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_4/data_log/job/2026/January/19012026/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_4/data_log/job/2026/January/19012026/coverage/htmlcov new path : /data_4/data_log/job/2026/January/19012026/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 : 10826 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 : 0.16336822509765625 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 Mon Jan 19 11:20:28 2026 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 : 10826 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 2026-01-19 11:20:31.857084: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-19 11:20:31.882502: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-19 11:20:31.883907: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f950c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-19 11:20:31.883940: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-19 11:20:31.886910: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-19 11:20:32.127637: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x6bfed50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-19 11:20:32.127686: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-19 11:20:32.129558: 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 2026-01-19 11:20:32.130205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:20:32.139034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:20:32.149166: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:20:32.151521: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:20:32.171842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:20:32.176466: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:20:32.217058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:20:32.218861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:20:32.219174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:20:32.220375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-19 11:20:32.220396: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-19 11:20:32.220406: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-19 11:20:32.222529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10034 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 2026-01-19 11:20:33.267769: 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 2026-01-19 11:20:33.267846: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:20:33.267867: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:20:33.267886: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:20:33.267904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:20:33.267922: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:20:33.267939: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:20:33.267957: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:20:33.269677: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:20:33.271070: 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 2026-01-19 11:20:33.271107: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:20:33.271129: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:20:33.271148: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:20:33.271167: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:20:33.271186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:20:33.271204: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:20:33.271223: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:20:33.272935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:20:33.272966: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-19 11:20:33.272978: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-19 11:20:33.272988: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-19 11:20:33.274765: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10034 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 : [] 2026-01-19 11:20:42.705612: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:20:42.894142: 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 356437 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5537 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 : 10826 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'] DEBUG bbox = [22, 0, 282, 186] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 0.0005421638488769531 nb_pixel_total : 15556 time to create 1 rle with old method : 0.0337977409362793 length of segment : 256 DEBUG bbox = [24, 29, 419, 591] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 0.0026726722717285156 nb_pixel_total : 145337 time to create 1 rle with old method : 0.30382704734802246 length of segment : 371 DEBUG bbox = [23, 485, 174, 636] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 0.0002186298370361328 nb_pixel_total : 14256 time to create 1 rle with old method : 0.03087902069091797 length of segment : 151 DEBUG bbox = [2, 280, 55, 481] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 0.00013756752014160156 nb_pixel_total : 5613 time to create 1 rle with old method : 0.013358354568481445 length of segment : 48 DEBUG bbox = [6, 456, 45, 547] DEBUG masks shape = (480, 640) time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 1826 time to create 1 rle with old method : 0.004377126693725586 length of segment : 39 time spent for convertir_results : 1.3162295818328857 time spend for datou_step_exec : 22.26561164855957 time spend to save output : 4.57763671875e-05 total time spend for step 1 : 22.265657424926758 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 3424 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 : 0.022684097290039062 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, 0.99552107, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (126, 30, 28), (10, 31, 1), (120, 31, 35), (8, 32, 13), (26, 32, 4), (115, 32, 41), (7, 33, 53), (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, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 42), (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)], ['16,276,8,273,3,265,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,29,32,30,33,59,33,60,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,150,45,158,40,166,34,172,29,188,26,193,25,200,25,219,24,220,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923798, [(313, 37, 28), (271, 38, 88), (252, 39, 131), (237, 40, 152), (199, 41, 196), (189, 42, 213), (180, 43, 239), (175, 44, 250), (172, 45, 258), (169, 46, 266), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 311), (153, 52, 317), (151, 53, 323), (150, 54, 329), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), (89, 111, 449), (89, 112, 450), (89, 113, 452), (89, 114, 453), (89, 115, 454), (89, 116, 455), (88, 117, 456), (88, 118, 457), (87, 119, 459), (87, 120, 459), (86, 121, 461), (86, 122, 461), (85, 123, 463), (84, 124, 464), (84, 125, 465), (83, 126, 466), (82, 127, 468), (82, 128, 468), (81, 129, 470), (80, 130, 471), (78, 131, 473), (77, 132, 475), (75, 133, 477), (73, 134, 480), (72, 135, 481), (70, 136, 484), (68, 137, 486), (67, 138, 488), (65, 139, 490), (64, 140, 492), (63, 141, 493), (61, 142, 496), (60, 143, 497), (59, 144, 499), (59, 145, 500), (58, 146, 501), (58, 147, 502), (57, 148, 504), (57, 149, 505), (56, 150, 507), (56, 151, 507), (55, 152, 509), (55, 153, 510), (54, 154, 511), (54, 155, 512), (54, 156, 513), (53, 157, 514), (53, 158, 514), (52, 159, 516), (52, 160, 516), (52, 161, 516), (51, 162, 517), (51, 163, 517), (50, 164, 518), (50, 165, 518), (49, 166, 519), (49, 167, 520), (48, 168, 521), (48, 169, 521), (47, 170, 522), (47, 171, 522), (46, 172, 523), (46, 173, 523), (46, 174, 523), (45, 175, 524), (45, 176, 523), (44, 177, 524), (44, 178, 524), (44, 179, 524), (43, 180, 525), (43, 181, 525), (42, 182, 525), (42, 183, 525), (42, 184, 525), (41, 185, 526), (41, 186, 526), (40, 187, 526), (39, 188, 526), (39, 189, 525), (38, 190, 526), (38, 191, 525), (37, 192, 525), (37, 193, 523), (36, 194, 523), (36, 195, 523), (36, 196, 522), (35, 197, 522), (35, 198, 521), (34, 199, 521), (34, 200, 521), (34, 201, 520), (34, 202, 520), (34, 203, 520), (34, 204, 519), (34, 205, 519), (33, 206, 520), (33, 207, 519), (33, 208, 519), (33, 209, 519), (33, 210, 518), (33, 211, 518), (33, 212, 518), (33, 213, 517), (32, 214, 518), (32, 215, 517), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 512), (32, 222, 511), (32, 223, 510), (32, 224, 508), (32, 225, 507), (32, 226, 505), (32, 227, 504), (32, 228, 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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)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,471,28,468,25,468,24,465,21,461,20,457,16,457,10,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1768818027_356254_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10826 ############################### 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 : 0.22997665405273438 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 Mon Jan 19 11:20:53 2026 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 : 10826 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-01-19 11:20:56.854897: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-19 11:20:56.882500: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-19 11:20:56.884107: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9510000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-19 11:20:56.884171: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-19 11:20:56.886640: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-19 11:20:57.101416: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7c9e0c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-19 11:20:57.101449: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-19 11:20:57.102349: 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 2026-01-19 11:20:57.102661: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:20:57.104679: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:20:57.106704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:20:57.107035: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:20:57.109182: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:20:57.110128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:20:57.114327: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:20:57.115872: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:20:57.115958: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:20:57.116692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-19 11:20:57.116708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-19 11:20:57.116716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-19 11:20:57.117996: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10034 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. 2026-01-19 11:20:57.225681: 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 2026-01-19 11:20:57.225807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:20:57.225826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:20:57.225842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:20:57.225858: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:20:57.225874: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:20:57.225904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:20:57.225920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:20:57.227150: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:20:57.228277: 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 2026-01-19 11:20:57.228311: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:20:57.228329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:20:57.228346: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:20:57.228363: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:20:57.228379: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:20:57.228395: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:20:57.228412: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:20:57.229571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:20:57.229605: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-19 11:20:57.229613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-19 11:20:57.229620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-19 11:20:57.230844: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10034 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 : [] 2026-01-19 11:21:05.850230: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:21:06.041468: 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 356815 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5537 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 : 10826 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'] DEBUG bbox = [0, 1092, 108, 1280] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.0006589889526367188 nb_pixel_total : 16901 time to create 1 rle with old method : 0.039778947830200195 length of segment : 107 DEBUG bbox = [16, 52, 668, 1128] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.018190860748291016 nb_pixel_total : 480734 time to create 1 rle with new method : 0.03126168251037598 length of segment : 632 DEBUG bbox = [0, 0, 116, 438] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.0006003379821777344 nb_pixel_total : 36579 time to create 1 rle with old method : 0.08008766174316406 length of segment : 132 DEBUG bbox = [0, 390, 54, 550] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.00011563301086425781 nb_pixel_total : 4793 time to create 1 rle with old method : 0.010526657104492188 length of segment : 51 time spent for convertir_results : 0.4316258430480957 time spend for datou_step_exec : 18.361024856567383 time spend to save output : 4.315376281738281e-05 total time spend for step 1 : 18.3610680103302 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 447 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 : 0.01673293113708496 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, 0.99883777, [(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, 11), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1274,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.99774206, [(711, 22, 21), (926, 22, 46), (608, 23, 146), (894, 23, 103), (598, 24, 233), (851, 24, 156), (590, 25, 427), (582, 26, 444), (575, 27, 458), 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(917855882, 492601069, 445, 390, 550, 0, 54, 0.939261, [(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (420, 25, 99), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1768818053_356254_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 : 0.21100115776062012 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 Mon Jan 19 11:21:14 2026 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 : 10826 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-01-19 11:21:17.303800: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-19 11:21:17.330518: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-19 11:21:17.332810: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9518000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-19 11:21:17.332839: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-19 11:21:17.337327: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-19 11:21:17.558649: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x77caae0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-19 11:21:17.558700: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-19 11:21:17.560184: 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 2026-01-19 11:21:17.560605: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:21:17.563912: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:21:17.567039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:21:17.567577: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:21:17.573426: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:21:17.574515: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:21:17.579073: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:21:17.580892: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:21:17.580987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:21:17.582039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-19 11:21:17.582059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-19 11:21:17.582072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-19 11:21:17.583816: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10034 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. 2026-01-19 11:21:17.698605: 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 2026-01-19 11:21:17.698739: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:21:17.698765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:21:17.698788: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:21:17.698810: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:21:17.698833: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:21:17.698854: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:21:17.698896: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:21:17.700555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:21:17.702058: 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 2026-01-19 11:21:17.702100: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:21:17.702122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:21:17.702144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:21:17.702165: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:21:17.702186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:21:17.702207: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:21:17.702228: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:21:17.703933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:21:17.703975: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-19 11:21:17.703987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-19 11:21:17.703997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-19 11:21:17.705749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10034 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 : [] 2026-01-19 11:21:27.808623: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:21:27.983836: 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 357300 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5537 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 : 10826 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'] DEBUG bbox = [118, 7, 2241, 2268] DEBUG masks shape = (2448, 2448) time for calcul the mask position with numpy : 0.31241869926452637 nb_pixel_total : 3693284 time to create 1 rle with new method : 0.34029674530029297 length of segment : 2041 time spent for convertir_results : 1.8658010959625244 time spend for datou_step_exec : 21.592894792556763 time spend to save output : 4.9591064453125e-05 total time spend for step 1 : 21.592944383621216 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 725 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.020726442337036133 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, 0.9850759, [(676, 120, 109), (520, 121, 480), (1053, 121, 378), (503, 122, 946), (486, 123, 981), (471, 124, 1013), (456, 125, 1045), (442, 126, 1091), (429, 127, 1136), (417, 128, 1168), (406, 129, 1186), (394, 130, 1205), (384, 131, 1221), (373, 132, 1239), (369, 133, 1249), (366, 134, 1258), (364, 135, 1265), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1308), (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, 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(860, 2121, 278), (863, 2122, 273), (866, 2123, 269), (869, 2124, 264), (872, 2125, 259), (874, 2126, 256), (877, 2127, 251), (880, 2128, 246), (883, 2129, 242), (886, 2130, 237), (889, 2131, 232), (892, 2132, 227), (896, 2133, 221), (899, 2134, 215), (902, 2135, 210), (906, 2136, 204), (909, 2137, 199), (913, 2138, 193), (916, 2139, 187), (920, 2140, 181), (924, 2141, 174), (928, 2142, 166), (932, 2143, 154), (936, 2144, 142), (945, 2145, 125), (956, 2146, 106), (966, 2147, 88), (977, 2148, 69), (989, 2149, 48), (1000, 2150, 28), (1012, 2151, 7)], ['936,2144,668,2065,610,2037,287,1973,214,1963,127,1970,54,1825,53,1749,39,1677,39,1453,29,1236,27,757,21,695,27,543,39,458,93,308,116,278,210,206,291,179,373,132,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2012,294,2094,411,2148,535,2168,613,2171,717,2165,833,2128,914,2091,1049,2032,1130,2009,1191,1950,1295,1931,1368,1876,1456,1846,1670,1788,1847,1760,1913,1719,1973,1662,2015,1581,2015,1501,2038,1419,2046,1177,2101,1093,2142,1019,2150'])], 'temp/1768818073_356254_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3689163 proportion of common points : 0.9991517470841306 #&_# 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 : 0.21734952926635742 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 Mon Jan 19 11:21:42 2026 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 : 0.001901865005493164 nb_pixel_total : 5629 time to create 1 rle with old method : 0.013212919235229492 time for calcul the mask position with numpy : 0.0015556812286376953 nb_pixel_total : 4277 time to create 1 rle with old method : 0.010323286056518555 time for calcul the mask position with numpy : 0.0015833377838134766 nb_pixel_total : 4211 time to create 1 rle with old method : 0.012343883514404297 time for calcul the mask position with numpy : 0.002664804458618164 nb_pixel_total : 3754 time to create 1 rle with old method : 0.012964010238647461 time for calcul the mask position with numpy : 0.0014142990112304688 nb_pixel_total : 16473 time to create 1 rle with old method : 0.037331581115722656 time for calcul the mask position with numpy : 0.0013353824615478516 nb_pixel_total : 2940 time to create 1 rle with old method : 0.006781816482543945 time for calcul the mask position with numpy : 0.0014081001281738281 nb_pixel_total : 6635 time to create 1 rle with old method : 0.015817642211914062 time for calcul the mask position with numpy : 0.001628875732421875 nb_pixel_total : 3924 time to create 1 rle with old method : 0.009327411651611328 time for calcul the mask position with numpy : 0.002032041549682617 nb_pixel_total : 83947 time to create 1 rle with old method : 0.1849226951599121 time for calcul the mask position with numpy : 0.001369476318359375 nb_pixel_total : 13934 time to create 1 rle with old method : 0.030675888061523438 time for calcul the mask position with numpy : 0.0013241767883300781 nb_pixel_total : 7679 time to create 1 rle with old method : 0.016558170318603516 time for calcul the mask position with numpy : 0.0013802051544189453 nb_pixel_total : 5313 time to create 1 rle with old method : 0.011872291564941406 time for calcul the mask position with numpy : 0.0013589859008789062 nb_pixel_total : 14604 time to create 1 rle with old method : 0.03186821937561035 time for calcul the mask position with numpy : 0.001283884048461914 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0027303695678710938 time for calcul the mask position with numpy : 0.0014281272888183594 nb_pixel_total : 343 time to create 1 rle with old method : 0.0015904903411865234 time for calcul the mask position with numpy : 0.0014460086822509766 nb_pixel_total : 10825 time to create 1 rle with old method : 0.022632598876953125 time for calcul the mask position with numpy : 0.001306295394897461 nb_pixel_total : 5762 time to create 1 rle with old method : 0.012091875076293945 time for calcul the mask position with numpy : 0.0012879371643066406 nb_pixel_total : 3324 time to create 1 rle with old method : 0.007025957107543945 time for calcul the mask position with numpy : 0.0014789104461669922 nb_pixel_total : 29466 time to create 1 rle with old method : 0.06256771087646484 time for calcul the mask position with numpy : 0.0013432502746582031 nb_pixel_total : 9505 time to create 1 rle with old method : 0.020168542861938477 time for calcul the mask position with numpy : 0.0013072490692138672 nb_pixel_total : 2453 time to create 1 rle with old method : 0.005304098129272461 time for calcul the mask position with numpy : 0.0013637542724609375 nb_pixel_total : 16338 time to create 1 rle with old method : 0.033916473388671875 time for calcul the mask position with numpy : 0.001283884048461914 nb_pixel_total : 832 time to create 1 rle with old method : 0.0018129348754882812 time for calcul the mask position with numpy : 0.001476287841796875 nb_pixel_total : 38957 time to create 1 rle with old method : 0.08127331733703613 time for calcul the mask position with numpy : 0.0012814998626708984 nb_pixel_total : 2081 time to create 1 rle with old method : 0.00432896614074707 time for calcul the mask position with numpy : 0.0012960433959960938 nb_pixel_total : 3164 time to create 1 rle with old method : 0.006780147552490234 time for calcul the mask position with numpy : 0.0012917518615722656 nb_pixel_total : 3903 time to create 1 rle with old method : 0.008317708969116211 time for calcul the mask position with numpy : 0.0012669563293457031 nb_pixel_total : 333 time to create 1 rle with old method : 0.0007436275482177734 time for calcul the mask position with numpy : 0.0013043880462646484 nb_pixel_total : 5506 time to create 1 rle with old method : 0.011517524719238281 time for calcul the mask position with numpy : 0.0012845993041992188 nb_pixel_total : 1485 time to create 1 rle with old method : 0.0032196044921875 time for calcul the mask position with numpy : 0.0012822151184082031 nb_pixel_total : 3560 time to create 1 rle with old method : 0.007732868194580078 time for calcul the mask position with numpy : 0.0013363361358642578 nb_pixel_total : 12994 time to create 1 rle with old method : 0.028254032135009766 time for calcul the mask position with numpy : 0.0012891292572021484 nb_pixel_total : 1418 time to create 1 rle with old method : 0.0031118392944335938 time for calcul the mask position with numpy : 0.0012938976287841797 nb_pixel_total : 1648 time to create 1 rle with old method : 0.003564596176147461 time for calcul the mask position with numpy : 0.0013091564178466797 nb_pixel_total : 8642 time to create 1 rle with old method : 0.01836395263671875 time for calcul the mask position with numpy : 0.001287698745727539 nb_pixel_total : 974 time to create 1 rle with old method : 0.002188444137573242 time for calcul the mask position with numpy : 0.0013337135314941406 nb_pixel_total : 9820 time to create 1 rle with old method : 0.02170729637145996 time for calcul the mask position with numpy : 0.00154876708984375 nb_pixel_total : 27944 time to create 1 rle with old method : 0.061071157455444336 time for calcul the mask position with numpy : 0.001294851303100586 nb_pixel_total : 2390 time to create 1 rle with old method : 0.005289793014526367 time for calcul the mask position with numpy : 0.0013072490692138672 nb_pixel_total : 2450 time to create 1 rle with old method : 0.005238056182861328 time for calcul the mask position with numpy : 0.001298666000366211 nb_pixel_total : 2726 time to create 1 rle with old method : 0.005884647369384766 time for calcul the mask position with numpy : 0.0013306140899658203 nb_pixel_total : 11905 time to create 1 rle with old method : 0.025890350341796875 time for calcul the mask position with numpy : 0.0014734268188476562 nb_pixel_total : 39173 time to create 1 rle with old method : 0.08237051963806152 time for calcul the mask position with numpy : 0.0013248920440673828 nb_pixel_total : 1055 time to create 1 rle with old method : 0.0024111270904541016 time for calcul the mask position with numpy : 0.0013022422790527344 nb_pixel_total : 2783 time to create 1 rle with old method : 0.00604557991027832 time for calcul the mask position with numpy : 0.001291036605834961 nb_pixel_total : 827 time to create 1 rle with old method : 0.0019354820251464844 time for calcul the mask position with numpy : 0.0013031959533691406 nb_pixel_total : 4133 time to create 1 rle with old method : 0.008957147598266602 time for calcul the mask position with numpy : 0.0013108253479003906 nb_pixel_total : 1029 time to create 1 rle with old method : 0.002380847930908203 time for calcul the mask position with numpy : 0.0012826919555664062 nb_pixel_total : 1625 time to create 1 rle with old method : 0.0035300254821777344 time for calcul the mask position with numpy : 0.0014252662658691406 nb_pixel_total : 10661 time to create 1 rle with old method : 0.022652626037597656 time for calcul the mask position with numpy : 0.0012941360473632812 nb_pixel_total : 1249 time to create 1 rle with old method : 0.002757549285888672 time for calcul the mask position with numpy : 0.0014472007751464844 nb_pixel_total : 4170 time to create 1 rle with old method : 0.00923776626586914 time for calcul the mask position with numpy : 0.0013370513916015625 nb_pixel_total : 2028 time to create 1 rle with old method : 0.0045011043548583984 time for calcul the mask position with numpy : 0.001285552978515625 nb_pixel_total : 860 time to create 1 rle with old method : 0.002069711685180664 time for calcul the mask position with numpy : 0.0014312267303466797 nb_pixel_total : 589 time to create 1 rle with old method : 0.0013115406036376953 time for calcul the mask position with numpy : 0.0012929439544677734 nb_pixel_total : 2323 time to create 1 rle with old method : 0.0050201416015625 time for calcul the mask position with numpy : 0.0012850761413574219 nb_pixel_total : 895 time to create 1 rle with old method : 0.0019788742065429688 time for calcul the mask position with numpy : 0.0012764930725097656 nb_pixel_total : 576 time to create 1 rle with old method : 0.0012671947479248047 time for calcul the mask position with numpy : 0.0012822151184082031 nb_pixel_total : 1075 time to create 1 rle with old method : 0.002348661422729492 time for calcul the mask position with numpy : 0.0012841224670410156 nb_pixel_total : 1792 time to create 1 rle with old method : 0.003870725631713867 time for calcul the mask position with numpy : 0.0014386177062988281 nb_pixel_total : 1677 time to create 1 rle with old method : 0.0038421154022216797 time for calcul the mask position with numpy : 0.0012824535369873047 