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/March/02032025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_2/data_log/job/2025/March/02032025/coverage/htmlcov new path : /data_2/data_log/job/2025/March/02032025/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 : 7037 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 : 0.9994180202484131 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 Sun Mar 2 05:20:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 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 2025-03-02 05:20:34.556369: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-02 05:20:34.564547: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-02 05:20:34.565971: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7804000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-02 05:20:34.566013: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-02 05:20:34.568623: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-02 05:20:34.785899: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xd0da0e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-02 05:20:34.785948: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-02 05:20:34.787327: 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 2025-03-02 05:20:34.787710: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:20:34.790564: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:20:34.793095: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:20:34.793564: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:20:34.796628: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:20:34.797558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:20:34.801454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:20:34.802806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:20:34.802858: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:20:34.803627: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-02 05:20:34.803644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-02 05:20:34.803652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-02 05:20:34.808511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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 2025-03-02 05:20:38.277321: 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 2025-03-02 05:20:38.277403: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:20:38.277420: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:20:38.277434: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:20:38.277448: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:20:38.277462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:20:38.277475: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:20:38.277489: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:20:38.278679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:20:38.279971: 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 2025-03-02 05:20:38.280044: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:20:38.280063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:20:38.280080: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:20:38.280096: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:20:38.280113: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:20:38.280129: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:20:38.280146: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:20:38.281385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:20:38.281416: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-02 05:20:38.281425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-02 05:20:38.281433: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-02 05:20:38.282706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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 : [] 2025-03-02 05:20:47.404721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:20:47.634269: 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 13546 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 236 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 : 10814 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 : 0.0005435943603515625 nb_pixel_total : 15551 time to create 1 rle with old method : 0.03783273696899414 length of segment : 256 time for calcul the mask position with numpy : 0.002676725387573242 nb_pixel_total : 145328 time to create 1 rle with old method : 0.31402087211608887 length of segment : 371 time for calcul the mask position with numpy : 0.00022602081298828125 nb_pixel_total : 14255 time to create 1 rle with old method : 0.031828880310058594 length of segment : 151 time for calcul the mask position with numpy : 0.00013208389282226562 nb_pixel_total : 5613 time to create 1 rle with old method : 0.013578414916992188 length of segment : 48 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 1825 time to create 1 rle with old method : 0.004578351974487305 length of segment : 39 time spent for convertir_results : 1.2937190532684326 time spend for datou_step_exec : 23.594631910324097 time spend to save output : 3.647804260253906e-05 total time spend for step 1 : 23.5946683883667 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 3305 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 : 0.02825927734375 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, 0.9954934, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (127, 30, 27), (10, 31, 1), (121, 31, 34), (8, 32, 13), (27, 32, 3), (116, 32, 40), (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, 137), (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, 46), (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)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,21,33,58,33,59,34,75,34,76,35,102,35,115,33,121,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,149,56,138,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,41,163,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923747, [(315, 37, 24), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 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(474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,529,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1740889231_13364_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10814 ############################### 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 : 0.1476907730102539 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 Sun Mar 2 05:21:16 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-02 05:21:19.273909: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-02 05:21:19.299121: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-02 05:21:19.300777: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7810000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-02 05:21:19.300834: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-02 05:21:19.303586: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-02 05:21:19.524806: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xe4e4990 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-02 05:21:19.524851: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-02 05:21:19.525871: 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 2025-03-02 05:21:19.526197: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:21:19.528444: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:21:19.530560: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:21:19.530940: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:21:19.533346: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:21:19.534441: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:21:19.539156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:21:19.540708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:21:19.540803: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:21:19.541555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-02 05:21:19.541571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-02 05:21:19.541580: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-02 05:21:19.542988: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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. 2025-03-02 05:21:19.672936: 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 2025-03-02 05:21:19.673068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:21:19.673089: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:21:19.673105: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:21:19.673121: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:21:19.673137: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:21:19.673152: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:21:19.673168: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:21:19.674370: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:21:19.675739: 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 2025-03-02 05:21:19.675796: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:21:19.675814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:21:19.675830: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:21:19.675845: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:21:19.675861: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:21:19.675878: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:21:19.675894: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:21:19.677110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:21:19.677150: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-02 05:21:19.677158: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-02 05:21:19.677165: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-02 05:21:19.678399: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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 : [] 2025-03-02 05:21:28.675716: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:21:28.859743: 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 15316 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 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 : 10814 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 : 0.0005929470062255859 nb_pixel_total : 16902 time to create 1 rle with old method : 0.04590582847595215 length of segment : 107 time for calcul the mask position with numpy : 0.015134572982788086 nb_pixel_total : 480736 time to create 1 rle with new method : 0.02944493293762207 length of segment : 632 time for calcul the mask position with numpy : 0.0005490779876708984 nb_pixel_total : 36584 time to create 1 rle with old method : 0.07850432395935059 length of segment : 132 time for calcul the mask position with numpy : 0.000102996826171875 nb_pixel_total : 4794 time to create 1 rle with old method : 0.01067805290222168 length of segment : 51 time spent for convertir_results : 0.44466423988342285 time spend for datou_step_exec : 18.685877323150635 time spend to save output : 4.1961669921875e-05 total time spend for step 1 : 18.685919284820557 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 400 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.018139362335205078 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, 0.9988397, [(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, 0.99774504, [(711, 22, 22), (925, 22, 47), (608, 23, 146), (893, 23, 104), (598, 24, 234), (849, 24, 159), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (489, 47, 589), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (477, 56, 607), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (414, 103, 676), (410, 104, 680), (406, 105, 684), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (369, 113, 721), (365, 114, 725), (362, 115, 728), (359, 116, 731), (356, 117, 734), (353, 118, 737), (351, 119, 739), (349, 120, 741), (346, 121, 744), (344, 122, 746), (342, 123, 748), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (287, 134, 802), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (234, 148, 855), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 876), 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0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (419, 25, 100), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['450,47,449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,419,25,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46,451,46'])], 'temp/1740889276_13364_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 : 0.19625329971313477 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 Sun Mar 2 05:21:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-02 05:21:40.199666: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-02 05:21:40.227040: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-02 05:21:40.229179: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7810000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-02 05:21:40.229239: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-02 05:21:40.233103: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-02 05:21:40.470966: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xeb41cb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-02 05:21:40.471012: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-02 05:21:40.472423: 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 2025-03-02 05:21:40.472867: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:21:40.476167: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:21:40.478656: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:21:40.479070: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:21:40.481811: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:21:40.483172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:21:40.488538: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:21:40.489994: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:21:40.490085: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:21:40.490814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-02 05:21:40.490828: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-02 05:21:40.490837: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-02 05:21:40.492188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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. 2025-03-02 05:21:40.600502: 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 2025-03-02 05:21:40.600631: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:21:40.600656: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:21:40.600678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:21:40.600700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:21:40.600721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:21:40.600741: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:21:40.600763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:21:40.602345: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:21:40.603835: 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 2025-03-02 05:21:40.603876: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:21:40.603898: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:21:40.603934: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:21:40.603955: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:21:40.603975: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:21:40.603995: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:21:40.604015: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:21:40.605223: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:21:40.605256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-02 05:21:40.605264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-02 05:21:40.605271: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-02 05:21:40.606470: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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 : [] 2025-03-02 05:21:50.609183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:21:50.780765: 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 19435 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5372 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 : 10661 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 : 0.3186478614807129 nb_pixel_total : 3693214 time to create 1 rle with new method : 0.7190375328063965 length of segment : 2042 time spent for convertir_results : 2.2811217308044434 time spend for datou_step_exec : 22.176005601882935 time spend to save output : 5.507469177246094e-05 total time spend for step 1 : 22.176060676574707 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 719 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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 : 0.01845407485961914 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, 0.9850348, [(676, 120, 110), (520, 121, 481), (1052, 121, 379), (503, 122, 947), (486, 123, 982), (471, 124, 1014), (456, 125, 1045), (442, 126, 1092), (429, 127, 1136), (417, 128, 1168), (406, 129, 1186), (394, 130, 1205), (384, 131, 1222), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (364, 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), (333, 152, 1362), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (324, 159, 1388), (322, 160, 1393), (321, 161, 1397), (320, 162, 1401), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (311, 168, 1428), (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), (281, 182, 1507), (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), (240, 196, 1623), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (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, 1776), (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), (150, 255, 1810), (149, 256, 1812), (148, 257, 1815), (146, 258, 1818), (145, 259, 1820), (143, 260, 1823), (142, 261, 1826), (140, 262, 1829), (138, 263, 1833), (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, 1911), (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, 1956), (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, 2006), (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, 2018), (68, 394, 2018), (68, 395, 2019), (67, 396, 2020), (67, 397, 2021), (67, 398, 2021), (66, 399, 2023), (66, 400, 2023), (66, 401, 2024), (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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['924,2141,775,2093,673,2067,614,2038,365,1986,195,1963,128,1971,103,1936,54,1825,39,1677,39,1455,29,1243,27,757,21,696,27,543,39,458,93,308,126,270,210,206,291,179,373,132,520,121,1430,121,1564,127,1663,142,1768,178,1904,204,1993,277,2094,411,2148,535,2168,614,2171,717,2165,833,2128,914,2112,994,2081,1068,2050,1101,1970,1250,1931,1368,1879,1444,1846,1670,1770,1892,1706,1986,1662,2015,1581,2015,1496,2039,1367,2063,1177,2101,1097,2141,1027,2150'])], 'temp/1740889296_13364_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3690472 proportion of common points : 0.9995062691361335 #&_# 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 : 0.2494354248046875 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 Sun Mar 2 05:22:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.002346515655517578 nb_pixel_total : 4275 time to create 1 rle with old method : 0.010593891143798828 time for calcul the mask position with numpy : 0.0014977455139160156 nb_pixel_total : 7607 time to create 1 rle with old method : 0.017788410186767578 time for calcul the mask position with numpy : 0.002490997314453125 nb_pixel_total : 83801 time to create 1 rle with old method : 0.19869709014892578 time for calcul the mask position with numpy : 0.0017137527465820312 nb_pixel_total : 5613 time to create 1 rle with old method : 0.018182754516601562 time for calcul the mask position with numpy : 0.001676321029663086 nb_pixel_total : 8562 time to create 1 rle with old method : 0.027294635772705078 time for calcul the mask position with numpy : 0.001669168472290039 nb_pixel_total : 3926 time to create 1 rle with old method : 0.009244918823242188 time for calcul the mask position with numpy : 0.001615285873413086 nb_pixel_total : 14575 time to create 1 rle with old method : 0.033811092376708984 time for calcul the mask position with numpy : 0.001508474349975586 nb_pixel_total : 342 time to create 1 rle with old method : 0.0008637905120849609 time for calcul the mask position with numpy : 0.0015523433685302734 nb_pixel_total : 16395 time to create 1 rle with old method : 0.0375058650970459 time for calcul the mask position with numpy : 0.001565694808959961 nb_pixel_total : 3782 time to create 1 rle with old method : 0.008758544921875 time for calcul the mask position with numpy : 0.0015192031860351562 nb_pixel_total : 2351 time to create 1 rle with old method : 0.0057370662689208984 time for calcul the mask position with numpy : 0.001539468765258789 nb_pixel_total : 13916 time to create 1 rle with old method : 0.03103470802307129 time for calcul the mask position with numpy : 0.0014750957489013672 nb_pixel_total : 4260 time to create 1 rle with old method : 0.009804964065551758 time for calcul the mask position with numpy : 0.0015480518341064453 nb_pixel_total : 6630 time to create 1 rle with old method : 0.014967203140258789 time for calcul the mask position with numpy : 0.0014355182647705078 nb_pixel_total : 2935 time to create 1 rle with old method : 0.006689548492431641 time for calcul the mask position with numpy : 0.0015332698822021484 nb_pixel_total : 10765 time to create 1 rle with old method : 0.023471355438232422 time for calcul the mask position with numpy : 0.0015053749084472656 nb_pixel_total : 29463 time to create 1 rle with old method : 0.06454849243164062 time for calcul the mask position with numpy : 0.0014545917510986328 nb_pixel_total : 887 time to create 1 rle with old method : 0.002216339111328125 time for calcul the mask position with numpy : 0.0014407634735107422 nb_pixel_total : 1226 time to create 1 rle with old method : 0.0030035972595214844 time for calcul the mask position with numpy : 0.0014843940734863281 nb_pixel_total : 2455 time