37, 40-47, 51-55, 59-63, 66-83, 86-106 /usr/lib/python3/dist-packages/chardet/charsetprober.py 55 36 35% 40-42, 45, 49, 52, 56, 59, 63-64, 81-101, 115-145 /usr/lib/python3/dist-packages/chardet/codingstatemachine.py 28 18 36% 56-61, 64, 69-78, 81, 84, 88 /usr/lib/python3/dist-packages/chardet/compat.py 10 4 60% 26-29 /usr/lib/python3/dist-packages/chardet/cp949prober.py 16 6 62% 36-41, 45, 49 /usr/lib/python3/dist-packages/chardet/enums.py 35 1 97% 62 /usr/lib/python3/dist-packages/chardet/escprober.py 58 45 22% 43-56, 59-67, 71, 75, 78-81, 84-101 /usr/lib/python3/dist-packages/chardet/escsm.py 17 0 100% /usr/lib/python3/dist-packages/chardet/eucjpprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/euckrfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euckrprober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/euctwfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euctwprober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/gb2312freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/gb2312prober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/hebrewprober.py 77 48 38% 155-162, 165-171, 175-176, 179, 193, 223-253, 259-280, 284, 289-292 /usr/lib/python3/dist-packages/chardet/jisfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/jpcntx.py 81 61 25% 124-129, 132-141, 144-168, 171, 175-178, 181, 185-186, 190, 193-210, 214-231 /usr/lib/python3/dist-packages/chardet/langbulgarianmodel.py 5 0 100% 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258-261, 271-296, 316-333 /usr/lib/python3/dist-packages/debtcollector/renames.py 16 5 69% 38-43 /usr/lib/python3/dist-packages/entrypoints.py 148 68 54% 37, 40, 47-48, 53-54, 57, 73, 82-83, 100, 103, 111, 127-156, 160-181, 197, 208-214, 221-225, 242-243 /usr/lib/python3/dist-packages/idna/__init__.py 2 0 100% /usr/lib/python3/dist-packages/idna/core.py 282 244 13% 37-41, 44, 47, 50, 55-57, 62-64, 70-124, 129-131, 136-140, 145-146, 151-194, 199-235, 240-267, 272-292, 297-313, 318-339, 346-372, 377-400 /usr/lib/python3/dist-packages/idna/idnadata.py 4 0 100% /usr/lib/python3/dist-packages/idna/intranges.py 29 24 17% 18-29, 32, 35, 40-53 /usr/lib/python3/dist-packages/idna/package_data.py 1 0 100% /usr/lib/python3/dist-packages/iso8601/__init__.py 1 0 100% /usr/lib/python3/dist-packages/iso8601/iso8601.py 79 64 19% 22, 76-134, 144-151, 158-172, 191-214 /usr/lib/python3/dist-packages/keyring/__init__.py 3 0 100% /usr/lib/python3/dist-packages/keyring/backend.py 84 20 76% 85-87, 90-91, 99, 108, 119, 133-140, 151, 157, 165, 168, 196-198 /usr/lib/python3/dist-packages/keyring/backends/OS_X.py 46 25 46% 30, 33-43, 47-58, 62-69 /usr/lib/python3/dist-packages/keyring/backends/SecretService.py 62 40 35% 10, 13-15, 31-39, 46-59, 64-72, 77-84, 89-94 /usr/lib/python3/dist-packages/keyring/backends/Windows.py 95 59 38% 16-18, 22-24, 56, 60, 64-71, 74-84, 87-94, 97-103, 106-114, 117-126, 129-138, 151, 155, 159, 163-165 /usr/lib/python3/dist-packages/keyring/backends/_OS_X_API.py 151 136 10% 24-345 /usr/lib/python3/dist-packages/keyring/backends/__init__.py 0 0 100% /usr/lib/python3/dist-packages/keyring/backends/chainer.py 36 18 50% 47-50, 53-57, 60-64, 67-70 /usr/lib/python3/dist-packages/keyring/backends/fail.py 8 2 75% 19-25 /usr/lib/python3/dist-packages/keyring/backends/kwallet.py 95 59 38% 14-18, 35, 40-48, 51-52, 55-75, 78-95, 100-107, 112-115, 121-126 /usr/lib/python3/dist-packages/keyring/core.py 80 42 48% 27, 34, 41-51, 57, 63, 69, 75, 79, 124-127, 135-138, 159-176, 181-185 /usr/lib/python3/dist-packages/keyring/credentials.py 37 14 62% 16, 20, 28-29, 33, 37, 46-47, 52-55, 