nb_pixel_total : 337 time to create 1 rle with old method : 0.0008265972137451172 time for calcul the mask position with numpy : 0.0014483928680419922 nb_pixel_total : 2408 time to create 1 rle with old method : 0.005627155303955078 time for calcul the mask position with numpy : 0.0012845993041992188 nb_pixel_total : 692 time to create 1 rle with old method : 0.0016107559204101562 time for calcul the mask position with numpy : 0.001361846923828125 nb_pixel_total : 1704 time to create 1 rle with old method : 0.0038230419158935547 time for calcul the mask position with numpy : 0.001291513442993164 nb_pixel_total : 873 time to create 1 rle with old method : 0.002031564712524414 time for calcul the mask position with numpy : 0.0012929439544677734 nb_pixel_total : 2769 time to create 1 rle with old method : 0.00616765022277832 time for calcul the mask position with numpy : 0.0012788772583007812 nb_pixel_total : 1197 time to create 1 rle with old method : 0.0027174949645996094 time for calcul the mask position with numpy : 0.0012824535369873047 nb_pixel_total : 586 time to create 1 rle with old method : 0.0014679431915283203 time for calcul the mask position with numpy : 0.0013511180877685547 nb_pixel_total : 615 time to create 1 rle with old method : 0.0013647079467773438 time for calcul the mask position with numpy : 0.0015168190002441406 nb_pixel_total : 27694 time to create 1 rle with old method : 0.05765414237976074 time for calcul the mask position with numpy : 0.001294851303100586 nb_pixel_total : 3092 time to create 1 rle with old method : 0.00664067268371582 time for calcul the mask position with numpy : 0.0013422966003417969 nb_pixel_total : 13014 time to create 1 rle with old method : 0.02698040008544922 time for calcul the mask position with numpy : 0.0012824535369873047 nb_pixel_total : 714 time to create 1 rle with old method : 0.0017099380493164062 time for calcul the mask position with numpy : 0.0013780593872070312 nb_pixel_total : 16746 time to create 1 rle with old method : 0.036392927169799805 time for calcul the mask position with numpy : 0.001455545425415039 nb_pixel_total : 8726 time to create 1 rle with old method : 0.018631458282470703 time for calcul the mask position with numpy : 0.0013015270233154297 nb_pixel_total : 7527 time to create 1 rle with old method : 0.01634836196899414 time for calcul the mask position with numpy : 0.0013928413391113281 nb_pixel_total : 1235 time to create 1 rle with old method : 0.002790212631225586 time for calcul the mask position with numpy : 0.001287221908569336 nb_pixel_total : 1031 time to create 1 rle with old method : 0.0023686885833740234 time for calcul the mask position with numpy : 0.0013265609741210938 nb_pixel_total : 8468 time to create 1 rle with old method : 0.017811059951782227 time for calcul the mask position with numpy : 0.0012738704681396484 nb_pixel_total : 1513 time to create 1 rle with old method : 0.003297090530395508 time for calcul the mask position with numpy : 0.001318216323852539 nb_pixel_total : 9082 time to create 1 rle with old method : 0.01912522315979004 time for calcul the mask position with numpy : 0.0012767314910888672 nb_pixel_total : 278 time to create 1 rle with old method : 0.0006499290466308594 time for calcul the mask position with numpy : 0.0012836456298828125 nb_pixel_total : 260 time to create 1 rle with old method : 0.0006392002105712891 time for calcul the mask position with numpy : 0.0012941360473632812 nb_pixel_total : 4232 time to create 1 rle with old method : 0.009796619415283203 time for calcul the mask position with numpy : 0.0012822151184082031 nb_pixel_total : 1334 time to create 1 rle with old method : 0.0030014514923095703 time for calcul the mask position with numpy : 0.0012791156768798828 nb_pixel_total : 1124 time to create 1 rle with old method : 0.002478361129760742 time for calcul the mask position with numpy : 0.0012705326080322266 nb_pixel_total : 942 time to create 1 rle with old method : 0.002257108688354492 time for calcul the mask position with numpy : 0.0013689994812011719 nb_pixel_total : 18548 time to create 1 rle with old method : 0.041699886322021484 time for calcul the mask position with numpy : 0.0013475418090820312 nb_pixel_total : 741 time to create 1 rle with old method : 0.0019197463989257812 time for calcul the mask position with numpy : 0.0012764930725097656 nb_pixel_total : 221 time to create 1 rle with old method : 0.0005345344543457031 time for calcul the mask position with numpy : 0.00128936767578125 nb_pixel_total : 1644 time to create 1 rle with old method : 0.0038526058197021484 time for calcul the mask position with numpy : 0.0012965202331542969 nb_pixel_total : 299 time to create 1 rle with old method : 0.0007472038269042969 time for calcul the mask position with numpy : 0.0012829303741455078 nb_pixel_total : 595 time to create 1 rle with old method : 0.0014066696166992188 time for calcul the mask position with numpy : 0.0013241767883300781 nb_pixel_total : 5014 time to create 1 rle with old method : 0.011034727096557617 time for calcul the mask position with numpy : 0.00131988525390625 nb_pixel_total : 1076 time to create 1 rle with old method : 0.002317667007446289 time for calcul the mask position with numpy : 0.0012784004211425781 nb_pixel_total : 865 time to create 1 rle with old method : 0.0019712448120117188 time for calcul the mask position with numpy : 0.0013124942779541016 nb_pixel_total : 1335 time to create 1 rle with old method : 0.0030832290649414062 time for calcul the mask position with numpy : 0.0012869834899902344 nb_pixel_total : 2196 time to create 1 rle with old method : 0.00497889518737793 time for calcul the mask position with numpy : 0.0012843608856201172 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0029833316802978516 time for calcul the mask position with numpy : 0.0013098716735839844 nb_pixel_total : 949 time to create 1 rle with old method : 0.002300739288330078 time for calcul the mask position with numpy : 0.0013391971588134766 nb_pixel_total : 1425 time to create 1 rle with old method : 0.003323793411254883 time for calcul the mask position with numpy : 0.0012860298156738281 nb_pixel_total : 888 time to create 1 rle with old method : 0.0019724369049072266 batch 1 Loaded 103 chid ids of type : 4677 Number RLEs to save : 9671 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 : 0.018752336502075195 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.430655717849731 time spend to save output : 0.019023418426513672 total time spend for step 1 : 13.449679136276245 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1768818102_356254_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 103 ############################### 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 : 0.18671917915344238 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 Mon Jan 19 11:21:56 2026 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/1768818116_356254_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 0.069s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 2.2944772243499756 time spend to save output : 4.76837158203125e-05 total time spend for step 1 : 2.294524908065796 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 : 0.02082657814025879 [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 : 0.016629457473754883 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, 0.06384016, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.052221175, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.01227113, None)], 'temp/1768818116_356254_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 : 0.17608928680419922 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 Mon Jan 19 11:21:59 2026 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 : 0.01162576675415039 time to convert the images to numpy array : 0.0008561611175537109 total time to convert the images to numpy array : 0.012945175170898438 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 : 6729 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 : 6729 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.009406566619873047 time used to do the prediction : 0.11115550994873047 save descriptor for thcl : 355 time to traite the descriptors : 0.06442904472351074 storage_type for insertDescriptorsMulti : 1 To insert : 916235064 Catched exception ! Connect or reconnect ! time to insert the descriptors : 1.9129643440246582 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 : 8.344650268554688e-06 save missing photos in datou_result : time spend for datou_step_exec : 7.505909442901611 time spend to save output : 2.5955169200897217 total time spend for step 1 : 10.101426362991333 step2:argmax Mon Jan 19 11:22:09 2026 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', 0.017712511, 332, '355'), 'temp/1768818118_356254_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 : 0.027570486068725586 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 : 0.020325899124145508 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 : 0.019545793533325195 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 : 6.198883056640625e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0002715587615966797 time spend to save output : 0.06820964813232422 total time spend for step 2 : 0.0684812068939209 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', 0.017712511, 332, '355'), 'temp/1768818118_356254_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 : 0.23325014114379883 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 Mon Jan 19 11:22:09 2026 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 2026-01-19 11:22:12.835128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-19 11:22:12.835814: 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 2026-01-19 11:22:12.835902: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:22:12.835947: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:22:12.838108: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:22:12.838181: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:22:12.841426: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:22:12.842474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:22:12.846341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:22:12.847544: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:22:12.847964: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-19 11:22:12.878661: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-19 11:22:12.880655: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9278000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-19 11:22:12.880698: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-19 11:22:12.884111: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x123d6d40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-19 11:22:12.884142: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-19 11:22:12.885336: 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 2026-01-19 11:22:12.885454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:22:12.885486: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-19 11:22:12.885574: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-19 11:22:12.885613: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-19 11:22:12.885660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-19 11:22:12.885728: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-19 11:22:12.885782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-19 11:22:12.887468: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-19 11:22:12.887545: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-19 11:22:12.887606: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-19 11:22:12.887622: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-19 11:22:12.887634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-19 11:22:12.889348: 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 : 6729 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 10.884619235992432 time used to load_weights : 0.15219402313232422 0it [00:00, ?it/s] 3it [00:00, 986.59it/s]2026-01-19 11:22:26.561592: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1768818129_356254_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1768818129_356254_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1768818129_356254_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.1267507076263428 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 time to traite the descriptors : 0.02885150909423828 storage_type for insertDescriptorsMulti : 3 To insert : 1171252784 To insert : 1171252764 To insert : 1171252487 time to insert the descriptors : 1.0012502670288086 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252784, 1171252764, 1171252487] Looping around the photos to save general results len do output : 3 /1171252784Didn't retrieve data . /1171252764Didn't retrieve data . /1171252487Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.01772141456604004 save_final save missing photos in datou_result : time spend for datou_step_exec : 20.98111867904663 time spend to save output : 0.01805567741394043 total time spend for step 1 : 20.99917435646057 step2:argmax Mon Jan 19 11:22:30 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 Inside saveOutput : final : True verbose : False photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.9677524, 4674, '3609'), 'temp/1768818129_356254_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.9853615, 4674, '3609'), 'temp/1768818129_356254_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.9262463, 4674, '3609'), 'temp/1768818129_356254_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.01829838752746582 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.0187380313873291 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.019489049911499023 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 4.291534423828125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00017571449279785156 time spend to save output : 0.06689023971557617 total time spend for step 2 : 0.06706595420837402 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252784': [(1171252784, 'jrm', 0.9677524, 4674, '3609'), 'temp/1768818129_356254_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853615, 4674, '3609'), 'temp/1768818129_356254_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262463, 4674, '3609'), 'temp/1768818129_356254_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.44638705253601074 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 Mon Jan 19 11:22:31 2026 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 l 3637 free memory gpu now : 3177 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_1[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_2[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 8.952237367630005 time used to load_weights : 0.35338640213012695 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.0905728340148926 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 time to traite the descriptors : 0.038922786712646484 storage_type for insertDescriptorsMulti : 3 To insert : 1171275314 To insert : 1171291875 To insert : 1171275372 time to insert the descriptors : 0.8239519596099854 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171275314, 1171291875, 1171275372] Looping around the photos to save general results len do output : 3 /1171275314Didn't retrieve data . /1171291875Didn't retrieve data . /1171275372Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.01973104476928711 save_final save missing photos in datou_result : time spend for datou_step_exec : 14.403477191925049 time spend to save output : 0.020094871520996094 total time spend for step 1 : 14.423572063446045 step2:argmax Mon Jan 19 11:22:45 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 Inside saveOutput : final : True verbose : False photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.9651602, 4723, '3655'), 'temp/1768818150_356254_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.970701, 4723, '3655'), 'temp/1768818150_356254_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96745974, 4723, '3655'), 'temp/1768818150_356254_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.02103257179260254 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.017679452896118164 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.017776012420654297 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 4.291534423828125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00016951560974121094 time spend to save output : 0.06721210479736328 total time spend for step 2 : 0.06738162040710449 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171275314': [(1171275314, 'tapis_vide', 0.9651602, 4723, '3655'), 'temp/1768818150_356254_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171291875': [(1171291875, 'tapis_vide', 0.970701, 4723, '3655'), 'temp/1768818150_356254_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.96745974, 4723, '3655'), 'temp/1768818150_356254_1171275372_76d81364ff7df843bff095f45c07ba35.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 51 thcl : 358 photo_hashtag_type : 337 ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.20914936065673828 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 Mon Jan 19 11:22:49 2026 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/1768818170_356254 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818170), 0.0, 0.0, 14, '', 0, 0, '1768818169_356254_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', 0, 2448, 3264, 0, 1768818170,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818170), 0.0, 0.0, 14, '', 0, 0, '1768818169_356254_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', 0, 3264, 2448, 0, 1768818170,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818170), 0.0, 0.0, 14, '', 0, 0, '1768818169_356254_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', 0, 2448, 3264, 0, 1768818170,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.1299505233764648 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 1.3418433666229248 time spend to save output : 2.956390380859375e-05 total time spend for step 1 : 1.3418729305267334 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 /1404582289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582291Didn'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 : 0.01882028579711914 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1404582289: ['917849322', 'temp/1768818169_356254_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1404582290: ['917849322', 'temp/1768818169_356254_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1404582291: ['917849322', 'temp/1768818169_356254_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 : 0.2106025218963623 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 Mon Jan 19 11:22:51 2026 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 : 0.0002551078796386719 time to convert the images to numpy array : 0.6194846630096436 total time to convert the images to numpy array : 0.6201226711273193 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3177 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 : 3177 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 1.4369380474090576 time used to do the prediction : 0.11473584175109863 save descriptor for thcl : 500 time to traite the descriptors : 0.06240439414978027 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.5983247756958008 time spend for datou_step_exec : 9.173078298568726 time spend to save output : 3.6716461181640625e-05 total time spend for step 1 : 9.173115015029907 step2:argmax Mon Jan 19 11:23:00 2026 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 : 0.00014972686767578125 time spend to save output : 2.956390380859375e-05 total time spend for step 2 : 0.000179290771484375 step3:rotate Mon Jan 19 11:23:00 2026 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/1768818181_356254 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.6645679473876953 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 : 0.7843594551086426 time spend to save output : 4.124641418457031e-05 total time spend for step 3 : 0.7844007015228271 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 /0Didn'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 : 0.0179293155670166 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {0: ['917849322', 'temp/1768818171_356254_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 : 0.18264436721801758 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 Mon Jan 19 11:23:01 2026 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 upload in portfolio : 30154916 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1768818188_356254 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818189), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', 0, 57, 51, 0, 1768818189,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818189), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', 0, 50, 45, 0, 1768818189,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818189), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', 0, 51, 54, 0, 1768818189,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818189), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', 0, 43, 52, 0, 1768818189,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! we have uploaded 4 photos in the portfolio 30154916 time of upload the photos Elapsed time : 7.435555458068848 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/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos upload in portfolio : 30154917 Result OK ! uploaded one batch 0 Elapsed time : 24.623390197753906 time spend for datou_step_exec : 35.449453592300415 time spend to save output : 1.4543533325195312e-05 total time spend for step 1 : 35.44946813583374 step2:tile Mon Jan 19 11:23:37 2026 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/1768818181_356254_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 ++++ created feed_id_new_photos : 30154919 with name tile_taggage_varroa feed_id_new_photos : 30154919 filename : temp/1768818181_356254_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/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.007820606231689453 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/1768818225_356254 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818226), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg', 0, 480, 480, 0, 1768818226,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! we have uploaded 1 photos in the portfolio 30154919 Importing ! upload mediasElapsed time : 0.7104196548461914 , 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 : 0.8016908168792725 time spend for datou_step_exec : 9.095279932022095 time spend to save output : 3.0517578125e-05 total time spend for step 2 : 9.09531044960022 step3:rotate Mon Jan 19 11:23:46 2026 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 feed_id_new_photos : 30154920 Needs to change image size ! time for calcul the mask position with numpy : 0.0005640983581542969 nb_pixel_total : 1389 time to create 1 rle with old method : 0.004624128341674805 .time for calcul the mask position with numpy : 0.0003650188446044922 nb_pixel_total : 1157 time to create 1 rle with old method : 0.003384113311767578 . 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 : 0.0003795623779296875 nb_pixel_total : 694 time to create 1 rle with old method : 0.0022056102752685547 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003733634948730469 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0034856796264648438 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003724098205566406 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006465911865234375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003592967987060547 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002777099609375 . 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 : 0.00035762786865234375 nb_pixel_total : 143 time to create 1 rle with old method : 0.00048232078552246094 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003592967987060547 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0030012130737304688 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003643035888671875 nb_pixel_total : 414 time to create 1 rle with old method : 0.0010633468627929688 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036644935607910156 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002824068069458008 . 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 : 0.00039386749267578125 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0028221607208251953 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035953521728515625 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027589797973632812 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034880638122558594 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007655620574951172 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003750324249267578 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003275156021118164 .time for calcul the mask position with numpy : 0.00037169456481933594 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027425289154052734 . 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 : 0.00036644935607910156 nb_pixel_total : 694 time to create 1 rle with old method : 0.0017361640930175781 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003619194030761719 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002733945846557617 . 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 : 0.00035881996154785156 nb_pixel_total : 221 time to create 1 rle with old method : 0.0007297992706298828 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003590583801269531 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002719879150390625 . 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 : 0.0003662109375 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004582405090332031 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036263465881347656 nb_pixel_total : 1160 time to create 1 rle with old method : 0.00270843505859375 . 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 : 0.0003807544708251953 nb_pixel_total : 414 time to create 1 rle with old method : 0.0010619163513183594 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003578662872314453 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002773761749267578 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003368854522705078 nb_pixel_total : 1 time to create 1 rle with old method : 2.3603439331054688e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.0003883838653564453 nb_pixel_total : 1204 time to create 1 rle with old method : 0.002778291702270508 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035500526428222656 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0028166770935058594 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034689903259277344 nb_pixel_total : 264 time to create 1 rle with old method : 0.000728607177734375 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00037384033203125 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0033025741577148438 .time for calcul the mask position with numpy : 0.0003552436828613281 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027577877044677734 . 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 : 0.00036144256591796875 nb_pixel_total : 727 time to create 1 rle with old method : 0.0018088817596435547 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003609657287597656 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0027778148651123047 . 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 : 0.0003609657287597656 nb_pixel_total : 250 time to create 1 rle with old method : 0.0007293224334716797 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.000362396240234375 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0027801990509033203 . 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 : 0.0003552436828613281 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005755424499511719 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003612041473388672 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0027785301208496094 . 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 : 0.00036644935607910156 nb_pixel_total : 450 time to create 1 rle with old method : 0.0015053749084472656 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.000362396240234375 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0035104751586914062 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003407001495361328 nb_pixel_total : 1 time to create 1 rle with old method : 2.574920654296875e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.00042700767517089844 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0029616355895996094 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035309791564941406 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0026640892028808594 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003421306610107422 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006735324859619141 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00041413307189941406 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003262042999267578 .time for calcul the mask position with numpy : 0.0003542900085449219 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002727985382080078 . 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 : 0.0003981590270996094 nb_pixel_total : 727 time to create 1 rle with old method : 0.001744985580444336 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003628730773925781 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0027341842651367188 . 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 : 0.00039649009704589844 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006802082061767578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003573894500732422 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0026917457580566406 . 