to create 1 rle with old method : 0.009083986282348633 time for calcul the mask position with numpy : 0.001566171646118164 nb_pixel_total : 3113 time to create 1 rle with old method : 0.007120609283447266 time for calcul the mask position with numpy : 0.0014638900756835938 nb_pixel_total : 2076 time to create 1 rle with old method : 0.004827260971069336 time for calcul the mask position with numpy : 0.0015337467193603516 nb_pixel_total : 13039 time to create 1 rle with old method : 0.02881598472595215 time for calcul the mask position with numpy : 0.0014696121215820312 nb_pixel_total : 2734 time to create 1 rle with old method : 0.006460428237915039 time for calcul the mask position with numpy : 0.0014460086822509766 nb_pixel_total : 1261 time to create 1 rle with old method : 0.0029022693634033203 time for calcul the mask position with numpy : 0.001489877700805664 nb_pixel_total : 16370 time to create 1 rle with old method : 0.03612780570983887 time for calcul the mask position with numpy : 0.0015323162078857422 nb_pixel_total : 9931 time to create 1 rle with old method : 0.022332429885864258 time for calcul the mask position with numpy : 0.0014824867248535156 nb_pixel_total : 5491 time to create 1 rle with old method : 0.012168169021606445 time for calcul the mask position with numpy : 0.0013670921325683594 nb_pixel_total : 3938 time to create 1 rle with old method : 0.008966684341430664 time for calcul the mask position with numpy : 0.0014507770538330078 nb_pixel_total : 3531 time to create 1 rle with old method : 0.007848739624023438 time for calcul the mask position with numpy : 0.0013403892517089844 nb_pixel_total : 2496 time to create 1 rle with old method : 0.005426168441772461 time for calcul the mask position with numpy : 0.0013346672058105469 nb_pixel_total : 2803 time to create 1 rle with old method : 0.00605010986328125 time for calcul the mask position with numpy : 0.0014107227325439453 nb_pixel_total : 13011 time to create 1 rle with old method : 0.029465675354003906 time for calcul the mask position with numpy : 0.0013740062713623047 nb_pixel_total : 5425 time to create 1 rle with old method : 0.012176513671875 time for calcul the mask position with numpy : 0.0016131401062011719 nb_pixel_total : 38841 time to create 1 rle with old method : 0.08611416816711426 time for calcul the mask position with numpy : 0.0015347003936767578 nb_pixel_total : 1644 time to create 1 rle with old method : 0.00390934944152832 time for calcul the mask position with numpy : 0.0013129711151123047 nb_pixel_total : 1627 time to create 1 rle with old method : 0.0037746429443359375 time for calcul the mask position with numpy : 0.0014307498931884766 nb_pixel_total : 12013 time to create 1 rle with old method : 0.026528120040893555 time for calcul the mask position with numpy : 0.0013713836669921875 nb_pixel_total : 3327 time to create 1 rle with old method : 0.007400989532470703 time for calcul the mask position with numpy : 0.001390218734741211 nb_pixel_total : 13123 time to create 1 rle with old method : 0.028427839279174805 time for calcul the mask position with numpy : 0.0014395713806152344 nb_pixel_total : 1025 time to create 1 rle with old method : 0.0022764205932617188 time for calcul the mask position with numpy : 0.001402139663696289 nb_pixel_total : 10619 time to create 1 rle with old method : 0.023303985595703125 time for calcul the mask position with numpy : 0.0013735294342041016 nb_pixel_total : 1206 time to create 1 rle with old method : 0.002717733383178711 time for calcul the mask position with numpy : 0.0013492107391357422 nb_pixel_total : 4139 time to create 1 rle with old method : 0.009158134460449219 time for calcul the mask position with numpy : 0.001336812973022461 nb_pixel_total : 4172 time to create 1 rle with old method : 0.00953221321105957 time for calcul the mask position with numpy : 0.0014271736145019531 nb_pixel_total : 8440 time to create 1 rle with old method : 0.017864465713500977 time for calcul the mask position with numpy : 0.0013835430145263672 nb_pixel_total : 875 time to create 1 rle with old method : 0.002074003219604492 time for calcul the mask position with numpy : 0.0014104843139648438 nb_pixel_total : 2267 time to create 1 rle with old method : 0.005176544189453125 time for calcul the mask position with numpy : 0.0013241767883300781 nb_pixel_total : 596 time to create 1 rle with old method : 0.0013666152954101562 time for calcul the mask position with numpy : 0.0014119148254394531 nb_pixel_total : 2324 time to create 1 rle with old method : 0.005388498306274414 time for calcul the mask position with numpy : 0.0014681816101074219 nb_pixel_total : 863 time to create 1 rle with old method : 0.0022420883178710938 time for calcul the mask position with numpy : 0.0014643669128417969 nb_pixel_total : 2768 time to create 1 rle with old method : 0.006372213363647461 time for calcul the mask position with numpy : 0.0014107227325439453 nb_pixel_total : 574 time to create 1 rle with old method : 0.0014066696166992188 time for calcul the mask position with numpy : 0.0013785362243652344 nb_pixel_total : 2431 time to create 1 rle with old method : 0.005995512008666992 time for calcul the mask position with numpy : 0.0014495849609375 nb_pixel_total : 692 time to create 1 rle with old method : 0.0017213821411132812 time for calcul the mask position with numpy : 0.0013968944549560547 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0025763511657714844 time for calcul the mask position with numpy : 0.0013823509216308594 nb_pixel_total : 338 time to create 1 rle with old method : 0.0008039474487304688 time for calcul the mask position with numpy : 0.00140380859375 nb_pixel_total : 1707 time to create 1 rle with old method : 0.003958463668823242 time for calcul the mask position with numpy : 0.0013318061828613281 nb_pixel_total : 590 time to create 1 rle with old method : 0.0013530254364013672 time for calcul the mask position with numpy : 0.001417398452758789 nb_pixel_total : 735 time to create 1 rle with old method : 0.001840829849243164 time for calcul the mask position with numpy : 0.0013682842254638672 nb_pixel_total : 967 time to create 1 rle with old method : 0.0021636486053466797 time for calcul the mask position with numpy : 0.0013227462768554688 nb_pixel_total : 1123 time to create 1 rle with old method : 0.0026412010192871094 time for calcul the mask position with numpy : 0.0015254020690917969 nb_pixel_total : 27512 time to create 1 rle with old method : 0.06150960922241211 time for calcul the mask position with numpy : 0.00139617919921875 nb_pixel_total : 1658 time to create 1 rle with old method : 0.003849029541015625 time for calcul the mask position with numpy : 0.0014529228210449219 nb_pixel_total : 8608 time to create 1 rle with old method : 0.018993377685546875 time for calcul the mask position with numpy : 0.0013310909271240234 nb_pixel_total : 876 time to create 1 rle with old method : 0.0020983219146728516 time for calcul the mask position with numpy : 0.0014662742614746094 nb_pixel_total : 941 time to create 1 rle with old method : 0.0024666786193847656 time for calcul the mask position with numpy : 0.0015406608581542969 nb_pixel_total : 16666 time to create 1 rle with old method : 0.03659248352050781 time for calcul the mask position with numpy : 0.0013375282287597656 nb_pixel_total : 1442 time to create 1 rle with old method : 0.0032453536987304688 time for calcul the mask position with numpy : 0.001325845718383789 nb_pixel_total : 3179 time to create 1 rle with old method : 0.006987094879150391 time for calcul the mask position with numpy : 0.0014638900756835938 nb_pixel_total : 1742 time to create 1 rle with old method : 0.004113674163818359 time for calcul the mask position with numpy : 0.0015017986297607422 nb_pixel_total : 5013 time to create 1 rle with old method : 0.011649370193481445 time for calcul the mask position with numpy : 0.001506805419921875 nb_pixel_total : 9078 time to create 1 rle with old method : 0.020452022552490234 time for calcul the mask position with numpy : 0.0015692710876464844 nb_pixel_total : 9548 time to create 1 rle with old method : 0.021654367446899414 time for calcul the mask position with numpy : 0.001466512680053711 nb_pixel_total : 711 time to create 1 rle with old method : 0.0017099380493164062 time for calcul the mask position with numpy : 0.001444101333618164 nb_pixel_total : 1513 time to create 1 rle with old method : 0.003592252731323242 time for calcul the mask position with numpy : 0.0015063285827636719 nb_pixel_total : 1347 time to create 1 rle with old method : 0.0032956600189208984 time for calcul the mask position with numpy : 0.0014128684997558594 nb_pixel_total : 1127 time to create 1 rle with old method : 0.0026426315307617188 time for calcul the mask position with numpy : 0.0014238357543945312 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006396770477294922 time for calcul the mask position with numpy : 0.0014345645904541016 nb_pixel_total : 835 time to create 1 rle with old method : 0.001991748809814453 time for calcul the mask position with numpy : 0.001512765884399414 nb_pixel_total : 39427 time to create 1 rle with old method : 0.08804869651794434 time for calcul the mask position with numpy : 0.0014636516571044922 nb_pixel_total : 1538 time to create 1 rle with old method : 0.0036575794219970703 time for calcul the mask position with numpy : 0.0014798641204833984 nb_pixel_total : 18446 time to create 1 rle with old method : 0.041387319564819336 time for calcul the mask position with numpy : 0.0014379024505615234 nb_pixel_total : 615 time to create 1 rle with old method : 0.0014967918395996094 time for calcul the mask position with numpy : 0.0014393329620361328 nb_pixel_total : 980 time to create 1 rle with old method : 0.0023963451385498047 time for calcul the mask position with numpy : 0.0014810562133789062 nb_pixel_total : 6158 time to create 1 rle with old method : 0.014624357223510742 time for calcul the mask position with numpy : 0.0016121864318847656 nb_pixel_total : 27952 time to create 1 rle with old method : 0.06467437744140625 time for calcul the mask position with numpy : 0.0014503002166748047 nb_pixel_total : 222 time to create 1 rle with old method : 0.0006082057952880859 time for calcul the mask position with numpy : 0.001420736312866211 nb_pixel_total : 247 time to create 1 rle with old method : 0.0006558895111083984 time for calcul the mask position with numpy : 0.0014462471008300781 nb_pixel_total : 3896 time to create 1 rle with old method : 0.009060144424438477 time for calcul the mask position with numpy : 0.0014345645904541016 nb_pixel_total : 1438 time to create 1 rle with old method : 0.003532886505126953 time for calcul the mask position with numpy : 0.0014407634735107422 nb_pixel_total : 1490 time to create 1 rle with old method : 0.0062062740325927734 time for calcul the mask position with numpy : 0.0014691352844238281 nb_pixel_total : 1633 time to create 1 rle with old method : 0.003945350646972656 time for calcul the mask position with numpy : 0.0014619827270507812 nb_pixel_total : 299 time to create 1 rle with old method : 0.0008220672607421875 time for calcul the mask position with numpy : 0.0015180110931396484 nb_pixel_total : 8547 time to create 1 rle with old method : 0.019336700439453125 time for calcul the mask position with numpy : 0.0014133453369140625 nb_pixel_total : 595 time to create 1 rle with old method : 0.001493215560913086 time for calcul the mask position with numpy : 0.00142669677734375 nb_pixel_total : 889 time to create 1 rle with old method : 0.0021677017211914062 time for calcul the mask position with numpy : 0.0014667510986328125 nb_pixel_total : 7488 time to create 1 rle with old method : 0.017162561416625977 time for calcul the mask position with numpy : 0.0014545917510986328 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0031311511993408203 time for calcul the mask position with numpy : 0.0013687610626220703 nb_pixel_total : 2819 time to create 1 rle with old method : 0.007147073745727539 time for calcul the mask position with numpy : 0.0013394355773925781 nb_pixel_total : 2248 time to create 1 rle with old method : 0.0053942203521728516 time for calcul the mask position with numpy : 0.0014338493347167969 nb_pixel_total : 948 time to create 1 rle with old method : 0.0023260116577148438 time for calcul the mask position with numpy : 0.0014133453369140625 nb_pixel_total : 880 time to create 1 rle with old method : 0.0020308494567871094 time for calcul the mask position with numpy : 0.001447916030883789 nb_pixel_total : 492 time to create 1 rle with old method : 0.0012669563293457031 batch 1 Loaded 104 chid ids of type : 4677 Number RLEs to save : 9988 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 : 0.21288108825683594 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.117423295974731 time spend to save output : 0.21322107315063477 total time spend for step 1 : 11.330644369125366 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1740889329_13364_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 104 ############################### TEST frcnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : frcnn list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2096545696258545 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 Sun Mar 2 05:22:21 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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/1740889340_13364_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 0.090s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 2.905545473098755 time spend to save output : 0.00010347366333007812 total time spend for step 1 : 2.905648946762085 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 : 0.015207290649414062 [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 : 0.014858245849609375 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, 0.06383922, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052290674, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271269, None)], 'temp/1740889340_13364_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 : 0.1325383186340332 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 Sun Mar 2 05:22:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 355 time to import caffe and check if the image exist : 0.01321864128112793 time to convert the images to numpy array : 0.0013797283172607422 total time to convert the images to numpy array : 0.015029430389404297 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 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'506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2940 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 : 2940 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.012180566787719727 time used to do the prediction : 0.08805727958679199 save descriptor for thcl : 355 time to traite the descriptors : 0.06969690322875977 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.6378157138824463 Inside saveOutput : final : False verbose : False time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 1.3113021850585938e-05 save missing photos in datou_result : time spend for datou_step_exec : 6.271178722381592 time spend to save output : 2.1447157859802246 total time spend for step 1 : 8.415894508361816 step2:argmax Sun Mar 2 05:22:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 355 Inside saveOutput : final : True verbose : False photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.01770635, 332, '355'), 'temp/1740889344_13364_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 : 0.01747584342956543 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 : 0.022403240203857422 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 : 0.20840096473693848 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 : 5.245208740234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.004285335540771484 time spend to save output : 0.24863791465759277 total time spend for step 2 : 0.25292325019836426 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', 0.01770635, 332, '355'), 'temp/1740889344_13364_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 : 0.2087993621826172 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 Sun Mar 2 05:22:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory 2025-03-02 05:22:42.190505: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-02 05:22:42.191202: 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 2025-03-02 05:22:42.191309: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:22:42.191373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:22:42.193323: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:22:42.193404: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:22:42.195653: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:22:42.196660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:22:42.200497: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:22:42.201419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:22:42.201776: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-02 05:22:42.231135: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-02 05:22:42.233000: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7578000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-02 05:22:42.233043: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-02 05:22:42.236388: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x10051c00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-02 05:22:42.236422: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-02 05:22:42.237403: 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 2025-03-02 05:22:42.237520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:22:42.237551: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-02 05:22:42.237638: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-02 05:22:42.237677: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-02 05:22:42.237725: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-02 05:22:42.237778: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-02 05:22:42.237832: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:22:42.239129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-02 05:22:42.239205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-02 05:22:42.239277: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-02 05:22:42.239294: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-02 05:22:42.239307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-02 05:22:42.240642: 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 : 2940 max_wait_temp : 6 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 : [] 2025-03-02 05:22:50.403151: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.02G (3246391296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory /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 : 10.668101072311401 time used to load_weights : 0.14726710319519043 0it [00:00, ?it/s] 3it [00:00, 627.80it/s]2025-03-02 05:22:55.367706: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-02 05:22:55.379965: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 2025-03-02 05:22:55.389745: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR temp/1740889352_13364_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1740889352_13364_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1740889352_13364_1171252764_29d5179a892cc50aadc9d67245534b59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : ERROR in datou_step_exec, will save and exit ! Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620] Function call stack: predict_function File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3146, in datou_step_tfhub2 classes, outputs, features = this_model.predict_image_paths(list_paths, keep_aspect_ratio=keep_aspect_ratio, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 288, in predict_image_paths Y_pred, F_pred = self.model.predict(valid_generator, validation_steps) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 88, in _method_wrapper return method(self, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 1268, in predict tmp_batch_outputs = predict_function(iterator) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 580, in __call__ result = self._call(*args, **kwds) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 650, in _call return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1661, in _filtered_call return self._call_flat( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1745, in _call_flat return self._build_call_outputs(self._inference_function.call( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 593, in call outputs = execute.execute( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, [1171252487, 1171252784, 1171252764] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.017359495162963867 save_final ERROR in last step tfhub_classification2, Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620] Function call stack: predict_function time spend for datou_step_exec : 22.36315894126892 time spend to save output : 0.0205838680267334 total time spend for step 0 : 22.383742809295654 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171252784': [(1171252784, 'jrm', 0.9677492, 4674, '3609'), 'temp/1687511175_1882837_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853587, 4674, '3609'), 'temp/1687511175_1882837_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262757, 4674, '3609'), 'temp/1687511175_1882837_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} got : None ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.28224945068359375 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 Sun Mar 2 05:22:55 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds l 3637 free memory gpu now : 6 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] 2025-03-02 05:25:14.423876: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 2025-03-02 05:25:14.427701: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 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_1" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_2 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_3 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_2[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_3[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 9.371164321899414 time used to load_weights : 0.14690470695495605 found 3 data found 0 labels begin to do the prediction : ERROR in datou_step_exec, will save and exit ! Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model_2/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_69245] Function call stack: predict_function File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2329, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2523, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3146, in datou_step_tfhub2 classes, outputs, features = this_model.predict_image_paths(list_paths, keep_aspect_ratio=keep_aspect_ratio, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 288, in predict_image_paths Y_pred, F_pred = self.model.predict(valid_generator, validation_steps) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 88, in _method_wrapper return method(self, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 1268, in predict tmp_batch_outputs = predict_function(iterator) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 580, in __call__ result = self._call(*args, **kwds) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 650, in _call return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1661, in _filtered_call return self._call_flat( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1745, in _call_flat return self._build_call_outputs(self._inference_function.call( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 593, in call outputs = execute.execute( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, [1171291875, 1171275372, 1171275314] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.3387274742126465 save_final ERROR in last step tfhub_classification2, Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model_2/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_69245] Function call stack: predict_function time spend for datou_step_exec : 138.65355014801025 time spend to save output : 0.33968186378479004 total time spend for step 0 : 138.99323201179504 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171291875': [(1171291875, 'tapis_vide', 0.97062814, 4723, '3655'), 'temp/1691745841_1143057_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9674145, 4723, '3655'), 'temp/1691745841_1143057_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96509415, 4723, '3655'), 'temp/1691745841_1143057_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} got : None ERROR tfhub2 FAILED ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 51 thcl : 358 photo_hashtag_type : 337 ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1348283290863037 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 Sun Mar 2 05:25:36 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_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/1740889537_13364 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 2.263868570327759 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 2.4777300357818604 time spend to save output : 2.4318695068359375e-05 total time spend for step 1 : 2.4777543544769287 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 /1340487441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487442Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487443Didn'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 : 0.04661870002746582 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1340487441: ['917849322', 'temp/1740889536_13364_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1340487442: ['917849322', 'temp/1740889536_13364_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1340487443: ['917849322', 'temp/1740889536_13364_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 : 0.11970663070678711 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 Sun Mar 2 05:25:39 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 : 0.00026917457580566406 time to convert the images to numpy array : 0.9664759635925293 total time to convert the images to numpy array : 0.9671676158905029 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 havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 6 wait 20 seconds l 3637 free memory gpu now : 6 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 : 3783 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 3.7873470783233643 time used to do the prediction : 0.21042108535766602 save descriptor for thcl : 500 time to traite the descriptors : 0.06273317337036133 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 1.7430481910705566 time spend for datou_step_exec : 31.939083576202393 time spend to save output : 5.030632019042969e-05 total time spend for step 1 : 31.939133882522583 step2:argmax Sun Mar 2 05:26:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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 : 0.0002353191375732422 time spend to save output : 2.9802322387695312e-05 total time spend for step 2 : 0.0002651214599609375 step3:rotate Sun Mar 2 05:26:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_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/1740889571_13364 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.718317985534668 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 : 0.815418004989624 time spend to save output : 3.314018249511719e-05 total time spend for step 3 : 0.8154511451721191 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 /1340487457Didn'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 : 0.028524160385131836 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1340487457: ['917849322', 'temp/1740889539_13364_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 : 0.1456129550933838 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 Sun Mar 2 05:26:12 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 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 : 20955099 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740889574_13364 we have uploaded 4 photos in the portfolio 20955099 time of upload the photos Elapsed time : 3.491530418395996 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/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1740889572_13364_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 : 20955100 Result OK ! uploaded one batch 0 Elapsed time : 19.43658947944641 time spend for datou_step_exec : 27.36474871635437 time spend to save output : 1.71661376953125e-05 total time spend for step 1 : 27.364765882492065 step2:tile Sun Mar 2 05:26:39 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/1740889572_13364_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 : 20955101 with name tile_taggage_varroa feed_id_new_photos : 20955101 filename : temp/1740889572_13364_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/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.00850677490234375 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/1740889606_13364 we have uploaded 1 photos in the portfolio 20955101 Importing ! upload mediasElapsed time : 0.7041871547698975 , 0Saving 4 CHIs. batch 1 Loaded 4 chid ids of type : 521 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 0.856499195098877 time spend for datou_step_exec : 7.515111684799194 time spend to save output : 3.0517578125e-05 total time spend for step 2 : 7.515142202377319 step3:rotate Sun Mar 2 05:26:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 20955102 Needs to change image size ! time for calcul the mask position with numpy : 0.00046539306640625 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0030472278594970703 .time for calcul the mask position with numpy : 0.00034308433532714844 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002614259719848633 . 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 : 0.00042247772216796875 nb_pixel_total : 694 time to create 1 rle with old method : 0.0016703605651855469 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003807544708251953 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0026252269744873047 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004317760467529297 nb_pixel_total : 221 time to create 1 rle with old method : 0.0005373954772949219 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003581047058105469 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0025932788848876953 . 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 : 0.0003685951232910156 nb_pixel_total : 143 time to create 1 rle with old method : 0.00036644935607910156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00032210350036621094 nb_pixel_total : 1161 time to create 1 rle with old method : 0.002605438232421875 . 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 : 0.0003628730773925781 nb_pixel_total : 414 time to create 1 rle with old method : 0.0009813308715820312 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003094673156738281 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0026137828826904297 . 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 : 0.0003571510314941406 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0028259754180908203 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003342628479003906 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0026617050170898438 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003390312194824219 nb_pixel_total : 264 time to create 1 rle with old method : 0.0006554126739501953 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00036406517028808594 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0031235218048095703 .time for calcul the mask position with numpy : 0.0003590583801269531 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0026924610137939453 . 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 : 0.00040221214294433594 nb_pixel_total : 694 time to create 1 rle with old method : 0.0016176700592041016 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035572052001953125 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002745389938354492 . 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 : 0.0004124641418457031 nb_pixel_total : 221 time to create 1 rle with old method : 0.0005695819854736328 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034999847412109375 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0026717185974121094 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003867149353027344 nb_pixel_total : 143 time to create 1 rle with old method : 0.00041675567626953125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003573894500732422 nb_pixel_total : 1160 time to create 1 rle with old method : 0.0027289390563964844 . 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 : 0.0003905296325683594 nb_pixel_total : 414 time to create 1 rle with old method : 0.001024484634399414 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003476142883300781 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002670764923095703 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003352165222167969 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 : 0.0003943443298339844 nb_pixel_total : 1204 time to create 1 rle with old method : 0.002683877944946289 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003437995910644531 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0028014183044433594 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003426074981689453 nb_pixel_total : 264 time to create 1 rle with old method : 0.0006873607635498047 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004031658172607422 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003175020217895508 .time for calcul the mask position with numpy : 0.00034332275390625 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027441978454589844 . 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 : 0.0004284381866455078 nb_pixel_total : 727 time to create 1 rle with old method : 0.0017826557159423828 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003476142883300781 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002681255340576172 . 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 : 0.00040841102600097656 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006501674652099609 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034689903259277344 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0027174949645996094 . 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 : 0.0004222393035888672 nb_pixel_total : 169 time to create 1 rle with old method : 0.00046896934509277344 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034737586975097656 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0027179718017578125 . 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 : 0.00041294097900390625 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011470317840576172 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.000347137451171875 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002707242965698242 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003352165222167969 nb_pixel_total : 1 time to create 1 rle with old method : 2.2411346435546875e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.00040340423583984375 nb_pixel_total : 1237 time to create 1 rle with old method : 0.002942800521850586 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034737586975097656 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0026602745056152344 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003364086151123047 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006194114685058594 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004062652587890625 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003151416778564453 .time for calcul the mask position with numpy : 0.00034236907958984375 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027146339416503906 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004036426544189453 nb_pixel_total : 727 time to create 1 rle with old method : 0.0016825199127197266 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034928321838378906 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0026276111602783203 . 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 : 0.0004010200500488281 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006279945373535156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034308433532714844 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002648591995239258 . 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 : 0.00040459632873535156 nb_pixel_total : 169 time to create 1 rle with old method : 0.00043892860412597656 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00041675567626953125 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0026235580444335938 . 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 : 0.00037550926208496094 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011181831359863281 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034046173095703125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0026166439056396484 . 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 : 0.0003829002380371094 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0027208328247070312 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003447532653808594 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027332305908203125 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003364086151123047 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006246566772460938 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 20955102 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740889609_13364 we have uploaded 24 photos in the portfolio 20955102 time of upload the photos Elapsed time : 6.01148247718811 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 : 10.832667589187622 time spend to save output : 8.463859558105469e-05 total time spend for step 3 : 10.832752227783203 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, '1340487486'] Looping around the photos to save general results len do output : 24 /1340487491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487508Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487515Didn'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, '1340487486', 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 : 0.024628877639770508 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1340487491: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1340487492: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1340487493: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1340487494: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1340487495: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1340487496: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1340487497: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1340487498: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1340487499: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1340487500: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1340487501: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1340487502: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1340487503: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1340487504: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1340487505: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1340487506: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1340487507: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1340487508: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1340487509: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1340487510: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1340487511: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1340487513: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1340487514: ['937852786', 'temp/1740889572_13364_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1340487515: ['937852786', 'temp/1740889572_13364_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 : 0.134352445602417 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 Sun Mar 2 05:27:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_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/1740889621_13364 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 1.0635173320770264 Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 batch 1 Loaded 12 chid ids of type : 741 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 1.6992378234863281 time spend to save output : 6.079673767089844e-05 total time spend for step 1 : 1.699298620223999 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 /1340487518 /1340487519 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 : 0.04337620735168457 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1340487518': ['911785586', 'temp/1740889620_13364_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1340487519': ['911785586', 'temp/1740889620_13364_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 : 0.17780756950378418 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 Sun Mar 2 05:27:03 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step 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 : 20955103 Result OK ! uploaded one batch 0 Elapsed time : 19.679980278015137 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 20.117823123931885 time spend to save output : 3.1948089599609375e-05 total time spend for step 1 : 20.117855072021484 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 /1340487520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1340487530Didn'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 : 0.018170595169067383 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1340487520': ['950103132', 'temp/1740889622_13364_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1340487522': ['950103132', 'temp/1740889622_13364_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1340487523': ['950103132', 'temp/1740889622_13364_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1340487525': ['950103132', 'temp/1740889622_13364_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1340487526': ['950103132', 'temp/1740889622_13364_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1340487528': ['950103132', 'temp/1740889622_13364_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1340487529': ['950103132', 'temp/1740889622_13364_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1340487530': ['950103132', 'temp/1740889622_13364_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 : 1.3237648010253906 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 Sun Mar 2 05:27:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of 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 : 0.08060193061828613 time spend to save output : 3.123283386230469e-05 total time spend for step 1 : 0.08063316345214844 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 : 0.13533926010131836 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 Sun Mar 2 05:27:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of 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 : 0.43916893005371094 time spend to save output : 4.291534423828125e-05 total time spend for step 1 : 0.4392118453979492 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 : 0.004160881042480469 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Sun Mar 2 05:27:25 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 0.34101152420043945 