59, 63 /usr/lib/python3/dist-packages/keyring/errors.py 26 1 96% 60 /usr/lib/python3/dist-packages/keyring/py27compat.py 35 6 83% 7-8, 17, 42, 49-50 /usr/lib/python3/dist-packages/keyring/py32compat.py 5 2 60% 3-4 /usr/lib/python3/dist-packages/keyring/py33compat.py 10 4 60% 28-31 /usr/lib/python3/dist-packages/keyring/util/__init__.py 13 2 85% 34-35 /usr/lib/python3/dist-packages/keyring/util/platform_.py 31 7 77% 8, 12, 16-18, 47-50 /usr/lib/python3/dist-packages/keyring/util/properties.py 14 6 57% 51-53, 56-58 /usr/lib/python3/dist-packages/keystoneauth1/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneauth1/_fair_semaphore.py 43 33 23% 35-42, 47-54, 58-60, 63-77, 87-88, 93-104 /usr/lib/python3/dist-packages/keystoneauth1/_utils.py 33 18 45% 30-33, 38-43, 57-59, 73-75, 82-83 /usr/lib/python3/dist-packages/keystoneauth1/adapter.py 167 135 19% 132-177, 181-196, 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/usr/lib/python3/dist-packages/keystoneauth1/exceptions/connection.py 16 2 88% 50-51 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/discovery.py 16 1 94% 39 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/http.py 147 55 63% 72-83, 254-259, 394-460 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/oidc.py 14 0 100% /usr/lib/python3/dist-packages/keystoneauth1/exceptions/response.py 7 2 71% 24-25 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/service_providers.py 7 3 57% 22-24 /usr/lib/python3/dist-packages/keystoneauth1/plugin.py 48 29 40% 33, 62, 95-100, 124-131, 149-154, 176-180, 193, 209, 224, 239, 254, 268, 287, 304, 315 /usr/lib/python3/dist-packages/keystoneauth1/session.py 539 443 18% 36-37, 65-70, 81-86, 91-106, 114, 118, 124-131, 138-139, 142-153, 162-195, 208-222, 238-240, 247-248, 251-254, 257-260, 352-388, 392-399, 403, 407, 410, 413-429, 434-441, 452-459, 464-517, 522-580, 593-623, 751-983, 1001-1107, 1115, 1123, 1131, 1139, 1147, 1155, 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125, 134-180, 183-186, 190-192 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/password.py 33 19 42% 23, 46-52, 55-67, 77-79, 83-86 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/token.py 21 10 52% 22, 34-35, 38-42, 46-48 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v2.py 108 62 43% 41-49, 56-61, 66, 71, 74-94, 131-144, 149, 154, 159, 164, 167-174, 178-181, 186-197, 213-214, 219, 224, 227-229, 233-239 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/__init__.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/base.py 107 68 36% 59-68, 73, 78, 83, 91-105, 130-132, 135-197, 217-222, 227, 261-263 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/federated.py 35 17 51% 46-48, 52-65, 70-79, 82, 106-116 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/password.py 29 16 45% 39-51, 78-89, 93-96 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/token.py 17 6 65% 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/usr/lib/python3/dist-packages/keystoneclient/i18n.py 4 0 100% /usr/lib/python3/dist-packages/keystoneclient/service_catalog.py 152 108 29% 51-56, 59-67, 80-85, 88, 138-178, 187-208, 255-294, 315-316, 323, 326-329, 332, 335, 338-346, 351-362, 372-374, 381, 384-387, 390-393, 396, 399-412, 417-427 /usr/lib/python3/dist-packages/keystoneclient/session.py 348 276 21% 48-58, 62, 66-82, 139-164, 169-178, 182-222, 225-255, 337-445, 460-519, 527, 535, 543, 551, 559, 567, 589-593, 597-607, 618-632, 635-641, 658-660, 686, 701-703, 737-757, 767-769, 785-787, 803-805, 834-837, 882-885, 901-909, 917-943, 961-967, 979-1018 /usr/lib/python3/dist-packages/keystoneclient/utils.py 