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 : 0.0003948211669921875 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005061626434326172 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003635883331298828 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0027685165405273438 . 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 : 0.000362396240234375 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011260509490966797 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035834312438964844 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002748727798461914 . 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 : 0.0004265308380126953 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0028388500213623047 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003516674041748047 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002836942672729492 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003426074981689453 nb_pixel_total : 234 time to create 1 rle with old method : 0.0006649494171142578 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 30154920 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1768818230_356254 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818235), 0.0, 0.0, 14, '', 0, 0, '1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', 0, 320, 320, 0, 1768818235,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! we have uploaded 24 photos in the portfolio 30154920 time of upload the photos Elapsed time : 6.965623617172241 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 : 12.0891752243042 time spend to save output : 6.914138793945312e-05 total time spend for step 3 : 12.089244365692139 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, '1404582329'] Looping around the photos to save general results len do output : 24 /1404582336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582341Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582356Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1404582360Didn'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, '1404582329', 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 : 0.03239035606384277 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1404582336: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1404582337: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1404582338: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1404582339: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1404582340: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1404582341: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1404582342: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1404582343: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1404582344: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1404582345: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1404582346: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1404582347: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1404582348: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1404582349: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1404582350: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1404582351: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1404582352: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1404582354: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1404582355: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1404582356: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1404582357: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1404582358: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1404582359: ['937852786', 'temp/1768818181_356254_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1404582360: ['937852786', 'temp/1768818181_356254_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 : 0.14580941200256348 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 Mon Jan 19 11:24:04 2026 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/1768818244_356254 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818244), 0.0, 0.0, 14, '', 0, 0, '1768818244_356254_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', 0, 640, 640, 0, 1768818244,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1768818244), 0.0, 0.0, 14, '', 0, 0, '1768818244_356254_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', 0, 640, 640, 0, 1768818244,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first Unexecpected behavior in 07/2025 that can be generalized l287 : type_extension .jpg This is a hack ! we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.816004753112793 Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 batch 1 Loaded 12 chid ids of type : 741 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.9545207023620605 time spend to save output : 4.458427429199219e-05 total time spend for step 1 : 0.9545652866363525 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 /1404582363 /1404582364 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 : 0.019196271896362305 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1404582363': ['911785586', 'temp/1768818244_356254_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1404582364': ['911785586', 'temp/1768818244_356254_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 : 0.12644243240356445 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 Mon Jan 19 11:24:05 2026 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 upload in portfolio : 30154921 Result OK ! uploaded one batch 0 Elapsed time : 9.195482730865479 Globalization didn't found path temp/1768818245_356254_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg in map_result_insert CONSIDER SERIOUSLY ! Globalization didn't found path temp/1768818245_356254_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg in map_result_insert CONSIDER SERIOUSLY ! Globalization didn't found path temp/1768818245_356254_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg in map_result_insert CONSIDER SERIOUSLY ! Globalization didn't found path temp/1768818245_356254_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg in map_result_insert CONSIDER SERIOUSLY ! Globalization didn't found path temp/1768818245_356254_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg in map_result_insert CONSIDER SERIOUSLY ! Globalization didn't found path temp/1768818245_356254_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg in map_result_insert CONSIDER SERIOUSLY ! Globalization didn't found path temp/1768818245_356254_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg in map_result_insert CONSIDER SERIOUSLY ! Globalization didn't found path temp/1768818245_356254_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg in map_result_insert CONSIDER SERIOUSLY ! Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 9.256112098693848 time spend to save output : 8.368492126464844e-05 total time spend for step 1 : 9.256195783615112 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 : 0 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 : 1 time used for this insertion : 0.016158580780029297 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {} 8 ERROR TEST CROP RLES Difference in size : ret_da_prev_reg : 8 and ret_da : 0 ERROR TEST CROP RLES no filename with crop_id_to_hash 1947670932 FOUND ERROR TEST CROP RLES different hash of same crop : hash_non_reg : f4e274dd08c90f327e74f1b91ebc5e43761642ac4456156033042a4b while hash_to_check : 741e1753b71b2b6b2879a507a69a00f8933bca84317a40e04a011d77 ERROR crop_rles FAILED ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : angular_coeff list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.15032100677490234 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 Mon Jan 19 11:24:14 2026 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 : 0.14674830436706543 time spend to save output : 4.029273986816406e-05 total time spend for step 1 : 0.1467885971069336 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 : 0.13325715065002441 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 Mon Jan 19 11:24:15 2026 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 : 0.15545058250427246 time spend to save output : 4.649162292480469e-05 total time spend for step 1 : 0.15549707412719727 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, ['491,350,489,350,488,349,487,350,483,350,480,348,480,341,482,339,482,337,485,334,487,334,491,330,494,330,495,328,498,326,501,326,503,324,507,325,509,323,514,321,516,319,518,321,520,321,521,319,522,319,524,321,527,321,530,317,530,315,531,314,535,313,540,309,543,310,544,311,542,313,542,314,544,316,541,318,541,322,536,322,535,323,533,323,532,322,528,322,527,321,524,321,522,323,518,322,516,324,517,327,516,328,512,327,510,329,512,332,513,332,515,330,516,331,516,333,514,332,511,333,511,336,514,337,516,336,516,339,515,339,513,338,511,340,512,341,512,342,510,343,507,343,502,347,500,347,497,349,492,349', '514,325,515,324,513,322,512,322,511,325,512,326', '522,327,521,327,521,326,522,325']), (946711423, 599722655, 631, 176, 535, 138, 264, 0.9818268, 1947740373, ['453,253,413,253,412,252,387,252,386,250,386,248,383,246,379,245,376,243,361,243,361,240,362,239,359,238,358,237,356,237,355,236,352,236,351,235,333,235,332,234,329,234,329,233,331,231,331,229,329,228,328,224,330,222,330,221,324,218,308,219,307,218,302,218,298,216,288,217,287,218,285,218,283,220,283,221,287,224,295,225,295,225,294,226,289,226,288,227,283,227,282,228,273,228,272,229,271,228,259,228,258,227,254,227,253,226,247,225,247,225,251,221,248,218,243,216,247,213,248,213,249,212,248,211,246,211,245,210,241,210,240,209,237,209,236,208,231,207,230,206,228,202,224,201,223,200,221,200,220,199,214,198,213,195,211,193,208,193,203,189,203,184,201,181,201,176,198,171,199,170,199,158,203,154,205,153,205,151,206,149,209,149,210,148,225,148,226,147,283,147,284,148,287,148,288,147,305,147,306,148,312,148,313,147,354,147,355,146,428,146,429,147,433,147,434,148,437,148,438,149,451,149,457,156,459,162,462,165,464,166,471,166,472,165,477,165,480,167,480,171,486,175,488,175,489,176,502,176,503,178,503,180,509,185,509,189,512,193,512,199,513,200,513,203,514,204,514,210,513,211,514,217,512,221,513,222,513,225,510,229,510,235,507,237,504,238,502,243,490,243,489,244,485,244,484,245,480,245,479,246,463,246,462,247,460,247,458,249,457,252,454,252', '528,212,528,207,526,206,524,203,526,203,527,202,528,202', '299,215,302,212,299,211,298,210,291,210,290,211,281,212,286,215,290,215,291,216', '375,242,376,240,375,238,363,239,368,242,371,242,372,243']), (946711423, 492844413, 631, 89, 163, 93, 144, 0.9772748, 1947740375, ['159,142,153,141,151,139,148,138,145,135,141,133,139,133,138,132,131,132,130,131,125,131,124,130,121,130,120,129,116,129,115,128,112,128,108,126,106,126,100,123,98,121,94,113,94,104,97,101,103,98,105,98,106,97,110,97,111,96,116,96,117,95,132,95,133,96,139,97,141,99,144,100,149,105,150,107,154,108,155,113,157,115,158,115,160,118,160,120,161,121,161,133,160,134,160,140']), (946711423, 2096875709, 631, 185, 431, 39, 136, 0.97171515, 1947740377, ['331,134,287,134,286,133,284,133,283,134,272,134,271,133,264,133,263,134,258,134,257,133,254,133,253,132,236,132,235,131,225,131,224,132,223,131,213,131,212,130,208,130,207,129,204,129,203,128,199,127,193,121,192,117,189,113,189,110,188,109,187,93,186,92,187,91,187,89,186,88,186,65,185,64,186,63,186,61,185,60,185,48,186,47,186,42,187,40,232,40,233,41,248,41,249,42,281,43,282,44,290,44,291,45,300,45,301,46,308,46,309,47,314,47,315,48,322,49,328,53,334,54,336,56,339,57,344,62,349,64,351,66,353,67,356,67,358,69,359,72,363,76,367,78,369,80,379,91,380,93,383,94,390,100,393,101,395,103,396,106,399,109,402,110,406,115,408,115,410,117,410,120,412,123,411,127,409,129,399,129,398,130,395,130,394,131,378,131,377,132,368,132,367,131,346,131,345,132,342,132,341,133,332,133']), (946711423, 2096875722, 631, 198, 395, 118, 142, 0.9699756, 1947740378, ['328,137,251,137,250,136,249,137,241,137,240,136,219,136,218,135,213,135,212,134,206,133,205,132,201,131,200,130,200,122,201,121,205,121,206,122,222,122,226,124,239,124,240,125,369,125,370,124,371,125,389,125,391,127,391,133,390,134,386,134,385,135,380,135,379,134,375,134,374,135,341,135,340,136,329,136']), (946711423, 499500794, 631, 93, 107, 127, 146, 0.9574813, 1947740379, ['101,143,98,143,95,139,95,131,97,129,100,129,101,133,102,134,102,136,103,137,103,140']), (946711423, 492925064, 631, 71, 125, 36, 95, 0.95296955, 1947740380, ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80,82,75,77,75,75,74,74,74,66,75,65,75,62,77,60,77,58,80,55,80,54,83,51,83,50,88,45,94,44,95,43,99,43,100,42,113,42,117,45,117,47,116,48,116,51,115,52,114,59,113,60,112,65,111,66,111,69,110,70,110,75,109,76,109,83,108,84,108,86,109,87,108,89']), (946711423, 492925064, 631, 101, 167, 38, 127, 0.9508439, 1947740381, 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'230,167,229,166,227,167,228,168']), (946711423, 495920967, 631, 202, 524, 112, 333, 0.45109355, 1947740396, 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'312,179,311,178,308,179,309,180', '268,269,264,269,259,266,259,262,261,258,261,250,265,245,269,250,270,257,274,260,278,265,275,267,269,268', '414,281,401,281,414,281']), (946711423, 2096875722, 631, 433, 558, 248, 286, 0.44133398, 1947740397, ['492,272,474,272,473,271,468,271,465,269,460,269,460,268,465,266,467,266,468,265,470,265,471,264,475,264,476,263,479,263,480,262,486,262,487,261,491,261,492,260,495,260,496,259,502,259,506,257,510,257,514,255,517,255,518,254,530,253,531,252,535,252,536,251,538,251,539,252,543,252,544,253,547,253,549,251,553,251,555,253,555,267,552,270,550,270,550,269,548,267,547,267,547,267,548,266,547,265,545,266,540,266,539,264,530,264,529,263,524,263,519,266,513,266,510,268,507,268,506,269,499,270,498,271,493,271', '438,279,435,279,435,273,436,272,448,271,449,272,448,274,443,274,440,277,440,278']), (946711423, 492654799, 631, 399, 569, 68, 251, 0.41876298, 1947740399, []), (946711423, 492624020, 631, 420, 552, 244, 293, 0.35962066, 1947740400, 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'519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] origin we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.005632877349853516 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Mon Jan 19 11:24:15 2026 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 : 0.3776998519897461 time spend to save output : 0.00011467933654785156 total time spend for step 1 : 0.37781453132629395 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 : 0.2677001953125 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 Mon Jan 19 11:24:16 2026 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/1768818255_356254_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.2615177631378174 time spend to save output : 3.910064697265625e-05 total time spend for step 1 : 0.26155686378479004 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 maybe url didn't existe for photo_id 987515175 , please check it BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFmaybe url didn't existe for photo_id 987515212 , please check it BFBFmaybe url didn't existe for photo_id 987515195 , please check it BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFmaybe url didn't existe for photo_id 987515248 , please check it BFBFBFFBFBFBFBFFBFFFBFBFBFBFwe have missing 4 photos in the step downloads : photo missing : [987515195, 987515212, 987515175, 987515248] try to delete the photos missing in DB HTTP Error 500: Internal Server Error HTTP Error 500: Internal Server Error HTTP Error 500: Internal Server Error HTTP Error 500: Internal Server Error length of list_filenames : 60 ; length of list_pids : 60 ; length of list_args : 60 time to download the photos : 138.97595524787903 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 Mon Jan 19 11:26:35 2026 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 : 0.006226062774658203 time to convert the images to numpy array : 0.042916297912597656 time to import caffe and check if the image exist : 0.004410266876220703 time to convert the images to numpy array : 0.0451662540435791 time to import caffe and check if the image exist : 0.006494760513305664 time to convert the images to numpy array : 0.05043339729309082 time to import caffe and check if the image exist : 0.010176658630371094 time to convert the images to numpy array : 0.047118186950683594 time to import caffe and check if the image exist : 0.008457422256469727 time to convert the images to numpy array : 0.04990410804748535 time to import caffe and check if the image exist : 0.015021562576293945 time to convert the images to numpy array : 0.04464578628540039 time to import caffe and check if the image exist : 0.01235508918762207 time to convert the images to numpy array : 0.04588460922241211 time to import caffe and check if the image exist : 0.010610818862915039 time to convert the images to numpy array : 0.05153465270996094 time to import caffe and check if the image exist : 0.013967037200927734 time to convert the images to numpy array : 0.04796433448791504 time to import caffe and check if the image exist : 0.015606880187988281 time to convert the images to numpy array : 0.046057701110839844 total time to convert the images to numpy array : 0.06608319282531738 list photo_ids error: [] list photo_ids correct : [987515188, 987515189, 987515190, 987515192, 987515193, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515245, 987515246, 987515247, 987515249, 987515250, 987515224, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515207, 987515208, 987515209, 987515211, 987515213, 987515215, 987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232] number of photos to traite : 60 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3177 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3177 max_wait_temp : 1 max_wait : 0 dict_keys(['res5b', 'prob']) time used to do the prepocess of the images : 0.05501079559326172 time used to do the prediction : 0.23121023178100586 save descriptor for thcl : 1528 time to traite the descriptors : 3.7223663330078125 storage_type for insertDescriptorsMulti : 1 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515223 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515245 To insert : 987515246 To insert : 987515247 To insert : 987515249 To insert : 987515250 To insert : 987515224 To insert : 987515239 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515238 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515213 To insert : 987515215 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515187 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515231 To insert : 987515232 time to insert the descriptors : 13.00492787361145 time spend for datou_step_exec : 20.629777908325195 time spend to save output : 0.00011491775512695312 total time spend for step 1 : 20.629892826080322 step2:argmax Mon Jan 19 11:26:55 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 time spend for datou_step_exec : 0.0009474754333496094 time spend to save output : 8.58306884765625e-06 total time spend for step 2 : 0.0009560585021972656 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515188': [('987515188', 'Carton', 0.9956571, 1927, '1528'), 'temp/1768818256_356254_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.99779165, 1927, '1528'), 'temp/1768818256_356254_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.97635627, 1927, '1528'), 'temp/1768818256_356254_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999112, 1927, '1528'), 'temp/1768818256_356254_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993957, 1927, '1528'), 'temp/1768818256_356254_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515196': [('987515196', 'Carton', 0.9846519, 1927, '1528'), 'temp/1768818256_356254_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.9660471, 1927, '1528'), 'temp/1768818256_356254_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.9858402, 1927, '1528'), 'temp/1768818256_356254_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99547845, 1927, '1528'), 'temp/1768818256_356254_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.99111146, 1927, '1528'), 'temp/1768818256_356254_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.99509406, 1927, '1528'), 'temp/1768818256_356254_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908634, 1927, '1528'), 'temp/1768818256_356254_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774534, 1927, '1528'), 'temp/1768818256_356254_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.52972096, 1927, '1528'), 'temp/1768818256_356254_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.9993692, 1927, '1528'), 'temp/1768818256_356254_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963756, 1927, '1528'), 'temp/1768818256_356254_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.99747545, 1927, '1528'), 'temp/1768818256_356254_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.99208814, 1927, '1528'), 'temp/1768818256_356254_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998142, 1927, '1528'), 'temp/1768818256_356254_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9771419, 1927, '1528'), 'temp/1768818256_356254_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8574905, 1927, '1528'), 'temp/1768818256_356254_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9271625, 1927, '1528'), 'temp/1768818256_356254_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.98998535, 1927, '1528'), 'temp/1768818256_356254_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.99778324, 1927, '1528'), 'temp/1768818256_356254_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515245': [('987515245', 'Carton', 0.8660612, 1927, '1528'), 'temp/1768818256_356254_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515246': [('987515246', 'Carton', 0.9992316, 1927, '1528'), 'temp/1768818256_356254_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.9996679, 1927, '1528'), 'temp/1768818256_356254_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515249': [('987515249', 'Carton', 0.98130155, 1927, '1528'), 'temp/1768818256_356254_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9808005, 1927, '1528'), 'temp/1768818256_356254_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515224': [('987515224', 'Carton', 0.90864193, 1927, '1528'), 'temp/1768818256_356254_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515239': [('987515239', 'Carton', 0.9997836, 1927, '1528'), 'temp/1768818256_356254_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.9995202, 1927, '1528'), 'temp/1768818256_356254_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.98213273, 1927, '1528'), 'temp/1768818256_356254_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9358671, 1927, '1528'), 'temp/1768818256_356254_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.87448174, 1927, '1528'), 'temp/1768818256_356254_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.816766, 1927, '1528'), 'temp/1768818256_356254_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515233': [('987515233', 'Carton', 0.98338586, 1927, '1528'), 'temp/1768818256_356254_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.9447601, 1927, '1528'), 'temp/1768818256_356254_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89204186, 1927, '1528'), 'temp/1768818256_356254_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.5362568, 1927, '1528'), 'temp/1768818256_356254_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7688084, 1927, '1528'), 'temp/1768818256_356254_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515238': [('987515238', 'Carton', 0.9995709, 1927, '1528'), 'temp/1768818256_356254_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.87388825, 1927, '1528'), 'temp/1768818256_356254_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.99171877, 1927, '1528'), 'temp/1768818256_356254_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9677076, 1927, '1528'), 'temp/1768818256_356254_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.9734485, 1927, '1528'), 'temp/1768818256_356254_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515213': [('987515213', 'Carton', 0.98689044, 1927, '1528'), 'temp/1768818256_356254_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939341, 1927, '1528'), 'temp/1768818256_356254_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515182': [('987515182', 'Carton', 0.9923994, 1927, '1528'), 'temp/1768818256_356254_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999213, 1927, '1528'), 'temp/1768818256_356254_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997323, 1927, '1528'), 'temp/1768818256_356254_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7970008, 1927, '1528'), 'temp/1768818256_356254_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.98469305, 1927, '1528'), 'temp/1768818256_356254_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.9811245, 1927, '1528'), 'temp/1768818256_356254_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.98695034, 1927, '1528'), 'temp/1768818256_356254_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9002211, 1927, '1528'), 'temp/1768818256_356254_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5213163, 1927, '1528'), 'temp/1768818256_356254_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.999406, 1927, '1528'), 'temp/1768818256_356254_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515231': [('987515231', 'Carton', 0.99942064, 1927, '1528'), 'temp/1768818256_356254_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992455, 1927, '1528'), 'temp/1768818256_356254_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg']} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detect_points list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2062215805053711 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detect_points Mon Jan 19 11:26:56 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : 1 To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 1 Inside predict_points step exec : nb paths : 1 treate image : temp/1768818415_356254_987515173_91fa471b1a04f95b356afdbaf021f623.jpg size of numpy array img : 2408584 scale method : caffe/skimage size of numpy array img_scale : 2408584 (448, 448, 3) nb_h 8 nb_w 8 size of sub images : (224, 224, 3) size of caffe_input : 38535320 (64, 3, 224, 224) time to do the preprocess : 0.04025745391845703 time to do a prediction : 0.3277435302734375 dict_keys(['prob']) shape of output (64, 10, 1, 1) shape of the out_put heatmap (10, 8, 8) number of sub_photos vertical and horizon 8 8 size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) time spend for datou_step_exec : 1.6501836776733398 time spend to save output : 3.886222839355469e-05 total time spend for step 1 : 1.6502225399017334 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.260247095019045e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4742171852998318e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.0754837909132675e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.460368359104905e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9216658984078094e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.760757317650132e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012292296742089093), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.943235813290812e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.3593283771106144e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2010835820651664e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3756151417965157e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4729556596648763e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1317320968373679e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015788347809575498), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004437033785507083), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.533502892125398e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3326862244866788e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.6362408814529772e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5206163627444766e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6137295233420446e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.2692265601071995e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.640543092042208e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.0003270501329097897), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003061702009290457), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8635353626450524e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.928380000521429e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.7040427085012197e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 1.8003935110755265e-05), (987515173, 1982, 'Autre_Environement', 240, -1, 208, -1, 2.3434977265424095e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 208, -1, 1.7026290151989087e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 208, -1, 4.552921836875612e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 208, -1, 8.798038834356703e-06), (987515173, 1982, 'Autre_Environement', 112, -1, 240, -1, 6.090705937822349e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 240, -1, 1.6487807670273469e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 240, -1, 1.9709355001396034e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 240, -1, 1.4365625702339457e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 240, -1, 7.865700354159344e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 240, -1, 1.2864522432209924e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 240, -1, 9.28897406993201e-06), (987515173, 1982, 'Autre_Environement', 336, -1, 240, -1, 2.1695350369554944e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 272, -1, 3.825338353635743e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 272, -1, 