time spend to save output : 6.580352783203125e-05 total time spend for step 1 : 0.3410773277282715 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 : 0.1431565284729004 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 Sun Mar 2 05:27:25 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blur_detection methode: ratio et variance treat image : temp/1740889645_13364_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.302386999130249 time spend to save output : 3.695487976074219e-05 total time spend for step 1 : 0.30242395401000977 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 BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFBFBFFFFwe 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 : 1.7944135665893555 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 Sun Mar 2 05:27:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 0.0012900829315185547 time to convert the images to numpy array : 0.005889415740966797 time to import caffe and check if the image exist : 0.00670623779296875 time to convert the images to numpy array : 0.04837465286254883 time to import caffe and check if the image exist : 0.00450444221496582 time to convert the images to numpy array : 0.05214285850524902 time to import caffe and check if the image exist : 0.010977506637573242 time to convert the images to numpy array : 0.046946048736572266 time to import caffe and check if the image exist : 0.010320425033569336 time to convert the images to numpy array : 0.04985928535461426 time to import caffe and check if the image exist : 0.009622812271118164 time to convert the images to numpy array : 0.051911354064941406 time to import caffe and check if the image exist : 0.013809680938720703 time to convert the images to numpy array : 0.04868435859680176 time to import caffe and check if the image exist : 0.015666484832763672 time to convert the images to numpy array : 0.04661226272583008 time to import caffe and check if the image exist : 0.01653265953063965 time to convert the images to numpy array : 0.04725337028503418 time to import caffe and check if the image exist : 0.021651029586791992 time to convert the images to numpy array : 0.0429539680480957 total time to convert the images to numpy array : 0.06826543807983398 list photo_ids error: [] list photo_ids correct : [987515223, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515224, 987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515248, 987515249, 987515250, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515202, 987515241, 987515242, 987515243, 987515244, 987515245, 987515246, 987515247, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515239, 987515240, 987515204, 987515205, 987515207, 987515208, 987515209, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222] number of photos to traite : 64 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 6 wait 20 seconds l 3637 free memory gpu now : 6 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 havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 6 wait 20 seconds WARNING: Logging before InitGoogleLogging() is written to STDERR F0302 05:28:11.663695 13364 syncedmem.cpp:78] 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/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 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290-293, 298, 302-309, 317, 321-330, 350, 367-376, 405, 415, 428, 436, 446-449, 453-458, 462, 483-485, 504-508, 518, 531-555, 590, 602, 606, 616, 620, 630, 638-641, 668-669, 689, 713-717, 727-728, 731, 735, 746-759, 774-776, 803-838, 845, 849, 853, 864, 879-881, 892, 896, 900, 908-910, 923-927, 931-937, 941, 959-964, 968, 985-986, 1003-1005, 1016-1023, 1035-1046, 1056-1058, 1076-1078, 1091, 1095, 1106-1111, 1115, 1126-1130, 1157, 1161-1174, 1178, 1187-1203, 1213, 1223, 1231, 1238-1243, 1271-1286, 1317, 1337-1373, 1380, 1397-1403, 1414-1417, 1435-1437, 1441, 1485, 1493, 1515-1519, 1596-1600, 1614, 1616-1617, 1635-1666, 1683-1700, 1704-1715, 1746-1751, 1816-1838 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/__init__.py 9 1 89% 10 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py 2254 2048 9% 98-102, 150-181, 193-195, 312-320, 323, 381-398, 461-511, 547-550, 585-591, 617-623, 681-692, 699-706, 761-776, 829-844, 849-851, 904-926, 966-974, 1022-1031, 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234, 237-252, 263-272, 275-279, 283-287, 291-292, 297-299, 302-311, 321-322, 325-329, 334-339, 346-351, 359, 367, 377, 387, 397-402, 412-417, 427, 434-436, 439-440, 443-444, 454, 464, 482-485, 490-497, 501-505, 515-536, 542-550, 567, 576-580, 584-585, 589-590, 594-595, 604-607, 655-663, 667-672, 677-681, 684-688, 692-716, 729-730, 733-740, 747-774, 777-778, 781-782, 787-796, 802-837, 850-851, 854-858, 861-878, 882-896, 899-903, 917, 921-922, 925, 930-932, 935-939, 951-952, 992-993, 1011-1013 /home/admin/.local/lib/python3.8/site-packages/matplotlib/bezier.py 222 186 16% 18-22, 42-62, 72-81, 91-92, 100-110, 151-178, 192-198, 214-215, 222, 227, 232, 237, 264-273, 291-305, 330-337, 348-401, 413-418, 424-429, 451-459, 474-533, 541-543, 554-594 /home/admin/.local/lib/python3.8/site-packages/matplotlib/category.py 85 50 41% 48-58, 80-85, 104-108, 112-113, 127, 131, 135, 147, 151, 155-156, 161-165, 178-181, 188-196, 211-223 /home/admin/.local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py 901 724 20% 64-98, 102-105, 114, 117, 120, 123, 130-133, 198-204, 207, 220-229, 233-243, 251-253, 258-273, 281-299, 308-321, 336-346, 371-373, 376-380, 386-395, 404-418, 435, 443, 451-456, 488-508, 513-514, 519, 536-557, 584-588, 603-604, 607-609, 620-621, 625-628, 631, 634, 638-639, 643-645, 653-655, 663-666, 670, 674-675, 686-698, 709-715, 719-729, 748-798, 839, 843-847, 853-865, 869-870, 874-877, 885-888, 892-894, 901, 942-945, 981-1023, 1057-1088, 1171-1289, 1303-1321, 1331-1334, 1343, 1351-1360, 1376-1420, 1476-1518, 1549-1556, 1587-1592, 1623-1630, 1661-1672, 1682, 1695-1725, 1732, 1763-1789, 1808-1828, 1841, 1895-1897, 1945-1949, 1976-2000, 2009-2045, 2050, 2053, 2056, 2059, 2062-2063, 2066, 2077-2088, 2095-2108, 2119-2126, 2131, 2145-2166, 2174, 2185-2188, 2197, 2205-2215, 2229-2243, 2265-2290, 2295-2297, 2302-2314, 2338-2341 /home/admin/.local/lib/python3.8/site-packages/matplotlib/cm.py 213 150 30% 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, 1764-1765, 1807-1821, 1824-1826, 1836-1846, 1849-1851, 1854, 1864-1866, 1870-1889, 1928-1940, 1943-1945, 1948-1949, 1973-1986, 1989, 1999, 2009-2020, 2028-2058, 2062-2107, 2110-2113 /home/admin/.local/lib/python3.8/site-packages/matplotlib/colorbar.py 698 608 13% 120, 125-127, 133, 136-138, 141-143, 151-152, 155-186, 190, 301-440, 446, 450-451, 456, 460-461, 466, 470-471, 476, 480-481, 485-489, 506-519, 527, 534-579, 584-601, 604-625, 628-644, 653-738, 765-818, 825-828, 840-876, 894-901, 912-915, 950, 956-957, 961-962, 985-989, 998, 1026, 1035-1063, 1071-1116, 1125-1148, 1152-1160, 1164-1165, 1173-1199, 1206-1219, 1228-1233, 1240-1271, 1280-1296, 1300-1301, 1305-1306, 1310-1312, 1316-1318, 1323, 1328, 1334-1339, 1343-1348, 1355-1371, 1375, 1381, 1409-1488, 1524-1594 /home/admin/.local/lib/python3.8/site-packages/matplotlib/colors.py 1035 681 34% 66-67, 70-71, 93, 137-140, 143, 146, 149, 172-184, 194-197, 204-210, 234, 241-243, 252-262, 291-293, 302-303, 320, 344, 350-354, 358-362, 370, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/container.py 44 25 43% 14, 18, 21-23, 26-31, 34, 71-75, 104-107, 138-142 /home/admin/.local/lib/python3.8/site-packages/matplotlib/contour.py 700 623 11% 43-45, 49-74, 175-233, 238, 244, 249, 253, 258-259, 264-266, 274-277, 281-290, 296-324, 346-414, 418-432, 436-441, 465-505, 509-511, 515-557, 560-561, 570, 595-606, 723-882, 889-894, 897-902, 926-962, 970-996, 1000-1015, 1021-1030, 1043-1046, 1050-1075, 1091-1118, 1124-1143, 1157-1180, 1205-1223, 1228-1246, 1249-1270, 1274, 1281-1282, 1326-1361, 1364-1366, 1384-1436, 1439-1461, 1468-1504, 1519-1545 /home/admin/.local/lib/python3.8/site-packages/matplotlib/dates.py 654 512 22% 222-234, 273, 300-302, 316-318, 332-342, 356-381, 404-415, 440-467, 486-491, 509-514, 543-544, 567, 590-608, 612-621, 645-647, 651-652, 655, 735-785, 789-791, 794-870, 873, 876, 958-965, 976, 979-996, 1016-1019, 1023-1025, 1028-1056, 1060-1063, 1068-1101, 1104-1114, 1117, 1136, 1147, 1151-1155, 1159-1162, 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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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748-749, 753, 764-765, 771, 787-790, 794, 797-817, 821-823, 841-846, 850-852, 859, 877-880, 884, 888-898, 902-905, 971-990, 1001-1004, 1008, 1012, 1016-1018, 1022-1029, 1040-1054, 1059-1065, 1068-1070, 1074-1086, 1096-1098, 1131-1140, 1161-1178, 1181-1183, 1186, 1189-1190, 1193, 1197, 1200, 1203-1208, 1212-1214, 1229, 1311-1341, 1345, 1349-1350, 1354, 1358-1359, 1362-1367, 1371-1374, 1377-1380, 1388-1392, 1396, 1400-1403, 1408-1411, 1419-1458, 1462-1475, 1508-1514, 1531-1541, 1544-1556, 1559-1563, 1566-1570, 1574-1575, 1578, 1581, 1584, 1589-1590, 1593-1597, 1600-1601, 1604-1609, 1614-1615, 1618-1619, 1622-1623 /home/admin/.local/lib/python3.8/site-packages/matplotlib/patches.py 1704 1259 26% 66-100, 109-114, 117-126, 136-156, 203-204, 232-233, 239-254, 260, 264, 271, 282, 286, 290, 294, 298, 302, 312-315, 318-330, 340-341, 344-348, 358-359, 374-375, 379-381, 392-397, 424-432, 442-445, 449, 468-470, 474, 488-490, 494, 525-527, 531, 544-580, 585-591, 601, 604, 608-610, 615, 637-644, 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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, 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/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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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 39 73% 124, 128-132, 284-289, 309-313, 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, 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/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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992-999, 1003, 1114, 1199, 1276, 1280, 1350, 1354, 1416-1434, 1438, 1501-1508, 1512, 1638-1642, 1647, 1707-1711, 1715, 1819-1822, 1826, 1921, 1925, 1967-1971, 1975, 2039, 2043, 2115, 2248-2257, 2364, 2450, 2455, 2532, 2536, 2620-2630, 2884, 2919-2925, 2930, 3051, 3056, 3120, 3161-3162, 3166, 3204-3213, 3319, 3427-3440, 3446, 3568-3581, 3587, 3708-3723, 3739, 3751, 3763, 3777, 3789 /home/admin/.local/lib/python3.8/site-packages/numpy/core/function_base.py 117 56 52% 122, 144-148, 153-159, 167, 170, 173, 180, 275-278, 283, 389-440, 453, 456, 463, 527-529 /home/admin/.local/lib/python3.8/site-packages/numpy/core/getlimits.py 199 27 86% 262, 271, 277-281, 287-288, 385-387, 398, 401, 403-406, 409, 440-451, 454-457, 517-518, 523, 553-560, 563 /home/admin/.local/lib/python3.8/site-packages/numpy/core/machar.py 188 178 5% 113-114, 117-325, 328-338, 342 /home/admin/.local/lib/python3.8/site-packages/numpy/core/memmap.py 91 73 20% 211-286, 289-298, 315-316, 319-331, 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 65 57% 173-181, 219-227, 271, 274, 278-279, 355, 420, 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 /home/admin/.local/lib/python3.8/site-packages/numpy/core/records.py 356 310 13% 81, 149-151, 156-172, 178-208, 211-221, 234-236, 239-241, 244-265, 269-279, 283-290, 295-299, 423-434, 437-440, 446-470, 480-507, 510-524, 528-557, 560-574, 578-586, 637-680, 725-763, 821-838, 841-846, 893-945, 1031-1092 /home/admin/.local/lib/python3.8/site-packages/numpy/core/shape_base.py 192 137 29% 67, 74, 78, 119-132, 136, 189-204, 209-214, 219, 276-282, 334-345, 349-354, 416-433, 448-449, 483-525, 531, 535, 576-594, 624-647, 659-666, 673-677, 830-849, 861-870, 874-889, 893-900 /home/admin/.local/lib/python3.8/site-packages/numpy/core/umath.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ctypeslib.py 213 175 18% 63-64, 67-82, 118-154, 158-161, 166-171, 177-192, 208, 217-220, 280-341, 347-351, 373-387, 391-393, 398-443, 451-456, 497, 509-518, 524-539 /home/admin/.local/lib/python3.8/site-packages/numpy/dual.py 43 10 77% 66, 72-79, 82-83 /home/admin/.local/lib/python3.8/site-packages/numpy/fft/__init__.py 8 0 100% /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 968 16% 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, 1686, 1689-1693, 1698, 1750, 1754, 1794-1798, 1833-1840, 1866-1869, 1887-1905, 1926-1934, 1939, 1945-1948, 2113, 2118, 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 438 334 24% 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, 1220-1231, 1233, 1236, 1239, 1246-1249, 1252-1254, 1257, 1260-1314, 1317, 1320, 1323, 1326-1330, 1333-1337, 1340-1341, 1344-1345, 1348-1353, 1356-1357, 1360-1361, 1364-1368, 1373-1377, 1382-1386, 1389-1391, 1395-1400, 1403-1411, 1414, 1427, 1440 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/scimath.py 70 38 46% 106-110, 135-138, 162-165, 189-192, 196, 239-240, 287-288, 337-338, 342, 376-378, 425-426, 430, 473-475, 519-520, 565-566, 616-617 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/shape_base.py 263 196 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 454 365 20% 38-46, 83-84, 96-97, 116, 123-124, 221, 260-277, 329-379, 391-407, 415-431, 452-482, 537-628, 672-677, 737-812, 835-947, 950-956, 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, 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204 11% 71-77, 130-153, 200-208, 248-254, 297-301, 366-368, 372-374, 422-443, 452-453, 469-483, 497-513, 527-529, 547-565, 571-578, 584-592, 606-680, 697-713, 732-750 /home/admin/.local/lib/python3.8/site-packages/numpy/random/__init__.py 17 1 94% 210 /home/admin/.local/lib/python3.8/site-packages/numpy/random/_pickle.py 22 12 45% 31-37, 54-60, 77-83 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/decorators.py 74 61 18% 61-65, 100-105, 143-186, 226-251, 282-304, 323-329 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/nosetester.py 174 157 10% 36-58, 96-109, 164-193, 212-230, 233-250, 259-260, 276-324, 397-463, 523-536, 540-544 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/utils.py 873 772 12% 59-75, 89-95, 109-113, 127-132, 146-151, 156-186, 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272-273, 289-290, 305-306, 317, 328, 340, 355, 371-380, 397, 408, 419, 428, 439, 450, 461, 470, 472, 474, 476, 482-484, 497, 527, 533, 547, 560 /home/admin/.local/lib/python3.8/site-packages/psutil/__init__.py 950 691 27% 37-38, 127-128, 131-132, 135-136, 139-140, 143-180, 248-259, 271-281, 290-292, 298-304, 346, 349-393, 396-411, 421-423, 426, 429-431, 436, 467-505, 518-549, 556-568, 574-579, 586-601, 617-618, 628-647, 654-686, 690, 694-697, 703-715, 722-724, 728, 732-737, 746, 752, 758, 764, 776, 793-798, 813-816, 829-837, 850, 858, 862-866, 872, 876, 886, 915-956, 993-1048, 1059, 1070, 1074, 1090, 1102-1119, 1133-1147, 1154, 1178, 1184-1202, 1211-1212, 1222-1223, 1233-1234, 1244-1245, 1254-1255, 1273-1275, 1322-1323, 1326, 1329-1331, 1334-1347, 1350-1356, 1360-1364, 1383-1385, 1393-1403, 1431-1482, 1521-1571, 1593-1599, 1632-1634, 1638-1640, 1647-1659, 1666-1675, 1679-1696, 1736-1783, 1808-1859, 1864, 1877-1907, 1918, 1981-1984, 2000, 2013, 2025, 2060-2072, 2111-2121, 2155, 2176-2203, 2218, 2237-2259, 2272, 2290, 2304, 2317, 2327-2337, 2406, 2409 /home/admin/.local/lib/python3.8/site-packages/psutil/_common.py 442 251 43% 29-30, 33-34, 39, 131-133, 144-145, 157, 161-162, 278-279, 282-283, 295-304, 307, 320-332, 340-350, 360-367, 377-384, 412, 447-457, 462, 466-469, 481-488, 496-503, 509-517, 524-545, 552-553, 565-566, 576-590, 605-606, 623-628, 634-639, 645-678, 682-690, 694-695, 703-704, 721-727, 739-747, 757-760, 770-780, 785-798, 803-832, 836-842, 846 /home/admin/.local/lib/python3.8/site-packages/psutil/_compat.py 243 215 12% 27, 30-41, 57-119, 132-272, 278-324, 330-345 /home/admin/.local/lib/python3.8/site-packages/psutil/_pslinux.py 1130 874 23% 56, 113, 121-124, 217-232, 239-245, 258-264, 298-305, 310-313, 344-371, 390-492, 498-546, 592-616, 622-652, 657-672, 683-727, 778-797, 800-813, 832-868, 873-908, 913-946, 949-977, 985, 992-1022, 1027-1043, 1058-1146, 1151-1182, 1203-1290, 1303-1322, 1332-1405, 1415-1428, 1434-1441, 1452, 1459-1484, 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268-288, 293-309, 328-341, 352-367, 372-378, 383-390, 395-402, 407-412, 417-423, 428-435, 440-448, 458-505 /home/admin/.local/lib/python3.8/site-packages/pygit2/config.py 186 125 33% 35-38, 44-45, 48, 51, 54-58, 61, 64, 69-70, 78-87, 91-95, 98-101, 104-109, 112-118, 121-128, 136-138, 141-151, 154-157, 166-170, 178-185, 191-196, 202-206, 216-221, 231-236, 241-243, 251-255, 263-267, 271-275, 283-289, 295, 301, 307, 321-336, 339-340, 345, 349, 353, 358, 363, 368 /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, 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/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 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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 5 77% 44, 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, 671-675, 683, 692-696, 704, 708, 717-721, 731-755, 764, 768, 777-781, 789, 797, 806-809, 817, 821, 829-833, 841, 849, 853, 861-865, 872, 880, 889, 897, 906, 914, 923, 931, 940, 948, 957, 966-986, 997-1013 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail_settings.py 93 58 38% 38-69, 77, 86, 94, 103, 111, 120, 128, 137, 145, 154, 162, 171, 179, 188, 196, 206, 215-243 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mime_type.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking.py 27 16 41% 16-23, 31, 40, 50, 65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking_substitution_tag.py 13 6 54% 12-15, 26, 39, 49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/personalization.py 130 83 36% 8-16, 19-29, 32-42, 51, 55, 62-78, 86, 90, 98, 106, 110, 118, 129, 133, 141, 145, 152, 160, 164, 171-174, 182, 186, 193, 203, 207, 216, 220-223, 232-252 /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 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/twilio_email.py 9 4 56% 63-73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/__check_build/__init__.py 18 12 33% 19-31, 45-46 /home/admin/.local/lib/python3.8/site-packages/sklearn/__init__.py 29 9 69% 69, 103-112 /home/admin/.local/lib/python3.8/site-packages/sklearn/_config.py 21 14 33% 27, 75-82, 144-150 /home/admin/.local/lib/python3.8/site-packages/sklearn/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/glm_distribution.py 86 54 37% 59-66, 132, 156, 175, 204, 208, 215-235, 246, 272-323, 329, 335, 341, 347 /home/admin/.local/lib/python3.8/site-packages/sklearn/base.py 259 188 27% 54-88, 108-138, 156-176, 193-200, 221-244, 251-293, 296-304, 307-319, 322, 325-333, 352-365, 412-439, 449-453, 460, 464-467, 499-500, 503, 552-554, 557, 583-584, 587, 599, 619-621, 639-640, 662-665, 697-702, 724, 750, 761, 767, 784, 800, 816, 840-857 /home/admin/.local/lib/python3.8/site-packages/sklearn/exceptions.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py 207 160 23% 81-101, 124-179, 197-207, 218-221, 238, 245-249, 252, 282-293, 309-314, 323-330, 353-357, 385-388, 485-489, 514-575, 588-642 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_bayes.py 203 180 11% 162-174, 197-300, 324-332, 343-356, 361-386, 515-526, 546-633, 641-650, 656-661, 685-694 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py 492 409 17% 58-75, 124-168, 311, 440-551, 706-717, 751-874, 879, 893-898, 1031, 1085-1146, 1157-1171, 1200-1356, 1518, 1526, 1529, 1532, 1725-1740, 1743, 1746, 1749, 1880-1889, 1913-1958, 1961, 2078-2087, 2263-2276, 2279, 2282, 2285, 2444, 2452, 2455, 2458 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/glm.py 158 122 23% 32-35, 40-48, 133-141, 161-298, 313-317, 333-335, 371-376, 380-388, 458, 465, 469-470, 540, 547, 551-552, 654, 664-666, 670-673 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/link.py 40 13 68% 68, 71, 74, 77, 84, 87, 90, 93, 100, 103, 106, 109-110 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_huber.py 88 74 16% 52-122, 229-234, 255-307 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_least_angle.py 435 384 12% 166-171, 301, 442-798, 917-926, 930-936, 940-994, 1017-1035, 1177-1188, 1195-1197, 1283-1310, 1438-1442, 1449, 1467-1529, 1685-1695, 1823-1832, 1835, 1858-1904 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py 546 500 8% 75-82, 114-133, 162-169, 202-246, 286-302, 343-355, 396-428, 432-458, 462-475, 632-819, 957-1009, 1261-1275, 1306-1435, 1463-1478, 1499, 1751-1767, 1789-2062, 2085-2088, 2091 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_omp.py 273 245 10% 72-138, 194-264, 349-408, 490-544, 632-636, 655-687, 735-764, 870-876, 894-919 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_passive_aggressive.py 34 20 41% 173-191, 216-228, 254-256, 401-418, 435-437, 464-466 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_perceptron.py 6 1 83% 164 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ransac.py 153 133 13% 47-54, 215-226, 256-464, 480-482, 502-504, 507 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py 614 520 15% 41-114, 118-132, 137-156, 161-217, 221-228, 232-235, 366, 385-518, 527-534, 539-600, 737, 762, 895-899, 924-946, 950, 954-966, 982-984, 995-999, 1002-1003, 1008, 1014, 1025-1029, 1032-1040, 1043-1050, 1057, 1060, 1070, 1073, 1122-1130, 1135, 1140-1143, 1176-1192, 1223-1236, 1261-1276, 1281-1290, 1297-1313, 1319-1338, 1347-1355, 1370-1385, 1394-1397, 1402-1412, 1421-1433, 1454-1581, 1590-1597, 1627-1665, 1917-1920, 1943-1959, 1963, 1966 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_sag.py 75 63 16% 67-85, 234-344 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py 446 345 23% 56-62, 65-68, 80-102, 117-119, 127-156, 160-168, 171-174, 178-182, 187-241, 258-281, 285-288, 298, 307, 315, 323, 331-355, 413-450, 478-488, 494-535, 539-578, 583-605, 616-649, 684-695, 729, 975, 986-987, 1027-1028, 1031-1069, 1096-1097, 1100, 1103, 1129, 1141-1166, 1192-1193, 1201-1225, 1252, 1270-1276, 1290, 1294-1363, 1582, 1593 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_theil_sen.py 113 89 21% 57-74, 112-128, 147, 178-193, 298-306, 309-343, 359-400 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/__init__.py 78 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_base.py 78 71 9% 67-131, 175-202, 234-251 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py 511 452 12% 48-52, 83-128, 132-137, 202-210, 296-355, 456-557, 618-639, 758-785, 852-875, 935-946, 1068, 1192-1200, 1214-1247, 1251-1261, 1269-1299, 1458-1540, 1653-1660, 1771-1778, 1846-1858, 1966-2060, 2135-2152, 2225-2281, 2365-2403, 2477-2506 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/base.py 37 33 11% 26-45, 81-114 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/confusion_matrix.py 61 47 23% 71-72, 107-162, 255-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/det_curve.py 44 36 18% 65-68, 88-129, 210-229 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/precision_recall_curve.py 47 35 26% 77-81, 107-140, 203-225 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/roc_curve.py 47 35 26% 73-77, 100-132, 210-230 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_ranking.py 336 292 13% 83-106, 199-224, 294-317, 326-349, 522-547, 595-648, 688-730, 811-823, 913-956, 1005-1046, 1090-1106, 1149-1191, 1238-1250, 1290-1299, 1303-1307, 1407-1411, 1458-1466, 1564-1569, 1646-1717 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_regression.py 168 137 18% 88-122, 182-194, 257-271, 335-351, 408-416, 477-492, 552-584, 676-723, 753-756, 808-821, 857, 896 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py 226 132 42% 52-60, 77, 81-92, 107-122, 133-134, 155-166, 169-171, 199, 204, 236-242, 276-288, 291, 326-362, 365, 383-392, 397, 426-459, 485-530, 614 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/__init__.