56 41 27% 27-46, 50-52, 60-71, 80-91, 111-118, 122-123 /usr/lib/python3/dist-packages/keystoneclient/v2_0/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneclient/v2_0/certificates.py 9 5 44% 19, 28-29, 38-40 /usr/lib/python3/dist-packages/keystoneclient/v2_0/client.py 53 33 38% 150-176, 193-219 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/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 /usr/lib/python3/dist-packages/keystoneclient/v3/client.py 104 64 38% 218-267, 270, 277-286, 313-352 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/endpoint_filter.py 82 62 24% 34-47, 50-64, 68-73, 77-82, 86-91, 95-99, 106-110, 117-122, 126-131, 135-140, 144-149, 156-160 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/endpoint_policy.py 59 39 34% 28-38, 42, 47, 52, 56-66, 70, 75, 80, 85-97, 102, 108, 114, 125-134, 146-153 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/base.py 19 8 58% 30, 33-40 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/core.py 16 7 56% 24-31 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/domains.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/identity_providers.py 22 8 64% 36-38, 54, 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 11 45% 22-31, 36-38, 62-65 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/request_tokens.py 33 20 39% 24-25, 30-36, 54-57, 60-73 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/utils.py 14 10 29% 28-38 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/simple_cert.py 11 6 45% 21-22, 31-33, 42-44 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/trusts.py 33 17 48% 59-74, 85, 90-92, 98, 102 /usr/lib/python3/dist-packages/keystoneclient/v3/credentials.py 17 5 71% 62, 80, 93, 119, 138 /usr/lib/python3/dist-packages/keystoneclient/v3/domain_configs.py 28 13 54% 37, 63-65, 78-79, 105-107, 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293-294, 297, 300-301, 308, 312 /usr/lib/python3/dist-packages/netaddr/ip/nmap.py 64 55 14% 22-45, 51-62, 69-87, 96-101, 115-117 /usr/lib/python3/dist-packages/netaddr/ip/rfc1924.py 28 18 36% 32-42, 49-61 /usr/lib/python3/dist-packages/netaddr/ip/sets.py 350 300 14% 27-53, 65-81, 105-122, 126, 133, 145-210, 216-217, 226, 238-245, 249, 257, 263, 281-296, 317-350, 361, 371-372, 376-378, 392-413, 417, 426-429, 438-441, 450-453, 462-465, 476-479, 488-494, 505-507, 518-551, 566-619, 631-673, 683-688, 696, 700, 711-718, 729-735, 744-748 /usr/lib/python3/dist-packages/netaddr/strategy/__init__.py 113 90 20% 44-56, 70-83, 97-106, 121-138, 154-160, 177-194, 207-226, 238-257, 270-273 /usr/lib/python3/dist-packages/netaddr/strategy/eui48.py 135 70 48% 144-152, 163-197, 209-216, 226, 237-245, 249-251, 255-257, 261-263, 267-269, 273-275, 279-281, 286-288, 292, 296 /usr/lib/python3/dist-packages/netaddr/strategy/eui64.py 122 66 46% 121-124, 133-139, 149-176, 187-192, 202-203, 214-222, 226-228, 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/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, 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/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 959 847 12% 53-63, 71-72, 75-76, 86-89, 128-135, 146-156, 160-190, 194-222, 230-232, 236-245, 250-260, 275-276, 279, 282, 285-288, 291, 294, 320-328, 331-368, 401-441, 445-450, 454, 458-472, 481, 485-521, 524-526, 531-532, 536-567, 573-574, 584-661, 683-745, 749-753, 765-768, 794-839, 857-875, 896-921, 952-1008, 1027-1048, 1068-1092, 1111-1133, 1155-1180, 1210-1242, 1262-1285, 1329-1400, 1420-1439, 1465-1503, 1529-1557, 1568-1578, 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334-354 /usr/lib/python3/dist-packages/urllib3/connection.py 173 116 33% 17-21, 27-30, 105-115, 134, 144, 151-175, 178-184, 187-188, 192-199, 206-234, 256-266, 297-310, 314-402, 409-420, 428 /usr/lib/python3/dist-packages/urllib3/connectionpool.py 318 257 19% 75-80, 83, 86, 89-91, 97, 182-215, 221-236, 250-275, 291-303, 309, 313, 317-325, 330-348, 369-451, 454, 460-472, 479-493, 601-854, 904-927, 935-947, 954-955, 961-991, 997-1004, 1035-1040, 1048-1058 /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/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 70 22% 18-20, 38-61, 82-91, 113-118, 150-156, 176-192, 205, 218-227, 233-246, 263-273 /usr/lib/python3/dist-packages/urllib3/filepost.py 43 30 30% 19-22, 33-42, 57-60, 74-98 /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 172 132 23% 89-114, 160-167, 170, 173-175, 187-202, 211, 224-234, 243-247, 257-271, 284-285, 297-307, 318-372, 411-431, 434-439, 448-456, 460-469, 473 /usr/lib/python3/dist-packages/urllib3/request.py 39 28 28% 42, 54, 70-79, 88-97, 144-171 /usr/lib/python3/dist-packages/urllib3/response.py 399 322 19% 34-36, 39, 42-61, 73-74, 77, 80-98, 103-118, 131, 134, 137-139, 143-152, 190, 214-258, 268-271, 274-278, 283-287, 291, 294, 302, 308-354, 362-373, 377, 383-399, 406-410, 421-467, 490-541, 559-567, 578-599, 603, 606, 610, 614-621, 625-634, 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 45 32% 17-26, 51-86, 90-94, 102-105, 118, 130-131 /usr/lib/python3/dist-packages/urllib3/util/queue.py 14 5 64% 7, 12, 15, 18, 21 /usr/lib/python3/dist-packages/urllib3/util/request.py 50 25 50% 13, 63, 65, 71, 74, 77, 80, 85, 95-105, 119-133 /usr/lib/python3/dist-packages/urllib3/util/response.py 35 29 17% 15-35, 54-71, 83-86 /usr/lib/python3/dist-packages/urllib3/util/retry.py 150 102 32% 186-187, 202-218, 223-232, 240-249, 253-265, 270-275, 278-283, 286-289, 300-305, 311, 317, 323-326, 335-341, 350-355, 376-442, 445 /usr/lib/python3/dist-packages/urllib3/util/ssl_.py 148 112 24% 31-34, 43-44, 50-56, 61-63, 104-149, 162-174, 192-201, 208-217, 256-293, 327-383, 393-396, 401-407 /usr/lib/python3/dist-packages/urllib3/util/timeout.py 63 42 33% 96-99, 102, 120-153, 169, 183, 191-194, 204-208, 220-226, 245-258 /usr/lib/python3/dist-packages/urllib3/util/url.py 205 152 26% 101-105, 112, 117-122, 127-129, 150-169, 172, 193-207, 214-241, 246-271, 275-299, 303-317, 322-327, 352-416, 431-432 /usr/lib/python3/dist-packages/urllib3/util/wait.py 76 58 24% 8-9, 43-68, 72-87, 91-107, 111, 118-124, 133-139, 146, 153 /usr/local/lib/python3.8/dist-packages/MySQLdb/__init__.py 46 13 72% 21, 36-37, 44-46, 63, 66, 69, 72, 75-76, 79 /usr/local/lib/python3.8/dist-packages/MySQLdb/_exceptions.py 12 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/compat.py 12 5 58% 4-8 /usr/local/lib/python3.8/dist-packages/MySQLdb/connections.py 146 34 77% 42, 138, 140, 143, 150, 161, 197, 204, 245, 249, 260, 262, 265, 269, 282, 286-293, 304-308, 314-317, 324-328 /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/CLIENT.py 18 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FIELD_TYPE.py 29 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FLAG.py 16 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/converters.py 35 13 63% 47-48, 52, 56, 63-68, 79, 82, 85 /usr/local/lib/python3.8/dist-packages/MySQLdb/cursors.py 261 96 63% 83-90, 93, 96-97, 107, 109, 114-120, 127, 135, 142-144, 160, 171, 187, 194-206, 227, 239-240, 259-260, 296-310, 328, 358-363, 368-372, 378, 391-400, 403-405, 417, 421-426, 431-434, 438-441, 444, 447-450 /usr/local/lib/python3.8/dist-packages/MySQLdb/release.py 3 