2.548887323428062e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 272, -1, 2.9596178592328215e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 272, -1, 2.7453793336462695e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 272, -1, 4.325318514020182e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 272, -1, 8.186508239305113e-06), (987515173, 1982, 'Autre_Environement', 304, -1, 272, -1, 1.1525326044647954e-05), (987515173, 1982, 'Autre_Environement', 336, -1, 272, -1, 3.934374763048254e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 304, -1, 1.2081116437911987e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 304, -1, 1.595494177308865e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 304, -1, 3.3318727219011635e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 304, -1, 0.00015496363630518317), (987515173, 1982, 'Autre_Environement', 240, -1, 304, -1, 0.0002597515704110265), (987515173, 1982, 'Autre_Environement', 272, -1, 304, -1, 0.00018801931582856923), (987515173, 1982, 'Autre_Environement', 304, -1, 304, -1, 0.0002146929909940809), (987515173, 1982, 'Autre_Environement', 336, -1, 304, -1, 0.00016468965623062104), (987515173, 1982, 'Autre_Environement', 112, -1, 336, -1, 4.550350240606349e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 336, -1, 1.7343405488645658e-05), (987515173, 1982, 'Autre_Environement', 176, -1, 336, -1, 4.9307840527035296e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 336, -1, 0.00012173973664175719), (987515173, 1982, 'Autre_Environement', 240, -1, 336, -1, 0.00019621651154011488), (987515173, 1982, 'Autre_Environement', 272, -1, 336, -1, 0.00018745995475910604), (987515173, 1982, 'Autre_Environement', 304, -1, 336, -1, 0.00012327509466558695), (987515173, 1982, 'Autre_Environement', 336, -1, 336, -1, 0.0002731324639171362), (987515173, 1982, 'Carton', 112, -1, 112, -1, 1.5752210913433373e-07), (987515173, 1982, 'Carton', 144, -1, 112, -1, 4.106095275346888e-06), (987515173, 1982, 'Carton', 176, -1, 112, -1, 7.065040335874073e-06), (987515173, 1982, 'Carton', 208, -1, 112, -1, 0.0008751273271627724), (987515173, 1982, 'Carton', 240, -1, 112, -1, 0.002642166567966342), (987515173, 1982, 'Carton', 272, -1, 112, -1, 0.003356782952323556), (987515173, 1982, 'Carton', 304, -1, 112, -1, 0.031360287219285965), (987515173, 1982, 'Carton', 336, -1, 112, -1, 0.05580176040530205), (987515173, 1982, 'Carton', 112, -1, 144, -1, 0.00012363301357254386), (987515173, 1982, 'Carton', 144, -1, 144, -1, 0.00020902878895867616), (987515173, 1982, 'Carton', 176, -1, 144, -1, 0.00036738597555086017), (987515173, 1982, 'Carton', 208, -1, 144, -1, 0.006830404978245497), (987515173, 1982, 'Carton', 240, -1, 144, -1, 0.015907278284430504), (987515173, 1982, 'Carton', 272, -1, 144, -1, 0.009420567192137241), (987515173, 1982, 'Carton', 304, -1, 144, -1, 0.009772772900760174), (987515173, 1982, 'Carton', 336, -1, 144, -1, 0.02212531305849552), (987515173, 1982, 'Carton', 112, -1, 176, -1, 0.021912895143032074), (987515173, 1982, 'Carton', 144, -1, 176, -1, 0.1948823481798172), (987515173, 1982, 'Carton', 176, -1, 176, -1, 0.09645115584135056), (987515173, 1982, 'Carton', 208, -1, 176, -1, 0.1235823780298233), (987515173, 1982, 'Carton', 240, -1, 176, -1, 0.5336247086524963), (987515173, 1982, 'Carton', 272, -1, 176, -1, 0.460528701543808), (987515173, 1982, 'Carton', 304, -1, 176, -1, 0.7704389691352844), (987515173, 1982, 'Carton', 336, -1, 176, -1, 0.8660241961479187), (987515173, 1982, 'Carton', 112, -1, 208, -1, 0.8504652976989746), (987515173, 1982, 'Carton', 144, -1, 208, -1, 0.984271228313446), (987515173, 1982, 'Carton', 176, -1, 208, -1, 0.9847410321235657), (987515173, 1982, 'Carton', 208, -1, 208, -1, 0.9919992089271545), (987515173, 1982, 'Carton', 240, -1, 208, -1, 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0.0007341557065956295)]} ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Step 4442 tile have less inputs used (1) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 4441 detect_points is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 4443 count_percent_refus is not consistent : 4 used against 3 in the step definition ! Step 4444 send_mail_dechet have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 1 of step 4440 have datatype=1 whereas input 0 of step 4443 have datatype=2 WARNING : type of output 1 of step 4441 doesn't seem to be define in the database( WARNING : type of input 4 of step 4443 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] origin Catched exception ! Connect or reconnect ! We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.23618721961975098 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 7 step1:init_dechet Mon Jan 19 11:26:58 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec debut step init detect dechets input : temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [987321136] Looping around the photos to save general results len do output : 1 /987321136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.019871234893798828 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.0001697540283203125 time spend to save output : 0.02017068862915039 total time spend for step 1 : 0.020340442657470703 step2:tile Mon Jan 19 11:26:58 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_correct_upm', 'ETA': 86400, 'new_width': 1500, 'new_height': 20000, 'host': 'www.fotonower.com', 'protocol': 'https', 'photo_tile_type': 1522, 'option_bande': 'True'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {406: 410} map_filenames : {987321136: 'temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 Loaded 0 chid ids of type : 0 created feed_id_new_photos : 30154924 with name tile_correct_upm feed_id_new_photos : 30154924 filename : temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea.jpg photo_id : 987321136 height_image_input : 439 width_image_input : 562 new_width : 1500 new_height : 20000 stride : 0 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 0 list_bib_to_crops : 1 [(0, 562, 0, 439, 0)] new_crops_tiles : 1 crop_transformed : 0 batch 1 Loaded 1 chid ids of type : 1522 treat the image : temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.008572578430175781 About to upload 1 photos upload in portfolio : 30154924 Result OK ! uploaded one batch 0 Elapsed time : 6.452221632003784 upload mediasElapsed time : 6.460864782333374 , 0Saving 0 CHIs. end of tileElapsed time : 6.476919651031494 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1404582423'] Looping around the photos to save general results len do output : 1 /1404582423Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1404582423', 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 : 0.015646934509277344 save_final save missing photos in datou_result : time spend for datou_step_exec : 14.712526798248291 time spend to save output : 0.015893936157226562 total time spend for step 2 : 14.728420734405518 step3:detect_points Mon Jan 19 11:27:12 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : False To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 2 Inside predict_points step exec : nb paths : 1 treate image : temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea_0.jpg size of numpy array img : 2960752 scale method : caffe/skimage size of numpy array img_scale : 2655880 (416, 532, 3) nb_h 7 nb_w 11 size of sub images : (224, 224, 3) size of caffe_input : 46362776 (77, 3, 224, 224) time to do the preprocess : 0.039359331130981445 time to do a prediction : 13.88127613067627 dict_keys(['prob']) shape of output (77, 10, 1, 1) shape of the out_put heatmap (10, 7, 11) number of sub_photos vertical and horizon 7 11 size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) Inside saveOutput : final : False verbose : False Inside savePoints : final : False verbose : False threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 final : False save missing photos in datou_result : time spend for datou_step_exec : 15.15826940536499 time spend to save output : 0.10274362564086914 total time spend for step 3 : 15.26101303100586 step4:count_percent_refus Mon Jan 19 11:27:28 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure debut step count percent refus (987321136, 0.9481481481481482) ('temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) list_photo : [987321136] list_photo_correc : [1404582423] debut step count percent refus Treating photo_id : 987321136 Calcul du count_res count res : ((492774966, 3), (2107752386, 7)) Hashtag_id : 492774966 Hashtag_id : 2107752386 We have 2 classes in this image Inside saveOutput : final : False verbose : False begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.07497239112854004 save missing photos in datou_result : time spend for datou_step_exec : 0.025087833404541016 time spend to save output : 0.07512140274047852 total time spend for step 4 : 0.10020923614501953 step5:brightness Mon Jan 19 11:27:28 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea.jpg Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.011939287185668945 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 : 0.01977062225341797 save missing photos in datou_result : time spend for datou_step_exec : 0.11712813377380371 time spend to save output : 0.037606239318847656 total time spend for step 5 : 0.15473437309265137 step6:blur_detection Mon Jan 19 11:27:28 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1768818418_356254_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.010985851287841797 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 : 0.020888805389404297 save missing photos in datou_result : time spend for datou_step_exec : 0.13381600379943848 time spend to save output : 0.03841567039489746 total time spend for step 6 : 0.17223167419433594 step7:send_mail_dechet Mon Jan 19 11:27:28 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail dechet Mail sent: {'message_id': '<202601191027.50396118169@smtp-relay.mailin.fr>', 'message_ids': None} retour de l'envoi du mail : None Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1404582423'] Looping around the photos to save general results len do output : 1 /987321136. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1404582423', 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 : 0.015607833862304688 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.9155416488647461 time spend to save output : 0.016799449920654297 total time spend for step 7 : 0.9323410987854004 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 30.0, 61.64383561643836, {'carton': 3, 'Papier_Magazine': 7}, {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 0.6164383561643836)} ############################### TEST image_temperature_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_temperature_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14982295036315918 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_temperature_detection Mon Jan 19 11:27:29 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanche_jaune_detection treat image : temp/1768818449_356254_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 time spend for datou_step_exec : 0.1907637119293213 time spend to save output : 6.771087646484375e-05 total time spend for step 1 : 0.19083142280578613 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {984484223: [(984484223, 1.004309911525615, 492630606)]} {984484223: [(984484223, 1.004309911525615, 492630606)]} ############################### TEST broca ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.02384161949157715 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:split_time_score Mon Jan 19 11:27:30 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec split portfolio by speed calcul order for each photo with time calcul time for a portfolio 2021-12-01 10:11:30 2021-12-01 10:11:32 2021-12-01 10:11:30 2021-12-01 10:11:34 2021-12-01 10:11:32 2021-12-01 10:11:40 2021-12-01 10:11:34 2021-12-01 10:12:17 2021-12-01 10:11:40 2021-12-01 10:12:24 2021-12-01 10:12:17 2021-12-01 10:12:27 2021-12-01 10:12:24 2021-12-01 10:12:29 2021-12-01 10:12:27 2021-12-01 10:12:56 2021-12-01 10:12:29 2021-12-01 10:13:04 2021-12-01 10:12:56 2021-12-01 10:13:13 2021-12-01 10:13:04 2021-12-01 10:13:04 distance 1.4513659170185111 2021-12-01 10:13:13 2021-12-01 10:13:22 2021-12-01 10:13:13 2021-12-01 10:13:30 2021-12-01 10:13:22 2021-12-01 10:16:14 2021-12-01 10:13:30 2021-12-01 10:13:30 distance 8.382409567451603 2021-12-01 10:16:14 2021-12-01 10:16:18 2021-12-01 10:16:14 2021-12-01 10:16:47 2021-12-01 10:16:18 2021-12-01 10:16:53 2021-12-01 10:16:47 2021-12-01 10:16:47 distance 8.03396608896571 2021-12-01 10:16:53 2021-12-01 10:16:57 2021-12-01 10:16:53 dict_time_useful: {0: [1098136690, 1098136784, 48.864288393888884, 2.19199505125, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94]], 1: [1098136974, 1098137007, 48.86291258986111, 2.19361357125, [datetime.datetime(2021, 12, 1, 10, 16, 14), datetime.datetime(2021, 12, 1, 10, 16, 47), 33]]} get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "CS"; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "OM"; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 time spend for datou_step_exec : 0.25088071823120117 time spend to save output : 8.225440979003906e-05 total time spend for step 1 : 0.2509629726409912 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(30154926, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]} résultat du premier test BROCA : True True ############################### TEST crop_conditional ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1335 frcnn is not linked in the step_by_step architecture ! WARNING : step 1336 crop_condition is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] origin We have 1 , BBBFBFBFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 time to download the photos : 0.42624878883361816 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:frcnn Mon Jan 19 11:27:30 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! Inside try reload ! To loadFromThcl() model_param file didn't exist model_name : learn_piece_voiture_0808_v2 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/learn_piece_voiture_0808_v2 /data/models_weight/learn_piece_voiture_0808_v2/caffemodel size_local : 350215080 size in s3 : 350215080 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 caffemodel already exist and didn't need to update /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt size_local : 7166 size in s3 : 7166 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt caffemodel : /data/models_weight/learn_piece_voiture_0808_v2/caffemodel Loaded network /data/models_weight/learn_piece_voiture_0808_v2/caffemodel About to compute detect_faster_rcnn : len(args) : 6 Inside frcnn step exec : nb paths : 6 image_path : temp/1768818450_356254_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg image_size (294, 285, 3) [[[ 29 29 29] [ 29 29 29] [ 30 30 30] ... [182 172 165] [141 131 124] [103 94 90]] [[ 29 29 29] [ 29 29 29] [ 31 31 31] ... [231 220 212] [202 193 184] [164 154 147]] [[ 30 30 30] [ 27 27 27] [ 26 26 26] ... [223 211 199] [229 219 209] [228 217 209]] ... [[ 22 27 25] [ 16 21 19] [ 11 16 14] ... [166 145 123] [168 147 125] [170 149 127]] [[ 20 25 23] [ 17 22 20] [ 15 20 18] ... [163 142 120] [165 144 122] [166 145 123]] [[ 13 18 16] [ 17 22 20] [ 20 25 23] ... [162 141 119] [163 142 120] [163 142 121]]] Detection took 0.229s for 300 object proposals image_path : temp/1768818450_356254_950003813_e28be02dfcce79cce594a390a9911a0a.jpg image_size (254, 229, 3) [[[202 190 186] [205 193 189] [205 194 190] ... [ 81 70 56] [ 80 69 55] [ 78 67 53]] [[198 187 183] [200 189 185] [198 189 185] ... [ 50 41 28] [ 44 36 23] [ 45 36 23]] [[192 187 184] [191 186 183] [191 186 183] ... [ 36 30 23] [ 32 29 21] [ 33 27 20]] ... [[187 186 190] [186 185 189] [188 184 189] ... [ 43 38 35] [ 37 33 28] [ 33 28 25]] [[184 185 189] [183 184 188] [184 183 187] ... [ 28 23 22] [ 29 24 21] [ 33 28 27]] [[181 185 186] [180 184 185] [182 184 185] ... [ 23 15 16] [ 22 14 14] [ 24 16 17]]] Detection took 0.047s for 300 object proposals image_path : temp/1768818450_356254_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg image_size (2160, 3840, 3) [[[111 118 91] [113 120 93] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 24 41 38]] [[111 118 91] [112 119 92] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 23 40 37]] [[113 118 91] [114 119 92] [115 120 93] ... [ 22 39 36] [ 23 40 37] [ 23 40 37]] ... [[120 125 94] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 23 37 35]] [[119 124 93] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]] [[118 123 91] [117 122 90] [117 122 91] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]]] Detection took 1.386s for 300 object proposals image_path : temp/1768818450_356254_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg image_size (480, 640, 3) [[[36 41 44] [36 41 44] [35 40 43] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[37 42 45] [36 41 44] [35 40 43] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[37 42 45] [36 41 44] [35 40 43] ... [ 3 5 5] [ 4 6 6] [ 4 6 6]] ... [[42 47 50] [41 46 49] [40 45 48] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[41 46 49] [41 46 49] [40 45 48] ... [ 0 2 2] [10 12 12] [22 24 24]] [[40 45 48] [40 45 48] [40 45 48] ... [10 12 12] [17 19 19] [26 28 28]]] Detection took 0.033s for 300 object proposals image_path : temp/1768818450_356254_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg image_size (480, 614, 3) [[[ 44 44 44] [ 49 51 51] [ 42 44 44] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 43 43 43] [ 36 38 38] [ 39 41 41] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[ 70 70 70] [ 40 42 42] [ 41 43 43] ... [ 4 6 6] [ 4 6 6] [ 4 6 6]] ... [[103 101 101] [110 108 108] [ 61 59 59] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 98 96 96] [115 113 113] [ 73 71 71] ... [ 0 2 2] [ 11 13 13] [ 21 23 23]] [[ 92 90 90] [114 112 112] [ 87 82 83] ... [ 10 12 12] [ 18 20 20] [ 25 27 27]]] Detection took 0.031s for 300 object proposals image_path : temp/1768818450_356254_950003696_11e3a77b72af4b332d366d98984039c7.jpg image_size (2160, 3264, 3) [[[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 48 60 64] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 47 59 63]] ... [[167 164 160] [167 164 160] [167 164 160] ... [ 44 59 61] [ 44 59 61] [ 44 59 61]] [[165 162 158] [165 162 158] [165 162 158] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]] [[164 161 157] [164 161 157] [164 161 157] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]]] Detection took 0.602s for 300 object proposals len de result frcnn : 6 time spend for datou_step_exec : 4.642401218414307 time spend to save output : 0.00014853477478027344 total time spend for step 1 : 4.642549753189087 step2:crop_condition Mon Jan 19 11:27:35 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 757 Loading chi in step crop for subpids : 6 ! batch 1 Loaded 32 chid ids of type : 757 begin to crop the class : phare param for this class : {'margin_type': 'margin', 'margin_value': 300, 'feed_id_new_photos': 1097966} filtre for class : phare hashtag_id of this class : 492624020 WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : phare begin to crop the class : aile-avant param for this class : {} filtre for class : aile-avant hashtag_id of this class : 2106233860 WARNING : margin is only used for type bib ! now we use margin_relative for the photo_id : 926687666 now we use margin_relative for the photo_id : 950003812 map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : aile-avant time spend for datou_step_exec : 0.4097895622253418 time spend to save output : 6.246566772460938e-05 total time spend for step 2 : 0.4098520278930664 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {1071808962: [926687666, 'temp/1768818450_356254_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1768818450_356254_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1768818450_356254_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1768818450_356254_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1768818450_356254_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]} ############################### TEST image_blanchir ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_blanchir list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2698476314544678 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_blanchir Mon Jan 19 11:27:36 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanchir_image feed_id_new_photos:30154927 treat image : temp/1768818455_356254_990111206_7ca22c7e68dd0a10509c7987af0cf549.png blanchir func Result OK ! time spend for datou_step_exec : 9.72590708732605 time spend to save output : 6.4373016357421875e-06 total time spend for step 1 : 9.725913524627686 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[1818, 0, 990111206, 1, 1, 1, None, 1, None]] time used for this insertion : 0.019443273544311523 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(990111206, '1404582434', 0, 300, 0, 381, 1, 1, 'blanc')] [(990111206, '1404582434', 0, 300, 0, 381, 1, 1, 'blanc')] ############################### TEST darker_image ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : darker_image list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.15009617805480957 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:darker_image Mon Jan 19 11:27:46 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec dans la step darker batch 1 Loaded 7 chid ids of type : 2228 +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points treat image : temp/1768818466_356254_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg Result OK ! batch 1 Loaded 7 chid ids of type : 2228 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! crops sauvegardes time spend for datou_step_exec : 9.38954210281372 time spend to save output : 2.0742416381835938e-05 total time spend for step 1 : 9.389562845230103 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[2085, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.017436981201171875 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, '1404582445', 0, 897, 0, 1431, 1, 1, 'darker')] [(989962950, '1404582445', 0, 897, 0, 1431, 1, 1, 'darker')] batch 1 Loaded 7 chid ids of type : 2228 ############################### TEST img_aug ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : data_aug list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.23910832405090332 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:data_aug Mon Jan 19 11:27:56 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec numpy.version est ancienne, on utilise l'ancien bit generator numpy.version est ancienne, on utilise l'ancien bit generator batch 1 Loaded 7 chid ids of type : 2228 +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points on traite des points Result OK ! batch 1 Loaded 7 chid ids of type : 2260 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1404582449 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1404582449 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1404582449 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1404582449 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1404582449 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1404582449 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1404582449 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! photo_uploade augmentation faite pour la photo : 989962950 time spend for datou_step_exec : 9.19190263748169 time spend to save output : 3.075599670410156e-05 total time spend for step 1 : 9.191933393478394 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[2041, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.017314672470092773 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, 1404582449, 0, 1431, 0, 897, 1, 1, 'img_aug')] [(989962950, 1404582449, 0, 1431, 0, 897, 1, 1, 'img_aug')] batch 1 Loaded 7 chid ids of type : 2260 ############################### TEST rubbia ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.052767276763916016 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Mon Jan 19 11:28:05 2026 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec begin split time score 2022-04-13 10:29:59 0 TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('05', 2), ('07', 25), ('06', 1), ('08', 96), ('09', 44), ('10', 64)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {1: 188, 2: 36, 3: 8} 07092021 4599398 Nombre de photos uploadées : 232 / 23040 (1%) 07092021 4599398 Nombre de photos taguées (types de déchets): 232 / 232 (100%) 07092021 4599398 Nombre de photos taguées (volume) : 232 / 232 (100%) elapsed_time : load_data_split_time_score 4.5299530029296875e-06 elapsed_time : order_list_meta_photo_and_scores 0.0002467632293701172 elapsed_time : fill_and_build_computed_from_old_data 0.02400994300842285 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.29322028160095215 Creating list_photo_total elapsed_time : select_descriptors 19.22420048713684 07092021 4599398 Nombre de photos avec descriptors (type 3963) : 232 / 232 (100%) ERROR : Hum hum, what can we do for different size of descriptors (ignore the difference ) : 0 vs 2048 photo_id : 1049293230 photo_id_prec : 0 0:00:00|ON:0:27:28.999934|OFF:1:46:59.999878|ON:0:00:20.000007|OFF:0:01:51.000162|ON:0:12:18.999909|OFF:0:01:01.000055|ON:0:08:50.000116|OFF:0:00:09.999867|ON:0:00:19.999899|OFF:0:00:09.000058|ON:0:00:29.999860|OFF:0:01:40.000249|ON:0:00:30.999931|OFF:0:07:40.000107|ON:0:00:28.999981|OFF:0:00:09.999968|ON:0:00:10.999986|OFF:0:08:09.999919|ON:0:00:40.000176|OFF:0:01:08.999784|ON:0:00:11.000245|OFF:0:00:39.999921|ON:0:00:19.000004|OFF:0:06:31.000039|ON:0:02:09.999929|OFF:0:01:40.000021|ON:0:00:39.000031|OFF:0:07:10.999966|ON:0:12:30.000101|OFF:0:00:18.999765|ON:0:00:39.999946|OFF:0:00:11.000212|ON:0:00:29.999851|OFF:0:00:20.000150|ON:0:00:30.000042|OFF:0:00:18.999771|ON:0:07:31.000243|OFF:0:00:09.999942|ON:0:00:08.999822|OFF:0:00:11.000172|ON:0:00:39.999914|OFF:0:00:20|ON:0:31:10.000147|OFF:0:12:18.999857|ON:0:01:39.999950|OFF:0:00:19.999947|ON:0:00:21.000213|OFF:0:00:28.999911|ON:0:00:21.000117|OFF:0:00:40.000020|ON:0:10:58.999762|OFF:0:00:41.000023|ON:0:00:09.000008|OFF:0:00:21.000234|ON:0:00:29.999765|OFF:0:00:28.999920|ON:0:00:21.000174|OFF:0:00:30.000078|ON:0:00:29.999938|OFF:0:00:29.999871|ON:0:00:08.999965|OFF:0:09:31.000234|ON:0:00:09.999916|OFF:0:00:20.000049|ON:0:04:09.999926|OFF:0:01:09.000014|ON:0:02:00.999957|OFF:0:00:08.999951|ON:0:00:21.000053|OFF:0:00:18.999927|ON:0:00:39.999997|OFF:0:00:30.000158|ON: 07092021 Removing 115 photos because of the 'same image' condition Total on : 7859.999814999999 list_time_on Total off : 10509.0002 list_time_off dist_desc begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 232 time used for this insertion : 0.5285468101501465 photos_removed : len 115 elapsed_time : remove_photo_duplicate 0.6298868656158447 Creating list_photo_total XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX elapsed_time : count_sum_diff_and_build_graph 0.05574369430541992 Total photos : 232 ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 10 hashtag : 2107750911 photo_id =1049308384 : rungis_plastique_fonce ..can't find max_score_info ...can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .....Change port : 25 hashtag : 2107750908 photo_id =1049311795 : rungis_papier ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 4 hashtag : 2107750911 photo_id =1049311961 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 1 hashtag : 2107750908 photo_id =1049312208 : rungis_papier .....Change port : 5 hashtag : 2107750911 photo_id =1049312420 : rungis_plastique_fonce .Change port : 1 hashtag : 2107750908 photo_id =1049312422 : rungis_papier ..can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049312438 : rungis_plastique_fonce ....can't find max_score_info ...can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info ....Change port : 12 hashtag : 2107750908 photo_id =1049312556 : rungis_papier .can't find max_score_info ..can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750911 photo_id =1049312984 : rungis_plastique_fonce ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .........can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 17 hashtag : 2107751280 photo_id =1049317359 : rungis_aluminium .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750913 photo_id =1049317524 : rungis_tetrapak .