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_bicluster.py 32 22 31% 12-17, 22-28, 38-45, 80-86 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_supervised.py 170 139 18% 43-69, 74-83, 127-149, 214-229, 289-299, 383-389, 453-473, 542, 611, 710, 768-798, 889-919, 998-1020, 1091-1100, 1115-1123 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_unsupervised.py 93 76 18% 33-34, 109-117, 135-149, 214-248, 281-298, 339-363 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/pairwise.py 412 339 18% 45-61, 135-164, 194-198, 272-323, 399-439, 451-508, 512-514, 587-601, 670-673, 722-723, 782-804, 833-841, 861-862, 880-886, 910-911, 967-978, 1004-1005, 1033-1041, 1067-1075, 1101-1108, 1136-1142, 1180-1191, 1241-1251, 1296-1298, 1342, 1347, 1354-1373, 1379-1405, 1422-1434, 1443-1470, 1594-1635, 1747-1790, 1845, 1937-1954 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/__init__.py 32 1 97% 37 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py 342 251 27% 96-116, 127-136, 141-142, 160-184, 244-265, 268, 274-306, 310-314, 380-386, 390-406, 421-429, 433, 437, 450, 473-489, 510-511, 514-523, 539-540, 556-557, 573-574, 590-591, 607-608, 624-625, 631-639, 643-644, 704, 708-721, 747-892, 896-968, 1278-1284, 1288, 1608-1611, 1619 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_split.py 467 353 24% 78-83, 92-95, 99, 106, 155-161, 183-185, 235, 238-245, 262-264, 273-298, 324-333, 354, 428, 432-444, 499, 502-536, 562, 636, 640-690, 693-695, 731-732, 831-834, 859-887, 934-944, 968-971, 997, 1057, 1060-1075, 1099-1102, 1128, 1156-1169, 1195-1202, 1226-1229, 1232, 1284, 1340, 1350-1354, 1386-1388, 1413, 1416, 1484-1489, 1492-1503, 1575-1580, 1583-1594, 1626, 1691-1696, 1699-1757, 1793-1794, 1803-1864, 1906-1909, 1933-1937, 1941-1945, 1966, 1972, 1993, 2017-2018, 2058-2073, 2168-2199, 2211-2241, 2250-2252 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py 380 335 12% 231-279, 288-304, 309-313, 438-446, 543-657, 666-709, 838-892, 937-964, 980-1021, 1039-1045, 1165-1182, 1189-1197, 1202-1209, 1353-1417, 1444-1476, 1483-1518, 1625-1645, 1671 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/__init__.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_data.py 810 681 16% 71-80, 161-217, 323-325, 335-341, 362-363, 386-417, 432-441, 456-463, 466, 545-561, 683-685, 695-699, 726-727, 762-860, 877-897, 914-937, 940, 1007, 1017-1020, 1040-1041, 1065-1085, 1100-1110, 1125-1134, 1137, 1198-1215, 1319-1323, 1344-1387, 1402-1416, 1431-1444, 1447, 1536-1555, 1632-1635, 1639-1641, 1646-1651, 1668-1681, 1701-1708, 1739-1837, 1891-1937, 2001-2002, 2023-2024, 2043-2045, 2048, 2083-2098, 2157-2158, 2179-2180, 2200-2205, 2208, 2253, 2272-2282, 2299-2310, 2313, 2321, 2350-2378, 2484-2489, 2499-2519, 2531-2568, 2589-2625, 2630-2694, 2699-2717, 2737-2750, 2768-2771, 2789-2793, 2796, 2920-2931, 3020-3022, 3043-3044, 3047, 3050-3077, 3092-3106, 3139-3152, 3158-3163, 3169-3185, 3192-3207, 3217-3219, 3228-3243, 3267-3290, 3293, 3394-3395 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_discretization.py 117 102 13% 131-134, 153-237, 242-271, 288-318, 337-353 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py 272 241 11% 42-67, 70-74, 77-110, 113-156, 159, 318-322, 325-333, 339-397, 416-420, 442-443, 459-505, 524-600, 617-635, 721-724, 743-771, 787-793, 809-844 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_function_transformer.py 41 26 37% 11, 91-97, 100-102, 106-109, 128-132, 147, 162, 166-171, 174 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_label.py 274 229 16% 100-102, 116-118, 132-138, 152-163, 166, 262-274, 289-298, 321, 343-350, 387-403, 406, 471-569, 577-613, 619-657, 725-726, 742-754, 773-798, 816-824, 827-831, 847-864, 881-898, 902 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_base.py 362 291 20% 39-60, 81-104, 108, 117, 152-240, 249-250, 253-255, 262-287, 291-323, 342-344, 347-361, 370-378, 393-400, 417-430, 433-440, 449-457, 471-496, 500-514, 517, 521-532, 542-544, 552-564, 592-595, 614-625, 632-636, 666-667, 670-676, 706-707, 710, 713-727, 730-738, 752-764, 768, 772, 791-830, 930-995 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_bounds.py 21 14 33% 54-74 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_classes.py 124 65 48% 187-198, 224-246, 249, 382-391, 417-432, 435, 657, 667, 877, 887, 1042, 1054, 1062, 1065, 1211, 1218, 1346, 1376-1379, 1396-1397, 1412, 1431-1432, 1440, 1448, 1451 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/__init__.py 366 300 18% 84, 87, 90, 93-96, 107, 125-132, 165-167, 172-179, 184-193, 198-205, 224-268, 312-346, 355-409, 502-563, 631, 651-661, 668-672, 708-722, 755-768, 778-783, 817-819, 845-851, 868-878, 898-903, 933-944, 976, 1019-1045, 1059-1062, 1080-1084, 1113-1182 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_encode.py 115 99 14% 30-50, 60-65, 84-102, 108-112, 115-117, 122-123, 128-144, 176-187, 215-269 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_estimator_html_repr.py 76 62 18% 39-50, 53, 61-76, 82-102, 111-143, 303-311 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_joblib.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_mask.py 20 14 30% 9-21, 41-54 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_show_versions.py 33 26 21% 24-32, 44-73, 82-93 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_tags.py 16 13 19% 50-67 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/class_weight.py 61 55 10% 41-72, 115-181 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/deprecation.py 56 11 80% 67-68, 86-87, 101-102, 117-123 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/extmath.py 223 193 13% 41-46, 69-78, 92-95, 111-115, 135-157, 211-242, 327-374, 424-448, 488-501, 535-547, 579-592, 619-626, 649-657, 686-690, 752-789, 841-867, 886-889, 907-914 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/fixes.py 82 53 35% 29-31, 44, 48, 57-60, 84-108, 171-201, 207-210, 216-218, 221-222 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/metaestimators.py 92 63 32% 26, 29-38, 43-55, 59-64, 67-76, 105-123, 141, 143, 199-218 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/multiclass.py 148 127 14% 24-27, 31, 74-106, 110, 141-165, 180-183, 250-309, 326-344, 370-420, 442-464 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/optimize.py 84 74 12% 40-52, 80-111, 161-204, 231-257 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/random.py 39 32 18% 40-94 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/sparsefuncs.py 206 178 14% 19-21, 25-26, 45-46, 63-64, 103-120, 186-217, 237-242, 259-264, 283-294, 313-346, 369-374, 393-402, 406-413, 417-436, 440-455, 459, 464, 494-500, 519-549, 558-568, 574-578, 596-610 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/stats.py 23 19 17% 33-61 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/validation.py 397 332 16% 66-74, 80, 86-111, 124, 163-177, 182, 189-212, 236-245, 259-262, 277-283, 298-300, 348-394, 398-400, 494-685, 691-702, 811-833, 852-864, 880-886, 914, 948-974, 1023-1041, 1057-1068, 1102-1110, 1194-1273, 1304-1326, 1351-1361, 1384-1400 /home/admin/.local/lib/python3.8/site-packages/tqdm/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_dist_ver.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_monitor.py 45 31 31% 31-39, 42-45, 49, 54-92, 95 /home/admin/.local/lib/python3.8/site-packages/tqdm/_tqdm_pandas.py 10 6 40% 12-24 /home/admin/.local/lib/python3.8/site-packages/tqdm/cli.py 188 173 8% 17-40, 54-97, 151-311 /home/admin/.local/lib/python3.8/site-packages/tqdm/gui.py 10 1 90% 181 /home/admin/.local/lib/python3.8/site-packages/tqdm/std.py 696 579 17% 46-49, 93-100, 103-104, 107-108, 111, 114, 118-121, 127-128, 154-161, 165, 169-184, 187-211, 227-229, 238-242, 390-398, 415-420, 437-439, 448-465, 537-663, 667-680, 685-687, 699-717, 722-726, 735-756, 761, 766-768, 805-950, 963-1104, 1107-1111, 1114, 1122-1130, 1133-1134, 1137, 1140-1146, 1149, 1152, 1156, 1159, 1165-1197, 1225-1264, 1268-1308, 1312-1324, 1339-1351, 1355-1359, 1371-1381, 1393-1395, 1399-1401, 1416-1432, 1438-1440, 1444-1445, 1450-1455, 1478-1499, 1515-1520, 1525 /home/admin/.local/lib/python3.8/site-packages/tqdm/utils.py 175 113 35% 22, 28-31, 70, 81-96, 108-109, 112-113, 119, 122, 125, 128, 131, 134, 139, 142, 146-149, 153, 159, 169-170, 176, 179, 191-210, 213-218, 222, 231-248, 252-262, 266-269, 273-278, 374, 382, 389-398 /home/admin/.local/lib/python3.8/site-packages/tqdm/version.py 8 6 25% 4-9 /home/admin/.local/lib/python3.8/site-packages/zipp.py 123 73 41% 28, 47-50, 62, 73-75, 78-79, 82, 89-92, 100-111, 121-124, 127-130, 223-224, 232-242, 246, 250, 254, 258, 262, 265-266, 269-270, 273, 276, 279, 282, 285, 288-291, 294, 297, 300-301, 307-312 /home/admin/mtr/.credentials/credentials.py 1 0 100% /home/admin/workarea/git/Velours/python/dev/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/dev/poly_crop_reduction.py 238 157 34% 9-20, 40, 45, 54-56, 58-59, 117, 119-120, 127-168, 172-226, 229-244, 260-310, 330-381 /home/admin/workarea/git/Velours/python/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/CacheModelData_queries.py 180 166 8% 18-61, 66-82, 87-102, 105-107, 111-133, 137-232, 240-256, 262-268, 293 /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 1108 25% 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, 744, 751, 761-765, 771-780, 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, 1206, 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, 1505-1506, 1516-1517, 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 83 44% 12-13, 33-34, 36-37, 45-46, 49, 54-61, 74, 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 558 468 16% 12, 50-75, 81-101, 107-123, 129-142, 148-161, 180-181, 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 511 402 21% 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, 589, 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 1161 34% 43-44, 75-101, 108-109, 112-113, 117-118, 153, 158, 161, 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, 1795-1804, 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, 2096, 2108, 2113-2122, 2126-2129, 2148-2149, 2154-2203, 2206-2241, 2258-2264, 2267-2277, 2280-2282, 2299, 2313, 2316-2328, 2331-2332, 2334-2369, 2371, 2375-2381, 2422, 2428, 2430, 2432, 2436, 2438, 2440, 2442, 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, 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, 2683-2688, 2701-2703, 2718-2720, 2736, 2742-2744, 2746, 2751, 2760-2762, 2771-2774, 2783-2787, 2803, 2808, 2811, 2819-2833, 2837-2843, 2855-2873 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_object.py 478 203 58% 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, 469-470, 495, 499-500, 512-513, 522-523, 570, 579, 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 749 29% 28-121, 138, 141-142, 149-153, 161, 163, 179, 187-189, 197-198, 209-215, 226-293, 327-328, 356-363, 369, 371-449, 454-458, 481-482, 495-503, 505-513, 538, 541-542, 547, 549, 555, 568, 586-621, 637-647, 653-655, 660-661, 665, 669-694, 700-701, 704-705, 714-721, 724-732, 735-736, 749-867, 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, 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198-201, 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 322 286 11% 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/mask_rcnn/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py 299 255 15% 35-43, 49-298, 304-342, 359, 373-374, 383-387, 400-418, 423-429, 444-549 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_segment.py 67 16 76% 40, 87, 107-117, 173, 190-191, 196-197, 222 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/svm_subroutines.py 69 63 9% 21-43, 50-99, 104-136 /home/admin/workarea/git/Velours/python/mtr/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 43 22% 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 1880 10% 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, 2019, 2023-2024, 2035, 2041-2042, 2044-2045, 2051, 2057-2064, 2074-2128, 2131, 2136-2234, 2237-2384, 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, 3339, 3347, 3359-3360, 3362-3363, 3387, 3409-3417, 3458, 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/MTRMongoClient.py 99 87 12% 21-92, 97-208, 213-241 /home/admin/workarea/git/Velours/python/mtr/utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cd.py 11 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cdn/s3_bucket_manager.py 112 88 21% 33-40, 43-48, 51-54, 57-69, 75-84, 97-104, 119-125, 128-132, 140-159, 162-166, 169-175, 179-182, 185-186, 189-190 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/swift_upload_manager.py 151 131 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/kmean_cloud_storage.py 15 5 67% 19-20, 23, 26, 29 /home/admin/workarea/git/Velours/python/mtr/utils/load_caffe.py 61 44 28% 23, 25-30, 39-44, 48, 53-67, 70, 76-94 /home/admin/workarea/git/Velours/python/mtr/utils/upload_batch.py 58 45 22% 26-78, 86-87 /home/admin/workarea/git/Velours/python/mtr/utils/utils_timer.py 11 8 27% 12-20 /home/admin/workarea/git/Velours/python/prod/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/prod/caffe_vision.py 1390 1299 7% 47, 68-72, 77-81, 89-215, 220-274, 279-280, 284-373, 378-426, 431-573, 577-592, 595-602, 606-759, 763-782, 787-810, 814-877, 883-889, 894-901, 907-926, 934-947, 954-958, 964-981, 986-1001, 1008-1028, 1034-1043, 1051-1112, 1116-1121, 1126-1333, 1401-2359 /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 1823 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, 2895, 2901-2903, 2913-2915, 2917-2919, 2933, 2946-2998, 3010-3056, 3066-3121, 3131-3186, 3196-3217, 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/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 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/usr/lib/python3/dist-packages/chardet/langturkishmodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/latin1prober.py 52 29 44% 98-101, 104-106, 110, 114, 117-128, 131-145 /usr/lib/python3/dist-packages/chardet/mbcharsetprober.py 44 33 25% 40-43, 46-51, 55, 59, 62-88, 91 /usr/lib/python3/dist-packages/chardet/mbcsgroupprober.py 14 3 79% 43-54 /usr/lib/python3/dist-packages/chardet/mbcssm.py 41 0 100% /usr/lib/python3/dist-packages/chardet/sbcharsetprober.py 75 60 20% 40-51, 54-61, 65-68, 72-75, 78-122, 125-132 /usr/lib/python3/dist-packages/chardet/sbcsgroupprober.py 19 8 58% 45-73 /usr/lib/python3/dist-packages/chardet/sjisprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/universaldetector.py 124 104 16% 82-92, 100-109, 125-218, 229-286 /usr/lib/python3/dist-packages/chardet/utf8prober.py 43 29 33% 39-42, 45-47, 51, 55, 58-74, 77-82 /usr/lib/python3/dist-packages/chardet/version.py 2 0 100% 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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 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32-42, 49-61 /usr/lib/python3/dist-packages/netaddr/ip/sets.py 350 300 14% 27-53, 65-81, 105-122, 126, 133, 145-210, 216-217, 226, 238-245, 249, 257, 263, 281-296, 317-350, 361, 371-372, 376-378, 392-413, 417, 426-429, 438-441, 450-453, 462-465, 476-479, 488-494, 505-507, 518-551, 566-619, 631-673, 683-688, 696, 700, 711-718, 729-735, 744-748 /usr/lib/python3/dist-packages/netaddr/strategy/__init__.py 113 90 20% 44-56, 70-83, 97-106, 121-138, 154-160, 177-194, 207-226, 238-257, 270-273 /usr/lib/python3/dist-packages/netaddr/strategy/eui48.py 135 70 48% 144-152, 163-197, 209-216, 226, 237-245, 249-251, 255-257, 261-263, 267-269, 273-275, 279-281, 286-288, 292, 296 /usr/lib/python3/dist-packages/netaddr/strategy/eui64.py 122 66 46% 121-124, 133-139, 149-176, 187-192, 202-203, 214-222, 226-228, 232-234, 238-240, 244-246, 250-252, 256-258, 263-265, 269, 273 /usr/lib/python3/dist-packages/netaddr/strategy/ipv4.py 103 51 50% 16, 91-107, 121, 141-148, 158-161, 171, 182, 186, 196-199, 212-214, 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71-81, 89-95, 103-110 /usr/lib/python3/dist-packages/rfc3986/iri.py 50 34 32% 49-57, 61-73, 76, 86-89, 111-142 /usr/lib/python3/dist-packages/rfc3986/misc.py 31 5 84% 116-121 /usr/lib/python3/dist-packages/rfc3986/normalizers.py 79 63 20% 24, 29-37, 42, 47, 52-67, 72-76, 81-83, 88-90, 101-105, 114-139, 144-167 /usr/lib/python3/dist-packages/rfc3986/parseresult.py 166 124 25% 34-46, 50, 55, 60, 65, 81-92, 98-112, 134-139, 152, 159-172, 176-181, 193-198, 208-220, 227-244, 267-273, 287, 294-314, 327-337, 342-364, 368-385 /usr/lib/python3/dist-packages/rfc3986/uri.py 30 17 43% 88-96, 102-115, 128, 144-147 /usr/lib/python3/dist-packages/rfc3986/validators.py 129 99 23% 60-73, 87-89, 103-105, 119-123, 135-136, 148-149, 165-174, 190-199, 220-240, 245-251, 256-258, 265-271, 284-289, 306-309, 324-329, 344, 359, 374, 389, 396, 411-430, 435-450 /usr/lib/python3/dist-packages/secretstorage/__init__.py 21 18 14% 17-53 /usr/lib/python3/dist-packages/secretstorage/collection.py 104 99 5% 22-201 /usr/lib/python3/dist-packages/secretstorage/defines.py 11 0 100% /usr/lib/python3/dist-packages/secretstorage/dhcrypto.py 28 22 21% 18-59 /usr/lib/python3/dist-packages/secretstorage/exceptions.py 5 0 100% /usr/lib/python3/dist-packages/secretstorage/item.py 73 68 7% 17-145 /usr/lib/python3/dist-packages/secretstorage/util.py 112 107 4% 15-180 /usr/lib/python3/dist-packages/simplejson/__init__.py 80 57 29% 120-122, 126-130, 248-279, 372-385, 457, 514-535, 539-562, 577 /usr/lib/python3/dist-packages/simplejson/compat.py 29 16 45% 5-18, 24, 26 /usr/lib/python3/dist-packages/simplejson/decoder.py 225 178 21% 14-15, 26-27, 60-133, 145-234, 237-270, 368-374, 387-400 /usr/lib/python3/dist-packages/simplejson/encoder.py 394 341 13% 13-14, 42-62, 69-103, 244, 247, 249, 251, 272, 284-302, 314-380, 400-404, 407-417, 441-722 /usr/lib/python3/dist-packages/simplejson/errors.py 29 23 21% 7-12, 16-23, 41-50, 53 /usr/lib/python3/dist-packages/simplejson/raw_json.py 3 1 67% 9 /usr/lib/python3/dist-packages/simplejson/scanner.py 64 53 17% 9-10, 21-83 /usr/lib/python3/dist-packages/six.py 491 239 51% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 308, 488, 496, 501-507, 519-525, 530-532, 538-540, 545, 550, 554-568, 583, 586, 589, 592, 600-616, 628, 631, 645-647, 653-673, 679, 683, 687, 691, 698-721, 737-738, 743-795, 797-804, 814-834, 845-861, 870-873, 893-898, 912-918, 932-937, 948-955, 976-977 /usr/lib/python3/dist-packages/stevedore/__init__.py 9 0 100% /usr/lib/python3/dist-packages/stevedore/driver.py 29 17 41% 51-53, 66, 100-105, 108-118, 139-141, 147-148 /usr/lib/python3/dist-packages/stevedore/enabled.py 13 7 46% 64-65, 77-84 /usr/lib/python3/dist-packages/stevedore/exception.py 3 0 100% /usr/lib/python3/dist-packages/stevedore/extension.py 104 71 32% 46-49, 58, 99-107, 141-146, 150-152, 155-156, 160-165, 176-179, 183, 187-214, 220-230, 237, 259-265, 269, 290, 294-301, 309, 317, 326, 331 /usr/lib/python3/dist-packages/stevedore/hook.py 11 6 45% 59, 74-78, 87-89 /usr/lib/python3/dist-packages/stevedore/named.py 34 24 29% 74-89, 123-129, 134-140, 143-146, 154-156 /usr/lib/python3/dist-packages/swiftclient/__init__.py 7 2 71% 31-32 /usr/lib/python3/dist-packages/swiftclient/client.py 959 847 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, 1641-1672, 1675-1679, 1682-1691, 1694-1702, 1712, 1721-1727, 1730-1791, 1795, 1804, 1812, 1818, 1827, 1836, 1842, 1848, 1855, 1861-1878, 1885-1906, 1914, 1920, 1927, 1933-1944, 1947-1950 /usr/lib/python3/dist-packages/swiftclient/exceptions.py 51 45 12% 25-36, 40-43, 48-81 /usr/lib/python3/dist-packages/swiftclient/utils.py 229 178 22% 41, 51-68, 100-197, 201-205, 209-216, 220-239, 248-253, 258, 261, 264, 285-287, 290, 298-307, 310, 313, 332-339, 342, 345, 348, 351-363, 367-369, 373-378, 382-387, 391-392, 396-397, 401-405, 410-416, 419, 424-427 /usr/lib/python3/dist-packages/swiftclient/version.py 6 3 50% 24-28 /usr/lib/python3/dist-packages/urllib3/__init__.py 33 8 76% 56-62, 86 /usr/lib/python3/dist-packages/urllib3/_collections.py 187 137 