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/times.py 76 49 36% 21, 25, 29, 34-37, 43-47, 53, 60-64, 75-76, 79-99, 102-113, 116-123, 127, 131 /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/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 /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/pyparsing.py 3062 1772 42% 122-123, 127-128, 134-137, 141-145, 149-150, 191-194, 233-266, 274-278, 295-296, 307-308, 321, 329-336, 339-346, 349, 354-359, 361, 410-453, 487, 490, 498, 500, 563, 567, 576, 580, 588-591, 606-611, 616-634, 650, 653-656, 659, 662, 675-694, 738-754, 794-798, 813, 829-832, 838-839, 844-845, 848-850, 869-874, 877, 880, 883-891, 908, 930-944, 961-1016, 1019-1023, 1050-1063, 1086-1128, 1155, 1159, 1166-1173, 1176, 1179, 1188-1207, 1222-1223, 1235, 1240-1245, 1248, 1251, 1254, 1258, 1297-1302, 1319-1337, 1344-1345, 1370, 1392, 1396-1398, 1464, 1504-1516, 1559, 1562, 1612-1613, 1616-1626, 1630, 1636, 1641, 1653-1674, 1694-1712, 1717-1720, 1729-1730, 1735-1738, 1741-1746, 1750-1768, 1783-1786, 1792, 1800-1826, 1853-1856, 1896, 1939, 1945-1955, 1990-2031, 2053-2079, 2102-2111, 2129-2136, 2166, 2171-2173, 2181, 2186-2188, 2195-2201, 2207-2213, 2236, 2238, 2246, 2248-2253, 2258-2260, 2263, 2265, 2267, 2276-2279, 2284, 2290, 2297, 2300, 2302-2304, 2311-2317, 2323-2329, 2335-2341, 2347-2353, 2359-2365, 2376, 2398-2412, 2465-2466, 2482-2490, 2496-2500, 2539-2543, 2549, 2557, 2563, 2571-2585, 2589, 2591, 2593, 2597, 2603, 2606, 2622-2626, 2724-2788, 2795-2799, 2802-2815, 2818, 2821, 2846-2850, 2853, 2876-2879, 2891-2893, 2932-2950, 2953-2969, 2972-2974, 2980, 2994-2998, 3001-3003, 3016, 3052-3058, 3061-3084, 3146, 3159, 3164, 3181, 3187, 3191-3192, 3208, 3213-3218, 3223, 3248-3253, 3262-3267, 3304, 3313-3324, 3335, 3337, 3348-3349, 3353-3359, 3362-3368, 3392-3408, 3451-3512, 3515-3547, 3550-3558, 3587, 3593, 3610-3620, 3630, 3682, 3687-3688, 3691-3703, 3718-3719, 3722-3728, 3731-3736, 3766-3768, 3780-3788, 3799-3803, 3814, 3817-3820, 3832-3834, 3837-3841, 3852-3855, 3858-3863, 3873, 3876, 3878, 3883, 3886-3889, 3893-3895, 3907-3916, 3919-3926, 3963-3966, 3975-3977, 4007-4009, 4014-4024, 4036-4043, 4056-4057, 4059-4067, 4075-4077, 4080-4084, 4088, 4114-4118, 4121-4124, 4127-4184, 4188-4190, 4193-4199, 4202-4204, 4207-4216, 4241, 4260-4263, 4271, 4274-4276, 4280, 4288-4290, 4293-4302, 4363-4367, 4370-4372, 4375-4421, 4424-4430, 4433-4435, 4448, 4464, 4474-4483, 4492-4496, 4499-4504, 4540-4541, 4546-4549, 4581-4601, 4604-4624, 4658-4660, 4664, 4677, 4682, 4691, 4696, 4702, 4704, 4716-4726, 4757, 4787, 4797, 4800, 4852-4856, 4863, 4936, 4942-4986, 5016, 5019-5029, 5032, 5035-5036, 5039-5043, 5046-5053, 5056-5073, 5076-5081, 5084-5092, 5131-5135, 5138-5145, 5213-5234, 5263, 5270-5271, 5273-5277, 5279, 5306-5324, 5346, 5373-5384, 5387-5393, 5410-5423, 5440-5452, 5494-5547, 5586, 5617-5628, 5634, 5661-5662, 5708-5709, 5715-5718, 5733, 5748, 5787, 5792-5793, 5811-5812, 5818-5819, 5873, 5931-5943, 5981-5982, 6060-6115, 6192-6229, 6312-6355, 6365, 6622-6627, 6647-6652, 6680, 6705-6713, 6734-6740, 6745, 6750, 6755, 6760, 6886, 6889-6902, 6906-6921, 6924, 6927, 6940-6943, 6952-6955, 6964-6967, 6981-7028, 7034-7035, 7040-7105 /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 -------------------------------------------------------------------------------------------------------------------------------------- TOTAL 130913 96957 26% ret : 34304 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 : 0 132.77user 40.98system 5:44.90elapsed 50%CPU (0avgtext+0avgdata 6545236maxresident)k 6148352inputs+70472outputs (7734major+6332239minor)pagefaults 0swaps