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049318212 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..........Change port : 12 hashtag : 2107750908 photo_id =1049318287 : rungis_papier ...can't find max_score_info ..Change port : 4 hashtag : 2107750911 photo_id =1049318294 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info . Total photos : 232 Number of lists : 15 counter photos in port : 117 hashtag : rungis_aluminium(2107751280) : 8 photos in 1 portfolios ! hashtag : rungis_carton(2107750907) : 0 photos in 0 portfolios ! hashtag : rungis_papier(2107750908) : 33 photos in 6 portfolios ! hashtag : rungis_plastique_clair(2107750909) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_dur(2107750910) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_fonce(2107750911) : 74 photos in 7 portfolios ! hashtag : rungis_tapis_vide(2107750912) : 0 photos in 0 portfolios ! hashtag : rungis_tetrapak(2107750913) : 2 photos in 1 portfolios ! elapsed_time : group_photo_by_moyenne_exp 0.013356685638427734 elapsed_time : compute_and_correct_tag_with_moyenne_mobile 3.337860107421875e-06 today str has not a value , we define it as the date of the first image todaystr_first : 07092021 attention , prev_timestamp is 0 , we do nothing *******o** BIG TIME 550.0000100135803 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049307693 2021-09-07 07:45:54.010041 id_data : 12 * BIG TIME 168.99982810020447 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049308235 2021-09-07 07:48:43.009869 id_data : 13 ** BIG TIME 499.9998118877411 (191.00026988983154, 2, 0, 0, 0.93498826, 0, 0, 0, 0, 0, 0.9018999995946884, 0.002, 0.002999984407424927, 2.9, 0.019000051021575926, -0.01666705330212911) on 7 1049309345 2021-09-07 07:58:43.009858 id_data : 20 * BIG TIME 371.0001440048218 (271.00081276893616, 5, 0.24365342, 0, 0, 0, 0, 0.38418204, 0, 0.34177557, 0.9720000085830689, 0.0041, 0.014999971222877502, 2.9, 0.061000473976135255, -0.2516670016447703) on 15 1049310132 2021-09-07 08:09:54.010082 id_data : 51 * BIG TIME 461.0001001358032 (301.0006546974182, 0, 0.5752453, 0, 0, 0, 0, 0, 0, 0, 1.0228999980926514, 0.0056, 0.02589997522830963, 2.9, 0.009999968051910401, -0.0) on 18 1049310905 2021-09-07 08:18:54.009936 id_data : 60 * BIG TIME 370.0001759529114 (359.0003435611725, 5, 0, 0, 0, 0, 0, 0.85983855, 0, 0, 1.0898999773979188, 0.0078, 0.04109999623298645, 2.9, 0.008999762058258056, -0.0) on 24 1049311767 2021-09-07 08:28:24.010105 id_data : 81 *** BIG TIME 411.0001149177551 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312208 2021-09-07 08:40:04.010052 id_data : 97 * BIG TIME 549.9999330043793 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312363 2021-09-07 08:49:14.009985 id_data : 98 ** BIG TIME 168.99987387657166 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312508 2021-09-07 08:58:23.009966 id_data : 123 * BIG TIME 259.99999809265137 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312556 2021-09-07 09:02:43.009964 id_data : 124 * BIG TIME 190.00016593933105 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312803 2021-09-07 09:07:34.010149 id_data : 135 * BIG TIME 180.0000081062317 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312984 2021-09-07 09:10:34.010157 id_data : 136 * BIG TIME 1480.0000269412994 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316209 2021-09-07 09:35:23.009898 id_data : 138 * BIG TIME 668.9998891353607 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316332 2021-09-07 09:47:53.009987 id_data : 147 * BIG TIME 649.9999890327454 (1086.000019311905, 5, 0, 0, 0, 0, 0, 0.69907516, 0, 0.23055789, 1.6300000094890594, 0.0168, 0.08039999685287476, 2.9, 0.01100021505355835, -0.0) on 68 1049317197 2021-09-07 10:02:34.010134 id_data : 168 * BIG TIME 540.0001981258392 (1189.0002081394196, 0, 0.8074409, 0, 0, 0, 0, 0, 0, 0, 1.7199999867916107, 0.0194, 0.09519999706745148, 2.9, 0.009999808073043823, -0.0) on 78 1049318212 2021-09-07 10:16:24.010117 id_data : 198 * BIG TIME 190.00007104873657 (1199.0000162124634, 5, 0, 0.22708784, 0, 0, 0, 0.7244179, 0, 0, 1.781000003194809, 0.0202, 0.10109996955394746, 2.9, 0.011000241041183472, -0.0) on 79 1049318219 2021-09-07 10:20:04.010153 id_data : 202 **Terminated /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:720: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_origin_portfolio_ids = [int(item) for item in options.list_origin_portfolio_ids.split(",")] if options.list_origin_portfolio_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:721: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_photo_ids = [int(item) for item in options.list_photo_ids.split(",")] if options.list_photo_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:722: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval = [int(item) for item in options.interval_rotation.split(",")] if options.interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:723: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval = [float(item) for item in options.interval_resize.split(",")] if options.interval_resize is not "" else None /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:750: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi = [float(item) for item in options.mother_crop_portfolio_multi.split(",")] if options.mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1515: 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:1966: 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:1967: 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:1973: 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:2157: 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:2158: 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:2164: 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/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/proto/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/proto/caffe_pb2.py 506 506 0% 4-6137 /home/admin/workarea/install/segment-anything/segment_anything/__init__.py 3 3 0% 7-15 /home/admin/workarea/install/segment-anything/segment_anything/automatic_mask_generator.py 128 128 0% 7-372 /home/admin/workarea/install/segment-anything/segment_anything/build_sam.py 23 23 0% 7-107 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307-359 /home/admin/.local/lib/python3.8/site-packages/botocore/loaders.py 139 98 29% 127-134, 154, 166-175, 192-199, 222-238, 242, 246, 267-287, 310, 327-340, 374-389, 393-400, 419-424, 430-441, 455-456, 460-461 /home/admin/.local/lib/python3.8/site-packages/botocore/model.py 398 242 39% 42, 81-93, 118-126, 145-150, 161, 164, 167, 172, 178-188, 192-195, 199-206, 212, 218, 222, 228, 259-266, 269, 273, 277-281, 284, 288, 292-297, 301-305, 309, 313, 327-330, 334-337, 348-351, 355, 359, 363, 367-370, 374-379, 382-385, 394-397, 401, 404, 437-444, 448-451, 465, 469, 473, 477, 483, 487, 491-495, 500-505, 510-514, 520, 524-525, 529, 533, 537, 541, 544, 547, 551-556, 560, 564, 567, 570, 574-581, 584, 599-600, 603-616, 624-636, 643, 647, 688-691, 702-703, 712-719, 724-734, 737-745, 748-752, 755-762, 765-773, 776, 779-782, 793, 821-823 /home/admin/.local/lib/python3.8/site-packages/botocore/monitoring.py 221 149 33% 42-43, 47-48, 56-61, 73, 89, 92, 97, 104-110, 114-124, 127-129, 136-138, 141, 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/home/admin/.local/lib/python3.8/site-packages/botocore/retries/bucket.py 70 48 31% 10, 13, 16, 24-32, 36, 40-56, 60, 64, 76-77, 80-100, 103, 106-114 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/quota.py 24 16 33% 12-16, 28-32, 46-53, 57 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/special.py 28 17 39% 23-27, 36-48 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/standard.py 200 132 34% 40-61, 71-73, 77-93, 108-129, 136-138, 172-189, 199-204, 215, 218, 223-224, 227, 230, 239-241, 257, 265, 268-272, 290-298, 301-310, 334-336, 341, 348, 351-354, 376-391, 396-398, 402-406, 421-422, 433, 439, 442, 458-460, 463-475, 478, 490-498 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/throttling.py 25 15 40% 13-17, 20-21, 24-28, 35-38, 50 /home/admin/.local/lib/python3.8/site-packages/botocore/retryhandler.py 158 118 25% 52-58, 68, 73-77, 85-87, 93-118, 124-128, 132-144, 148-155, 173-174, 183-187, 219-225, 228, 231, 245-247, 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/home/admin/.local/lib/python3.8/site-packages/botocore/validate.py 164 114 30% 46-49, 55-56, 59-64, 71-84, 89, 92-94, 97-100, 103-128, 132-137, 140, 160-162, 165-166, 169-173, 179-182, 187-202, 215, 219-222, 226-231, 235, 238-244, 250, 254, 260, 266-269, 273-279, 284-285, 288-294 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/exceptions.py 26 6 77% 21-27 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/exceptions.py 67 15 78% 17-18, 22, 28-29, 33, 73-78, 85-87, 135-138 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/six.py 444 211 52% 49-72, 98-99, 112, 118-121, 131-133, 145, 154-157, 192-193, 203, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /home/admin/.local/lib/python3.8/site-packages/botocore/waiter.py 175 140 20% 44-73, 79-82, 87, 90-93, 113-121, 124-125, 132-136, 147-154, 158-162, 167-171, 175-186, 197-208, 212-219, 222-239, 242-259, 262-271, 274-284, 303-307, 310-367 /home/admin/.local/lib/python3.8/site-packages/cached_property.py 93 61 34% 14-15, 30-37, 40-47, 57-59, 62-74, 85-91, 94-95, 98-115, 118, 121, 124-128, 143-144, 147-148 /home/admin/.local/lib/python3.8/site-packages/cffi/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/cffi/api.py 544 341 37% 8-11, 52-59, 82, 97-98, 115-117, 121-123, 129-130, 133-135, 147, 160, 166, 169, 174, 190, 200, 203-211, 217-221, 227-229, 238-240, 284-291, 299, 318, 335, 361-365, 382, 392-403, 411-419, 431, 454-473, 476, 478, 483, 486-487, 495-508, 511-515, 526-538, 541, 544, 547, 556-577, 580-585, 589-635, 638-647, 652-658, 661-684, 687-695, 699-707, 720-725, 735-751, 754-777, 780, 788-801, 807-809, 815-816, 820-827, 842-847, 852-864, 867, 871, 879, 887, 889-895, 897, 903, 915-921, 923-925, 927-936, 939-940, 945, 947-948, 955-965 /home/admin/.local/lib/python3.8/site-packages/cffi/commontypes.py 37 10 73% 12-13, 31, 34-44, 56, 80 /home/admin/.local/lib/python3.8/site-packages/cffi/cparser.py 672 327 51% 12, 16-17, 23-24, 67-96, 116-142, 151-158, 161-163, 174-176, 182-186, 196, 201-203, 207, 234-242, 287-288, 337-338, 349-357, 360-367, 372-377, 379-380, 404, 415, 420, 426, 432-446, 449-454, 457-469, 473-479, 491, 493, 495, 507-549, 559, 564-568, 574, 583, 585, 594, 611, 621, 651, 667, 677-685, 692-699, 706, 718-720, 726, 734, 741, 777-778, 781-783, 788-795, 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/home/admin/.local/lib/python3.8/site-packages/charset_normalizer/cd.py 189 164 13% 24-50, 63-71, 80-91, 100-112, 120-129, 138-164, 175-244, 253-283, 291-311, 319-338, 350-388 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/constant.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/legacy.py 19 14 26% 22-50 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/models.py 174 110 37% 20-34, 37-43, 49-62, 66, 70-72, 75, 78-86, 90, 97-103, 107, 111, 119, 127-147, 151, 155-157, 161, 165, 172, 176, 180, 184-192, 201, 208-212, 219, 229, 232, 239-246, 249, 252, 259-272, 278-280, 286, 308-318, 322, 337 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/utils.py 214 158 26% 24-28, 40-46, 54-60, 65-69, 74-78, 83-93, 98-108, 113-118, 123-128, 133, 137-139, 144-149, 154-159, 164-169, 174-179, 184-189, 194, 199, 212-237, 245, 266-276, 280, 284-296, 300-310, 314-334, 342, 353-358, 372-414 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/colorama/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/colorama/ansi.py 74 8 89% 16, 19, 22, 38, 40, 42, 44, 46 /home/admin/.local/lib/python3.8/site-packages/colorama/ansitowin32.py 131 100 24% 13, 25-26, 29, 35, 38, 41, 44-53, 57-61, 75-104, 114, 117-158, 161-167, 171-174, 183-190, 194-196, 200-202, 206-220, 224-242, 246-257 /home/admin/.local/lib/python3.8/site-packages/colorama/initialise.py 48 32 33% 19-20, 25-48, 52-55, 60-64, 68-71, 75-80 /home/admin/.local/lib/python3.8/site-packages/colorama/win32.py 78 68 13% 11, 17-152 /home/admin/.local/lib/python3.8/site-packages/colorama/winterm.py 119 90 24% 25-34, 37, 40-42, 45-47, 50-58, 61-69, 72-75, 78-83, 86-91, 94-101, 104-109, 115-141, 147-166, 169 /home/admin/.local/lib/python3.8/site-packages/cryptography/__about__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/__init__.py 7 1 86% 18 /home/admin/.local/lib/python3.8/site-packages/cryptography/exceptions.py 37 5 86% 11, 33-34, 61-62 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/_oid.py 122 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/aead.py 143 127 11% 10-19, 28-46, 50-62, 70-85, 97-137, 141-145, 149-158, 162-166, 170-175, 179-187, 199-245, 257-310 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/backend.py 1215 945 22% 189, 200, 219, 229-231, 234-244, 250-264, 269-276, 279-285, 294, 299, 304, 308, 323-327, 334-336, 339-342, 346-349, 357-368, 372, 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138, 142, 147-162, 165-179, 182-184, 195, 209-214, 219-226, 230, 234, 237-255, 258-279, 286-302, 312-317 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ed25519.py 70 54 23% 17, 22-23, 30-44, 49-56, 59-77, 82-83, 86-94, 97-117, 125-143, 148-155 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ed448.py 72 54 25% 15, 23-24, 31-45, 50-57, 60-78, 83-84, 87-95, 98-118, 126-144, 149-156 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/hashes.py 45 12 73% 11, 29, 47-53, 65, 79-86 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/hmac.py 46 34 26% 15, 26-48, 52, 55-62, 67-69, 72-79, 82-84 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/poly1305.py 34 23 32% 15, 20-47, 50-54, 57-62, 65-67 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/rsa.py 257 136 47% 37, 46-59, 68-95, 107-162, 172, 180-201, 226-227, 233-234, 242-243, 250-261, 288-289, 306-322, 332-358, 383-384, 395-396, 436, 439-444, 447-451, 494, 520-532, 536, 539, 542-549, 559, 570-571, 581-586 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/utils.py 34 22 35% 11, 15-41, 53, 56 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/x448.py 53 37 30% 15, 22-23, 30-44, 49-56, 61-62, 65-73, 76-79, 87-105, 110-117 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/_conditional.py 74 34 54% 9, 25, 32, 38, 46, 52, 58, 65, 72, 79, 92, 98, 106, 118, 126, 132, 146, 155, 175, 181, 189, 199, 205, 211, 218, 227, 239, 247, 253, 257, 261, 265, 271, 275 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/binding.py 84 16 81% 25-28, 42, 88-99, 110-111, 161, 185, 200 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_asymmetric.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_cipheralgorithm.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_serialization.py 79 35 56% 37-42, 64-67, 83-87, 90-99, 109-116, 126-133, 141-144, 163-168 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/dh.py 112 47 58% 17-19, 24-39, 42-45, 50-54, 58, 62, 66, 71-80, 83-86, 92-96, 100, 104, 109-118, 121-124, 130-134, 138, 142 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/dsa.py 127 57 55% 128-139, 143, 147, 151, 154-158, 161-164, 167, 175-184, 188, 192, 195-199, 202-205, 211, 219-227, 231, 235, 238-242, 245-248, 256-258, 264-266, 270-278, 282-288 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ec.py 216 56 74% 171-184, 315, 321, 327-329, 337-348, 353-361, 364-368, 372, 376, 380, 383-386, 394, 397, 407-417, 422-426, 430, 434, 437-440, 446, 477-480 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ed25519.py 39 14 64% 18-26, 43, 57-65, 69-77, 101 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ed448.py 37 14 62% 15-23, 40, 54-61, 65-73, 103 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/padding.py 46 20 57% 44-57, 69-74, 85-88, 95-101 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/rsa.py 181 91 50% 129-132, 136-143, 156-190, 194-201, 208-214, 221, 229, 237, 254-288, 310, 316, 331, 335, 339, 343, 347, 351, 363-367, 372-375, 386, 402, 416-420, 423, 426-429, 432 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/types.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/utils.py 13 5 62% 15-19, 23 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/x25519.py 40 6 85% 19, 55, 63-71 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/x448.py 35 14 60% 15-23, 40, 48-55, 59-67, 91 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/aead.py 187 139 26% 19-30, 34, 37, 45-56, 66-71, 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11 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/hashes.py 153 27 82% 79, 89, 97, 102-104, 108, 185-191, 195, 203-209, 213, 229-232, 236, 246-249, 253 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/kdf/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/kdf/scrypt.py 36 25 31% 33-56, 59-66, 71-73 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/padding.py 116 71 39% 32-36, 42-54, 62-66, 72-84, 92-104, 109-110, 113, 116, 123-125, 128-131, 134, 137-141, 148-150, 153-156, 159-163, 168-169, 172, 175, 182-184, 187-190, 193, 196-200, 207-209, 212-215, 218-224 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/base.py 21 10 52% 22-24, 32-34, 40-42, 62-64, 70-72 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97, 118, 139, 164, 186, 204 /home/admin/.local/lib/python3.8/site-packages/filetype/types/base.py 16 5 69% 16, 20, 23, 26, 29 /home/admin/.local/lib/python3.8/site-packages/filetype/types/font.py 30 4 87% 22, 55, 88, 110 /home/admin/.local/lib/python3.8/site-packages/filetype/types/image.py 135 19 86% 82, 87, 125, 145, 172, 194, 216, 235, 255, 276, 297-305, 321 /home/admin/.local/lib/python3.8/site-packages/filetype/types/isobmff.py 20 12 40% 19-23, 26-33 /home/admin/.local/lib/python3.8/site-packages/filetype/types/video.py 86 23 73% 23-30, 47, 70-72, 89-91, 107-111, 128, 153, 180, 201, 222 /home/admin/.local/lib/python3.8/site-packages/filetype/utils.py 29 13 55% 6-7, 39-42, 68, 73-82 /home/admin/.local/lib/python3.8/site-packages/fontTools/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/cffLib/__init__.py 1891 1514 20% 62, 67-107, 110-112, 115-117, 120-125, 128-130, 133-135, 170-203, 206, 209, 212, 218-228, 239-292, 305-321, 325-389, 397-469, 472-500, 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89-92, 104-105, 117, 135-136, 151-152, 167-168, 183-184, 201-211, 226-234, 247-248, 261-262, 277-282, 295-298, 308, 348-359, 419-422 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/bezierTools.py 541 442 18% 14-18, 70, 76-78, 93-99, 125-126, 138, 148, 183, 218-234, 251, 287-295, 316-329, 357, 393-409, 430-447, 486-504, 542-549, 579-586, 609-610, 633-634, 657-658, 685-698, 702-725, 729-760, 779-798, 822-838, 879-930, 939-946, 950-960, 974-977, 981-990, 994-1006, 1021-1024, 1042, 1055-1057, 1070-1083, 1105-1108, 1112-1118, 1127-1137, 1141-1143, 1169-1221, 1228-1231, 1235-1244, 1268-1282, 1286-1290, 1294-1302, 1308-1369, 1393-1394, 1430-1445, 1453-1458, 1465-1466, 1470-1473 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/classifyTools.py 59 49 17% 13-17, 23-59, 65-66, 69-79, 89-90, 98-99, 107-108, 164-166, 170-172 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/cliTools.py 18 14 22% 37-52 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/configTools.py 128 66 48% 56, 63, 72, 81-86, 105-112, 116, 132-133, 150, 156, 162, 165, 168, 230-233, 236-248, 275-294, 321-326, 329, 332, 335, 338-339, 342, 345, 348 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/dictTools.py 26 13 50% 31, 34, 40, 43, 48, 53, 58, 63, 68, 73, 81-83 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/encodingTools.py 7 4 43% 69-72 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/etree.py 263 257 2% 47-478 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/filenames.py 76 64 16% 99-134, 168-191, 221-239, 243-246 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/fixedTools.py 35 20 43% 81, 109-110, 137-138, 157-158, 188-190, 212-213, 231-239, 251-253 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/intTools.py 9 2 78% 9, 25 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/loggingTools.py 240 173 28% 57-76, 79-86, 134-185, 192-226, 294-296, 306, 312, 316-319, 327-336, 340-342, 348-357, 370, 374-375, 384, 387, 390, 421-423, 426-434, 439-444, 447-456, 459-464, 467, 470-478, 511-514, 519, 528-533, 541-543 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/plistlib/__init__.py 259 182 30% 66-78, 82, 103-105, 109, 112, 117-122, 125, 131-142, 188-201, 204-207, 210-212, 215, 218-220, 225-238, 241-243, 250-252, 256-258, 262-264, 268-270, 274, 278, 282, 286, 290, 294, 298-301, 305, 331-333, 337-339, 343-347, 351-353, 359-373, 379-386, 390-392, 396-403, 407-416, 426, 479-490, 509-519, 544-554, 576-577, 612-632, 672-681 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/psCharStrings.py 950 717 25% 21-31, 35, 39-40, 44-45, 49-50, 54-55, 59-60, 64-65, 69-81, 131, 214-243, 255-259, 267-294, 303-309, 312-317, 320-348, 351-353, 356-358, 361, 364-365, 368, 371, 374-376, 379-381, 384, 387, 390, 393, 396-400, 405-406, 410, 413, 416, 419, 422, 425, 428, 431, 434, 437, 440, 443, 446, 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22-30, 33, 36-41, 44-46, 49, 52, 57, 60, 63, 68, 71, 74, 77, 80, 83-86, 89, 92, 97, 101, 105, 110-111, 116-120, 124-129, 133, 139, 143 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/visitor.py 88 42 52% 22, 54, 58-70, 82-94, 98, 102-103, 107-108, 113, 131-143 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/xmlWriter.py 146 118 19% 20-55, 58, 61, 64-65, 69, 73, 80, 84, 88-94, 97-102, 105-111, 114-116, 119-123, 126-130, 133-144, 147, 150-151, 154-167, 171-176, 180-182, 188-195, 199-204 /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/builder.py 1114 926 17% 55-59, 99-123, 130-137, 140, 149, 153, 156, 165-170, 173-177, 180-184, 187-191, 194-198, 201-208, 218, 249-250, 253, 262-265, 268, 271, 279, 284, 287, 293-296, 302-306, 309-319, 322-329, 334, 339-346, 349-359, 368-429, 432-475, 478-547, 550-559, 562-580, 583, 593-599, 612-621, 624-633, 636-640, 643-647, 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/home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/G_S_U_B_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/H_V_A_R_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/M_A_T_H_.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/S_V_G_.py 127 98 23% 55-97, 102-153, 156-168, 171-181, 186-189, 192, 212, 215 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/TupleVariation.py 522 459 12% 39-40, 43-46, 49, 53-57, 65, 68-106, 109-128, 131-166, 169-177, 180-196, 200-205, 215-270, 275-321, 324-340, 359-371, 375-385, 389-415, 419-461, 466-488, 492-497, 503-510, 516-519, 529-530, 540-550, 553-577, 580-581, 584-614, 618-622, 628-638, 645-705, 718-744, 750-790, 804-808 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/tables/V_O_R_G_.py 111 96 14% 16-47, 50-71, 74-96, 99-113, 116-123, 126-133, 136, 141-142, 145-152, 155-159 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425-439, 443-455, 459, 467-477, 482, 494-545, 550-554, 564-575 /home/admin/.local/lib/python3.8/site-packages/google/api_core/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/google/api_core/client_options.py 24 19 21% 95-108, 111, 122-130 /home/admin/.local/lib/python3.8/site-packages/google/api_core/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/_credentials_base.py 11 3 73% 47, 63, 73 /home/admin/.local/lib/python3.8/site-packages/google/auth/_default.py 225 188 16% 73-76, 113-126, 165-170, 178-221, 226-245, 251-279, 286-306, 318-343, 378-413, 419-430, 434-444, 448-458, 462-508, 512-522, 527-529, 533-544, 644-697 /home/admin/.local/lib/python3.8/site-packages/google/auth/_exponential_backoff.py 63 39 38% 73-85, 90, 95, 98-99, 102-108, 117, 120-121, 124-136, 145, 148-149, 152-164 /home/admin/.local/lib/python3.8/site-packages/google/auth/_helpers.py 61 34 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149-154 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/__init__.py 20 4 80% 55, 60, 65, 103 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/_http_client.py 42 24 43% 36-38, 42, 46, 50, 80-113 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/_mtls_helper.py 134 107 20% 61-65, 81-88, 109-114, 134-144, 148-186, 190-193, 197-207, 211-222, 241-276, 310-337, 360-365, 401-407 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/mtls.py 35 29 17% 27-39, 53-67, 89-112 /home/admin/.local/lib/python3.8/site-packages/google/auth/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/oauth2/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/google/oauth2/_client.py 126 100 21% 57-69, 85-110, 123-133, 174-217, 259-271, 297-319, 344-367, 396-419, 439-450, 494-508 /home/admin/.local/lib/python3.8/site-packages/google/oauth2/service_account.py 249 165 34% 174-197, 215, 243-246, 260-263, 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569-578, 582-615, 650-681, 687-705, 711-727, 733-748, 754-769, 787-801, 820-829 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/enum_type_wrapper.py 33 19 42% 53-64, 69-73, 83, 103, 108-114 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/type_checkers.py 121 59 51% 61, 71-76, 80, 92-101, 123-132, 142, 153-155, 157, 164, 172, 175-181, 184, 195-217, 220, 261-272, 275 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/well_known_types.py 447 337 25% 68-72, 76-80, 85, 89, 108-126, 139-190, 194, 198, 202, 207, 212, 216-217, 221-222, 226-227, 231-232, 236, 249-250, 267-288, 301-321, 326, 330-331, 335-336, 340, 344, 349, 355, 361-362, 366, 372, 378-382, 386-395, 406-411, 421-424, 428-433, 437-440, 444-446, 458-459, 463-467, 471-477, 492-493, 499-508, 513-516, 522-546, 551-561, 581-583, 587-588, 604-613, 617-618, 627-635, 639-643, 649, 658-660, 666-698, 703-711, 715-732, 736-750, 759, 762, 765, 768, 771, 774, 777, 780, 783, 787-790, 794-797, 800-801, 812, 815, 818-819, 823, 826, 829, 832-833, 837-840, 844-847 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/wire_format.py 105 49 53% 75, 77, 89, 97, 105-107, 112-114, 123, 127, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 194, 199, 209-221, 227, 237-248, 268 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/json_format.py 423 362 14% 121-127, 158-165, 169, 184-191, 194-195, 199-206, 210-270, 274-313, 317-334, 340, 344-356, 360, 365-369, 372, 377, 381-386, 391-400, 419-425, 444-446, 456-457, 469-476, 488-594, 598-619, 625-628, 632-645, 650-655, 659-667, 671-672, 685-697, 715-752, 767-777, 782-813, 829-839 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/message.py 90 46 49% 81-83, 87, 91, 94, 98, 102, 115, 126-129, 133, 143, 152, 191, 198-199, 215, 230, 261, 279, 293, 309, 327, 335, 343, 350, 360, 364, 368, 389, 393, 397-403, 406-413, 419-421 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/message_factory.py 56 39 30% 49, 78-85, 100-121, 137-157, 176-185 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/pyext/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/pyext/cpp_message.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/reflection.py 13 5 62% 75-78, 95 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/symbol_database.py 44 21 52% 95, 108, 118-120, 130, 148, 169-186 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/text_encoding.py 32 13 59% 73-80, 96-107 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/text_format.py 722 458 37% 79-89, 92, 95, 101, 104, 107, 110, 171-192, 198-202, 226-239, 257-263, 281-287, 302-312, 375-391, 395-407, 410-418, 426-457, 461-512, 516-541, 545-548, 553-560, 563-579, 590-629, 682, 755-759, 811-813, 852-875, 878-907, 912-915, 923-925, 929, 932, 941-943, 959-964, 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142-144, 148-151, 158-167 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/_helpers.py 53 29 45% 113-130, 137-138, 153-163, 183-188, 204-207 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/channel.py 71 46 35% 104-107, 135-138, 200-207, 218-233, 245-248, 267-278, 299-308 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/discovery.py 586 487 17% 59, 136-139, 161-165, 179-189, 279-343, 360-369, 404-453, 457-460, 538-722, 748-763, 775-782, 799, 830-847, 878-888, 923-935, 952-956, 971-975, 1014-1025, 1038-1066, 1079-1331, 1351-1392, 1429-1442, 1451-1452, 1460-1464, 1472-1474, 1477, 1480, 1487, 1490-1492, 1496-1535, 1541-1573, 1581-1604, 1617-1627, 1640, 1659-1662 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/errors.py 86 43 50% 40-46, 51, 55-85, 88-102, 166-168, 171-174, 185, 195 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/http.py 672 543 19% 43-44, 90-147, 170-229, 243-244, 253-256, 269-270, 279-282, 318, 326, 334, 342, 355, 367, 376, 389-396, 405, 419-425, 469-478, 486, 494, 502, 510, 523-524, 536, 545, 549, 592-601, 606-607, 616, 620-621, 658-659, 695-713, 734-780, 801-804, 816-820, 852-874, 896-939, 951, 992-1093, 1109-1126, 1133-1141, 1146-1149, 1162, 1210-1247, 1259-1277, 1290-1296, 1313-1319, 1331-1367, 1379-1395, 1405-1408, 1440-1453, 1468-1525, 1544-1606, 1624-1630, 1638, 1681-1682, 1700-1720, 1732-1744, 1755-1759, 1762, 1794-1796, 1808-1826, 1848-1877, 1898-1930, 1944-1962 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/mimeparse.py 56 41 27% 45-56, 73-83, 95-120, 133, 147-149, 166-177, 181-183 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/model.py 170 103 39% 38, 51, 79, 94, 118-130, 152-182, 193-205, 209-215, 230-241, 252, 264, 284, 287-293, 296-307, 311, 327, 331, 347, 351, 375, 378, 381, 385, 409-429 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/sample_tools.py 31 22 29% 58-108 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/schema.py 110 86 22% 81-84, 99-114, 127, 142-145, 158, 167, 184-200, 208, 216, 225-232, 236, 240, 251-302, 316-317 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/httplib2/__init__.py 914 737 19% 59-61, 151-188, 192-194, 202-217, 228-229, 233-245, 258-274, 281, 290-292, 296-304, 342-386, 390-412, 416-429, 433-471, 475-478, 482, 498-502, 505-506, 510-511, 516, 526, 529, 532, 535, 538, 541, 544, 547, 552, 557, 567-579, 583-615, 618-630, 639-678, 682-689, 705-708, 721, 726-730, 740-765, 770, 791-794, 797-805, 808-811, 814-816, 824, 827, 830-832, 840, 843-845, 877-881, 900, 911, 914, 918-929, 932, 942-949, 955-979, 994-998, 1002-1066, 1093-1114, 1118-1202, 1272, 1313-1317, 1320-1327, 1330-1331, 1337-1341, 1346, 1351, 1356-1357, 1360-1431, 1439-1510, 1513, 1542-1751, 1775-1793, 1796-1799 /home/admin/.local/lib/python3.8/site-packages/httplib2/auth.py 40 17 58% 40-49, 54-69 /home/admin/.local/lib/python3.8/site-packages/httplib2/certs.py 29 9 69% 10-11, 17, 30-33, 35, 38, 42 /home/admin/.local/lib/python3.8/site-packages/httplib2/error.py 25 3 88% 10-12 /home/admin/.local/lib/python3.8/site-packages/httplib2/iri2uri.py 41 30 27% 49-57, 64-72, 76-124 /home/admin/.local/lib/python3.8/site-packages/httplib2/socks.py 244 201 18% 44, 127, 139-142, 155-162, 169-175, 181-183, 190-206, 209-210, 241, 256-352, 358, 364, 371, 378-422, 429-467, 477-518 /home/admin/.local/lib/python3.8/site-packages/imgaug/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/base.