27% 5-6, 9-16, 47-51, 55-58, 61-73, 76-80, 83-84, 87, 92-99, 102-103, 141-149, 152-153, 156-157, 160, 163, 166-170, 175, 178-179, 184, 188-189, 198-206, 209-212, 223-228, 235-256, 261-268, 275-286, 297, 300-305, 308-310, 314-317, 321-323, 326, 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/appdirs.py 257 211 18% 29-39, 77-97, 131-163, 195-203, 236-254, 291-300, 302-304, 310, 345-353, 388-404, 411-415, 419, 424, 429, 434, 439, 444, 449, 460-476, 480-503, 507-530, 533-556, 559-571, 577-608 /usr/local/lib/python3.8/dist-packages/boto/__init__.py 281 191 32% 75, 88-97, 102-111, 125-126, 140-141, 155-156, 170-171, 185-186, 206-207, 223-224, 239-240, 254-255, 270-271, 286-287, 302-303, 317-318, 332-333, 351-352, 366-367, 381-382, 397-398, 414-415, 437-453, 470-471, 494-508, 530-545, 562-563, 577-578, 599-607, 626-627, 643-644, 660-661, 683-684, 702-703, 720-721, 737-738, 747-748, 767-768, 788-789, 811-812, 834-835, 856-857, 878-879, 901-902, 924-925, 947-948, 970-971, 993-994, 1016-1017, 1039-1040, 1062-1063, 1078-1079, 1094-1095, 1143-1197, 1209-1214 /usr/local/lib/python3.8/dist-packages/boto/auth.py 584 467 20% 49-51, 102-105, 108-115, 118-121, 124-128, 132-134, 137-140, 143-144, 155, 158, 167-169, 172-173, 176-193, 203-205, 208-209, 212-221, 232-233, 236-247, 258-259, 266-271, 280-282, 290-298, 309-322, 334-340, 343-350, 357-367, 370-374, 377-383, 388-395, 404-414, 417-419, 422-430, 433-441, 444-451, 454-459, 462, 465-479, 482-487, 490-504, 512-516, 519-525, 536-582, 592-595, 598-601, 606-611, 617-622, 625-629, 636-645, 658-690, 696, 703-738, 741-744, 747-753, 764-806, 825, 828-834, 837-845, 856-874, 885-896, 908-910, 913-924, 935-955, 967-982, 1007-1033, 1037-1054, 1059-1098 /usr/local/lib/python3.8/dist-packages/boto/auth_handler.py 10 2 80% 52, 60 /usr/local/lib/python3.8/dist-packages/boto/cacerts/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/compat.py 47 26 45% 28-29, 35-36, 45-47, 68-102 /usr/local/lib/python3.8/dist-packages/boto/connection.py 605 493 19% 79-80, 84-85, 123, 131, 138, 146-158, 173-181, 189-190, 197-199, 234-238, 243-246, 249, 255, 264-269, 276-280, 290-300, 341-358, 361, 367-384, 390-391, 402-413, 474-572, 575, 578, 581, 587, 591, 594, 598, 602, 608, 614, 622-642, 645-662, 665-698, 701-705, 708-721, 724-778, 781, 784-851, 854-855, 858-859, 863-874, 877-881, 884, 897-1033, 1037-1059, 1066-1070, 1077-1078, 1091, 1103, 1106, 1109-1116, 1119-1122, 1158-1162, 1168-1186, 1190-1208, 1211-1227 /usr/local/lib/python3.8/dist-packages/boto/endpoints.py 79 57 28% 44-50, 55-56, 61-78, 81-82, 87-92, 100-103, 118-121, 126, 130, 149, 163-166, 177, 186, 197, 210-222, 227-232, 237-239 /usr/local/lib/python3.8/dist-packages/boto/exception.py 287 166 42% 42-43, 46, 49, 79-135, 138-142, 145-148, 151, 155, 159, 162-170, 173-176, 181-185, 188, 191-196, 204-205, 208-211, 254-256, 259, 262-267, 270-272, 280-281, 284, 287, 295-296, 299, 303-306, 310-312, 334-340, 343-347, 350-353, 356-359, 376-383, 403-405, 408, 411-416, 458-459, 466-467, 474-475, 482-483, 490-491, 532-534, 537, 549-551, 554, 565-566, 574-575, 578, 592-593 /usr/local/lib/python3.8/dist-packages/boto/gs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/gs/acl.py 187 133 29% 58-59, 63, 67-75, 80-82, 87-88, 91-93, 96-97, 100-107, 110-115, 118-126, 132-135, 138-141, 144-149, 152-155, 158-164, 172-175, 178, 181-205, 208-216, 219-223, 243-250, 254-264, 267-271, 274-284, 287-308 /usr/local/lib/python3.8/dist-packages/boto/gs/user.py 26 20 23% 25-29, 32, 35, 38-43, 46-54 /usr/local/lib/python3.8/dist-packages/boto/handler.py 29 19 34% 30-32, 35-38, 41-46, 49, 54-57, 60 /usr/local/lib/python3.8/dist-packages/boto/https_connection.py 49 34 31% 41-44, 47, 59-62, 75-83, 105-114, 118-135 /usr/local/lib/python3.8/dist-packages/boto/jsonresponse.py 108 89 18% 30-32, 35-41, 44-47, 50, 53-55, 64-74, 77-86, 89-91, 94-109, 112-119, 127-132, 135-137, 140-155, 158-168 /usr/local/lib/python3.8/dist-packages/boto/plugin.py 38 22 42% 53-56, 60-66, 70-78, 86, 91-93 /usr/local/lib/python3.8/dist-packages/boto/provider.py 247 178 28% 183-214, 217-219, 222, 227-229, 232, 237-239, 242, 247-263, 267-378, 383-402, 413-434, 437-441, 444-465, 468-473, 476, 479, 484 /usr/local/lib/python3.8/dist-packages/boto/pyami/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/pyami/config.py 158 108 32% 42, 47-49, 59, 61, 65-69, 78, 88-93, 96-103, 111-121, 124, 127, 130-134, 137-141, 144-148, 151, 166-169, 172-180, 183-186, 189-191, 194-202, 205-217, 220-235 /usr/local/lib/python3.8/dist-packages/boto/regioninfo.py 89 66 26% 44, 56-57, 77-82, 100-115, 121-134, 161-182, 208-220, 226-229, 235-239, 247-249, 259-262, 265, 268, 271-276, 289-290 /usr/local/lib/python3.8/dist-packages/boto/resultset.py 111 98 12% 47-62, 65-76, 79-82, 85-134, 140-142, 145-148, 151, 154, 157-160, 163-176 /usr/local/lib/python3.8/dist-packages/boto/s3/__init__.py 17 11 35% 43-44, 54-55, 63-74 /usr/local/lib/python3.8/dist-packages/boto/s3/acl.py 111 89 20% 34-36, 39-51, 54-64, 67-72, 75-82, 88-89, 92, 95-97, 100-101, 104-108, 111-114, 117-121, 130-135, 138-140, 143-156, 159-171 /usr/local/lib/python3.8/dist-packages/boto/s3/bucket.py 700 576 18% 71, 95-97, 100, 103, 106, 109, 112-117, 131, 143, 175-194, 197-231, 282, 328, 363, 369-390, 394-411, 424-426, 469-472, 521-522, 535, 606-609, 623-625, 630, 662-730, 757-759, 766-788, 847-889, 894-908, 912-922, 926-936, 940-944, 948-963, 989-1002, 1028-1040, 1043-1046, 1072-1080, 1110-1119, 1123-1124, 1134-1146, 1162-1171, 1193-1197, 1206-1207, 1216-1227, 1235-1240, 1243-1249, 1253-1260, 1288-1308, 1323-1339, 1350-1366, 1377-1389, 1396-1404, 1438-1441, 1448, 1454-1461, 1483, 1489-1493, 1522-1526, 1530-1538, 1544-1551, 1560-1563, 1570-1576, 1586-1594, 1598-1606, 1618-1632, 1644, 1651-1658, 1669-1673, 1679-1687, 1736-1767, 1775-1806, 1815-1822, 1826, 1829-1835, 1838-1845, 1849-1864, 1867, 1870-1878 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlistresultset.py 67 54 19% 29-41, 54-59, 62, 73-86, 99-105, 108, 121-134, 147-151, 154 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlogging.py 50 41 18% 28-33, 36-47, 50, 53-57, 60-65, 69-83 /usr/local/lib/python3.8/dist-packages/boto/s3/connection.py 282 212 25% 58-62, 67-69, 76, 79-82, 85-88, 91-95, 98-99, 106, 113, 119, 122-126, 132-135, 176-199, 205-208, 211-212, 215, 226, 232-237, 298-355, 361-380, 386-438, 443-453, 468-469, 508-511, 528-555, 577-581, 606-627, 644-647, 653-667 /usr/local/lib/python3.8/dist-packages/boto/s3/cors.py 80 69 14% 65-78, 81, 84, 87-100, 103-117, 126-130, 133, 140-144, 194-210 /usr/local/lib/python3.8/dist-packages/boto/s3/deletemarker.py 28 23 18% 26-31, 34-38, 41-55 /usr/local/lib/python3.8/dist-packages/boto/s3/key.py 690 590 14% 106-135, 138-147, 150, 154-157, 160, 163, 168-169, 172-175, 180-184, 187-192, 197-207, 210, 219-223, 226-231, 234-242, 246-259, 262-273, 280, 304-334, 348, 352-361, 381-385, 397-402, 408-415, 441-451, 501-507, 515-519, 522-542, 551, 557, 561, 566-573, 576, 580-581, 584-585, 588-589, 592-593, 596, 605-610, 624-635, 639, 695-713, 760, 767-966, 969-1021, 1036-1045, 1108-1132, 1214-1311, 1374-1375, 1437-1444, 1493, 1503-1575, 1602, 1658-1664, 1722-1739, 1795-1804, 1829-1831, 1853-1856, 1859-1867, 1875-1889, 1893-1912, 1929-1935 /usr/local/lib/python3.8/dist-packages/boto/s3/keyfile.py 74 53 28% 35-44, 47-49, 52-85, 88-89, 92-94, 97, 102, 107, 110, 113, 116, 119, 122, 125, 128, 131, 134 /usr/local/lib/python3.8/dist-packages/boto/s3/lifecycle.py 148 113 24% 48-65, 68, 71-76, 79-86, 89-99, 111-112, 115, 118-121, 124-128, 131-137, 152-154, 157-161, 164-171, 178-182, 185, 188-203, 210-213, 230-231, 234-236, 242, 246, 250, 259-263, 266, 273-278, 310-311 /usr/local/lib/python3.8/dist-packages/boto/s3/multidelete.py 64 48 25% 41-44, 47-50, 53, 56-66, 82-85, 88-92, 95, 98-107, 121-123, 126-134, 137 /usr/local/lib/python3.8/dist-packages/boto/s3/multipart.py 160 133 17% 46-52, 55, 59, 62-71, 86-90, 93-96, 99, 102-111, 118-125, 134-146, 149, 152, 155-162, 165-175, 178-200, 211-226, 253-261, 290-301, 317-318, 330 /usr/local/lib/python3.8/dist-packages/boto/s3/prefix.py 16 10 38% 24-25, 28, 31-34, 38-41 /usr/local/lib/python3.8/dist-packages/boto/s3/tagging.py 52 34 35% 7-8, 11, 14-17, 20, 24, 29-33, 36, 39-40, 43-47, 54-58, 61, 64-68, 71 /usr/local/lib/python3.8/dist-packages/boto/s3/user.py 23 18 22% 24-28, 31, 34-39, 42-49 /usr/local/lib/python3.8/dist-packages/boto/s3/website.py 122 85 30% 24-26, 57-63, 66-72, 75, 78-89, 94-98, 101, 104-106, 109-114, 131-133, 136, 152-153, 156-159, 162, 165, 168-171, 191-192, 195-198, 201, 204-209, 213, 218-223, 245-247, 250, 283-288, 291 /usr/local/lib/python3.8/dist-packages/boto/storage_uri.py 488 377 23% 57, 62, 66, 69-70, 77-78, 82-83, 87-89, 103-149, 152, 158-160, 165-172, 176-177, 180-184, 187-191, 194-196, 199-203, 209-218, 224-227, 231-234, 237-240, 288-299, 302-318, 321, 328-332, 335-344, 348-355, 365-366, 379-390, 402-413, 417-421, 425-429, 433-438, 441-443, 446-453, 457-464, 468-470, 475-491, 496-504, 508-515, 518-520, 524, 528, 536, 540, 544, 548, 552, 556, 560, 564, 568-576, 579-581, 584-585, 588-591, 596-605, 610-619, 624-625, 630-631, 636-640, 646-649, 653-655, 661-675, 680-696, 700-706, 713-724, 733-735, 738-740, 743-745, 749-754, 757-759, 762-764, 767-769, 773, 779-787, 791-795, 800-802, 805-811, 816-822, 826-833, 838-840, 845-849, 875-880, 889, 893, 897, 901, 905, 909-911, 915, 919, 923, 927, 932, 937, 944 /usr/local/lib/python3.8/dist-packages/boto/utils.py 575 467 19% 108-111, 119-169, 173-183, 187-202, 212-237, 241, 246-266, 269-270, 273-337, 340-343, 346-347, 350-351, 354-355, 358-359, 383, 399-405, 413-427, 432-441, 454-460, 464-466, 470-481, 485-498, 505-508, 518-543, 549-554, 557-576, 579, 582, 588, 616-619, 629-646, 689-691, 694, 697-700, 703, 706-709, 712, 715-717, 720-728, 731, 734-741, 744-750, 753-765, 780-782, 785-787, 790, 793-797, 800-803, 808-859, 863-872, 876-881, 897-899, 920-944, 959-972, 1000, 1004-1029, 1038, 1048-1049, 1060, 1070-1083, 1093-1098 /usr/local/lib/python3.8/dist-packages/boto/vendored/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/exceptions.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/regions.py 81 60 26% 58, 65, 85, 94-96, 99-102, 106-116, 120-124, 128-152, 156-160, 163-177, 180-182, 186 /usr/local/lib/python3.8/dist-packages/boto/vendored/six.py 444 208 53% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /usr/local/lib/python3.8/dist-packages/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 /usr/local/lib/python3.8/dist-packages/joblib/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/joblib/_compat.py 15 3 80% 11, 24-25 /usr/local/lib/python3.8/dist-packages/joblib/_memmapping_reducer.py 180 132 27% 28, 38-39, 70, 73-78, 81-95, 98, 107-119, 152-178, 183, 189-202, 213-236, 243-252, 281-286, 293-298, 301-361, 374-434 /usr/local/lib/python3.8/dist-packages/joblib/_memory_helpers.py 65 63 3% 5-105 /usr/local/lib/python3.8/dist-packages/joblib/_multiprocessing_helpers.py 34 11 68% 20-21, 34-37, 51-53, 61-64 /usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py 271 174 36% 38-39, 78-79, 92, 99, 123, 132-136, 153, 163-184, 188, 203-205, 209-212, 216-220, 230-238, 242-245, 249, 253, 258-260, 282-284, 288-344, 348-360, 367-368, 392-399, 407-409, 432-462, 467-489, 493-497, 509-519, 523-547, 551-555, 561-564, 567-574, 579-583, 590, 593, 604, 607-624, 631 /usr/local/lib/python3.8/dist-packages/joblib/_store_backends.py 196 137 30% 26-31, 152-174, 179-193, 198-200, 205-208, 212, 217-223, 227-238, 242-243, 247-249, 253-260, 264-270, 274, 278, 282-294, 298-322, 326-328, 332, 345-348, 352, 356-388, 395-415 /usr/local/lib/python3.8/dist-packages/joblib/backports.py 48 37 23% 22-30, 37-76, 80-81 /usr/local/lib/python3.8/dist-packages/joblib/compressor.py 315 209 34% 12-13, 17-18, 22-23, 27-28, 61, 65, 73, 78, 107-110, 115, 127, 130-131, 136-140, 145-152, 164, 168-172, 178-186, 202, 206-209, 220, 225-235, 239-243, 248-249, 289-321, 330-348, 353, 357-358, 362, 366-367, 371-372, 377-383, 386-388, 391-393, 396-401, 406-424, 430-440, 446-470, 478-485, 492-493, 502-511, 515-520, 537-562, 566-568 /usr/local/lib/python3.8/dist-packages/joblib/disk.py 59 42 29% 27-38, 44-52, 59-63, 90-101, 106-124 /usr/local/lib/python3.8/dist-packages/joblib/executor.py 32 21 34% 28-50, 58-59, 63-65, 68-69, 72-73 /usr/local/lib/python3.8/dist-packages/joblib/externals/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle.py 623 480 23% 65-66, 85, 88-95, 109-115, 119-124, 128-139, 151-164, 169-202, 209-227, 257-274, 334-339, 343-384, 388, 406, 410-432, 440-443, 448-465, 473-477, 480-488, 491, 496-499, 505-509, 517-541, 551-556, 579-582, 591-608, 617-692, 706-760, 767-812, 820-847, 850-851, 854, 865-879, 886-892, 900-941, 944, 948, 953-954, 961-967, 974-989, 996-1033, 1036, 1039, 1050, 1055, 1060, 1066, 1073, 1083-1089, 1093-1094, 1109, 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/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat_posix.py 4 1 75% 11 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/context.py 135 95 30% 37-85, 91-97, 101, 118-153, 164-165, 170-171, 175-206, 214-215, 219-220, 224-225, 229-230, 234-235, 239-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/process.py 57 42 26% 20-31, 35-39, 42-64, 67-81, 89, 100-108 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/queues.py 131 102 22% 36-62, 66-67, 72-75, 79-111, 121-175, 182-183, 187-189, 195-210, 214-215, 219, 225-229, 234-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/reduction.py 126 59 53% 27-30, 63-66, 79-82, 87, 91, 101, 109, 113, 121, 127-129, 146, 149, 154-165, 176-177, 185-197, 202-210, 218, 223, 232-234, 240, 246-250, 256 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/utils.py 94 75 20% 11-12, 20-21, 25-28, 32-46, 52-60, 67-116, 125-138, 143-145, 149-172 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/cloudpickle_wrapper.py 60 44 27% 7-8, 16-17, 20-24, 29-31, 38, 42-44, 48-49, 55-83, 95-113 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py 507 411 19% 88-89, 94, 132-138, 143, 146-147, 150-155, 158-159, 171-174, 177-179, 182-184, 189-197, 214, 217, 223-229, 232, 236-237, 245-248, 254-256, 262-268, 271-272, 275, 283-286, 289-312, 317-325, 337, 342-347, 372-465, 487-505, 542-757, 766-785, 794-797, 803-810, 882-933, 939-951, 955-994, 998-1014, 1019-1022, 1025-1048, 1073-1081, 1084-1117 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/reusable_executor.py 91 70 23% 34-37, 84-142, 150-155, 158-159, 163-194, 199-207, 212-213 /usr/local/lib/python3.8/dist-packages/joblib/format_stack.py 209 188 10% 34-35, 45-68, 72, 88-94, 104-116, 120-147, 151-176, 181-322, 337-365, 371-401 /usr/local/lib/python3.8/dist-packages/joblib/func_inspect.py 176 154 12% 47-79, 84-93, 110-162, 173-177, 190-193, 198-203, 228-318, 322-325, 330-349, 356-359 /usr/local/lib/python3.8/dist-packages/joblib/hashing.py 117 85 27% 24, 33-42, 49, 58-64, 67-75, 78-94, 101-103, 111-127, 141-150, 155, 174-182, 189-242, 258-267 /usr/local/lib/python3.8/dist-packages/joblib/logger.py 76 56 26% 28-31, 35-36, 40-44, 48-57, 74, 77, 81, 85, 96-124, 136-156 /usr/local/lib/python3.8/dist-packages/joblib/memory.py 374 296 21% 57-63, 92-100, 105-141, 146-148, 153-160, 166-182, 225-244, 248-253, 257-277, 281, 284, 293-295, 306-307, 310-313, 316-317, 320-325, 329, 332-333, 352, 355, 358, 361, 365, 415-453, 483-545, 562-563, 568, 574-576, 583, 588-590, 594-595, 605-625, 636-713, 717-724, 730-745, 764-795, 804, 872-907, 914-921, 949-962, 971-974, 978-979, 990-992, 999, 1008-1010 /usr/local/lib/python3.8/dist-packages/joblib/my_exceptions.py 53 20 62% 24, 27-33, 46-48, 51-59, 75, 80, 84, 89, 94-99, 112-113 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle.py 204 161 21% 13-14, 78-82, 91-104, 112-161, 165-178, 195-209, 234-249, 253-260, 272-295, 320-332, 342-355, 361, 415-515, 526-548, 588-607 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_compat.py 105 75 29% 21-25, 38-61, 71-75, 90-92, 96-120, 140-142, 148-154, 164-173, 176, 185-192, 198, 227-247 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_utils.py 92 65 29% 22-23, 27-28, 36-37, 45-49, 54-56, 73-90, 95-100, 105-112, 144-182, 187-197, 229-245 /usr/local/lib/python3.8/dist-packages/joblib/parallel.py 362 293 19% 40-41, 65-73, 83-124, 181-209, 212, 215, 218-222, 234, 241-249, 254-255, 259, 267-270, 282-291, 297-311, 327-329, 332-340, 360-363, 388-389, 620-696, 699-701, 704-705, 709-725, 728-730, 733-734, 744-759, 769-771, 783-836, 842-849, 855-887, 895-940, 943-1032, 1035 /usr/local/lib/python3.8/dist-packages/joblib/pool.py 116 83 28% 42-43, 75-87, 91-99, 120-127, 130-131, 135-137, 140, 143-177, 199-207, 210-216, 296-313, 316-329 /usr/local/lib/python3.8/dist-packages/pooch/__init__.py 20 14 30% 36-50 /usr/local/lib/python3.8/dist-packages/pooch/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/pooch/core.py 120 84 30% 197-230, 410, 413, 424, 545-568, 575-576, 589-590, 610-637, 658-673, 702-711, 725-733 /usr/local/lib/python3.8/dist-packages/pooch/downloaders.py 82 69 16% 14-15, 47-60, 139-143, 161-203, 253-261, 277-310 /usr/local/lib/python3.8/dist-packages/pooch/processors.py 75 50 33% 38, 64-81, 88, 117-133, 162-182, 213, 240-252, 262-279 /usr/local/lib/python3.8/dist-packages/pooch/utils.py 101 54 47% 33, 96, 150, 173-193, 214-216, 246, 248, 255-256, 261-271, 308-315, 346-362, 386-393, 430-436 /usr/local/lib/python3.8/dist-packages/pooch/version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/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, 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98 71 28% 15-23, 87-88, 91, 95, 99, 103, 106, 125-131, 135-159, 168-180, 184-202, 207, 216-222, 227 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_numpy_compat.py 275 254 8% 18-50, 58-87, 92-102, 109-197, 203-289, 294-568, 573-781 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_testutils.py 77 62 19% 31-73, 81-82, 89-104, 108-119, 126-145 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_threadsafety.py 33 9 73% 33-37, 40-41, 45-46 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_uarray/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/_uarray/_backend.py 93 47 49% 48-57, 61-74, 96-99, 194, 213, 275, 306, 338-340, 343, 346, 363, 379-392, 404-417 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_util.py 166 118 29% 19-24, 42-60, 90-100, 111-126, 134-136, 164, 167-170, 186-196, 235-253, 320-347, 367-389, 393, 396-397, 400-401, 404-405, 408-409, 412-414, 418-422 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_version.py 76 33 57% 59, 72, 82-87, 91-95, 101-112, 116, 124-132, 140, 143, 146, 155 /usr/local/lib/python3.8/dist-packages/scipy/_lib/decorator.py 252 115 54% 51-68, 77-78, 102, 114, 121-122, 124, 126-127, 140, 146, 150, 162-163, 175, 181, 192-195, 244, 253, 259-261, 275-276, 282, 290-292, 294, 297, 310-319, 328-424 /usr/local/lib/python3.8/dist-packages/scipy/_lib/deprecation.py 15 4 73% 11-14, 18-20 /usr/local/lib/python3.8/dist-packages/scipy/_lib/doccer.py 97 21 78% 53, 116-125, 140, 145, 165, 171, 200, 250, 268-274 /usr/local/lib/python3.8/dist-packages/scipy/_lib/six.py 177 113 36% 43-65, 75-76, 87-92, 106-114, 119-121, 127, 132-142, 155, 160, 165, 170, 173, 176-177, 185-192, 202-204, 210-270, 277 /usr/local/lib/python3.8/dist-packages/scipy/_lib/uarray.py 13 5 62% 17-20, 24-25 /usr/local/lib/python3.8/dist-packages/scipy/constants/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/constants/codata.py 97 15 85% 1566, 1616-1617, 1641-1642, 1692-1704, 1725 /usr/local/lib/python3.8/dist-packages/scipy/constants/constants.py 142 21 85% 220-246, 278, 307 /usr/local/lib/python3.8/dist-packages/scipy/fft/__init__.py 27 2 93% 112-113 /usr/local/lib/python3.8/dist-packages/scipy/fft/_backend.py 33 13 61% 19-23, 36-37, 40, 90-91, 122-123, 151-152 /usr/local/lib/python3.8/dist-packages/scipy/fft/_basic.py 64 23 64% 9-13, 154, 252, 339, 434, 510, 570, 668, 764, 857, 947, 1040, 1083, 1183, 1226, 1333, 1375, 1463, 1506 /usr/local/lib/python3.8/dist-packages/scipy/fft/_helper.py 10 2 80% 62, 101 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/basic.py 133 87 35% 16-30, 44-58, 72-90, 103, 110, 117, 124, 131, 138, 146-161, 172-186, 198-223, 234-251 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/helper.py 106 84 21% 28-38, 43-77, 86-95, 102-106, 111-133, 137-141, 147-153, 158-170, 190-195, 210 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/realtransforms.py 64 42 34% 19-46, 71-99 /usr/local/lib/python3.8/dist-packages/scipy/fft/_realtransforms.py 28 8 71% 63, 121, 179, 237, 387, 451, 568, 618 /usr/local/lib/python3.8/dist-packages/scipy/integrate/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_bvp.py 378 352 7% 29-57, 75-116, 124-142, 152-158, 243-276, 310-317, 322-347, 418-502, 506, 513, 556-577, 590-602, 632, 642-710, 1001-1159 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/base.py 99 80 19% 7-23, 118-151, 155-158, 170-191, 201-209, 212, 215, 231-234, 250-253, 256, 266-267, 270-275 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/bdf.py 245 222 9% 21-26, 31-34, 39-70, 188-242, 245-295, 298-438, 441, 447-451, 454-467 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/common.py 214 189 12% 13-17, 22-24, 39-40, 46-57, 62, 100-120, 153-176, 181-190, 206-238, 292-320, 325-363, 367-432 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/dop853_coefficients.py 153 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/ivp.py 162 143 12% 31-51, 77-78, 109-128, 146-154, 504-662 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/lsoda.py 57 46 19% 108-138, 141-161, 164-172, 177-180, 183-188 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/radau.py 262 230 12% 88-137, 169-177, 286-334, 337-387, 390-529, 532-533, 536, 541-545, 548-562 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/rk.py 190 127 33% 62-72, 89-104, 107, 110, 113-177, 180-181, 480-485, 488-494, 497-503, 506-524, 529-533, 