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/batches.py 266 116 56% 24, 168, 285-311, 374, 380, 386, 392, 398, 415, 431, 583-610, 624, 630, 717, 751, 771-785, 803-816, 853-888, 943-954, 979-981, 997, 1018-1029, 1051-1068, 1081-1088 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/bbs.py 484 365 25% 49-58, 73-76, 91, 106, 121, 136, 148, 160, 172, 184, 196, 214-218, 249-253, 282, 318-322, 355, 381-387, 406, 433-437, 459-464, 493-501, 521-523, 548-552, 581-585, 592, 614-628, 647, 673-677, 721-723, 784-795, 843-895, 946-957, 1003-1085, 1101-1103, 1122-1124, 1158-1173, 1201-1203, 1228-1244, 1277, 1319, 1332, 1345, 1348, 1351, 1382-1383, 1397, 1411, 1425, 1439, 1451, 1473-1481, 1500, 1529-1544, 1574-1580, 1601-1606, 1620, 1652-1670, 1694-1695, 1735-1748, 1773-1778, 1800, 1825, 1847, 1854, 1870-1876, 1887, 1913-1915, 1958-1960, 1974-1989, 2015-2028, 2042-2045, 2066-2072, 2094-2100, 2113, 2128, 2141, 2144, 2147, 2158-2165, 2169-2187, 2190, 2194-2195, 2203-2227, 2230-2246, 2251-2258, 2261-2271 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/heatmaps.py 143 116 19% 45-96, 113-125, 156-187, 221-254, 280-286, 323-333, 381-396, 413-414, 435-436, 446, 467-475, 494-496, 536-537, 580-584, 614-659, 671, 682 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/kps.py 364 260 29% 37-67, 100-102, 114, 126, 140, 154, 184-186, 213, 235-239, 265, 287-289, 308, 347-408, 456-487, 512-526, 554, 575, 601, 604, 647, 661, 673, 685, 718-726, 744, 785-790, 819, 845, 863, 879, 901-903, 922, 936, 975, 996-1006, 1077-1097, 1139-1184, 1214-1228, 1280-1334, 1351, 1374-1384, 1405-1411, 1452, 1467, 1480, 1483, 1486 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/lines.py 517 418 19% 48-67, 80-82, 94, 106, 122, 138, 153-155, 170-172, 192-198, 211-213, 240-262, 285-288, 313, 343-344, 371, 400-409, 431-433, 456-463, 495-502, 520-614, 634-671, 697-699, 742-744, 772-778, 812-823, 854-864, 907-923, 967-1052, 1097-1104, 1176-1210, 1266-1305, 1325-1327, 1356-1360, 1372-1373, 1385-1390, 1405-1406, 1443-1444, 1483-1484, 1520-1539, 1563-1565, 1587, 1609, 1624, 1637, 1640, 1643-1645, 1690-1698, 1712, 1726, 1738, 1760-1768, 1787, 1815-1818, 1837-1838, 1913-1923, 1948-1951, 1973, 1998, 2018, 2046-2050, 2076, 2102-2104, 2147-2149, 2163-2165, 2191-2216, 2232-2238, 2262-2277, 2302-2308, 2334-2340, 2353, 2368, 2381, 2384, 2387, 2392-2397, 2401-2414, 2418-2442 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/normalization.py 574 394 31% 14, 16-19, 29-32, 37-46, 64-65, 77-87, 98-99, 111-121, 132, 134-138, 149, 153, 171-187, 203-230, 245, 247-250, 256-257, 260-261, 264-301, 319-384, 433-516, 527-530, 532-565, 570-575, 583, 597-626, 638-669, 675-679, 681-686, 690-700, 703-713, 720-762, 773-861, 898-1040, 1193-1202, 1223-1252, 1262, 1265, 1273-1281, 1286-1288 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/polys.py 731 611 16% 49-70, 104-142, 157, 169, 181, 197, 213, 231-233, 245-248, 263-264, 279-280, 310-311, 338, 369-380, 402-405, 435-438, 459, 480, 511-536, 543, 575-650, 676-678, 721-723, 818-902, 928-957, 993-1008, 1031-1041, 1062-1069, 1089, 1101-1103, 1125, 1138-1142, 1157-1159, 1178-1181, 1208-1223, 1250, 1297-1306, 1340-1342, 1364, 1385, 1400, 1413, 1416, 1419-1423, 1455-1456, 1470, 1484, 1496, 1517-1525, 1543, 1637-1653, 1678-1682, 1702, 1725, 1747, 1777-1781, 1806, 1831-1833, 1876-1878, 1896-1898, 1916, 1930-1932, 1964-1988, 2003-2009, 2032-2047, 2072-2078, 2104-2110, 2123, 2138, 2151, 2154, 2157, 2164-2182, 2188-2230, 2233-2281, 2284-2299, 2303-2317, 2321-2328, 2331-2392, 2397-2407, 2413-2526, 2531-2573, 2579-2589, 2592-2760, 2777-2782, 2806-2829 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/segmaps.py 124 101 19% 21, 103-167, 195-205, 211, 236-255, 312-381, 418-421, 466-476, 482, 505-507, 536-540, 568-572 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/utils.py 114 88 23% 12, 25-37, 57-59, 90-111, 139-142, 167-173, 204-218, 252-270, 290-296, 323-334, 339-342, 348-361 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/__init__.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/arithmetic.py 791 668 16% 101-118, 130-161, 165-204, 247-261, 274-283, 298-325, 383-400, 411-442, 448-492, 548-565, 571-583, 587-628, 686, 754-781, 816-847, 877-893, 943-1004, 1038, 1132-1186, 1190-1193, 1199-1202, 1207-1211, 1236-1252, 1259-1261, 1268-1281, 1287-1309, 1335, 1369, 1407-1470, 1552-1559, 1564-1607, 1611, 1698-1705, 1710-1729, 1733, 1836-1845, 1959-1968, 2075-2081, 2167-2174, 2179-2221, 2225, 2311-2318, 2323-2353, 2357, 2365-2373, 2554-2570, 2576-2587, 2591-2627, 2631-2663, 2680-2698, 2703, 2792-2794, 2803-2834, 2993-3010, 3113-3128, 3132-3165, 3171-3209, 3214, 3301-3315, 3319-3353, 3357-3359, 3364-3366, 3371, 3483-3491, 3496-3537, 3541, 3612, 3675, 3816-3831, 3905-3913, 4035-4055, 4132-4139, 4259-4279, 4414-4431, 4436-4460, 4464-4485, 4496, 4505-4510, 4579, 4664-4665, 4742-4748, 4754-4765, 4769 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/artistic.py 105 84 20% 99-157, 164-170, 175-189, 194-198, 203-206, 211-221, 226-239, 244-246, 365-381, 385-396, 400-401, 413 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/base.py 14 7 50% 21-27, 43, 49 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/blend.py 584 447 23% 38-43, 111-211, 216-242, 247-258, 403-422, 426-472, 476, 481, 486, 491-498, 616-648, 652-695, 704-718, 724-785, 789, 794, 799, 804-810, 958-962, 970, 1160-1190, 1411-1442, 1581, 1704, 1833, 1962, 2064, 2196, 2325, 2407-2409, 2418-2423, 2429-2451, 2562-2578, 2586-2592, 2598, 2607-2626, 2667-2684, 2690-2694, 2701-2703, 2708-2735, 2740-2744, 2752-2762, 2773-2777, 2783, 2792-2801, 2842-2881, 2942, 2982, 3041, 3081, 3134-3142, 3153-3158, 3164-3175, 3205-3242, 3275, 3291, 3305, 3315-3320, 3348-3359, 3434-3450, 3462-3469, 3475-3490, 3518-3528, 3598-3614, 3626-3637, 3643-3658, 3686-3699, 3724-3725, 3734-3740, 3758, 3782, 3809, 3842 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/blur.py 259 182 30% 174-181, 186-215, 219, 221, 223, 225, 242, 263, 265, 329-367, 372, 376, 461, 473, 570-616, 622-686, 690, 768-781, 787-815, 819, 932-942, 949-975, 979, 1075-1088, 1098-1101, 1106-1134, 1213-1220, 1227-1236, 1240-1241, 1251 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/collections.py 50 33 34% 192-218, 233, 254-293, 340-341 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/color.py 642 462 28% 69-72, 240-310, 374-395, 405-407, 414, 436-454, 459-853, 888-950, 983, 994, 1063-1069, 1073-1093, 1096-1100, 1104, 1108, 1111, 1226-1234, 1238-1266, 1270-1277, 1282-1285, 1289-1293, 1298, 1303, 1307, 1396-1402, 1414, 1497, 1571, 1666-1675, 1679-1688, 1692-1712, 1716-1744, 1747-1751, 1755, 1759, 1762, 1891-1974, 2048, 2117, 2196-2204, 2380-2398, 2401-2438, 2442-2483, 2501-2511, 2516-2521, 2525, 2530-2543, 2547-2554, 2558-2564, 2572-2582, 2653, 2726, 2858-2905, 2908-2913, 2917-2939, 2943, 3021, 3076-3083, 3087-3095, 3100, 3115-3126, 3129-3139, 3143-3153, 3157-3210, 3218, 3228-3238, 3380, 3396, 3399, 3409, 3484-3526, 3663, 3679, 3682, 3824, 3836, 3858, 3889, 3957-3974, 3993-3995, 4015-4017, 4023, 4031, 4039-4053, 4081, 4133-4136, 4165 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/contrast.py 303 239 21% 37-45, 49-90, 94, 149-172, 233-258, 318-340, 388-421, 489-493, 589-599, 676-681, 754-761, 788-803, 806-898, 902, 1001-1012, 1017-1082, 1086, 1265-1275, 1280-1309, 1313-1315, 1388, 1394-1420, 1424, 1541-1549, 1554-1583, 1587-1588 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/convolutional.py 149 115 23% 126-143, 150-234, 238, 313-322, 330-331, 334-353, 424-433, 441-442, 445-464, 521-527, 535, 538-556, 654-664, 672-673, 676-717 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/debug.py 470 319 32% 42-77, 138-140, 145, 150, 154-159, 171, 176, 181-184, 188-191, 203-205, 210, 215, 219-250, 255-256, 271-272, 277, 282, 286-294, 305, 314, 323, 332, 340-341, 358-359, 367-373, 470-496, 502, 509, 515-527, 533-558, 564-593, 599-611, 617-671, 677-690, 696-708, 714-756, 762-825, 832-843, 849-850, 863, 868, 873, 878-882, 887, 892-894, 899-901, 906-908, 913-915, 920, 925-926, 931-932, 937, 942-943, 948-949, 953-957, 961-965, 970, 975, 980, 985-987, 992, 997, 1002, 1007-1010, 1051, 1055-1056, 1060-1061, 1071-1075, 1079-1080, 1086, 1123-1124, 1128-1130, 1170-1174, 1178-1193, 1256-1266, 1273-1274 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/edges.py 94 68 28% 93-101, 110-114, 117-159, 162, 327-375, 382-415, 419-468, 472, 476 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/flip.py 114 92 19% 722-724, 728, 734-761, 805, 811, 817, 872-875, 879-925, 929, 984-987, 991-1039, 1043 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/geometric.py 1751 1369 22% 92-96, 102, 106-120, 129, 139, 143-150, 158, 162, 169, 185-188, 215-244, 267, 269, 298-312, 315, 317, 323-351, 399-443, 475-508, 543-582, 607, 611, 639, 672, 681, 1178-1181, 1224-1229, 1253-1268, 1279-1284, 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5849-5862, 5866-5935, 5939-5955, 5961-5970, 5976-5986, 5991-5996, 6001-6007, 6011, 6018-6021 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/imgcorruptlike.py 197 121 39% 96-112, 144-212, 243-257, 297, 329, 361, 393, 425, 457, 470-515, 550, 582, 614, 646, 678, 710, 742, 774, 806, 838, 870, 902, 934, 1009-1014, 1020-1029, 1033-1037, 1042, 1098, 1157, 1216, 1275, 1334, 1393, 1452, 1511, 1570, 1629, 1688, 1747, 1806, 1865, 1924, 1983, 2042, 2101, 2164, 2171-2177 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/meta.py 1062 774 27% 47, 53, 59-61, 68-72, 77-100, 108-117, 122-125, 130-142, 147-149, 153-157, 166-173, 207-210, 215, 219, 296, 299-304, 313-314, 320, 370-528, 550, 598-602, 610-616, 622-624, 626-632, 649, 654-659, 668-670, 713-737, 762-771, 821-822, 874, 901, 940, 966, 1010, 1071, 1116, 1180, 1246, 1315, 1364, 1406, 1449, 1479, 1524-1529, 1558, 1592-1596, 1631-1656, 1682-1687, 1933-1934, 1945-1957, 1984, 1995-2000, 2003, 2081-2082, 2139-2186, 2213-2214, 2252-2256, 2272-2287, 2296, 2357-2373, 2393-2397, 2443-2449, 2479-2487, 2552-2613, 2627, 2664, 2686-2696, 2740-2757, 2780, 2805-2813, 2871-2888, 2897, 2935-2947, 2958, 2975, 2978, 2981-2983, 3094, 3100, 3107, 3119, 3132-3137, 3141, 3152, 3156, 3159-3164, 3287-3315, 3319-3343, 3346-3355, 3358-3362, 3366-3374, 3378-3411, 3414-3419, 3423, 3434, 3438, 3441-3446, 3512, 3599-3607, 3612-3641, 3644-3653, 3657, 3661-3666, 3669-3673, 3746-3766, 3770-3811, 3816, 3824-3831, 3841-3849, 3859-3861, 3865-3869, 3873-3878, 3882-3898, 3901-3905, 3909, 3913, 3916-3920, 3980, 3986, 3991, 4035, 4222-4231, 4234-4236, 4239-4252, 4255-4269, 4273-4287, 4291-4311, 4316-4333, 4338-4363, 4367, 4497-4504, 4521-4522, 4526-4529, 4669-4677, 4697-4718, 4725-4733, 4741, 4746-4748, 4754, 4757-4759, 4765, 4768-4770, 4776, 4779-4781, 4787, 4791-4794, 4800, 4803-4805, 4811, 4815-4818, 4892-4906, 4910-4922, 4926, 4972-4992, 5088-5092, 5096-5103, 5108, 5176, 5182-5188, 5193 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/pillike.py 343 243 29% 80-90, 122, 151, 182, 211, 244-249, 294-313, 320-347, 352-362, 415-426, 432, 446-531, 536-554, 597, 641, 684, 727, 732-750, 787, 824, 861, 898, 936, 973, 1010, 1047, 1084, 1121, 1132-1189, 1278-1316, 1373, 1444, 1451-1454, 1459, 1533-1540, 1558-1562, 1568-1574, 1578, 1583, 1643, 1709, 1775, 1841, 1855-1858, 1862-1865, 1870, 1917, 1967, 2018, 2070, 2122, 2173, 2224, 2274, 2324, 2375, 2489-2503, 2507-2513, 2520-2544, 2549-2567, 2572 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/pooling.py 113 76 33% 26-36, 49-59, 66-76, 83-92, 96-111, 115, 120, 126-150, 154-168, 172-174, 179-181, 186-188, 193, 312, 318, 433, 441, 556, 564, 679, 687 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/segmentation.py 353 269 24% 56-66, 212-222, 226-283, 287-319, 323, 357-378, 383-388, 392-393, 398-416, 421-441, 598-611, 615-634, 637-654, 658, 776, 940, 1122, 1174-1187, 1245-1248, 1253-1257, 1260-1263, 1267-1279, 1283-1286, 1290-1313, 1316, 1319, 1377-1381, 1387-1391, 1396-1410, 1413, 1417, 1463-1468, 1474-1495, 1498-1505, 1508-1512, 1515-1518, 1522-1538, 1541, 1545, 1580, 1585-1596, 1600-1603, 1608-1620, 1623, 1626, 1664-1671, 1676-1682, 1688-1692, 1695, 1699 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/size.py 1205 1047 13% 48-66, 70-72, 77-90, 95, 102, 110-147, 154-166, 170-174, 179-219, 225-253, 257, 262-279, 285-330, 348-350, 425-553, 607-621, 680-695, 734-766, 807-838, 874-902, 937-965, 1007-1037, 1084-1115, 1123, 1267-1272, 1276-1359, 1363-1378, 1382-1410, 1414-1434, 1438-1461, 1465-1475, 1478-1490, 1494-1529, 1533, 1539-1548, 1553, 1558, 1804-1828, 1833-1847, 1852-1902, 1906-1964, 1968-1997, 2001-2019, 2024-2043, 2047-2056, 2059-2169, 2173, 2377-2399, 2563-2585, 2731-2756, 2764-2790, 2794-2812, 2816-2831, 2836-2860, 2863-2884, 2889-2904, 2908, 2972, 3109-3121, 3129-3153, 3157-3171, 3175-3189, 3193-3204, 3210-3223, 3226-3245, 3249, 3310, 3381-3386, 3390-3412, 3417, 3477, 3551-3557, 3561-3583, 3588, 3657, 3739-3744, 3748-3770, 3775, 3835, 3914-3920, 3924-3946, 3951, 4019, 4085-4089, 4093-4118, 4123, 4180, 4248-4253, 4257-4279, 4284, 4340, 4399, 4461, 4523, 4579, 4701-4737, 4742-4781, 4787-4807, 4813-4825, 4830-4838, 4843-4855, 4858-4901, 4904-4908, 4912, 4916, 4919-4927 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/weather.py 226 166 27% 140-150, 153-159, 163-186, 190, 353-366, 371-379, 383, 394-414, 422-451, 457-466, 471-478, 484-495, 563-594, 666, 855-876, 880-888, 892, 903-953, 957-958, 965-969, 975, 979-988, 997-1004, 1012-1017, 1022-1023, 1028-1029, 1204-1217, 1301, 1311, 1317-1319, 1328-1339, 1428-1441 /home/admin/.local/lib/python3.8/site-packages/imgaug/dtypes.py 148 109 26% 20, 35-49, 54-97, 104, 108-112, 116-135, 150-172, 177-183, 187-189, 196-197, 201-207, 217-253, 258-282, 319-345 /home/admin/.local/lib/python3.8/site-packages/imgaug/external/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/external/opensimplex.py 1412 1385 2% 14-15, 85, 100-113, 116-120, 123-129, 132-140, 148-244, 252-740, 748-1934 /home/admin/.local/lib/python3.8/site-packages/imgaug/imgaug.py 599 461 23% 14-15, 86-87, 106, 147, 165-187, 192, 365, 382, 400-402, 419, 440-448, 462, 491-506, 525-526, 541-542, 567-572, 586-587, 610-613, 632-633, 656-657, 670-671, 703-726, 761-802, 839-846, 866, 889-906, 928-957, 983-1003, 1029-1056, 1083-1106, 1142-1146, 1200-1216, 1273-1306, 1403-1572, 1576-1578, 1608-1619, 1691-1744, 1791, 1838, 1882, 1926, 1976-2034, 2061-2062, 2095-2123, 2142-2143, 2150-2151, 2153, 2180, 2227-2273, 2352-2355, 2369-2371, 2389-2391, 2402-2404, 2415-2417, 2455-2461 /home/admin/.local/lib/python3.8/site-packages/imgaug/parameters.py 1056 827 22% 37, 40-43, 51-62, 74-99, 112-153, 161-187, 197-247, 254-293, 299-301, 306-314, 320-336, 342, 387, 420, 423-425, 431-433, 439-441, 447-453, 459-461, 467-469, 475-477, 483-485, 491-493, 499-501, 507-513, 519-521, 527-529, 535-537, 551, 562, 594-628, 659, 663, 675, 678-682, 706-727, 731-739, 743, 747-750, 787-799, 802-856, 859, 862, 899-900, 903-907, 910, 913, 955-958, 962-968, 971, 974, 1014-1016, 1019-1022, 1025, 1028, 1068-1071, 1075-1080, 1083, 1086, 1143-1149, 1153-1170, 1173, 1176, 1220-1223, 1227-1232, 1235, 1238, 1273-1275, 1279-1281, 1284, 1287, 1323-1325, 1329-1331, 1334, 1337, 1379-1382, 1386-1392, 1395, 1398, 1438-1445, 1448-1452, 1455, 1458, 1538-1589, 1592-1653, 1656, 1659-1670, 1703-1715, 1718-1723, 1726, 1729-1737, 1766-1769, 1772-1791, 1794, 1797-1798, 1846-1850, 1853-1867, 1870, 1873, 1926-1930, 1934-1958, 1961, 1964, 2018-2022, 2025-2038, 2041, 2044, 2093-2097, 2100-2113, 2116, 2119, 2169-2173, 2176-2202, 2205, 2208, 2229-2233, 2236-2237, 2240, 2243-2244, 2269-2280, 2283-2300, 2303, 2306-2307, 2347-2364, 2367-2404, 2407, 2410-2411, 2449, 2491, 2570-2622, 2628-2659, 2662, 2665-2666, 2731-2746, 2771, 2774-2789, 2792, 2795-2796, 2870-2890, 2895-2906, 2909-2934, 2937-2977, 2980, 2983, 3072-3093, 3103-3114, 3117-3177, 3181-3188, 3191, 3194, 3201 /home/admin/.local/lib/python3.8/site-packages/imgaug/random.py 378 202 47% 67, 172, 185, 202-203, 224, 241, 256, 272, 293, 304-305, 318, 336, 348-352, 384, 396-398, 438, 455, 476, 489, 496, 500, 507, 512, 517, 521, 526, 530, 534, 538, 542, 546, 550, 554, 559, 563, 567, 571, 575, 580, 585, 590, 594, 599, 604, 608, 613, 617, 621, 633-643, 654-665, 676-686, 691, 695, 700, 705, 709, 714, 719, 738, 756, 770, 783-785, 798, 811-814, 831, 880-883, 891, 895, 940, 961, 970, 975-978, 985-992, 996-1003, 1022, 1032, 1065-1066, 1080-1082, 1087, 1091, 1154-1156, 1163-1166, 1170-1173, 1196-1198, 1219, 1237-1240, 1260, 1284-1286, 1314-1319, 1324-1325, 1346-1348, 1356, 1373-1376, 1384, 1405-1407, 1411-1431, 1435-1444, 1466, 1477-1478, 1515-1519, 1521, 1550-1561, 1586-1587, 1590-1592, 1595-1596 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_common.py 101 56 45% 35-46, 56, 68-72, 77, 82, 87, 95-104, 112-114, 129-141, 145, 156-158, 167, 176, 184-185, 194-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_compat.py 58 36 38% 13, 20-23, 28-29, 42, 46, 49-75, 101-103, 107, 117-126 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/abc.py 65 23 65% 26, 39, 47, 52, 79-80, 86-87, 109-124, 130, 161, 164, 167, 170 /home/admin/.local/lib/python3.8/site-packages/jmespath/__init__.py 12 4 67% 10-12, 19, 23 /home/admin/.local/lib/python3.8/site-packages/jmespath/ast.py 44 22 50% 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46, 50, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90 /home/admin/.local/lib/python3.8/site-packages/jmespath/compat.py 40 21 48% 16-48 /home/admin/.local/lib/python3.8/site-packages/jmespath/exceptions.py 68 36 47% 13-19, 23-24, 34-37, 41-42, 50-57, 60-61, 68-71, 74, 82-85, 91, 103-106, 109, 117 /home/admin/.local/lib/python3.8/site-packages/jmespath/functions.py 228 141 38% 73-81, 84-91, 94-97, 104-120, 124-134, 137-161, 166, 170-173, 177-179, 183-186, 190-193, 198-211, 215, 219, 223, 227, 231-234, 238, 242, 246, 250-253, 257-260, 264-267, 271-274, 278, 282, 288, 292, 296-307, 311-327, 331-337, 341-347, 350-359, 362 /home/admin/.local/lib/python3.8/site-packages/jmespath/lexer.py 139 120 14% 27-110, 114-118, 121-127, 130-135, 140-156, 159-176, 180-188, 193-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/jmespath/parser.py 313 244 22% 79-82, 85-92, 95-105, 108-116, 119-135, 138, 141, 144-152, 155-160, 163, 166, 169-171, 174-177, 180-181, 184-198, 205-213, 219-237, 240, 243-244, 247-259, 262-263, 266-267, 270-271, 274-291, 295-301, 304, 307, 310, 313, 316, 319, 322-325, 328-344, 347-353, 356-357, 360-369, 372-389, 393-406, 417-434, 437-440, 443, 447-451, 455-458, 461, 464, 467, 470, 473-476, 480-488, 492-493, 498, 504-505, 508-510, 522-524, 527 /home/admin/.local/lib/python3.8/site-packages/jmespath/visitor.py 212 161 24% 9-12, 32-35, 43, 54-56, 70-71, 76-77, 80, 85, 88-94, 97, 113-123, 126, 129-132, 135-138, 142-158, 161, 164, 167-171, 174-184, 187-197, 200, 205-210, 213-216, 219-222, 225, 228, 231-236, 239-244, 247-250, 253-256, 259-264, 267-270, 273-281, 284-294, 300, 304, 309-311, 314-319, 322-328 /home/admin/.local/lib/python3.8/site-packages/lxml/__init__.py 11 9 18% 12-22 /home/admin/.local/lib/python3.8/site-packages/matplotlib/__init__.py 517 265 49% 165-178, 190-191, 223, 240-243, 276-277, 356-460, 465-480, 505, 511, 514-515, 521-537, 608-609, 617, 702-706, 708-709, 711-714, 717-718, 721-722, 724-725, 731-734, 737-740, 745-748, 758-764, 767, 775, 788-789, 803, 808-814, 821-828, 863-865, 872, 875-878, 889-902, 927-945, 977, 1033-1052, 1074-1077, 1090-1092, 1115-1119, 1168-1177, 1233-1240, 1252, 1263, 1270, 1288-1293, 1308-1316, 1320-1325, 1348, 1366, 1368, 1448-1472 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_afm.py 242 190 21% 54, 61-65, 69, 73-74, 78, 82-85, 105-168, 206-237, 252-269, 306-323, 339-355, 362-364, 367-369, 376-394, 398-424, 428, 432-434, 440-442, 446, 450-452, 458-459, 466, 470, 474, 478-481, 485-493, 498, 502, 506, 510, 514, 518, 525, 532 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/__init__.py 126 30 76% 47, 58, 83, 89-93, 124, 128-131, 158-168, 187, 191-192, 256, 270, 281, 336, 341, 357-359, 382 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/deprecation.py 173 33 81% 28-29, 142-143, 156-159, 162-164, 167-169, 292-296, 310, 370-373, 387, 392, 400-403, 449, 486-503 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_blocking_input.py 8 7 12% 21-30 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm.py 141 12 91% 59-64, 145-152 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm_listed.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_color_data.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_constrained_layout.py 373 352 6% 102-149, 162-194, 202-240, 247-260, 264-297, 303-335, 347-440, 447-479, 507-576, 583-596, 615-624, 632-665, 689-751, 761-768, 772-783 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_docstring.py 39 4 90% 35, 53, 59-60 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_enums.py 57 36 37% 24, 89-111, 161-177 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_fontconfig_pattern.py 46 7 85% 89-91, 97, 101-105, 114-118 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_layoutgrid.py 208 174 16% 40-103, 106-118, 126-128, 132-137, 144-162, 166, 173-206, 213-245, 266-267, 287-288, 303-304, 322-323, 339-347, 352, 359-367, 374-391, 398-411, 418-429, 436-448, 455-466, 473-484, 490, 497, 502-547 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext.py 1244 988 21% 57-66, 100-102, 105-111, 116-147, 170-171, 180, 218-219, 227-228, 234, 240, 247, 255, 264, 274-282, 285-295, 298-300, 304-323, 334-343, 349, 353-358, 380-387, 392-405, 464, 489-519, 524, 527-586, 589-592, 599-617, 621-632, 699-704, 709-753, 757-773, 912-919, 926, 929, 932, 939, 949-952, 955-959, 962, 969, 976, 993-1002, 1005, 1008-1015, 1018, 1027-1034, 1037, 1042-1047, 1057-1061, 1064-1065, 1068, 1077-1083, 1086, 1093-1104, 1108-1113, 1120-1123, 1133-1148, 1187-1226, 1233-1234, 1258-1305, 1320-1321, 1324, 1331-1334, 1341-1342, 1368-1375, 1378-1381, 1391, 1401, 1419-1420, 1423, 1426-1428, 1441-1467, 1480-1495, 1508-1637, 1646-1649, 1664-1668, 1671, 1675, 1679-1681, 1685, 1701-1711, 1800-1955, 1964-1977, 1981, 1985, 1989, 1992, 1995, 1998-2000, 2003-2009, 2019-2028, 2046-2048, 2051, 2054-2096, 2099, 2127-2141, 2148-2150, 2153-2185, 2188-2192, 2195-2196, 2199, 2204-2205, 2208-2209, 2212-2216, 2219-2221, 2224-2226, 2229, 2232-2391, 2394-2429, 2432, 2435, 2441, 2446, 2451, 2456-2483, 2488-2525, 2528-2544, 2547-2566, 2569 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext_data.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_pylab_helpers.py 67 27 60% 41, 55-67, 72-75, 80-83, 88, 93, 98, 115, 130-132 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_text_helpers.py 23 1 96% 34 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_bbox.py 47 44 6% 18-70, 80-84 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_layout.py 133 125 6% 48-157, 170-191, 226-301 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_type1font.py 396 320 19% 56-58, 61, 65, 69, 73, 77, 81, 84, 91, 94, 101, 108, 115, 118, 137-141, 145-151, 158, 165, 168-171, 190-270, 294-315, 364-373, 377-402, 415-441, 457-463, 482-489, 498-592, 595-626, 630-653, 660-680, 684-692, 714-770 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_version.py 11 2 82% 5-6 /home/admin/.local/lib/python3.8/site-packages/matplotlib/artist.py 664 261 61% 36-37, 58-61, 66-67, 70, 75, 77, 81-82, 97-98, 113, 145, 217-221, 241-259, 304, 317, 327, 350, 367-376, 405, 415, 457, 462, 483-485, 504-508, 518, 531-555, 590, 602, 616, 630, 641, 713-717, 727-728, 735, 754, 813-814, 816, 822-823, 825-826, 828-829, 832, 864, 879-881, 892, 896, 908-910, 959-964, 985-986, 1003-1005, 1017, 1020, 1036, 1040-1046, 1076-1078, 1127, 1178, 1193, 1197, 1271-1286, 1317, 1337-1373, 1397-1403, 1414-1417, 1435-1437, 1441, 1485, 1493, 1515-1519, 1596-1600, 1614, 1616-1617, 1635-1666, 1683-1700, 1704-1715, 1746-1751, 1816-1838 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/__init__.py 9 1 89% 10 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py 2254 1940 14% 98-102, 158, 172, 179, 193-195, 317, 323, 381-398, 461-511, 547-550, 585-591, 617-623, 681-692, 704, 761-776, 829-844, 849-851, 904-926, 966-974, 1022-1031, 1072-1111, 1152-1191, 1304-1437, 1772-1776, 1821-1829, 1872-1876, 1919-1923, 1996, 2073-2105, 2174-2176, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py 901 551 39% 64-98, 102-105, 207, 220-229, 260, 269, 289, 317-321, 336-346, 371-373, 376-380, 386-395, 404-418, 435, 443, 451-456, 489, 492, 496-497, 501-505, 508, 513-514, 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, 723, 748-798, 839, 846-847, 853-865, 869-870, 874-877, 885-888, 901, 942-945, 981-1023, 1058, 1060, 1070, 1073-1076, 1081, 1084-1087, 1171-1289, 1303-1321, 1360, 1376-1420, 1476-1518, 1549-1556, 1587-1592, 1623-1630, 1661-1672, 1697, 1711-1715, 1720, 1722, 1766, 1770, 1784, 1808-1828, 1841, 1895-1897, 1945-1949, 1976-2000, 2034, 2053, 2056, 2059, 2062-2063, 2066, 2077-2088, 2095-2108, 2119-2126, 2131, 2145-2166, 2185-2188, 2197, 2205-2215, 2229-2243, 2269, 2280, 2295-2297, 2307, 2309-2313, 2338-2341 /home/admin/.local/lib/python3.8/site-packages/matplotlib/cm.py 213 73 66% 91, 102, 133-146, 178-181, 200-210, 254-257, 285-293, 341-343, 364-371, 416-418, 464, 469-472, 475, 479-483, 486-488, 510-511, 515, 527, 531, 537, 561, 563, 573, 598-606, 610, 643-647, 655, 719 /home/admin/.local/lib/python3.8/site-packages/matplotlib/collections.py 835 484 42% 182, 187, 195, 205, 208, 216, 219, 232, 257, 263, 266, 277-280, 305, 328-330, 332-334, 346, 358-359, 362, 365-366, 384-391, 394, 397, 400-405, 434, 443-471, 494, 535, 545-553, 612-617, 635, 638, 649, 652, 677, 680-684, 722-723, 749, 758, 764, 769-775, 777-779, 799, 822, 825, 850-853, 869, 874, 876-886, 894, 901, 906-925, 943, 958-959, 1069-1144, 1170-1173, 1189-1215, 1221-1226, 1238-1244, 1264-1271, 1315-1320, 1323, 1326, 1330-1337, 1408-1412, 1415-1421, 1434-1448, 1451, 1454, 1457, 1460, 1473, 1478, 1541-1549, 1555-1556, 1560-1571, 1575-1580, 1585, 1591, 1598-1603, 1613-1618, 1622, 1626-1635, 1639, 1643-1652, 1656, 1659, 1663, 1680-1683, 1711-1718, 1723-1760, 1764-1765, 1807-1821, 1824-1826, 1836-1846, 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 594 43% 66-67, 70-71, 93, 137-140, 143, 146, 149, 172-184, 194-197, 206-210, 243, 252-262, 320, 344, 350-354, 358-362, 370, 378, 381, 395, 432, 438-441, 445-446, 450, 452, 454, 464, 471, 474, 483, 487, 490, 496, 515-518, 608-644, 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166, 168, 170, 172, 174, 176, 178, 220-224, 236-237, 262-282, 304-308, 314, 363-364, 379, 385-388, 411-414, 421-425, 429, 433, 441, 451, 457, 467, 477, 489-490, 516-527, 615-641, 747, 752-755, 765, 906-909, 911-914, 930-933, 938-941, 954, 973, 1009, 1128-1150, 1277-1315, 1346-1357, 1400-1418, 1460-1478, 1501-1502, 1587-1614, 1639-1641, 1680-1693, 1706-1709, 1713-1718, 1722-1726, 1732-1737, 1766-1808, 1812-1827, 1831-1837, 1949-2148, 2219-2252, 2256, 2260, 2266, 2276-2277, 2280, 2292-2308, 2316, 2332, 2335, 2350, 2358-2373, 2399, 2402, 2509-2517, 2519-2526, 2528-2532, 2566, 2569, 2600-2601, 2612-2620, 2654, 2656, 2661, 2663, 2665, 2669, 2675-2678, 2684, 2698-2700, 2736-2744, 2763-2766, 2793, 2814-2819, 2827, 2851-2856, 2889-2890, 2909-2923, 3007-3024, 3057, 3060, 3065, 3091, 3095, 3099, 3109-3110, 3127, 3144, 3153, 3162, 3168-3171, 3179, 3192-3194, 3200, 3203-3220, 3223-3247, 3372-3375, 3429-3474, 3484-3494, 3507-3509, 3539-3549, 3600-3629 /home/admin/.local/lib/python3.8/site-packages/matplotlib/font_manager.py 563 282 50% 135-136, 177, 190-191, 207-212, 217-244, 250-258, 269-291, 295-301, 305-307, 347-456, 474-524, 608, 626-631, 648, 664, 715, 773-781, 799-807, 827-828, 834-836, 853-857, 888-891, 907-920, 938, 958-962, 991-1024, 1037-1047, 1053, 1060, 1073, 1078, 1095, 1097, 1107-1110, 1127, 1139, 1172, 1191-1199, 1264, 1269, 1331-1339, 1344-1356, 1369, 1372, 1383, 1396, 1399-1417, 1427-1442, 1454-1458, 1539-1540, 1545-1548 /home/admin/.local/lib/python3.8/site-packages/matplotlib/gridspec.py 277 100 64% 49, 52, 59-63, 83, 97-99, 110-111, 121, 132-133, 143, 170-175, 214-224, 238, 242, 245-249, 255-256, 276, 304-305, 307-308, 316, 400-410, 426-428, 443, 467-474, 501-505, 511-521, 529, 558, 574-581, 587, 591, 593-597, 600, 629-630, 635-636, 641-645, 648, 651, 654, 657, 679-683, 691, 697, 739 /home/admin/.local/lib/python3.8/site-packages/matplotlib/hatch.py 143 101 29% 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, 185-189, 205-225 /home/admin/.local/lib/python3.8/site-packages/matplotlib/image.py 760 617 19% 83-110, 134-157, 171-213, 221-227, 259-274, 277-281, 285, 289-292, 302-306, 318, 325-326, 358-587, 607, 615, 620-646, 650-677, 681-683, 695-731, 743, 754, 771-776, 788-792, 796-797, 811-814, 818, 830-831, 835, 846-850, 854, 920-922, 936-938, 942-949, 954, 977-1002, 1006-1014, 1025-1041, 1058-1059, 1063, 1067-1133, 1148-1165, 1168, 1177-1180, 1183-1185, 1188, 1191, 1194-1196, 1199-1201, 1245-1248, 1252-1281, 1284, 1304-1338, 1341, 1345-1354, 1379-1389, 1393-1394, 1399-1410, 1416-1417, 1440-1451, 1454-1462, 1466-1476, 1480-1486, 1538, 1541-1553, 1558, 1621, 1627-1633, 1641, 1648, 1657, 1665-1666, 1679-1686, 1711-1724, 1734-1754, 1796-1818 /home/admin/.local/lib/python3.8/site-packages/matplotlib/layout_engine.py 69 39 43% 63-64, 70, 78-80, 88-90, 96, 103, 122-124, 130, 158-162, 181-189, 207-209, 249-259, 269-274, 303-305 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend.py 470 191 59% 69-74, 77-80, 83-90, 93-94, 343, 423, 428-430, 460, 469-470, 477, 483, 497-501, 509-511, 518-528, 533, 538, 556, 591, 596-598, 600, 623, 625-647, 649-650, 655-657, 684, 695, 702-704, 712, 721-722, 731, 769, 774, 804-806, 845-853, 921-941, 945, 949, 953, 957, 963, 977-979, 983, 1016, 1020-1022, 1026, 1030, 1040-1041, 1076, 1080-1081, 1121-1158, 1161-1164, 1190-1191, 1196, 1202, 1209-1238, 1243-1250, 1304, 1309, 1314-1346 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend_handler.py 343 231 33% 41-43, 82, 164, 189-192, 195-206, 231-236, 249-273, 290-312, 347, 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 679 268 61% 43-52, 56-58, 65, 78-106, 118-201, 262-271, 314, 316, 326, 366, 370, 400, 440-484, 492, 506-508, 518, 537-538, 616-618, 622-624, 627-635, 654, 661, 666, 680-688, 709-710, 739-744, 749-750, 765-766, 774, 778-791, 818, 821-822, 829-833, 843, 859, 869-871, 882, 890, 906, 914, 922, 930, 940-946, 956, 960, 973, 981, 989, 997, 1006-1010, 1019-1023, 1035-1037, 1090, 