536-549, 554-557, 560-576 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ode.py 507 401 21% 348-353, 357, 361-369, 382-394, 422-437, 441-445, 532-536, 540-541, 545-546, 562-567, 584-587, 620-625, 628-633, 637-656, 660, 673-686, 690-694, 722-723, 739-742, 751-754, 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/usr/local/lib/python3.8/dist-packages/scipy/interpolate/_bsplines.py 324 291 10% 20-22, 27-30, 38-43, 182-226, 235-239, 245, 303-308, 332-355, 358, 367-370, 391-397, 425-437, 488-571, 581-588, 593, 597-604, 608-617, 735-861, 969-1022 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_cubic.py 259 230 11% 28-72, 142-158, 235-240, 245-255, 268-304, 343-350, 405-435, 438, 445, 450, 619-770, 784-837 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_fitpack_impl.py 413 388 6% 44-48, 215-311, 443-524, 580-607, 654-668, 710-733, 773-791, 890-988, 1039-1057, 1079-1080, 1128-1146, 1196-1229, 1288-1311 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_pade.py 27 22 19% 46-67 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack.py 65 49 25% 156-158, 289-290, 353-368, 417-429, 477-491, 531-534, 586-601, 654-657, 719-722 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack2.py 366 304 17% 171-196, 201-208, 211-232, 235-241, 244-256, 265-276, 303-317, 324-326, 330-332, 342, 380, 405-408, 415-421, 468-471, 524-525, 602-618, 741-770, 793, 801, 805, 841-880, 955-961, 983, 1002-1004, 1048-1069, 1110-1136, 1172-1198, 1273-1281, 1304, 1385-1396, 1486-1504, 1670-1727 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/interpolate.py 920 819 11% 32-34, 84-94, 201-252, 279-313, 318-328, 335, 431-540, 547, 552-581, 585, 591-614, 623-631, 637-646, 649, 652-654, 660-669, 687-700, 708-751, 754-758, 770-777, 784-787, 808-870, 902-923, 986, 1013-1031, 1063-1085, 1108-1167, 1217-1239, 1268, 1284-1296, 1313-1332, 1422, 1443-1477, 1502-1533, 1558-1597, 1600-1603, 1621-1639, 1705-1757, 1814-1843, 1873-1883, 1946-1965, 1978-1984, 1987-1991, 1994-1997, 2028-2062, 2069-2096, 2103-2136, 2163-2169, 2197-2203, 2236-2264, 2287-2312, 2415-2452, 2468-2501, 2505-2516, 2519-2521, 2525-2541, 2601-2676, 2689-2704, 2707, 2710-2712, 2717-2724 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/ndgriddata.py 47 38 19% 59-65, 77-81, 193-228 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/polyint.py 203 169 17% 18, 56-60, 78-80, 86, 90-92, 96-103, 106-111, 114-131, 134-139, 177-188, 216-218, 293-316, 319-326, 329-355, 400-406, 448-461, 502-513, 532-537, 559-577, 599, 602-617, 666 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/rbf.py 105 83 21% 145, 148, 151, 154, 157, 160, 163, 167-216, 223-268, 274-275, 278, 281-290 /usr/local/lib/python3.8/dist-packages/scipy/linalg/__init__.py 36 4 89% 222-223, 227-228 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_ldl.py 85 74 13% 123-156, 207-241, 268-297, 335-354 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_polar.py 16 11 31% 98-112 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_qz.py 127 110 13% 19-34, 38-43, 47-52, 56-61, 65-71, 76-145, 262-265, 358-405 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_expm_frechet.py 153 138 10% 91-114, 122-127, 166-173, 177-187, 191-203, 207-222, 226-278, 298, 334-350, 393-411 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_matfuncs_sqrtm.py 85 74 13% 52-116, 163-196 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_procrustes.py 18 13 28% 76-91 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_sketches.py 15 8 47% 49-54, 167-168 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_solvers.py 216 194 10% 86-107, 159-199, 214-218, 228-233, 306-324, 446-528, 652-736, 778-844 /usr/local/lib/python3.8/dist-packages/scipy/linalg/basic.py 385 358 7% 27-37, 137-258, 330-359, 433-472, 568-596, 669-702, 706-711, 862-907, 950-983, 1034-1043, 1157-1246, 1304-1318, 1373-1391, 1451-1470, 1575-1619 /usr/local/lib/python3.8/dist-packages/scipy/linalg/blas.py 86 18 79% 296-310, 341, 352, 362, 377, 381-384, 387 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp.py 364 338 7% 41-47, 51-73, 78-115, 214-267, 374-489, 503-530, 640-695, 767, 858, 951, 1031, 1124-1194, 1199-1203, 1252-1283, 1368-1431 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_cholesky.py 72 61 15% 19-44, 90-92, 154-156, 194-213, 274-286, 334-353 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_lu.py 48 38 21% 71-86, 135-148, 209-223 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_qr.py 130 121 7% 16-25, 121-173, 251-320, 386-424 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_schur.py 104 85 18% 119-178, 191-197, 201-210, 266-295 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_svd.py 88 74 16% 109-139, 225-232, 273-281, 323-330, 384-391, 459-496 /usr/local/lib/python3.8/dist-packages/scipy/linalg/flinalg.py 30 23 23% 14-19, 23, 32-58 /usr/local/lib/python3.8/dist-packages/scipy/linalg/lapack.py 45 14 69% 776, 805-814, 823-832 /usr/local/lib/python3.8/dist-packages/scipy/linalg/linalg_version.py 5 0 100% /usr/local/lib/python3.8/dist-packages/scipy/linalg/matfuncs.py 130 103 21% 52-55, 84-89, 136-138, 195-208, 255-256, 291-295, 330-334, 371-372, 409-410, 447-448, 485-486, 551-590, 626-670 /usr/local/lib/python3.8/dist-packages/scipy/linalg/misc.py 42 32 24% 141-181, 190-194 /usr/local/lib/python3.8/dist-packages/scipy/linalg/special_matrices.py 223 197 12% 62-73, 104-106, 138-140, 193-203, 239-244, 290-301, 344-358, 413-430, 465-471, 535-553, 600-617, 654-662, 698-700, 759-777, 840-863, 938-973, 1033-1043, 1109-1119, 1172-1196 /usr/local/lib/python3.8/dist-packages/scipy/misc/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/scipy/misc/common.py 73 65 11% 35-47, 85-120, 154-159, 195-204, 297-303 /usr/local/lib/python3.8/dist-packages/scipy/misc/doccer.py 29 8 72% 15, 21, 27, 33, 39, 44, 49, 54 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_docstrings.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_support.py 43 36 16% 41-52, 60-68, 72-84, 88-92 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/filters.py 399 336 16% 51, 79-96, 129-133, 140-164, 212-217, 288-303, 332-340, 369-377, 404-419, 447-449, 487-495, 525-544, 583-591, 598-623, 648, 754, 783-795, 842-857, 897-909, 954-966, 971-1033, 1069, 1106, 1113-1162, 1201-1202, 1240, 1280, 1348-1364, 1421-1448 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/fourier.py 69 58 16% 42-55, 59-70, 120-129, 179-187, 241-249, 298-306 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/interpolation.py 210 182 13% 93-105, 127-139, 245-263, 329-351, 433-487, 520-539, 589-616, 677-746 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/measurements.py 315 288 9% 178-236, 298-305, 377-458, 463-466, 500-573, 617-618, 669-670, 721-722, 773, 782-884, 946, 1025, 1086, 1155-1164, 1210-1219, 1275-1293, 1355-1364, 1419-1424, 1459-1498 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/morphology.py 403 373 7% 50-53, 106-122, 207-213, 218-285, 381, 501-513, 629-636, 775-782, 871-892, 1022, 1097-1107, 1213-1216, 1338-1364, 1443-1447, 1526-1530, 1636-1643, 1681-1695, 1739-1752, 1797-1810, 1872-1947, 1986-2063, 2175-2230 /usr/local/lib/python3.8/dist-packages/scipy/optimize/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py 219 182 17% 20, 23-24, 27-31, 34, 61-92, 102-146, 151-173, 177-178, 208-216, 219, 222-232, 238-242, 246-247, 264-265, 268-270, 278-280, 283-286, 304-305, 313-315, 321, 626-701, 705-707, 711-716, 720-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_constraints.py 180 157 13% 94-101, 136-139, 168-170, 173-176, 215-251, 267-274, 284-294, 304-307, 312-317, 324-411, 419-450 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentiable_functions.py 351 317 10% 31-160, 163-165, 168-170, 173-175, 178-181, 184-187, 190-193, 196-200, 223-420, 423-425, 428-429, 432-434, 437-439, 442-444, 447-449, 452-454, 458-461, 472-489, 492-494, 497-501, 504-505, 508-510, 521-528 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentialevolution.py 364 310 15% 294-308, 475-599, 607-637, 644-652, 666-687, 694, 702-704, 711, 730-849, 869-889, 894-908, 928, 948-959, 962, 965, 969-970, 974-975, 1011-1020, 1035-1144, 1150, 1154, 1158-1159, 1163-1195, 1199-1200, 1205-1206, 1211-1216, 1220-1224, 1228-1233, 1237-1242, 1249-1253, 1261-1262, 1265, 1294-1322, 1325, 1341-1346 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_dual_annealing.py 285 250 12% 55-71, 79-111, 115-127, 153-159, 166-195, 199-205, 209-210, 240-256, 259-277, 280-306, 315-355, 362-370, 373-374, 388-405, 409-425, 602-689 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_hessian_update_strategy.py 134 94 30% 52, 70, 87, 100, 136-145, 151-159, 162, 180-201, 217-220, 231-237, 279, 282-288, 311-312, 329-330, 334-375, 407-408, 412-430 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog.py 78 65 17% 78-110, 155-161, 510-581 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_ip.py 247 217 12% 84-119, 189-329, 345-349, 368-375, 399-412, 427-432, 447-453, 471-502, 534-544, 698-822, 1083-1127 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_rs.py 190 171 10% 47-99, 108-134, 161-237, 249-269, 277, 285-288, 295-310, 327-402, 522-558 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_simplex.py 107 98 8% 89-95, 154-166, 212-229, 355-435, 591-659 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_util.py 489 470 4% 57-68, 92-99, 118-121, 183-386, 497-778, 873-881, 971-1085, 1092, 1100-1130, 1138-1145, 1170-1173, 1241-1293, 1350-1396, 1473-1485 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsap.py 20 17 15% 79-105 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/bvls.py 116 109 6% 13-16, 20-177 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/common.py 295 261 12% 36-56, 108-170, 196-221, 235-247, 284-301, 316-324, 350-363, 371, 392-400, 418-439, 448-466, 499-510, 515-541, 548, 555-565, 571, 578-588, 597-600, 605-615, 620-631, 637-648, 660-671, 679-689, 697-707, 712-722, 730-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/dogbox.py 149 138 7% 65-77, 94-106, 125-149, 154-330 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/least_squares.py 255 229 10% 43-92, 98-105, 109-126, 130-149, 153-162, 169-177, 181-186, 190-195, 199-204, 212-237, 748-940 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/lsq_linear.py 82 70 15% 16-24, 218-317 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf.py 290 278 4% 121-126, 133-205, 210-402, 410-564 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf_linear.py 144 132 8% 53-69, 74-90, 95-142, 147-248 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_minimize.py 176 155 12% 479-636, 756-794, 799-806, 811-829 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_numdiff.py 254 237 7% 46-91, 100-103, 107-114, 147-175, 330-398, 404-441, 445-481, 486-561, 625-639 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_remove_redundancy.py 147 136 7% 30-31, 53-54, 83-92, 96-104, 139-230, 266-357, 393-449 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root.py 89 69 22% 153-203, 207-208, 246-257, 266-305, 369, 434, 476, 513, 550, 590, 654 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root_scalar.py 124 104 16% 30-33, 38-43, 47-49, 53-55, 58, 181-287, 306, 325, 343, 366, 392, 423, 442, 461 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo.py 657 595 9% 417-447, 456-655, 673-707, 721-737, 744-757, 762-778, 782-789, 793-795, 799-800, 803-804, 814-835, 838-845, 855-868, 871-879, 888-899, 907-910, 917-955, 974-1020, 1024-1028, 1031-1034, 1044-1052, 1070-1086, 1102-1104, 1124-1182, 1190-1199, 1203-1206, 1225-1240, 1245-1282, 1290-1292, 1302-1357, 1366-1375, 1384-1390, 1395-1403, 1407-1409, 1415-1421, 1431-1458, 1466-1472, 1476-1508, 1514-1531, 1534-1542, 1550, 1555-1568, 1574-1596, 1601-1605, 1610-1617, 1620-1628, 1631-1645, 1651-1671 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/sobol_seq.py 122 114 7% 30-40, 53-58, 97-102, 140-145, 197-372 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/triangulation.py 359 321 11% 8-46, 49, 56-85, 89-115, 119-141, 146-159, 163-174, 182, 195-225, 231-243, 258-298, 315-362, 372-451, 456-464, 467, 470-471, 477-480, 487-490, 497, 503-504, 513-517, 528-530, 536-569, 572, 575-585, 588-592, 596-600, 603-609, 616-626, 629-661 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_spectral.py 106 93 12% 66-164, 207-238, 245-251, 255, 259 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trlib/__init__.py 6 3 50% 7-12 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion.py 135 114 16% 25-35, 38, 43-45, 50-52, 57-59, 62-65, 70-72, 80-95, 98, 133-266 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/canonical_constraint.py 253 234 8% 43-48, 53-69, 78-91, 101-149, 153-181, 185-221, 225-261, 265-327, 337-390 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py 105 96 9% 14-15, 50-218 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py 175 152 13% 29-30, 33-36, 46-48, 51-57, 62-100, 107-112, 314-544 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/projections.py 164 145 12% 10, 41-55, 62-90, 96-172, 179-233, 240-287, 364-406 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/qp_subproblem.py 215 201 7% 45-63, 99-149, 189-234, 286-303, 308, 313, 364-409, 492-638 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/report.py 32 10 69% 11-16, 23-28, 32 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py 148 124 16% 38-57, 60-61, 64, 67, 80-86, 93-96, 100, 108-114, 130-136, 140, 143-163, 179-195, 199-205, 209-221, 226-240, 251-264, 287-347 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_dogleg.py 40 30 25% 31-35, 47-51, 57-62, 98-124 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_exact.py 139 124 11% 35-41, 80-122, 137-143, 173-185, 218-254, 264-285, 290-432 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_krylov.py 11 7 36% 22-59 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_ncg.py 51 42 18% 33-39, 71-128 /usr/local/lib/python3.8/dist-packages/scipy/optimize/cobyla.py 72 60 17% 139-170, 196-258 /usr/local/lib/python3.8/dist-packages/scipy/optimize/lbfgsb.py 136 123 10% 174-208, 265-380, 412-422, 440-456, 468-478 /usr/local/lib/python3.8/dist-packages/scipy/optimize/linesearch.py 320 299 7% 69-103, 150-186, 267-322, 384-468, 481-502, 512-523, 532-603, 644-659, 666-668, 685-726, 773-802, 852-883 /usr/local/lib/python3.8/dist-packages/scipy/optimize/minpack.py 284 255 10% 26-45, 139-165, 206-259, 384-457, 461-478, 482-491, 495-508, 683-813, 821-844, 848, 852, 856-870, 914-916 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nnls.py 21 16 24% 60-82 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nonlin.py 628 491 22% 139, 144-147, 152-154, 158-160, 269-367, 375-415, 434-453, 456-472, 521-530, 533, 536, 539, 542-547, 552-558, 562, 566, 573-658, 667-678, 681, 684-688, 703-708, 712-718, 723-746, 750-752, 756-758, 762-764, 768-770, 773-781, 784-790, 794-797, 803-808, 814-819, 850-884, 951-973, 977-978, 981, 984-988, 991, 994, 997, 1000-1006, 1046-1051, 1115-1121, 1124-1144, 1147-1167, 1170-1192, 1225-1226, 1229-1230, 1233, 1236, 1239, 1242, 1245, 1248, 1275-1276, 1279, 1282, 1285, 1288, 1291, 1294, 1325-1328, 1331-1332, 1335, 1338, 1341, 1344, 1347, 1350-1353, 1438-1476, 1479-1481, 1484-1491, 1494-1498, 1501-1508, 1511-1524 /usr/local/lib/python3.8/dist-packages/scipy/optimize/optimize.py 1239 1166 6% 59-61, 64-67, 70-74, 115-118, 124-129, 132, 140-145, 152, 159-164, 197-200, 229-238, 270-277, 310-317, 321-329, 435-453, 498-682, 690-707, 765, 809-813, 818-820, 841-869, 945-964, 990-1098, 1253-1271, 1297-1406, 1500-1519, 1545-1672, 1742-1750, 1771-1894, 1901-1910, 1914, 1918-1945, 1949-2053, 2056-2059, 2132-2138, 2158-2171, 2234-2239, 2253-2315, 2352-2417, 2426-2430, 2550-2570, 2599-2712, 2716-2728, 2903-2985, 2993-2994, 2998, 3069-3166, 3170-3242, 3246 /usr/local/lib/python3.8/dist-packages/scipy/optimize/slsqp.py 190 178 6% 57-66, 181-212, 237-475, 483-531 /usr/local/lib/python3.8/dist-packages/scipy/optimize/tnc.py 102 76 25% 243-277, 342-413, 420-441 /usr/local/lib/python3.8/dist-packages/scipy/optimize/zeros.py 471 417 11% 54-62, 65-68, 73-81, 86-93, 266-365, 377-469, 546-554, 643-651, 773-781, 878-886, 896-899, 904-908, 919-927, 932-937, 949-972, 980-992, 1002, 1013-1037, 1048-1064, 1067-1077, 1081-1085, 1089, 1092, 1096-1123, 1127-1132, 1139-1213, 1218-1244, 1353-1375 /usr/local/lib/python3.8/dist-packages/scipy/signal/__init__.py 44 2 95% 330, 343 /usr/local/lib/python3.8/dist-packages/scipy/signal/_arraytools.py 49 41 16% 43-46, 54, 92-106, 143-155, 196-209, 237-243 /usr/local/lib/python3.8/dist-packages/scipy/signal/_max_len_seq.py 31 25 19% 104-137 /usr/local/lib/python3.8/dist-packages/scipy/signal/_peak_finding.py 225 201 11% 66-81, 138, 194, 248-250, 264-267, 281-293, 307-319, 459-462, 584-590, 625-640, 673-678, 713-723, 935-1006, 1055-1126, 1164-1190, 1283-1299 /usr/local/lib/python3.8/dist-packages/scipy/signal/_savitzky_golay.py 81 70 14% 98-141, 153-165, 179-209, 219-223, 328-353 /usr/local/lib/python3.8/dist-packages/scipy/signal/_upfirdn.py 40 30 25% 59-63, 67-69, 75-86, 90-100, 207-210 /usr/local/lib/python3.8/dist-packages/scipy/signal/bsplines.py 202 177 12% 19, 28-43, 60-114, 125-129, 148-149, 157-167, 175-185, 189-193, 197, 202-206, 210-237, 241-252, 256-267, 292-295, 319-322, 337-358, 373-394 /usr/local/lib/python3.8/dist-packages/scipy/signal/filter_design.py 1094 1014 7% 47-56, 94-117, 180-193, 255-272, 424-477, 562-583, 662-693, 698-706, 821-828, 881-932, 987-1000, 1064-1071, 1095-1130, 1168, 1193-1200, 1230-1240, 1245-1250, 1421-1515, 1539-1561, 1592-1632, 1691-1704, 1765-1787, 1851-1876, 1938-1965, 2017-2046, 2155-2175, 2294-2386, 2393-2398, 2457-2474, 2520-2534, 2583-2600, 2652-2679, 2731-2759, 2866, 2983, 3094, 3218, 3380, 3385, 3389, 3421-3445, 3525-3615, 3693-3753, 3833-3915, 3993-4054, 4067-4074, 4091-4112, 4129-4154, 4161-4163, 4167-4179, 4201-4263, 4285-4288, 4318-4332, 4340-4357, 4371-4396, 4404-4435, 4449-4463, 4540-4568, 4648, 4728, 4764-4803 /usr/local/lib/python3.8/dist-packages/scipy/signal/fir_filter_design.py 272 250 8% 26-32, 79-85, 127-128, 250-261, 389-482, 593-684, 836-855, 968-1068, 1082-1090, 1215-1265 /usr/local/lib/python3.8/dist-packages/scipy/signal/lti_conversion.py 160 142 11% 76-114, 118-121, 125-126, 130-133, 137-139, 143-148, 176-195, 256-284, 304, 334, 405-504 /usr/local/lib/python3.8/dist-packages/scipy/signal/ltisys.py 919 757 18% 54-58, 66-70, 75, 79-82, 87, 92, 103-106, 117-120, 131-134, 206-221, 229, 236, 243, 250, 274, 284, 295, 383-398, 406-409, 414, 418, 425, 432, 439, 468, 479, 561-578, 583-592, 596, 606, 610-617, 622, 626, 638-639, 651, 663, 676, 697-702, 722-727, 800, 940-958, 963-972, 976, 987, 991-998, 1003, 1007, 1012, 1016, 1028-1030, 1042, 1055, 1067, 1131, 1207-1208, 1304-1317, 1322-1333, 1337, 1347, 1365-1404, 1411-1421, 1428, 1434-1473, 1479-1482, 1485-1488, 1491-1494, 1501-1508, 1513, 1517, 1522, 1526-1527, 1532, 1536-1537, 1542, 1546, 1558-1561, 1578, 1596, 1609, 1677, 1799-1853, 1861-1867, 1926-2032, 2058-2064, 2114-2133, 2200-2222, 2277-2292, 2358-2373, 2432-2437, 2494-2522, 2529, 2540-2580, 2590-2600, 2613-2636, 2655-2710, 2720-2763, 2777-2886, 2896-2912, 3095-3263, 3322-3379, 3431-3465, 3516-3550, 3615-3648, 3712-3722 /usr/local/lib/python3.8/dist-packages/scipy/signal/signaltools.py 1148 1072 7% 27, 47-50, 55-58, 77-93, 197-259, 265-270, 305-331, 365-391, 419-428, 523-544, 572-655, 736-860, 876-881, 889-892, 904-935, 963-977, 984-985, 996-1002, 1019-1037, 1146-1174, 1267-1296, 1353-1359, 1394-1409, 1437-1460, 1537-1549, 1627-1644, 1681-1692, 1817-1885, 1928-1953, 1997-2009, 2098-2120, 2145-2180, 2208-2210, 2265-2297, 2356-2372, 2377-2397, 2401-2424, 2503-2539, 2598-2644, 2648-2675, 2733-2749, 2832-2924, 3054-3134, 3184-3212, 3254-3297, 3391-3433, 3489-3505, 3557-3684, 3844-3885, 3890-3920, 3924-3927, 4000-4039, 4130-4151, 4202-4245 /usr/local/lib/python3.8/dist-packages/scipy/signal/spectral.py 363 339 7% 142-158, 268-289, 452-457, 584-601, 734-771, 870-895, 996-1022, 1172-1178, 1348-1456, 1566-1576, 1669-1870, 1896-1920, 1959-1981, 2001-2002 /usr/local/lib/python3.8/dist-packages/scipy/signal/waveforms.py 120 107 11% 58-88, 139-162, 224-262, 427-430, 440-483, 577-580, 591-593, 669-681 /usr/local/lib/python3.8/dist-packages/scipy/signal/wavelets.py 136 123 10% 29-76, 90-92, 127-198, 253-261, 301-308, 384-388, 462-473 /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/windows.py 289 243 16% 21-23, 28-31, 36-39, 112-121, 168-174, 226-238, 290-302, 349-357, 442, 501, 548, 609-610, 700-708, 790, 795, 856-878, 925-933, 1020, 1098, 1207-1216, 1271-1279, 1342-1349, 1438-1476, 1545-1563, 1616-1622, 1698-1710, 1876-1970, 1975-1982, 2095-2124 /usr/local/lib/python3.8/dist-packages/scipy/sparse/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/_index.py 221 190 14% 24-27, 35-75, 78-126, 129-150, 157-180, 185-191, 196-202, 205, 208, 211, 214, 217, 220, 223, 226, 229, 232, 235, 238, 242-244, 252-283, 288-322, 326-328 /usr/local/lib/python3.8/dist-packages/scipy/sparse/_matrix_io.py 42 32 24% 16, 63-80, 131-156 /usr/local/lib/python3.8/dist-packages/scipy/sparse/base.py 455 345 24% 71-75, 81-82, 86, 123-131, 157, 182-189, 194-203, 207-208, 212, 223, 239, 250, 254, 257-258, 263-281, 284-287, 295, 313-328, 340, 344, 348, 363, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 407, 410-424, 427, 430-443, 446-455, 466-530, 534, 537, 540, 543, 546-554, 561-564, 567-570, 577-617, 620, 624, 628, 632, 635, 638, 641, 644, 647, 650, 653-675, 678-691, 718, 736-741, 744, 756, 759, 762, 780-782, 791-799, 808-816, 851, 883, 894, 902, 910, 918, 926, 937, 945, 953, 993-1025, 1064-1097, 1124, 1146-1149, 1152-1176, 1179-1189, 1219 /usr/local/lib/python3.8/dist-packages/scipy/sparse/bsr.py 315 266 16% 123-214, 225-271, 279, 283-287, 292-293, 299-308, 317, 320, 330, 336, 339, 342-351, 354-364, 367-421, 436-441, 444-463, 468, 479-505, 508, 513-534, 546-561, 568-594, 599-607, 613-627, 635-676, 684-688, 722 /usr/local/lib/python3.8/dist-packages/scipy/sparse/compressed.py 737 654 11% 31-108, 111-123, 130-136, 148-195, 212-215, 219-248, 252-279, 283-313, 316, 322, 328, 334, 344-352, 355, 358, 365-458, 465-475, 478-489, 492-528, 531-539, 548-568, 571, 577, 592-611, 630-635, 642-647, 650-653, 657-668, 672-673, 678-698, 704-730, 736-758, 764-775, 782-793, 797-798, 801-802, 806-823, 826-853, 856-872, 880-911, 918-931, 942-1003, 1010-1017, 1023-1037, 1050-1053, 1068-1074, 1077-1079, 1089-1098, 1110-1113, 1116, 1123-1125, 1135-1138, 1143-1153, 1156-1187, 1201-1207, 1212-1242, 1248-1269, 1273-1290 /usr/local/lib/python3.8/dist-packages/scipy/sparse/construct.py 232 206 11% 62, 138-188, 218, 252-273, 311-355, 384-398, 406-431, 465, 499, 545-623, 668-677, 750-793, 842 /usr/local/lib/python3.8/dist-packages/scipy/sparse/coo.py 294 251 15% 129-198, 201-236, 242-263, 271-291, 294-300, 306-317, 323-330, 352-372, 394-414, 417-420, 425-443, 448-454, 459-475, 480-513, 521-525, 533-537, 541-554, 561-564, 571-579, 583-586, 589-593, 619 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csc.py 87 58 33% 111-119, 125-126, 129-132, 137-155, 164-180, 188-194, 200-206, 209, 212-214, 217-219, 222, 225, 228, 235, 261 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_laplacian.py 51 44 14% 69-81, 85, 89-111, 115-128 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_validation.py 31 25 19% 17-58 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csr.py 135 103 24% 129-137, 143-156, 161-164, 169-186, 191-222, 231, 234-242, 248-256, 263-271, 275, 278-307, 311-313, 316, 319, 322-325, 351 /usr/local/lib/python3.8/dist-packages/scipy/sparse/data.py 184 138 25% 23, 26, 29, 33-35, 38, 41, 44, 47, 50-53, 56-60, 63-68, 71-79, 84-89, 94, 99, 113-119, 126, 135-136, 148-156, 166-187, 191-215, 218-252, 255-289, 321, 353, 376, 399 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dia.py 224 188 16% 79-146, 149-150, 158-164, 167-168, 171-181, 187-225, 230-241, 244, 247-276, 279-282, 287-305, 311-318, 323-343, 349-365, 374-377, 380-392, 420 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dok.py 275 213 23% 23, 79-111, 115, 122, 125-128, 133-136, 139, 145, 151-158, 161, 164, 167, 170-191, 194, 197, 200-201, 204-205, 209-216, 220-227, 230-234, 237-246, 249-277, 280-301, 304-309, 312-316, 320-323, 327-332, 335-338, 341-346, 349-352, 358, 365-374, 380-384, 387-389, 394-404, 409-411, 416, 421-429, 457 /usr/local/lib/python3.8/dist-packages/scipy/sparse/extract.py 22 14 36% 38-42, 101-103, 162-164, 168-171 /usr/local/lib/python3.8/dist-packages/scipy/sparse/lil.py 291 232 20% 89-132, 135-136, 139-140, 143-147, 150-154, 160-172, 175, 181-185, 190-193, 198-206, 210-216, 220-226, 229-231, 234-235, 238, 241, 244-245, 248, 251-252, 255-256, 260-261, 265-271, 289-300, 303, 307-308, 314-324, 328-336, 339-350, 353-360, 363-369, 374-401, 406-426, 431-435, 440, 445-448, 454-484, 512-527, 553 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_expm_multiply.py 255 225 12% 18-23, 28-33, 38-43, 48-55, 140-144, 172-197, 204-223, 313, 342-346, 352, 358-360, 366-369, 375, 399, 414-417, 455-475, 506-511, 556-629, 639-648, 655-677, 684-713 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_norm.py 70 63 10% 15-19, 110-184 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_onenormest.py 199 177 11% 86-119, 130-139, 154-157, 162, 166-174, 178-180, 187-190, 194-197, 204-211, 215, 219, 261-322, 366-468 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/_add_newdocs.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py 197 173 12% 56-59, 63-82, 132-233, 302-324, 386-410, 442-469, 528-607 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py 726 642 12% 280-281, 297-299, 307, 313-364, 367-377, 435-533, 536-573, 576-595, 636-719, 722-759, 762-896, 900-904, 914-917, 922-927, 937-939, 942, 949-951, 961-974, 977-982, 992-1020, 1023-1028, 1032, 1037-1044, 1048-1054, 1058-1089, 1251-1349, 1554-1689, 1694-1713, 1717, 1721, 1804-1910 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py 323 310 4% 34-44, 52-57, 64-72, 77-79, 84-114, 119-125, 287-711 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/interface.py 346 245 29% 140-152, 160-168, 173-175, 184, 196, 222-243, 269-290, 294-298, 324-339, 364-377, 381-384, 387, 390, 407-419, 423-426, 429-432, 435-438, 441-444, 447-450, 453, 456, 459-465, 481, 491, 497, 501, 509-518, 521-524, 527, 530-533, 536-539, 542, 553-556, 559, 562, 565, 568, 573-576, 580, 583, 587, 590, 593-598, 603-610, 613, 616, 619, 622, 625-626, 631-639, 642, 645, 648, 651, 654-655, 660-666, 669, 672, 675, 678, 681-682, 687-695, 698-701, 704, 707, 710, 713, 716-717, 722-725, 728, 731-733, 737-740, 744, 747, 752, 755, 758, 761, 764, 767, 795-823 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/_gcrotmk.py 192 182 5% 66-182, 267-490 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/iterative.py 421 386 8% 73-77, 97-118, 137-198, 209-265, 276-337, 347-414, 514-647, 717-802 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lgmres.py 69 59 14% 128-235 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsmr.py 185 177 4% 197-482 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsqr.py 200 192 4% 81-95, 311-570 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/minres.py 203 196 3% 71-343, 347-363 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/utils.py 56 46 18% 23-27, 31, 65-123 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/matfuncs.py 354 279 21% 77-82, 103-115, 120-130, 155-173, 179-188, 191-193, 196-200, 203-205, 209, 218-235, 238-240, 243-246, 249-251, 255-256, 296, 338, 367-383, 387-390, 394-397, 401-404, 408-411, 415-418, 422-424, 428-430, 434-436, 440-442, 446-454, 458-466, 470-478, 482-490, 493-498, 501-506, 509-514, 517-525, 528-547, 595, 603-677, 700-709, 736-740, 762-764, 783-813, 833-855 /usr/local/lib/python3.8/dist-packages/scipy/sparse/sputils.py 176 146 17% 42-53, 58-63, 70, 80-90, 94, 105-118, 143-171, 176-180, 185, 194-207, 215-225, 229, 235, 241, 245-264, 269-314, 326-331, 338-339, 346-349, 353-356, 360-363 /usr/local/lib/python3.8/dist-packages/scipy/spatial/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/_plotutils.py 78 65 17% 11-27, 31-35, 81-90, 136-148, 212-264 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_procrustes.py 25 20 20% 101-132 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_spherical_voronoi.py 68 57 16% 133-166, 171-206, 217-243, 273-277 /usr/local/lib/python3.8/dist-packages/scipy/spatial/distance.py 633 560 12% 132-165, 170-172, 176-178, 182-183, 187, 192-195, 200-220, 224-240, 244-259, 263-269, 273-286, 290-292, 296-310, 314-329, 334-339, 343-346, 350-357, 451-457, 508-524, 580-581, 620, 661-674, 708-721, 766, 813-820, 883-894, 941-950, 985-991, 1031-1037, 1079-1084, 1122-1130, 1170-1178, 1221-1236, 1286-1296, 1337-1342, 1353, 1397-1412, 1457-1462, 1507-1519, 1565-1578, 1624-1638, 1714-1733, 1991-2094, 2149-2212, 2254-2302, 2330-2359, 2378-2380, 2399-2409, 2708-2793 /usr/local/lib/python3.8/dist-packages/scipy/spatial/kdtree.py 419 379 10% 39-55, 78-83, 93-95, 98, 102, 120-126, 140, 154, 168, 182, 243-251, 256, 259, 262, 265, 268, 272-273, 277-281, 284-323, 329-407, 492-547, 550-572, 625-636, 663-705, 731-812, 842-889, 912-942, 978-996 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_groups.py 56 48 14% 6-58, 62-76, 80-90, 94-99, 103-105, 109-140 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_spline.py 176 159 10% 18-25, 30, 48-65, 83-104, 124-151, 168, 188, 220-248, 331-361, 364-404, 424-456 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/rotation.py 470 416 11% 15-17, 29-142, 146-150, 154-158, 162-173, 369-394, 407, 474-479, 568-612, 618, 674-707, 803-860, 907-910, 964-999, 1004, 1052-1073, 1159-1179, 1298-1329, 1397-1406, 1439-1443, 1467-1475, 1510-1527, 1560-1617, 1655, 1703, 1722-1727, 1768-1775, 1783-1838, 1915-1968, 2049-2070, 2090-2114 /usr/local/lib/python3.8/dist-packages/scipy/special/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/_basic.py 524 449 14% 100-112, 179-213, 250-254, 279-285, 305, 325, 345, 365, 397-401, 433-437, 469-473, 481-487, 515-519, 547-551, 602-606, 635-639, 667-671, 699-703, 747-755, 799-807, 888, 926-928, 941-943, 956-958, 971-973, 988, 1032-1034, 1072-1093, 1129-1150, 1201-1226, 1284-1307, 1343-1360, 1373-1380, 1424-1431, 1449-1460, 1476-1487, 1535-1538, 1586-1589, 1624-1639, 1666-1676, 1703-1713, 1740-1749, 1762-1764, 1777-1779, 1792-1794, 1800-1802, 1815-1817, 1830-1832, 1845-1847, 1860-1862, 1878-1880, 1904-1911, 1928-1935, 1984-1996, 2035-2050, 2065-2070, 2120-2158, 2195-2215, 2260-2270, 2333-2336 /usr/local/lib/python3.8/dist-packages/scipy/special/_ellip_harm.py 16 6 62% 97, 155-156, 160, 208-209 /usr/local/lib/python3.8/dist-packages/scipy/special/_logsumexp.py 34 28 18% 94-129, 215 /usr/local/lib/python3.8/dist-packages/scipy/special/_spherical_bessel.py 18 12 33% 53-56, 104-107, 153-156, 202-205 /usr/local/lib/python3.8/dist-packages/scipy/special/lambertw.py 4 1 75% 107 /usr/local/lib/python3.8/dist-packages/scipy/special/orthogonal.py 525 464 12% 128-151, 154-157, 160-173, 191-216, 264-290, 331-346, 394-404, 442-457, 503-524, 569-586, 630, 662-676, 744-760, 789-796, 825-831, 860-871, 898-910, 947-1020, 1050-1067, 1113-1124, 1157-1172, 1229-1248, 1282-1297, 1345-1364, 1400-1408, 1455-1463, 1499-1512, 1557-1566, 1603-1608, 1652-1659, 1696-1714, 1758-1765, 1802-1821, 1865-1866, 1894-1902, 1945-1952, 1980-1985, 2029-2039, 2080-2094, 2137-2143, 2170-2182 /usr/local/lib/python3.8/dist-packages/scipy/special/sf_error.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/spfun_stats.py 14 8 43% 86-95 /usr/local/lib/python3.8/dist-packages/scipy/stats/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_binned_statistic.py 159 145 9% 167-182, 336-349, 514-634, 640-675, 681-706 /usr/local/lib/python3.8/dist-packages/scipy/stats/_constants.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_continuous_distns.py 2994 1970 34% 35-36, 50-54, 97, 100, 103, 106, 109, 152, 155, 158, 161, 164, 180, 184, 188, 192, 196, 200, 204, 208, 235, 239, 242, 245, 248, 251, 254, 257, 260, 263, 266, 273-300, 342, 345, 348, 351, 354, 382, 385, 388, 391, 394, 422, 425, 428, 431-435, 438, 449, 461-463, 470-472, 482-486, 514, 520, 523-525, 528, 531, 534-539, 542-553, 563-658, 690-693, 697, 700, 703, 706-724, 754, 757, 760, 763-775, 778-779, 834-840, 843-852, 855, 858, 861, 864, 867, 870-887, 890-894, 948, 951, 954, 957, 960, 963, 969, 972-973, 1011, 1014, 1017, 1021, 1024, 1027, 1030, 1033, 1036, 1039, 1068, 1071, 1074, 1077, 1080, 1083, 1086, 1090-1091, 1130-1131, 1137, 1140-1141, 1144, 1147, 1150-1155, 1187, 1191, 1194, 1197, 1200, 1203, 1206, 1209-1213, 1242, 1245, 1248, 1251, 1281-1284, 1288-1289, 1292-1293, 1296-1297, 1300-1301, 1304-1305, 1308-1309, 1338-1341, 1345-1347, 1350-1351, 1354-1355, 1358-1360, 1363, 1369, 1401, 1405, 1408, 1411, 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976-977, 981-985, 988-992, 995-999, 1040-1043, 1046, 1049, 1052, 1055, 1058, 1061, 1064-1077 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distn_infrastructure.py 1393 1036 26% 42, 361-363, 367-395, 402-406, 413-417, 424-432, 436, 440, 443, 446, 449, 452, 455, 458, 461-463, 466, 469, 472-474, 477, 480, 483, 486, 489, 492, 495, 498, 501, 508-512, 516, 541-545, 567-571, 575, 579, 607, 611, 614, 617-620, 640-649, 665, 668, 671, 679, 733-734, 742-751, 768, 771, 778, 785-788, 801-859, 871-874, 895, 898-899, 902-903, 912-914, 917-918, 921, 924-925, 928, 931, 961-995, 1024-1110, 1135-1146, 1165-1192, 1219, 1241-1245, 1267-1271, 1293-1295, 1321-1328, 1349-1351, 1362-1368, 1612, 1621, 1629, 1643-1651, 1654, 1657-1678, 1683, 1686-1687, 1692, 1695, 1698, 1702, 1705-1706, 1709, 1736-1752, 1778-1795, 1819-1837, 1861-1880, 1904-1922, 1949-1968, 1992-2014, 2038-2060, 2063, 2066-2072, 2082-2089, 2092-2102, 2109-2114, 2118-2121, 2132-2176, 2276-2314, 2336-2380, 2402-2412, 2415-2438, 2516-2540, 2546-2550, 2554-2609, 2663-2676, 2822, 2851, 2878, 2885, 2893, 2912-2921, 2924, 2927, 2930, 2933-2935, 2938-2939, 2968-2969, 2991-3006, 3028-3044, 3066-3084, 3106-3125, 3147-3164, 3189-3207, 3229-3248, 3270-3294, 3297-3301, 3356-3386, 3394-3428, 3449-3463, 3475-3517, 3532, 3535, 3539-3541, 3544-3546, 3551-3557, 3560, 3563-3564, 3593-3602 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distr_params.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_hypotests.py 43 36 16% 80-132 /usr/local/lib/python3.8/dist-packages/scipy/stats/_multivariate.py 972 690 29% 50-53, 81-88, 108, 156-173, 177-179, 203, 207, 210-213, 223, 227, 361, 373-426, 434-444, 469-471, 493-497, 519-523, 550-555, 587-594, 626-633, 657-661, 681-683, 733-741, 744-747, 750, 753, 756-759, 762, 774-776, 938, 948-1005, 1013-1019, 1050-1055, 1078-1085, 1107, 1131-1142, 1175-1179, 1182-1186, 1189, 1192, 1227-1233, 1237-1277, 1299, 1412, 1429-1430, 1448-1452, 1470-1474, 1490-1493, 1510-1514, 1531-1539, 1559-1561, 1569-1570, 1573, 1576, 1579, 1582, 1585, 1588, 1739, 1742-1768, 1775-1805, 1808-1819, 1851-1864, 1887-1894, 1917, 1933, 1948-1950, 1966-1970, 1988-1990, 2006-2010, 2025-2027, 2051-2072, 2098-2118, 2142-2150, 2169, 2197-2199, 2223-2225, 2250-2253, 2256-2260, 2263, 2266-2267, 2270-2271, 2274-2275, 2278-2281, 2284, 2325-2357, 2471, 2495-2515, 2538-2542, 2566, 2582-2586, 2605-2607, 2623, 2639-2641, 2657-2664, 2682-2684, 2708-2733, 2757-2769, 2773, 2798-2812, 2815-2818, 2821, 2824-2825, 2828-2829, 2832-2833, 2836-2841, 2845, 3002, 3011-3023, 3032-3047, 3050-3058, 3061, 3082-3094, 3115, 3130-3132, 3147-3156, 3182-3196, 3218-3220, 3244-3251, 3254, 3257, 3260, 3263, 3266, 3269, 3348, 3355-3359, 3378-3401, 3430-3431, 3434, 3495-3499, 3518-3538, 3601-3615, 3631-3648, 3657-3689, 3718-3730, 3787-3791, 3810-3824 /usr/local/lib/python3.8/dist-packages/scipy/stats/_rvs_sampling.py 33 28 15% 123-169 /usr/local/lib/python3.8/dist-packages/scipy/stats/_stats_mstats_common.py 107 98 8% 93-144, 232-266, 271-285, 374-404 /usr/local/lib/python3.8/dist-packages/scipy/stats/_tukeylambda_stats.py 54 40 26% 68-102, 164-201 /usr/local/lib/python3.8/dist-packages/scipy/stats/contingency.py 36 28 22% 57-62, 99-109, 243-275 /usr/local/lib/python3.8/dist-packages/scipy/stats/distributions.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/kde.py 183 151 17% 194-209, 231-265, 293-320, 344-355, 375-388, 411-438, 465-475, 485, 495, 548-565, 571-581, 593, 600-632, 636-640, 644-648 /usr/local/lib/python3.8/dist-packages/scipy/stats/morestats.py 829 760 8% 127-136, 194-211, 278-306, 349-358, 416-421, 440-452, 457-471, 585-627, 705-718, 797-812, 894-910, 916-945, 1034-1059, 1129-1165, 1173-1202, 1270, 1345-1363, 1370-1385, 1470-1481, 1533-1536, 1606, 1661-1678, 1771-1825, 1853-1869, 1897-1906, 2014-2070, 2113-2166, 2225-2247, 2307-2371, 2421-2465, 2472-2475, 2548-2600, 2670-2725, 2847-2938, 3080-3162, 3167-3186, 3225-3255, 3295-3307, 3349-3361 /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats_basic.py 947 845 11% 61-67, 71-79, 83-89, 116-128, 151-160, 196-211, 235-259, 299-324, 328-330, 335, 373-399, 459-528, 569-663, 676-727, 759-782, 798-828, 870-884, 924-938, 942-949, 982-996, 1037-1066, 1096-1114, 1144-1165, 1214-1229, 1258-1282, 1322-1343, 1372-1410, 1465-1468, 1500, 1535-1543, 1556-1561, 1579-1586, 1604-1610, 1646-1690, 1718-1736, 1786, 1825-1831, 1876-1878, 1927-1929, 1967-1974, 2030-2071, 2101-2135, 2176-2177, 2205-2222, 2262-2283, 2343-2351, 2367-2381, 2411-2430, 2460-2490, 2521-2526, 2628-2654, 2664-2668, 2714-2720, 2736-2749, 2798-2801, 2824-2837, 2867-2888, 2931-2977 /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats_extras.py 159 141 11% 62-103, 128-129, 156-190, 235-241, 260-286, 316-320, 346-371, 400-404, 429-444, 463-477 /usr/local/lib/python3.8/dist-packages/scipy/stats/stats.py 1635 1486 9% 172, 217-227, 231-245, 249-274, 328-339, 391-406, 459-503, 531-549, 594-599, 648-654, 706-717, 768-779, 828, 877-883, 950-970, 974-1021, 1064-1072, 1149-1175, 1259-1288, 1357-1374, 1437-1463, 1520-1560, 1625-1637, 1681-1693, 1738-1740, 1805-1820, 1825-1864, 1934-1954, 2008-2036, 2116-2118, 2195-2199, 2258-2281, 2336-2346, 2408-2419, 2465-2468, 2567-2594, 2727-2764, 2858-2882, 2893-2942, 2961-2986, 3051-3062, 3105-3124, 3156-3177, 3228-3247, 3323-3353, 3360-3363, 3370-3373, 3496-3557, 3624-3719, 3821-3877, 3967-3968, 4059-4169, 4307-4347, 4357-4360, 4363-4369, 4412-4430, 4434, 4640-4713, 4743-4773, 4792-4817, 4842-4869, 4891-4895, 4966-4985, 4990-4994, 4999-5004, 5008-5017, 5021-5024, 5131-5138, 5237-5266, 5333-5367, 5487-5528, 5549-5560, 5721-5763, 5877, 5916-5962, 5989-5998, 6047-6084, 6183-6282, 6323-6328, 6387-6429, 6467-6478, 6550-6601, 6644-6670, 6754-6815, 6890-6927, 7008, 7090, 7138-7177, 7202-7221, 7264, 7290-7291, 7316-7321, 7377-7407 /usr/local/lib/python3.8/dist-packages/scipy/version.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/skimage/__init__.py 28 9 68% 93-100, 114, 121-122 /usr/local/lib/python3.8/dist-packages/skimage/_shared/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/skimage/_shared/_warnings.py 54 42 22% 46-70, 109-145 /usr/local/lib/python3.8/dist-packages/skimage/_shared/utils.py 123 96 22% 42-45, 48-68, 90-99, 102-115, 135-137, 141-172, 179, 231-246, 251-253, 270-278, 298-304, 330-337, 361-375 /usr/local/lib/python3.8/dist-packages/skimage/_shared/version_requirements.py 60 51 15% 6, 10-12, 48-62, 67-69, 95-117, 141-155, 177-179 /usr/local/lib/python3.8/dist-packages/skimage/data/__init__.py 114 53 54% 98, 143-164, 196-204, 219, 241-244, 264-265, 278, 300, 366, 414, 426-429, 484, 503, 518, 549, 574, 585, 613, 627, 641, 657, 674, 694, 712, 731, 755, 782, 799, 810, 833, 890-892, 926 /usr/local/lib/python3.8/dist-packages/skimage/data/_binary_blobs.py 12 10 17% 47-57 /usr/local/lib/python3.8/dist-packages/skimage/data/_registry.py 4 0 100% /usr/local/lib/python3.8/dist-packages/skimage/measure/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/skimage/measure/_find_contours.py 60 11 82% 118, 121, 124, 126, 128-133, 139, 155 /usr/local/lib/python3.8/dist-packages/skimage/measure/_label.py 3 1 67% 93 /usr/local/lib/python3.8/dist-packages/skimage/measure/_marching_cubes_classic.py 52 44 15% 103-109, 118-152, 188-194, 257-301 /usr/local/lib/python3.8/dist-packages/skimage/measure/_marching_cubes_lewiner.py 70 56 20% 126-143, 260-265, 277-338, 342-346, 363-388 /usr/local/lib/python3.8/dist-packages/skimage/measure/_marching_cubes_lewiner_luts.py 48 0 100% /usr/local/lib/python3.8/dist-packages/skimage/measure/_moments.py 73 60 18% 45, 111-146, 191, 241-250, 296-305, 348, 372-376, 406-428, 461-469 /usr/local/lib/python3.8/dist-packages/skimage/measure/_polygon.py 61 21 66% 32, 134-168 /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/skimage/measure/_regionprops.py 304 167 45% 118-124, 132-135, 147-161, 166, 176, 181, 185, 190, 195-196, 200-201, 207-210, 214-217, 221-224, 228, 232, 237-238, 243, 248-249, 254, 260-262, 265, 269-270, 275, 279, 283, 287-288, 292-293, 298-299, 304-306, 311, 316, 321-328, 333, 337, 341-342, 347-348, 354, 359-360, 366, 371, 374-390, 393-397, 400-411, 499-525, 624-640, 852-896, 935-962 /usr/local/lib/python3.8/dist-packages/skimage/measure/_structural_similarity.py 8 2 75% 10-13 /usr/local/lib/python3.8/dist-packages/skimage/measure/block.py 18 15 17% 62-86 /usr/local/lib/python3.8/dist-packages/skimage/measure/entropy.py 5 2 60% 39-40 /usr/local/lib/python3.8/dist-packages/skimage/measure/fit.py 230 204 11% 9-10, 14-15, 20, 26, 81-100, 120-131, 156-171, 193-194, 216-217, 270-294, 313-320, 339-346, 414-482, 501-544, 564-577, 598-617, 785-876 /usr/local/lib/python3.8/dist-packages/skimage/measure/pnpoly.py 5 2 60% 29, 53 /usr/local/lib/python3.8/dist-packages/skimage/measure/profile.py 35 29 17% 95-127, 154-174 /usr/local/lib/python3.8/dist-packages/skimage/measure/simple_metrics.py 18 6 67% 13-16, 37-40, 61-64 /usr/local/lib/python3.8/dist-packages/skimage/metrics/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/skimage/metrics/_adapted_rand_error.py 19 15 21% 49-76 /usr/local/lib/python3.8/dist-packages/skimage/metrics/_contingency_table.py 15 11 27% 30-40 /usr/local/lib/python3.8/dist-packages/skimage/metrics/_structural_similarity.py 100 92 8% 89-232 /usr/local/lib/python3.8/dist-packages/skimage/metrics/_variation_of_information.py 33 24 27% 43-46, 64-71, 92-117, 133-136 /usr/local/lib/python3.8/dist-packages/skimage/metrics/simple_metrics.py 40 32 20% 15-18, 42-44, 92-105, 139-160 /usr/local/lib/python3.8/dist-packages/skimage/util/__init__.py 19 2 89% 22-25 /usr/local/lib/python3.8/dist-packages/skimage/util/_invert.py 14 11 21% 62-74 /usr/local/lib/python3.8/dist-packages/skimage/util/_map_array.py 70 56 20% 26-58, 107-110, 114, 122-126, 130, 133, 136-153, 156, 159-180, 183-187 /usr/local/lib/python3.8/dist-packages/skimage/util/_montage.py 32 29 9% 90-142 /usr/local/lib/python3.8/dist-packages/skimage/util/_regular_grid.py 27 24 11% 61-83, 112-116 /usr/local/lib/python3.8/dist-packages/skimage/util/apply_parallel.py 50 46 8% 23-44, 48-52, 109-147 /usr/local/lib/python3.8/dist-packages/skimage/util/arraycrop.py 17 14 18% 45-63 /usr/local/lib/python3.8/dist-packages/skimage/util/compare.py 25 21 16% 36-60 /usr/local/lib/python3.8/dist-packages/skimage/util/dtype.py 142 122 14% 51-54, 77, 98-101, 126-173, 224-349, 375, 401, 430, 454, 479, 503, 527 /usr/local/lib/python3.8/dist-packages/skimage/util/lookfor.py 4 1 75% 24 /usr/local/lib/python3.8/dist-packages/skimage/util/noise.py 54 50 7% 87-192 /usr/local/lib/python3.8/dist-packages/skimage/util/shape.py 48 41 15% 74-95, 209-248 /usr/local/lib/python3.8/dist-packages/skimage/util/unique.py 9 7 22% 39-50 /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 215243 164303 24% 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 : 0 101.77user 44.49system 8:08.84elapsed 29%CPU (0avgtext+0avgdata 6000272maxresident)k 5132096inputs+68264outputs (7782major+5318642minor)pagefaults 0swaps