1117, 1132, 1172, 1175, 1203-1206, 1279-1284, 1300-1305, 1329-1332, 1395, 1403, 1443, 1451, 1472-1481, 1484-1521, 1525-1526, 1566-1575, 1590, 1594-1599 /home/admin/.local/lib/python3.8/site-packages/matplotlib/markers.py 427 260 39% 260-261, 267-268, 342, 344, 346, 349, 358-362, 386, 402-405, 412-413, 424-429, 445-458, 474-480, 486-488, 491, 494, 497-515, 523-541, 552-556, 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, 825-828, 831-832, 835-836, 839-840, 845-849, 852-853, 856-857, 860-861, 866-867, 870-871, 874-875, 878-879, 887-890, 898-901, 911-922, 932-943 /home/admin/.local/lib/python3.8/site-packages/matplotlib/mathtext.py 114 67 41% 55-57, 61-63, 70, 76, 83, 90, 100-105, 108, 114-116, 119-125, 130-139, 142-144, 147-148, 161-163, 166-167, 170, 173, 225-226, 230-252, 278-287 /home/admin/.local/lib/python3.8/site-packages/matplotlib/mlab.py 275 235 15% 69, 80, 108-127, 152-157, 179, 198-213, 246-250, 255-288, 298-446, 455-472, 584-587, 638-651, 772-790, 829-840, 888-925, 929, 932, 959-985 /home/admin/.local/lib/python3.8/site-packages/matplotlib/offsetbox.py 659 353 46% 66-67, 73, 131-154, 196-197, 199-200, 271-278, 325-326, 336-337, 362, 387-388, 393-394, 399, 403-404, 481-483, 514, 552-564, 568-569, 573-583, 586-589, 593-594, 631, 635-636, 665, 678, 680, 683, 702, 748-749, 753, 764-765, 771, 794, 807-809, 841-846, 850-852, 859, 877-880, 884, 888-898, 902-905, 971-990, 1001-1004, 1008, 1012, 1016-1018, 1022-1029, 1040-1054, 1059-1065, 1068-1070, 1074-1086, 1131-1140, 1161-1178, 1181-1183, 1186, 1189-1190, 1193, 1197, 1200, 1203-1208, 1212-1214, 1229, 1311-1341, 1345, 1349-1350, 1354, 1358-1359, 1362-1367, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/pyplot.py 860 439 49% 119-120, 136, 145-157, 163-167, 175, 180-182, 188-191, 208, 234-267, 276-279, 304-322, 337, 352, 362-373, 375, 445-446, 476, 512-516, 552-556, 576-584, 589, 594, 599-601, 609, 614, 619, 658-686, 804-807, 814-825, 831, 846, 849-853, 867, 889-890, 906, 911, 921-923, 940, 945, 950, 969-991, 1017, 1022-1025, 1032, 1118-1126, 1133-1135, 1142-1143, 1149, 1290-1352, 1613-1621, 1664-1683, 1696-1699, 1712-1715, 1726-1734, 1753-1756, 1791-1795, 1828-1832, 1881-1883, 1893, 1941-1958, 2020-2029, 2088-2097, 2105-2108, 2116-2118, 2130-2138, 2155-2159, 2165, 2184-2190, 2195, 2242-2252, 2267-2274, 2284, 2295, 2303, 2315, 2324, 2335, 2343, 2349, 2355, 2361, 2370, 2380, 2389, 2395, 2401, 2407, 2413, 2419, 2425, 2431, 2439, 2447, 2457, 2467, 2483, 2500, 2508, 2517, 2527-2531, 2537-2541, 2550, 2564, 2578, 2588, 2598, 2608, 2627-2635, 2645, 2658, 2669-2674, 2682, 2695-2704, 2716, 2722, 2730, 2739, 2745, 2751, 2759-2764, 2773-2778, 2786, 2799, 2822, 2833, 2843-2847, 2853, 2874, 2880, 2890-2897, 2905-2909, 2917, 2929, 2940, 2953-2963, 2974, 2985, 2991, 2999, 3008-3010, 3016-3018, 3026-3030, 3036, 3045, 3058, 3069, 3107, 3113, 3124, 3135, 3146, 3157, 3168, 3179, 3190, 3201, 3212, 3223, 3234, 3245, 3256, 3267, 3278, 3289, 3300, 3311, 3322 /home/admin/.local/lib/python3.8/site-packages/matplotlib/quiver.py 390 338 13% 291-314, 318, 322-345, 348, 357-362, 365, 373-374, 377-385, 407-437, 441-443, 477-506, 516-527, 530-536, 540-544, 549-571, 575-577, 592-596, 599-605, 608-663, 670-723, 897-941, 967-973, 1024-1117, 1122-1162, 1173-1180 /home/admin/.local/lib/python3.8/site-packages/matplotlib/rcsetup.py 414 127 69% 68-69, 75-82, 99, 110, 127, 135-136, 159, 169-170, 185, 188-189, 218, 230-234, 238, 260, 282, 288, 290, 292, 294, 296-300, 304, 344-347, 354, 366-367, 381-384, 395-398, 411, 415, 427-428, 438, 457-483, 506-524, 534, 537-541, 549-552, 560, 568, 583-589, 683, 686, 689-692, 695-698, 705, 716-718, 738-739, 746, 750, 758, 761, 783, 786-792 /home/admin/.local/lib/python3.8/site-packages/matplotlib/scale.py 274 146 47% 69, 76, 111, 145-150, 153, 156, 180-182, 186, 190-198, 205-209, 213, 218-236, 239, 246-247, 250, 253, 256, 281-282, 288-291, 297, 301-304, 333-335, 339, 343, 350-361, 364-371, 374, 382-388, 391-398, 401, 440-441, 449-453, 457, 465-469, 472, 475, 483-484, 487, 490, 551-557, 562, 565-574, 581-584, 588-593, 596, 599, 606-607, 611, 614, 617, 646-648, 652, 657-665, 677-679, 726 /home/admin/.local/lib/python3.8/site-packages/matplotlib/spines.py 315 142 55% 33, 90-99, 103-109, 113-114, 126-131, 137-138, 156, 171-176, 189-194, 231, 234, 240, 243-270, 282, 314, 317, 319, 329-330, 338-341, 351, 357-386, 408-419, 423, 438, 448-451, 456-460, 476-477, 491, 494-505, 508-512, 546, 549, 554-555, 560-563, 566, 568-571, 578, 582 /home/admin/.local/lib/python3.8/site-packages/matplotlib/stackplot.py 42 37 12% 71-127 /home/admin/.local/lib/python3.8/site-packages/matplotlib/streamplot.py 370 328 11% 91-241, 247-248, 274-284, 288, 291, 294, 297, 300-301, 304-305, 308-311, 314, 321-362, 366, 372, 386-396, 399, 403-404, 408-409, 417-426, 443-502, 535-602, 607-624, 633-667, 678-707 /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/core.py 92 45 51% 22, 127-180, 220-224, 242, 256, 262-266 /home/admin/.local/lib/python3.8/site-packages/matplotlib/table.py 335 95 72% 101, 118, 143, 160-163, 188-189, 202, 207, 211-217, 302, 354-355, 365, 402, 404, 407, 431-444, 448, 452-457, 500-508, 512-516, 520-521, 526, 532, 543-545, 568-570, 585, 598-605, 614, 616, 618, 620, 627, 629, 631, 633, 650, 738, 744-746, 752, 757, 760-763, 774-777, 780-781, 787-790, 796, 821-827 /home/admin/.local/lib/python3.8/site-packages/matplotlib/texmanager.py 151 103 32% 48-49, 105-106, 110-115, 120-130, 134-171, 178-187, 194-195, 200, 205-207, 246-249, 253-275, 284-305, 314-329, 334-344, 357-361, 366-373 /home/admin/.local/lib/python3.8/site-packages/matplotlib/text.py 812 359 56% 41-49, 67-90, 130, 228, 233, 236-239, 246-268, 279, 292-313, 318, 390, 479, 486-489, 492, 494, 496, 498, 531-552, 559, 571-582, 589, 598-599, 633-652, 659-675, 681-685, 697-736, 761-762, 768-769, 785, 789-790, 796, 814, 824, 834, 844, 864, 874, 884, 916, 941, 952, 954, 977-983, 1026-1028, 1065-1066, 1080-1081, 1095-1096, 1126, 1148, 1181-1182, 1234, 1246-1247, 1297-1299, 1301, 1303, 1332, 1371, 1395-1397, 1407-1408, 1412, 1415-1419, 1436-1454, 1470-1478, 1483-1488, 1490-1496, 1498-1499, 1501, 1503, 1505, 1508, 1510-1513, 1517, 1524, 1526, 1537-1538, 1545-1546, 1548, 1550-1552, 1557, 1562, 1569, 1606-1607, 1612, 1616-1617, 1639-1654, 1673, 1856, 1872-1880, 1888-1895, 1908, 1918, 1922, 1938, 1946, 1960-2016, 2024, 2030-2032, 2041-2058, 2062-2064 /home/admin/.local/lib/python3.8/site-packages/matplotlib/textpath.py 192 152 21% 34-37, 40, 46, 49-70, 112-134, 142-164, 173-215, 221-223, 230-280, 287-298, 354-369, 373-374, 378, 385-386, 393, 402-408 /home/admin/.local/lib/python3.8/site-packages/matplotlib/ticker.py 1228 801 35% 165-167, 170, 173, 176, 179, 182, 186, 196-197, 213, 225, 233, 269, 283-284, 294-297, 300, 303, 316-317, 325, 328, 331, 346, 354, 365, 374, 452, 485-486, 498, 512, 531, 555-556, 559-564, 573, 588, 620-622, 626-650, 654-666, 673, 676-692, 712-713, 736, 749-750, 753-754, 761-762, 764, 772, 774, 789, 792, 805, 809, 874-883, 893, 902, 914, 925, 933-984, 987-993, 997-1016, 1019-1020, 1024, 1028-1046, 1054-1062, 1072, 1076-1110, 1120-1125, 1167-1172, 1184, 1193, 1205, 1218, 1221-1260, 1263-1286, 1289-1292, 1295-1313, 1318-1322, 1388-1392, 1395, 1398-1401, 1406, 1409-1412, 1417-1421, 1438-1473, 1503-1506, 1510-1512, 1536-1556, 1559, 1570-1579, 1583, 1616, 1623, 1631, 1645, 1685-1686, 1690-1693, 1697-1698, 1701, 1723-1724, 1741-1747, 1791-1795, 1800, 1804, 1808-1811, 1815-1816, 1819-1830, 1835-1850, 1860, 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216-218, 228-247 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tricontour.py 54 38 30% 29, 35-51, 54-79, 245-246, 271-272 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trifinder.py 26 15 42% 20-21, 38-42, 55-63, 79, 86, 93 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triinterpolate.py 535 450 16% 34-56, 157-207, 228, 258-261, 265, 270, 275-283, 381-418, 421, 426, 431-446, 466-476, 497-515, 539-543, 561-571, 689-706, 727-762, 783-787, 803-828, 846-879, 896-909, 935-978, 996-1004, 1007, 1013-1017, 1043-1058, 1065-1068, 1084-1105, 1112-1127, 1135-1153, 1163-1164, 1172-1210, 1224-1227, 1234-1235, 1243-1248, 1254-1259, 1265-1269, 1272, 1277-1280, 1312-1350, 1406-1426, 1440-1472, 1479, 1486, 1494-1514, 1531-1544, 1556-1574 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tripcolor.py 62 56 10% 61-154 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triplot.py 28 23 18% 38-86 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trirefine.py 93 81 13% 43-44, 62, 94-131, 157-169, 191-307 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tritools.py 77 65 16% 29-30, 44-47, 79-115, 165-190, 220-238, 260-263 /home/admin/.local/lib/python3.8/site-packages/matplotlib/units.py 61 10 84% 66, 117, 122, 132, 150-156, 176 /home/admin/.local/lib/python3.8/site-packages/matplotlib/widgets.py 1888 1585 16% 43-45, 49-51, 55, 59, 63, 76, 80, 91, 107, 133-135, 144-145, 149-150, 194-215, 218-221, 224-228, 231-241, 249, 253, 265-301, 305-315, 326, 330-331, 430-503, 507-527, 531-552, 556-561, 571-586, 603, 703-803, 814-824, 828-835, 839-846, 850, 854-865, 869-906, 910-920, 930, 940, 950-969, 986, 990-991, 1053-1107, 1111-1118, 1121-1143, 1156-1159, 1173-1176, 1190-1196, 1214-1246, 1256-1260, 1268, 1277, 1281, 1287-1305, 1311-1335, 1383-1420, 1424, 1435-1458, 1461-1465, 1468-1501, 1504-1511, 1515-1532, 1536-1548, 1551-1563, 1566, 1569-1575, 1583, 1592, 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175-200, 203-208, 212, 215, 221, 232-233, 244, 249, 253, 270-300, 308, 311, 314, 384-416, 421-576 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/axes_size.py 158 102 35% 20, 23-26, 31-32, 35-37, 44, 47-49, 58-59, 62-64, 74, 77-79, 85-88, 98-102, 105-114, 124-128, 131-141, 151-153, 156, 159-164, 173, 182, 193-195, 198-204, 215-216, 219-222, 234-239, 245, 248-254, 267-269, 272-276, 292-294, 297-304 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py 89 63 29% 8, 11-12, 15-16, 23-24, 27-38, 41, 45, 49-52, 61-72, 76-77, 82-83, 88, 91-94, 97, 101-128 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py 131 100 24% 12-16, 19-24, 31-37, 42-50, 53-54, 57, 60-69, 81-82, 111-120, 123-139, 142-144, 147-151, 160-164, 173-177, 186-193, 201-206, 209-216, 220-226, 247-253 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/art3d.py 488 390 20% 28-31, 36-39, 62-73, 97-98, 102, 116-119, 129-130, 144-146, 150-159, 164, 179-180, 208-209, 224-229, 248-254, 265, 269-273, 289-290, 296-300, 306-314, 320-329, 337-344, 354-355, 361-362, 368-377, 382-384, 404-405, 421-422, 426, 429-434, 455-456, 472-473, 476-481, 486-489, 494-496, 501-506, 529-531, 534, 546-547, 551-552, 570-580, 583-592, 595-602, 605, 611-613, 636-640, 643-645, 649-650, 668-695, 698-700, 703-705, 708, 720-721, 724-754, 758-768, 771-778, 781, 787-789, 809-816, 871-896, 914-916, 920-928, 944-947, 953-955, 960-966, 970-971, 977-1040, 1044-1045, 1049-1050, 1054-1065, 1070-1073, 1078-1081, 1097-1101, 1111-1118, 1127-1132, 1141-1146, 1173-1189, 1198-1227 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py 1305 1135 13% 122-180, 183-184, 187-188, 195, 200-207, 211-213, 217, 231, 234-235, 246, 249-253, 257, 260-275, 325-360, 372-381, 407-419, 422-436, 440-492, 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/home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_hash.py 21 12 43% 34-37, 47-50, 60-63 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_kx.py 40 27 32% 47-52, 69-81, 103-139, 161-197 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_pwhash.py 187 99 47% 194-226, 238-262, 295-322, 348-366, 382-399, 404-456, 486-527, 552-570, 585-597 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_scalarmult.py 51 34 33% 44-49, 61-66, 83-103, 120-140, 163-191, 212-240 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_secretbox.py 31 20 35% 41-54, 69-86 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_secretstream.py 76 54 29% 58-63, 76-82, 100-126, 154-193, 214-246, 270-331, 352-357 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_shorthash.py 26 13 50% 45-53, 67-81 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_sign.py 89 50 44% 38-44, 58, 106, 122-133, 147-158, 172-175, 189-192, 203-209, 224-235, 253-276, 296-327 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/randombytes.py 13 8 38% 30-32, 44-51 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/sodium_core.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/utils.py 54 46 15% 24-38, 53-64, 78-85, 101-110, 128-141 /home/admin/.local/lib/python3.8/site-packages/nacl/encoding.py 57 10 82% 56, 60, 66, 70, 76, 80, 86, 90, 96, 100 /home/admin/.local/lib/python3.8/site-packages/nacl/exceptions.py 26 2 92% 84, 88 /home/admin/.local/lib/python3.8/site-packages/nacl/public.py 113 69 39% 41-46, 53, 56, 59-61, 64, 93-108, 132-144, 147, 150, 153-155, 158, 167, 197-203, 209, 219-224, 246-263, 287-305, 319, 347-360, 365, 387-391, 411-423 /home/admin/.local/lib/python3.8/site-packages/nacl/signing.py 86 25 71% 56, 74, 77, 88, 91-93, 96, 121, 127, 135, 146-147, 177, 183, 195, 198, 201-203, 206, 215, 248-250 /home/admin/.local/lib/python3.8/site-packages/nacl/utils.py 30 11 63% 41-44, 51, 58, 63, 67, 71, 86-88 /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, 126-129, 136, 182-191 /home/admin/.local/lib/python3.8/site-packages/numpy/compat/py3k.py 59 24 59% 39-41, 44-46, 49-51, 57, 60, 65, 68-71, 74-77, 85, 103, 106, 109, 134-135 /home/admin/.local/lib/python3.8/site-packages/numpy/core/__init__.py 85 14 84% 23-48, 62-68, 125-126, 135, 142, 150-151 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs.py 261 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs_scalars.py 48 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_asarray.py 34 12 65% 19, 95, 109, 114-115, 121, 123-124, 126-127, 133-134 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype.py 157 126 20% 27-28, 35-42, 46-49, 60, 65, 95-100, 104-156, 163-175, 180-186, 191-230, 245-253, 257-279, 286-296, 300-301, 310, 313, 316, 326, 330, 340 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype_ctypes.py 54 36 33% 33, 37-68, 84-93, 106, 108, 110, 112, 116 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_exceptions.py 98 57 42% 11-14, 35, 42-44, 47, 58-59, 62, 75-78, 85-86, 90-91, 103-104, 108-109, 128-138, 145-146, 150-153, 160-190, 193-194 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_internal.py 430 234 46% 16-17, 24, 27-51, 57-76, 89-133, 141, 158-203, 207, 209, 211, 213, 215, 220, 222-223, 227, 230-233, 242, 246, 251-265, 282-284, 291-293, 300-302, 320, 332, 343, 352, 361-363, 370-372, 379-381, 388-392, 400-416, 431-434, 457-466, 490-495, 579, 627-630, 637-638, 643, 655, 662-663, 668-674, 690-696, 703, 717, 751-757, 761-779, 782-785, 798-803, 810-811, 830, 871, 877-878 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_methods.py 155 50 68% 52, 58, 64, 82-84, 95, 98-99, 109, 114-123, 127, 135-136, 138-139, 141, 153, 156, 169, 176-177, 184, 187, 191, 202, 207, 220, 225, 235-242, 251, 256, 262-272, 282-287, 290 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_string_helpers.py 15 5 67% 68-69, 97-100 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_type_aliases.py 122 13 89% 47-53, 108, 224-230 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1744-1760, 1769-1789, 1825-1828, 1864-1867, 1880-1884, 1894-1921 /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py 238 158 34% 15-33, 69, 123-128, 131-139, 142, 149, 151, 153, 157, 160, 173-187, 190-213, 216-221, 224, 227-228, 231, 234-235, 238, 244-251, 257-260, 284, 319, 372, 411, 445, 479, 513, 546, 569, 609, 644, 683, 718, 757, 790, 830-835, 865, 894, 933, 966, 998-1001, 1011-1032, 1089-1111 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/__init__.py 18 7 61% 171-180 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/_polybase.py 419 296 29% 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 193-198, 216, 234, 252, 280-288, 291-304, 307-311, 314-324, 327-329, 337-367, 375-380, 389-394, 398-403, 409, 413-462, 469-473, 476, 481-483, 486, 489, 494, 497, 500-505, 508-513, 516-521, 527-532, 535-538, 541-544, 547-556, 559-561, 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250-253, 309, 349, 389, 427-438, 480-504, 550, 587, 645-670, 756-792, 864-887, 936, 989, 1040, 1096, 1143-1156, 1209, 1263, 1395, 1426-1441, 1495-1505, 1537-1548, 1587-1615, 1641-1642 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/laguerre.py 252 200 21% 134-138, 179-193, 245-248, 304, 345, 385, 427-439, 481-505, 551, 588, 646-674, 761-798, 870-893, 942, 995, 1046, 1102, 1149-1162, 1215, 1269, 1400, 1429-1444, 1498-1508, 1547-1572, 1598-1599 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/legendre.py 261 209 20% 140-145, 193-207, 261-264, 319, 361, 405, 447-461, 505-529, 578, 609, 672-701, 789-829, 891-914, 963, 1016, 1067, 1123, 1161-1176, 1229, 1283, 1411, 1441-1455, 1506-1516, 1555-1584, 1611-1612 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polynomial.py 221 166 25% 145-148, 212, 248, 285, 317-325, 361-363, 400-421, 460, 515-542, 623-661, 745-757, 835-845, 895, 948, 999, 1055, 1096-1109, 1157, 1211, 1361, 1390-1401, 1454-1464, 1514, 1518, 1522-1529 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polyutils.py 229 204 11% 71-77, 130-153, 200-208, 248-254, 297-301, 366-368, 372-374, 422-443, 452-453, 469-483, 497-513, 527-529, 547-565, 571-578, 584-592, 606-680, 697-713, 732-750 /home/admin/.local/lib/python3.8/site-packages/numpy/random/__init__.py 17 1 94% 210 /home/admin/.local/lib/python3.8/site-packages/numpy/random/_pickle.py 22 12 45% 31-37, 54-60, 77-83 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/decorators.py 74 61 18% 61-65, 100-105, 143-186, 226-251, 282-304, 323-329 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/nosetester.py 174 157 10% 36-58, 96-109, 164-193, 212-230, 233-250, 259-260, 276-324, 397-463, 523-536, 540-544 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/utils.py 873 732 16% 59-75, 89-95, 109-113, 127-132, 146-151, 156-186, 196-208, 220-244, 249-272, 325-432, 463-473, 545-599, 660-698, 716, 741-745, 749, 753, 758-763, 778-781, 784-787, 790, 795-796, 806-852, 933-934, 1015-1046, 1127-1128, 1135, 1165-1206, 1231-1252, 1289-1290, 1300, 1329-1330, 1353-1354, 1380-1401, 1437-1447, 1455-1473, 1583-1594, 1639-1646, 1681-1709, 1717-1724, 1730-1738, 1743-1749, 1797-1803, 1808-1814, 1844-1850, 1878-1918, 1935-1939, 1956-1961, 2009-2011, 2014-2019, 2022-2027, 2105-2112, 2115-2124, 2127-2149, 2171, 2201, 2205-2234, 2237-2242, 2246-2293, 2300-2305, 2310-2342, 2386-2392, 2403-2409, 2414-2431, 2439-2460, 2465-2476, 2481-2500, 2509-2520 /home/admin/.local/lib/python3.8/site-packages/numpy/version.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/oauth2client/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/oauth2client/_helpers.py 98 60 39% 119-133, 139-140, 156-159, 174-179, 194-202, 222-227, 243-246, 250-255, 272-274, 278, 302-307, 323-328, 333-334, 339-341 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_openssl_crypt.py 40 38 5% 18-135 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pkce.py 14 8 43% 41-49, 66-67 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pure_python_crypt.py 63 39 38% 55-62, 73, 88-92, 113-125, 136, 147-148, 166-184 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pycrypto_crypt.py 38 22 42% 34, 48-49, 64-75, 87, 98-99, 116-124 /home/admin/.local/lib/python3.8/site-packages/oauth2client/client.py 708 520 27% 147-148, 184-187, 213, 222, 231, 239, 255-274, 283, 298-314, 328, 351, 358-359, 367-368, 378, 388, 395, 405-409, 419-423, 434-438, 489-506, 535-536, 545, 554, 562, 580-581, 595-596, 610-633, 641-652, 660-664, 677, 689-697, 701, 705-707, 711-712, 716-722, 726-733, 748-763, 774-819, 827, 841-863, 871, 885-902, 944, 956-960, 971, 980, 996-1005, 1014-1030, 1039-1045, 1101, 1111, 1118, 1124-1147, 1152, 1171-1174, 1187-1190, 1207-1230, 1249-1261, 1271, 1287-1298, 1310-1315, 1331-1340, 1344-1351, 1362-1379, 1385-1415, 1420, 1427, 1433-1435, 1439-1441, 1472-1481, 1484-1491, 1495, 1503, 1515, 1525-1526, 1552-1561, 1575-1584, 1601-1614, 1668-1680, 1733-1737, 1754-1775, 1780-1802, 1877-1893, 1913-1941, 1951-1989, 2014-2089, 2133-2169 /home/admin/.local/lib/python3.8/site-packages/oauth2client/clientsecrets.py 48 35 27% 78-106, 110-111, 115-116, 120-126, 162-173 /home/admin/.local/lib/python3.8/site-packages/oauth2client/crypt.py 83 57 31% 41, 46-48, 64-65, 87-102, 117-123, 142-150, 178-203, 224-250 /home/admin/.local/lib/python3.8/site-packages/oauth2client/transport.py 89 66 26% 39, 42, 45, 58, 73, 86, 101-107, 123-134, 150-201, 217-251, 279-280 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/__init__.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/cupy.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/dispatch.py 55 33 40% 37-44, 64-69, 79-88, 97-106, 132, 139, 145 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/jax.py 13 8 38% 17-27 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/object_arrays.py 24 20 17% 33-60 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/tensorflow.py 63 50 21% 17-34, 41-56, 65-76, 83-94, 103-106, 113, 120-122, 126-128 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/theano.py 25 18 28% 16-24, 30-42, 47-53 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/backends/torch.py 61 47 23% 22-28, 34, 42-45, 51-95, 100-105, 127-128 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/blas.py 77 72 6% 55-120, 168-243 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/contract.py 315 279 11% 30-44, 48-71, 75-87, 196-330, 337-353, 358, 365-366, 373-374, 470-507, 511, 518-527, 536-600, 611-626, 634-647, 657-667, 674-678, 681-686, 691-693, 710-719, 735-771, 774-778, 781-787, 797, 861-882 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/helpers.py 70 58 17% 42-50, 76-79, 125-134, 168-173, 228-283 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/parser.py 124 106 15% 32, 46, 64-66, 77-84, 97-99, 113-119, 137-138, 154, 183-186, 199-206, 212-243, 272-356 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/path_random.py 165 131 21% 18-25, 87-101, 107, 111, 116-139, 144-155, 158-160, 163, 166-204, 208-209, 247-283, 289-304, 310-319, 355-359, 367-370, 376-378, 384-385 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/paths.py 440 368 16% 51-56, 60, 71-77, 88-97, 130-138, 146-149, 183-236, 243, 247, 257, 267, 274, 312-319, 323, 352-448, 452-453, 462-472, 476-482, 486-496, 502-505, 514-615, 657-661, 700-719, 749-767, 778, 786-794, 809-816, 825-832, 842, 853-865, 903-916, 955-1053, 1057-1058, 1076-1077, 1092-1095, 1117, 1125-1129 /home/admin/.local/lib/python3.8/site-packages/opt_einsum/sharing.py 96 58 40% 26, 32, 36, 40-43, 68-74, 81, 88-90, 98-103, 112-119, 130-139, 150-168, 185-190, 196-201 /home/admin/.local/lib/python3.8/site-packages/packaging/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/packaging/_structures.py 36 16 56% 8, 11, 14, 17, 20, 23, 26, 29, 37, 40, 43, 46, 49, 52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/packaging/version.py 163 65 60% 69, 76, 81-84, 87-90, 93-96, 99-102, 105-108, 198, 228, 236-261, 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/pandas/__init__.py 33 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_config/__init__.py 11 4 64% 34-35, 39-40 /home/admin/.local/lib/python3.8/site-packages/pandas/_config/config.py 313 149 52% 119-121, 123, 144-171, 175-184, 188-201, 205-206, 217-226, 229-240, 243, 266-268, 433-438, 441-444, 447-449, 489, 491, 502, 504, 511, 517, 566-571, 589-593, 606-607, 623, 634, 644, 659-675, 681-700, 705-734, 812, 834, 838, 849-855, 881-882, 907-909 /home/admin/.local/lib/python3.8/site-packages/pandas/_config/dates.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_config/display.py 24 7 71% 27-28, 32-38, 42 /home/admin/.local/lib/python3.8/site-packages/pandas/_config/localization.py 46 33 28% 41-51, 71-78, 98, 137-169 /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/tslibs/__init__.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/window/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/__init__.py 396 256 35% 121-125, 253-255, 265, 276, 292-317, 325-330, 338, 343, 350-351, 358-359, 363-367, 371-385, 389-392, 396-399, 403, 407-410, 416-418, 424, 428-429, 433-437, 441-450, 462-468, 473-477, 481, 485, 489-492, 496-497, 501-503, 509-511, 515, 519, 524-525, 529-530, 534-543, 547, 551-552, 585-664, 739-771, 775-798, 802-805, 818, 822, 830, 834, 840, 859-872, 891-892, 909-911, 934-942, 959-967, 975, 979, 983, 987, 991, 995, 1005-1047 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_io.py 130 98 25% 30, 76-79, 102-111, 209-248, 267-278, 303-308, 329-337, 358-366, 388-418, 426-435 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_random.py 9 3 67% 14-19, 29 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_warnings.py 61 48 21% 86-102, 112-115, 126-150, 163-188, 196-199, 205-216 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/asserters.py 402 358 11% 92-142, 163-170, 176-177, 234-343, 352-377, 394-416, 420-435, 440-443, 471-504, 525-535, 539-542, 548-554, 560-564, 570-599, 633-679, 732-782, 877-1032, 1152-1224, 1253-1280, 1292-1313, 1317-1318, 1330-1336, 1350, 1358-1364, 1373-1378 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/compat.py 10 6 40% 10-14, 22-24 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/contexts.py 86 61 29% 46-47, 72-89, 114-134, 145-150, 173-184, 189-202, 206-213 /home/admin/.local/lib/python3.8/site-packages/pandas/_typing.py 149 33 78% 34-84, 204, 209, 213, 217, 223, 229, 233, 238, 243, 249, 253, 257, 262, 314 /home/admin/.local/lib/python3.8/site-packages/pandas/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/extensions/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/indexers/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/interchange/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/api/types/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/arrays/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/compat/__init__.py 33 14 58% 41-44, 56, 68, 80, 92, 104, 118, 131, 148-154 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/_constants.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/compat/_optional.py 49 29 41% 71-88, 145, 149-173 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/compressors.py 27 12 56% 16-17, 30-41, 54, 69 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/numpy/__init__.py 18 2 89% 19, 25 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/numpy/function.py 161 57 65% 68-86, 99-103, 113-115, 125-127, 158-164, 175, 180, 192-201, 221-226, 325-330, 345-351, 365-371, 388-391 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/pickle_compat.py 93 66 29% 27-57, 147-149, 157-174, 181-190, 195-196, 209-220, 233-234, 244-249 /home/admin/.local/lib/python3.8/site-packages/pandas/compat/pyarrow.py 17 6 65% 17-22 /home/admin/.local/lib/python3.8/site-packages/pandas/core/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/_numba/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/_numba/executor.py 18 10 44% 41-59 /home/admin/.local/lib/python3.8/site-packages/pandas/core/accessor.py 84 27 68% 28, 34, 44-46, 55, 58, 61, 96, 99, 112, 224-230, 306-319, 324-326, 331-333, 338-340 /home/admin/.local/lib/python3.8/site-packages/pandas/core/algorithms.py 440 392 11% 87-97, 128-181, 200-214, 221-230, 262-266, 281-288, 390, 408-413, 418-437, 457-531, 570-593, 747-797, 830-904, 923-936, 960-965, 985-1006, 1040-1065, 1103-1157, 1245-1259, 1315-1348, 1378-1461, 1519-1599, 1604-1614, 1624-1629, 1655-1672 /home/admin/.local/lib/python3.8/site-packages/pandas/core/api.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/apply.py 624 504 19% 60-67, 83-90, 112-139, 143, 154-174, 191-241, 247-265, 271-287, 297-372, 382-468, 479-496, 507, 519-551, 560-578, 593, 597, 609, 614, 619, 625, 631, 635, 639, 643, 648-678, 681-701, 710-741, 746-767, 770-795, 798-801, 804-820, 823-841, 846-851, 859, 863, 867, 874-908, 915-916, 920-942, 946, 950, 959-971, 975-984, 999-1001, 1011-1025, 1028-1053, 1056-1057, 1063-1087, 1100-1102, 1112, 1115, 1129, 1139, 1142, 1184-1203, 1227, 1258-1288, 1303, 1338-1382, 1410-1422, 1450-1466, 1492-1502 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/datetimelike_accumulations.py 26 17 35% 34-55, 59, 63, 67 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/masked_accumulations.py 30 20 33% 45-76, 80, 84, 88, 92 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/masked_reductions.py 48 32 33% 49-60, 71, 84, 112-124, 134, 144, 154-156, 167-172, 185-190 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/putmask.py 50 35 30% 26, 42-59, 75-101, 110-115, 122-129, 141-152 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/quantile.py 46 37 20% 34-39, 77-106, 135-143, 179-216 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/replace.py 48 37 23% 33-40, 63-106, 128-150 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/take.py 196 163 17% 32-33, 44, 55, 95-117, 127-166, 204-224, 237-284, 296-322, 336-349, 356-369, 375-384, 520-532, 544-561, 571-594 /home/admin/.local/lib/python3.8/site-packages/pandas/core/array_algos/transforms.py 21 17 19% 13-42 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arraylike.py 220 143 35% 36, 40, 44, 48, 52, 56, 60, 66, 70, 74, 78, 82, 86, 90, 96, 186, 190, 194, 198, 202, 206, 210, 214, 218, 222, 226, 230, 234, 238, 242, 246, 261-412, 422-427, 437-462, 469-473, 484-489, 496-527 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/__init__.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/_mixins.py 197 129 35% 69-74, 85-92, 114, 118, 126-153, 163-173, 178-182, 186-187, 190, 193, 198-201, 206-209, 212-213, 222-228, 237-238, 242-245, 248-250, 253, 257, 264, 270-286, 292-293, 299-331, 337-339, 358-360, 378-381, 402-413, 433-451, 460-467, 475, 494-496 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/_ranges.py 75 67 11% 49-90, 121-157, 167-207 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/_arrow_utils.py 24 16 33% 17-20, 43-61 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/array.py 1003 805 20% 105-109, 117-120, 142-147, 154-159, 168-181, 234-245, 252-285, 294-348, 374-417, 423-429, 433, 437, 440, 443, 446, 449, 454-456, 459-460, 463-487, 490-536, 539, 542, 545-549, 556, 563, 573, 577-585, 589, 597, 655, 713, 723-732, 735-748, 751, 754, 766, 776, 785-834, 838-844, 860-861, 868-895, 898, 927, 936-944, 1006-1042, 1051-1081, 1091-1104, 1123-1151, 1168-1175, 1207-1225, 1251-1345, 1369-1424, 1438-1491, 1508-1530, 1548-1571, 1575-1588, 1613-1632, 1658-1685, 1689, 1698-1700, 1708-1718, 1723-1733, 1736-1739, 1742-1745, 1756-1764, 1767-1774, 1779-1781, 1786-1788, 1791-1804, 1807-1824, 1827, 1830-1832, 1835-1837, 1842-1846, 1853-1857, 1860, 1863, 1866, 1869, 1872, 1875, 1878, 1881, 1884, 1887, 1890, 1893, 1896, 1899, 1902, 1905-1909, 1912-1916, 1919-1923, 1931-1940, 1943-1946, 1949-1951, 1954-1956, 1959, 1964-1967, 1970-1981, 1984-1986, 1989-1991, 1994-1996, 1999-2001, 2010-2016, 2019-2021, 2024-2026, 2029-2033, 2037, 2041, 2045, 2052, 2058, 2061, 2065, 2069, 2073, 2077, 2081, 2085, 2089, 2093, 2097-2102, 2106, 2109, 2118-2146, 2154, 2162, 2170, 2173-2181, 2189-2206 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/dtype.py 146 97 34% 36, 89, 91, 98, 105-150, 162-168, 172-175, 180, 191-193, 207, 212-236, 247-265, 273, 284, 290-304, 310-312 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/base.py 385 253 34% 92, 261, 285, 304, 311, 315, 350, 395, 405, 414-415, 424-434, 447, 454, 485-490, 501, 508, 517, 524, 533, 541, 545, 549, 571-592, 614, 624, 651, 689-692, 723-726, 751-754, 790-811, 822, 857-872, 882-883, 934-937, 957-971, 987, 1011, 1059-1066, 1117-1119, 1218, 1228, 1248-1250, 1257-1269, 1272-1285, 1311-1313, 1326, 1330, 1349, 1371, 1380, 1412, 1438-1444, 1467-1469, 1472-1473, 1497-1501, 1523-1528, 1545-1553, 1565-1571, 1585-1588, 1610-1618, 1635-1640, 1660, 1663-1686, 1691, 1694, 1710, 1714-1731, 1735, 1739-1744, 1748, 1752-1757, 1826-1865, 1869, 1873 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/boolean.py 169 122 28% 36-38, 71, 75, 79, 90, 93, 97, 101, 109-142, 162-229, 297-303, 307, 319-333, 341-343, 346-378, 383-392 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/categorical.py 725 567 22% 112, 128-188, 224-243, 368-457, 464, 470-471, 477, 481, 485, 489, 503-550, 556, 582-625, 669-692, 724, 731, 749-751, 774-788, 803-804, 815-818, 829, 840, 891-908, 971-980, 1016-1023, 1068-1092, 1135-1148, 1184-1197, 1270-1280, 1293-1297, 1317-1326, 1342-1348, 1352-1374, 1381-1391, 1395, 1420, 1439, 1461, 1484-1503, 1522-1529, 1544-1548, 1552-1553, 1610, 1620, 1626, 1701-1711, 1725-1728, 1749-1767, 1774, 1777-1779, 1784-1786, 1794-1797, 1804-1807, 1814, 1821-1829, 1835-1855, 1861-1884, 1887-1888, 1893-1899, 1905-1914, 1920-1949, 1977-1983, 2003-2018, 2035-2050, 2053-2062, 2096, 2100-2101, 2115-2120, 2126-2150, 2166-2169, 2184, 2195-2205, 2248-2258, 2261-2294, 2304-2309, 2313-2315, 2445-2449, 2453-2454, 2457, 2460, 2467-2469, 2472-2477, 2489-2495, 2523-2538, 2557-2579, 2599-2604 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/datetimelike.py 925 699 24% 152, 171-182, 207, 212, 223, 243, 265, 285, 293, 299, 302-305, 318, 333, 337, 344-346, 350, 357, 369-378, 384-405, 421-430, 435, 442-489, 493, 497, 501, 505, 511, 522-541, 545-547, 554-587, 613-646, 663-673, 676-724, 727-732, 739-745, 759-761, 776-818, 824, 831, 838, 859-865, 875-877, 886-891, 895-901, 909, 916, 920, 924, 930-985, 1009-1019, 1028-1035, 1039-1071, 1075-1081, 1085-1098, 1102-1111, 1115-1132, 1136-1144, 1147, 1157-1167, 1179-1185, 1189-1199, 1206-1217, 1230-1237, 1243-1264, 1282-1300, 1303-1309, 1315-1365, 1369, 1373-1423, 1426-1458, 1461-1467, 1470-1476, 1487, 1501-1505, 1519-1523, 1549-1560, 1564-1570, 1573-1580, 1637-1638, 1799-1866, 1870, 1877, 1881-1888, 1903-1928, 1937, 1943, 1950, 1953-1967, 1974-1980, 1985-1993, 1997-2017, 2026, 2035, 2044, 2051, 2056, 2062, 2077-2091, 2100-2109, 2117-2147, 2152, 2157, 2178-2184, 2210-2221, 2241-2249, 2265-2267 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/datetimes.py 617 485 21% 90-91, 109-112, 117-148, 198, 263-265, 275-287, 291, 309-370, 389-502, 508-514, 517, 520-522, 529-531, 556, 569, 574, 584, 591, 595, 601-605, 615-634, 641-701, 709-713, 722-729, 733-748, 756-782, 794-797, 863-873, 1021-1058, 1071, 1111-1118, 1164-1190, 1244-1250, 1301-1307, 1319-1321, 1330, 1343-1345, 1381-1390, 1911-1917, 1971-1979, 2022-2134, 2172-2202, 2229-2256, 2281-2290, 2315-2344, 2370-2397, 2422-2440, 2446-2453, 2478-2486, 2521-2595 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/floating.py 34 3 91% 36, 40, 51 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/integer.py 70 9 87% 36, 40, 50-57 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/interval.py 638 486 24% 104, 220, 238-275, 288-293, 305-379, 389, 395-400, 458-460, 536-548, 608-631, 645-661, 674-677, 684, 688, 693, 699, 702, 706, 710, 715-731, 734-738, 742-817, 821, 825, 829, 833, 837, 841, 851-859, 864-878, 881-895, 926-935, 956-996, 999-1002, 1023-1033, 1043-1046, 1049, 1052-1077, 1133-1146, 1150-1168, 1171-1182, 1185-1205, 1225, 1233-1260, 1266-1270, 1273-1274, 1284-1286, 1293-1295, 1302, 1309-1313, 1372-1385, 1396, 1438-1444, 1466-1474, 1487-1498, 1504-1550, 1573-1577, 1582-1591, 1608-1613, 1616-1624, 1632-1635, 1683-1686, 1691-1720, 1724-1730, 1736-1748, 1753-1757, 1776-1796 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/masked.py 572 451 21% 88-90, 121-134, 140-141, 145, 149, 153, 158-167, 173-198, 204, 214-231, 234-248, 251-264, 267, 271, 275, 278-280, 283-285, 288-290, 294-296, 300, 326-330, 336, 339, 342, 345, 415-439, 443-446, 450, 454, 458, 461-504, 513, 521-592, 598-600, 609, 614-625, 628-730, 735-773, 782-821, 824, 828, 832, 840-842, 854-876, 881-898, 901-904, 914-915, 924-932, 939-971, 975, 994-1021, 1025-1037, 1050-1078, 1084-1097, 1100-1109, 1119-1135, 1147-1155, 1160-1167, 1174-1182, 1189-1197, 1202-1203, 1211-1212, 1281-1298, 1362-1380, 1385-1391 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/numeric.py 152 112 26% 41, 52, 56, 60, 64, 72-114, 118, 125-136, 145, 149-235, 248-263, 267-268, 274-280, 286-289 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/numpy_.py 186 130 30% 80-95, 101-120, 123, 130, 136, 143-188, 194-202, 205, 208-211, 214-218, 231-233, 243-245, 250-254, 259-263, 273-277, 287-291, 302-304, 315-319, 331-335, 347-351, 363-367, 378-382, 393-397, 408-420, 426, 429, 432, 435, 438-459, 466-472 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/period.py 420 311 26% 83-92, 105-107, 179, 215-236, 247-249, 259-274, 280, 297-298, 302-319, 331-338, 341, 344-346, 353, 361, 364-370, 376-397, 479, 497-536, 541, 589-609, 615-617, 626-643, 650-664, 672-677, 680-688, 708-712, 715-718, 731-740, 754-781, 803-818, 837-847, 912-943, 948, 953, 977-993, 1018-1033, 1037-1081, 1094-1128, 1132-1143 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/accessor.py 110 78 29% 20, 30-31, 34, 46-47, 50, 53-58, 102-108, 185-190, 216-218, 232-234, 265-287, 309-312, 334-355, 362-363, 367-386 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/array.py 786 648 18% 107-123, 155-158, 180-266, 275-287, 378-497, 506-510, 538-554, 557-579, 585-586, 590, 594, 604, 618, 622, 631, 635, 642-645, 649-651, 654, 658, 661-664, 668, 682, 696, 701-706, 746-772, 775-799, 809-820, 823-834, 838, 849-853, 868-893, 900, 907, 913-1001, 1004-1012, 1017-1030, 1035-1097, 1100-1120, 1128-1133, 1136-1137, 1143-1195, 1254-1274, 1316-1326, 1336, 1341-1344, 1351-1361, 1364-1367, 1374-1384, 1398-1405, 1419-1426, 1454-1470, 1490-1498, 1512-1521, 1538-1539, 1556-1557, 1572-1592, 1595-1618, 1621-1624, 1627-1630, 1639-1702, 1709-1739, 1742-1765, 1774-1782, 1785, 1788, 1791, 1794, 1800-1803, 1808, 1832-1870, 1875, 1880, 1885-1892 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/dtype.py 143 94 34% 39, 85-99, 104, 109-140, 156, 159-160, 178, 182, 186, 193, 197, 201, 205, 208, 219-221, 250-273, 297-307, 311-317, 357-370, 396-398, 403-426 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/string_.py 257 193 25% 58-65, 105, 110-120, 124, 154-165, 180-185, 193-215, 225-227, 315-320, 324-336, 340-365, 371, 375-377, 383-390, 393-396, 399-429, 435, 438-468, 473-476, 479-483, 486-490, 493-497, 500-503, 512-517, 520-547, 560-608 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/string_arrow.py 200 142 29% 53-55, 112-116, 128, 132-152, 158, 165, 168-170, 174-185, 188-201, 204-216, 231-283, 288-306, 309-310, 313-314, 325-331, 336-338, 343-345, 348-349, 352-353, 356-357, 360-361, 364-365, 368-369, 372-373, 376-377, 380-381, 384-385, 388, 391, 394-398, 401-405, 408-412 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/timedeltas.py 440 333 24% 78, 83-96, 142, 164-167, 186, 197-199, 207-214, 218-227, 242-271, 279-319, 325-331, 334, 338, 348-368, 371-384, 400-407, 419-426, 432-441, 447-449, 454-460, 466-467, 473-500, 509-550, 553-559, 567-581, 586-610, 615-631, 635-660, 664-678, 683-685, 690-692, 697-702, 707-712, 715-718, 721, 725, 784-785, 795, 826-853, 896-966, 985-1006, 1039-1042, 1046-1062 /home/admin/.local/lib/python3.8/site-packages/pandas/core/base.py 322 197 39% 75-82, 113, 120, 126-131, 138-144, 163, 172-178, 196-200, 204-207, 212, 217-230, 233-246, 261, 264, 283, 288, 299-300, 320, 324, 331, 348-350, 357, 364, 429, 526-561, 566, 608-610, 666-678, 722-724, 730-742, 763, 780-784, 799, 802, 818-823, 849-926, 1015, 1025-1031, 1068-1071, 1082, 1093-1095, 1106-1108, 1135-1144, 1164-1177, 1293, 1302, 1311-1323, 1331-1333, 1337, 1340-1350, 1357 /home/admin/.local/lib/python3.8/site-packages/pandas/core/common.py 195 137 30% 57, 77-81, 85-92, 123-147, 163-169, 176, 183, 190, 197, 204, 211, 221, 226, 230-258, 276-287, 291-293, 300-303, 310, 322, 334, 342, 352-364, 378-381, 405-413, 418, 425, 453-478, 510-518, 527-533, 543-548, 563-568, 575-576, 626, 634, 653 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/align.py 99 76 23% 32-35, 42-51, 57, 64, 71-80, 87-142, 149-165, 188-213 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/api.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/check.py 8 1 88% 8 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/common.py 29 23 21% 14-16, 24-48 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/engines.py 50 23 54% 23, 37-42, 53-55, 63, 77-82, 88, 113-121, 134, 137 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/eval.py 111 90 19% 27, 51-72, 88-89, 95-99, 119-120, 147-149, 153-167, 299-413 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/expr.py 361 213 41% 65-66, 84-91, 114-117, 165-166, 262, 314, 397-401, 404-415, 418-421, 424, 428-451, 454-459, 462-482, 491, 504-532, 535-537, 540, 543-545, 548, 551, 554, 557, 560-561, 564-565, 571, 574-591, 595-605, 617-635, 638-655, 658-706, 709, 712-730, 733-735, 738-746, 768, 776, 804-809, 813, 816, 819, 822, 828, 835-837 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py 107 70 35% 24, 46, 58-61, 68-70, 75-89, 93-130, 171, 176-188, 196-199, 211-220, 235-240, 255-256, 267-268, 272-273, 281-283 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/ops.py 293 182 38% 72-75, 81-87, 91, 94, 97, 100, 103-117, 129-135, 139, 143-152, 158, 162-167, 171, 175, 179, 183, 188, 191, 195, 200, 214-216, 219, 226-227, 232-234, 238-240, 244, 248, 252-257, 265-273, 281-289, 346-355, 359, 374-387, 405-408, 427-457, 464-490, 493-511, 515, 529-539, 565-571, 577-579, 582, 586-593, 598-599, 603-605, 608-609, 614-617, 620 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/parsing.py 45 33 27% 35-67, 90-93, 125-130, 159-164, 181-195 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/pytables.py 352 257 27% 49-50, 57-61, 64, 68-78, 83, 88-89, 92, 103-106, 109, 112-151, 155-159, 164, 172, 177, 182, 187, 191-192, 200-256, 259, 266-268, 272-278, 282, 285-310, 313-316, 321, 324, 329, 336, 342, 345-368, 373-374, 379-393, 401-404, 411-416, 419, 422-425, 430-444, 449-470, 473, 476, 497-503, 545-585, 588-590, 594-609, 616-619, 623-631, 636-641 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/scope.py 125 84 33% 36-40, 49-53, 60, 76-82, 88-89, 118-120, 153-188, 191-193, 207, 226-246, 261-271, 285-294, 304-314, 330-338, 343, 356-357 /home/admin/.local/lib/python3.8/site-packages/pandas/core/config_init.py 197 20 90% 40-42, 54-56, 68-70, 290-292, 307-310, 345, 421-423, 641-643, 664-672 /home/admin/.local/lib/python3.8/site-packages/pandas/core/construction.py 218 188 14% 67-71, 290-379, 386, 393, 441-455, 462-473, 480-491, 519-614, 622-633, 640-641, 655-680, 692-700, 708-711, 734-767 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/api.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/astype.py 114 92 19% 40, 50, 57, 83-140, 149-159, 177-193, 220-251, 266-306 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/base.py 140 59 58% 33-36, 104, 124-133, 138, 141, 152, 164, 180, 189, 199, 210, 226-227, 275-284, 310-327, 338, 356, 380-384, 391, 404, 410-412, 416, 420, 472, 478, 482, 486, 492, 509, 517 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/cast.py 756 672 11% 102-103, 129-140, 151, 172-179, 194-210, 222-236, 245-251, 256, 261, 269-317, 324, 331, 350-422, 437-445, 475-492, 512-527, 532, 537, 544-556, 587-618, 626, 632-739, 760-764, 779-781, 795-873, 888, 926-944, 972-978, 990-995, 1000-1007, 1051-1168, 1191-1204, 1227-1254, 1273-1294, 1326-1348, 1369-1385, 1401-1411, 1416, 1421, 1426, 1446-1481, 1487-1509, 1531-1560, 1566-1570, 1593-1595, 1636-1707, 1723-1747, 1767-1910, 1919-1921 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/common.py 323 241 25% 82-89, 105-109, 128-139, 144, 152, 186, 231-234, 265-272, 303-306, 341-348, 379-383, 413-419, 451-457, 489-495, 502, 537-548, 578-598, 646, 702, 760, 809, 864, 900-906, 944-953, 987, 1025, 1060-1070, 1111-1124, 1165, 1202, 1239, 1285-1302, 1310-1317, 1330, 1380-1386, 1397, 1432, 1450-1456, 1478-1491, 1508-1535, 1557-1602, 1620-1625, 1650-1653, 1674, 1676, 1692-1694, 1706, 1719-1728 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/concat.py 101 87 14% 30, 55-121, 223-286, 291-293, 310-323 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/dtypes.py 621 400 36% 56-64, 102, 105, 109, 114, 187, 193-195, 201-207, 277-306, 329, 337, 340-347, 353-354, 359-365, 380-426, 429-437, 441-477, 488-490, 508-509, 526-545, 560-578, 585, 592, 596-598, 601-628, 679, 684, 687-716, 723, 730, 737, 748-750, 770, 777-785, 789, 794, 799, 802-807, 817-818, 865-885, 888, 895, 899-909, 924-927, 931, 935, 939, 943, 947, 950-963, 966, 972, 980-990, 1001-1003, 1011-1034, 1081-1141, 1145-1153, 1157, 1164, 1175-1177, 1186, 1191, 1203, 1206-1211, 1215, 1218-1230, 1236-1239, 1247-1255, 1263-1287, 1290-1302, 1325-1328, 1331, 1338, 1345, 1352, 1357, 1361, 1365-1373, 1384-1386, 1393, 1400, 1414, 1419, 1423, 1428, 1439, 1446-1459, 1464-1478 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/generic.py 37 5 86% 11-31, 44, 51 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/inference.py 60 29 52% 71, 96, 129-132, 157, 181-186, 218, 259, 292-293, 325, 361-362, 390-395, 426-431 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/missing.py 249 189 24% 57-66, 80, 87, 92, 98, 103, 183, 205-232, 256-263, 282-304, 309-321, 326, 333, 338, 344, 349, 429-432, 449-452, 496-542, 546, 550, 554-581, 588-593, 602-613, 620-622, 652-666, 673-676, 692-727, 734-759 /home/admin/.local/lib/python3.8/site-packages/pandas/core/flags.py 34 21 38% 50-51, 83, 87-96, 99-102, 105-107, 110, 113-115 /home/admin/.local/lib/python3.8/site-packages/pandas/core/frame.py 2171 1754 19% 239-244, 634, 649-855, 887-889, 908, 930, 963-968, 975-983, 990-1010, 1019-1020, 1033-1073, 1079-1080, 1088-1094, 1102-1138, 1163, 1188, 1250-1271, 1291-1293, 1347-1352, 1396-1403, 1469-1487, 1493, 1497, 1501, 1580-1614, 1620, 1624, 1630, 1636-1643, 1738-1774, 1836-1842, 1851-1864, 1872, 1876, 1986-1988, 2086-2088, 2184-2319, 2404-2479, 2514-2529, 2645-2686, 2712-2714, 2750-2763, 2776, 2789, 2887-2889, 2981-2983, 3014, 3043, 3106-3129, 3300-3342, 3354-3358, 3454-3464, 3563-3605, 3634, 3651-3671, 3681, 3691-3692, 3700-3711, 3714-3784, 3792-3811, 3815-3845, 3866-3883, 3909-3919, 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10051-10092, 10207-10223, 10297-10359, 10428-10451, 10464-10530, 10539-10558, 10598, 10604-10623, 10629-10648, 10654-10659, 10741-10751, 10761, 10771, 10781, 10874-10960, 10971, 10994, 11066-11076, 11123-11133, 11204-11241, 11277-11281, 11360, 11371, 11382, 11393, 11403, 11414, 11425, 11436, 11446, 11457, 11471, 11492, 11504, 11516, 11527, 11545, 11557, 11569, 11580, 11595-11599, 11605-11620 /home/admin/.local/lib/python3.8/site-packages/pandas/core/generic.py 2254 1667 26% 193-200, 273-281, 292-311, 331-333, 351-353, 357, 398, 448-451, 457-467, 478, 488, 503-507, 512-515, 520-521, 525-527, 533-538, 543-569, 573-579, 590-595, 601, 605, 612, 621, 644, 670, 708, 714-721, 729-731, 744-780, 848-850, 853-856, 962-963, 987-1042, 1055, 1068, 1081, 1216-1256, 1313-1324, 1331, 1414-1417, 1424-1438, 1442-1453, 1457-1462, 1466, 1507-1518, 1588-1589, 1593, 1597, 1629-1631, 1659-1662, 1689, 1713-1735, 1769-1796, 1825-1872, 1891, 1905, 1917-1918, 1922, 1927, 1987, 1997-2010, 2016, 2023-2024, 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/home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/generic.py 708 575 19% 98-99, 141, 146-155, 158, 216, 220-279, 284-290, 293-330, 355-401, 406-420, 469, 476-488, 494-517, 557-575, 586-635, 639, 649-793, 890-899, 980-981, 1042-1049, 1054-1055, 1061-1066, 1072-1077, 1081-1082, 1086-1087, 1096-1099, 1105-1108, 1113, 1118, 1136-1151, 1156, 1160-1161, 1260-1327, 1332-1354, 1357-1372, 1375-1391, 1400-1448, 1464-1503, 1512-1535, 1538-1578, 1637, 1642-1652, 1655-1688, 1731-1757, 1760-1771, 1786-1819, 1824-1832, 1840-1846, 1849, 1852-1853, 1862-1873, 1920-1935, 2011-2021, 2097-2107, 2222, 2344-2353, 2448-2449, 2521-2528, 2533-2534, 2543-2546, 2555-2558, 2580-2599, 2605, 2616-2624, 2630-2651 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/groupby.py 1169 895 23% 137, 584, 587-591, 594-600, 635, 640, 648, 653, 661, 670-712, 719, 725-740, 744, 775, 795-802, 814-819, 913-944, 947-952, 959-992, 998, 1010-1063, 1072-1089, 1093-1108, 1113, 1117-1124, 1146-1166, 1175, 1182-1199, 1216-1237, 1248-1273, 1284-1310, 1321-1365, 1402-1406, 1422-1428, 1438-1472, 1486-1512, 1517, 1521-1553, 1560-1578, 1585-1598, 1613-1636, 1644-1647, 1655-1678, 1706, 1726, 1740-1771, 1850-1860, 1883-1888, 1941-1971, 2024-2029, 2051-2165, 2193-2209, 2224-2240, 2251-2270, 2275, 2288-2293, 2309-2314, 2369-2384, 2428-2443, 2462-2486, 2495-2524, 2624-2626, 2758-2760, 2776-2778, 2792-2794, 2826-2885, 2910, 2935, 3029, 3036-3093, 3144-3310, 3376-3394, 3451-3453, 3527-3546, 3564-3569, 3582-3587, 3602-3610, 3627-3635, 3686-3774, 3804-3820, 3844-3861, 3884-3902, 3939-3940, 3978-3983, 4000-4006, 4040-4104, 4202-4235, 4247-4258, 4282-4292 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/grouper.py 405 322 20% 54, 250-254, 265-278, 294-309, 332-395, 400-409, 414-420, 425-431, 436-442, 447-454, 458-465, 520-611, 614, 617, 621, 625-639, 646-654, 658, 663-667, 671, 679-686, 692-698, 702-720, 725-783, 787, 822-1019, 1023, 1027-1044 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/indexing.py 105 78 26% 24-28, 114-120, 126-149, 152-155, 158-169, 172-184, 187-226, 230-235, 239-244, 250, 283-284, 293, 300, 303 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/numba_.py 52 39 25% 45-56, 93-119, 153-179 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/ops.py 533 383 28% 99, 121-123, 156-179, 197-220, 234-265, 268-284, 287-296, 312-322, 337-381, 386-397, 408-419, 434-461, 475-497, 519-615, 631-657, 692-697, 701, 705, 708, 712, 725-727, 736-737, 742-748, 754-782, 787-792, 802-819, 824, 828, 832, 839-845, 850-855, 861, 869, 873-878, 883-884, 891-897, 902, 906-908, 912-917, 925-935, 953-959, 987-1001, 1007-1025, 1068-1074, 1081-1086, 1091, 1096-1100, 1111-1125, 1129-1137, 1141-1151, 1160, 1164-1167, 1171, 1175, 1179-1185, 1189-1196, 1211-1216, 1221, 1226, 1229-1239, 1243, 1246, 1252-1254, 1264-1266, 1272-1278 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/objects.py 130 95 27% 55-59, 70, 85-100, 119, 140-142, 153-213, 228, 268-282, 315-318, 336-375, 390 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/utils.py 148 128 14% 29-30, 54-57, 77, 94-99, 114-118, 152-186, 226-234, 269-285, 300-331, 342-343, 358-370, 390-396, 403-414, 513-555 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/accessors.py 140 92 34% 43, 56-64, 67-80, 85-114, 117, 123-144, 163-170, 173-191, 194-210, 213, 216-229, 332, 336, 368, 438, 467, 475, 554-580 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/api.py 126 99 21% 94-95, 103-109, 138-157, 174-191, 210-308, 332-346, 362-364, 368-369 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py 2276 1814 20% 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6807-6817, 6821-6822, 6825, 6828, 6831, 6835, 6876-6882, 6923-6929, 6936-6944, 6948-6956, 6960-6968, 6972-6993, 6997-7018, 7029, 7062-7069, 7103-7128, 7135-7140, 7155-7159, 7163-7164, 7171-7180, 7196-7199, 7215-7228, 7232-7243 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/category.py 123 74 40% 178, 182, 194, 213-223, 246-273, 285-296, 303, 311-319, 322-326, 332, 337-340, 356-368, 376-381, 384-396, 401, 470-471, 475-486 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/datetimelike.py 384 272 29% 73, 91, 95, 100, 104, 109, 114, 119, 125, 131-161, 165-170, 173-174, 189-200, 206, 212, 218-226, 230-234, 243, 247, 251-269, 273-277, 295-321, 340-354, 386, 392-401, 423, 437-438, 441-442, 447, 451-473, 478, 487-490, 493, 496-512, 516-519, 523-526, 532-548, 552-568, 572-586, 592-616, 622-651, 655-667, 676-679, 684-687, 691, 695-696, 705-721, 727-748, 752-754, 758-762, 776-787 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/datetimes.py 285 213 25% 64, 75-100, 254, 264-265, 269-270, 279-280, 284-287, 291-292, 296-297, 301, 320-354, 367-372, 375-376, 382-386, 393-396, 404-416, 428-445, 456-471, 491-510, 513-523, 530-534, 544-586, 591-598, 617-659, 667, 690-705, 731-755, 942-956, 1031-1049, 1063-1064 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/extension.py 83 37 55% 28-29, 62, 71-78, 81, 90, 96-105, 154, 160-171, 177, 188, 191-192 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/frozen.py 44 21 52% 45-47, 63-65, 74-76, 79-81, 84-86, 91, 96, 100, 106, 109, 112 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/interval.py 376 267 29% 113-123, 127-137, 145, 221-232, 263-267, 299-303, 334-336, 344-348, 363-373, 377, 380-386, 391, 398, 408, 415-432, 482, 502-506, 525-571, 574-597, 639-667, 676-693, 699-724, 733-736, 744-768, 772, 779-790, 798, 801, 804-807, 813, 817, 821, 825, 833, 839, 844, 854-867, 882-889, 907-918, 926, 932, 941, 955-957, 1059-1137 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/multi.py 1380 1162 16% 105, 143-153, 184-194, 206-211, 326-359, 377-383, 407-445, 489-510, 561-597, 645-658, 717-722, 729-750, 754, 766, 776-779, 782, 790, 800-804, 818-846, 931-940, 956, 972, 979, 990-1016, 1074-1078, 1089-1113, 1119, 1123-1125, 1128-1137, 1180-1204, 1208, 1212-1214, 1218-1223, 1227, 1232-1235, 1243, 1248, 1261-1270, 1279-1280, 1285-1316, 1328-1390, 1396, 1425-1454, 1479, 1482-1506, 1513-1537, 1545, 1550, 1554-1557, 1567, 1571-1580, 1599-1605, 1655-1657, 1661-1665, 1727-1755, 1787, 1823, 1835-1837, 1873-1898, 1941-1989, 1996-2002, 2007-2032, 2044-2050, 2067-2086, 2113-2137, 2140-2144, 2148-2152, 2184-2221, 2226-2242, 2287-2298, 2334-2344, 2357-2363, 2425-2480, 2483-2496, 2499-2506, 2512-2516, 2524, 2529-2539, 2542-2559, 2565-2569, 2619-2621, 2677, 2680-2727, 2748-2752, 2795-2872, 2915-2926, 2934-3075, 3085-3188, 3226-3319, 3343-3407, 3435-3447, 3463-3514, 3521-3527, 3533-3564, 3567, 3575-3579, 3587-3596, 3599-3600, 3603-3608, 3611-3627, 3633-3644, 3647-3658, 3674-3696, 3714-3731, 3743-3744, 3753-3768, 3804-3808, 3812-3836, 3840-3845, 3862-3878, 3897-3901, 3908-3918 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/period.py 191 122 36% 61-67, 157, 162, 175-176, 180-181, 186, 191, 196, 211-265, 272, 291-308, 314-322, 338-343, 351-356, 362, 372-378, 400-440, 443-455, 458-463, 467-470, 473-474, 478-482, 537-547 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/range.py 508 403 21% 103, 117-142, 155-161, 170-179, 183-188, 199, 209, 213-214, 217-219, 228-231, 235, 239-245, 255, 262, 270, 277-278, 306, 310, 315, 319, 323, 326-331, 335, 342-351, 360-381, 388, 393, 397, 401-413, 416-418, 422-424, 427-433, 437-439, 443-445, 460-471, 478-483, 489-491, 500-526, 534-575, 579-580, 589-597, 602-608, 630-683, 687-770, 773-782, 791-808, 811-829, 840-890, 896, 900, 906-924, 930-931, 935-947, 953, 956, 961-964, 975-1037 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/timedeltas.py 69 38 45% 110, 121, 136-176, 184, 197-204, 208-209, 213-215, 221, 308-315 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexing.py 917 770 16% 83, 137, 285, 548, 613, 661, 676-683, 689-716, 728-775, 789-829, 833-849, 871, 879-893, 900-910, 919-921, 926-928, 932-940, 950-965, 970-1026, 1033-1084, 1087, 1091-1103, 1106, 1109, 1112, 1115, 1120-1123, 1144-1158, 1161, 1173-1192, 1215-1219, 1238-1242, 1269-1273, 1278-1289, 1293, 1297-1307, 1310-1343, 1350-1362, 1379-1434, 1459-1464, 1479-1518, 1529-1554, 1566-1569, 1587-1589, 1594-1598, 1617-1621, 1624-1658, 1662-1669, 1675, 1679-1685, 1701-1837, 1844-1928, 1933-1950, 1953-1999, 2011-2036, 2042-2078, 2084-2181, 2188-2198, 2217-2299, 2302-2348, 2360, 2363-2371, 2374-2386, 2401-2404, 2409-2410, 2413-2419, 2422-2430, 2441-2444, 2462-2464, 2471-2473, 2502-2525, 2533-2541, 2549-2552, 2559-2562, 2572-2579, 2589, 2602, 2613-2627 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/dataframe_protocol.py 101 1 99% 158 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/from_dataframe.py 171 151 12% 48-54, 73-91, 108-137, 156-164, 181-216, 233-307, 313-340, 357-376, 411-436, 469-499 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/utils.py 44 11 75% 75-90 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/api.py 36 26 28% 50-81, 88-97 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/array_manager.py 583 443 24% 130, 134-138, 142, 150, 155, 160-161, 165-167, 170, 176, 179, 182-183, 186-193, 216-255, 261-310, 313-319, 327, 330, 333-339, 347-348, 351, 354-361, 366-370, 375-378, 381-401, 404, 407-411, 423-425, 434, 438, 442, 447, 453, 457, 460-468, 479, 490, 509-529, 544-545, 578-632, 644-660, 665-679, 686-689, 698, 708-715, 718-736, 756-768, 771-781, 787-788, 794, 802, 822-861, 872-881, 894-916, 922-927, 944-969, 983-1001, 1008-1013, 1023-1034, 1052-1089, 1115-1144, 1158, 1166-1177, 1180-1185, 1192, 1196-1199, 1203, 1207, 1211, 1215, 1219, 1223, 1227-1230, 1234-1238, 1242, 1245, 1248-1253, 1256-1258, 1261-1265, 1276-1278, 1284-1289, 1293-1296, 1305, 1311-1314, 1331, 1335, 1351-1361 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/base.py 88 44 50% 44, 48, 52, 56, 61-70, 85, 98-100, 114, 121-130, 138, 142, 148, 151, 154, 160, 169, 181-193, 196-201, 205, 221-224 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/blocks.py 1041 814 22% 120-121, 135-139, 167, 175-178, 186, 190, 196, 201-203, 207, 211, 221-228, 240-244, 249-256, 260, 273-277, 288-293, 298-299, 315-318, 329-331, 337-348, 353-369, 376-385, 402-408, 422-424, 430-447, 457-459, 472-474, 481, 509-526, 531-532, 537-544, 567-647, 678-701, 715-806, 839-862, 878, 884, 890, 899, 906, 922-924, 936-963, 991-1007, 1034-1068, 1087-1141, 1160-1260, 1275-1311, 1333-1393, 1398-1399, 1412-1433, 1459-1467, 1484-1500, 1512-1551, 1556, 1563, 1570, 1573, 1608-1639, 1645-1709, 1715-1778, 1782-1789, 1793, 1799-1803, 1806, 1819-1825, 1854-1870, 1875-1877, 1888-1905, 1910-1912, 1919-1934, 1944-1976, 1981, 1985, 2006-2025, 2034-2035, 2040-2041, 2052-2053, 2070-2094, 2103, 2107, 2110-2112, 2115, 2134, 2140, 2163-2166, 2171-2173, 2181-2186, 2197, 2211-2238, 2271-2298, 2321-2331, 2348-2369, 2379-2382, 2390-2398, 2419-2440, 2450-2459, 2467-2478, 2486-2494, 2507-2581, 2593-2607 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/concat.py 350 303 13% 68-69, 90-117, 139-173, 194-252, 262-290, 303-319, 336-395, 402-406, 409, 413-418, 422-428, 435-458, 462-487, 492-569, 576-615, 622-638, 651-668, 678-681, 707, 720-738, 749-791 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py 428 385 10% 112-159, 174-194, 205-231, 244-388, 400-408, 428-481, 495-507, 514, 529-560, 567-571, 577-613, 621-672, 682-703, 707-721, 730-739, 766-768, 792-841, 847-852, 861-883, 909-920, 931-942, 971-998, 1023-1069 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py 951 771 19% 155, 161, 165, 177-181, 188-192, 196-209, 212, 219-221, 225-226, 231, 235, 243-244, 252, 259-266, 273-274, 277-278, 292, 295-304, 327-356, 359-365, 374, 386-394, 397-403, 413-414, 417, 422-426, 429-433, 443-451, 460-468, 471-475, 484, 494-505, 512, 516, 521, 526-536, 539-540, 553-565, 574-580, 586-614, 618, 634-665, 675-681, 712-766, 795-901, 908-927, 952-963, 988-1002, 1005-1011, 1022, 1039-1082, 1088-1096, 1106-1108, 1121-1136, 1148-1279, 1299-1326, 1340-1353, 1364-1385, 1398-1427, 1441-1444, 1453-1466, 1472-1479, 1496-1515, 1530-1539, 1545, 1552, 1580-1592, 1610-1655, 1670-1675, 1702-1739, 1750-1800, 1809-1811, 1814-1821, 1828-1832, 1840, 1857-1858, 1869-1871, 1880-1881, 1887-1892, 1900, 1903-1919, 1922-1939, 1942, 1946, 1951, 1956, 1960-1979, 1984-1994, 1998, 2002, 2005, 2009, 2013, 2017, 2020-2022, 2026, 2038-2042, 2050-2054, 2061, 2073-2074, 2082-2086, 2106-2116, 2136-2143, 2153-2169, 2179-2188, 2192-2236, 2241, 2250-2259, 2267-2276, 2282-2311, 2316-2320, 2326-2343 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/ops.py 62 46 26% 12-15, 33-52, 61-86, 93-95, 107-136, 143-147 /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/describe.py 148 115 22% 46, 80-95, 108, 127-130, 153-159, 162-176, 180-196, 201-207, 220-247, 263-279, 295-327, 341-351, 364-373, 387-408 /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/selectn.py 120 97 19% 34, 42-47, 50, 54, 58, 67-69, 88-156, 176-182, 185-262 /home/admin/.local/lib/python3.8/site-packages/pandas/core/missing.py 314 268 15% 46, 53-61, 81-118, 123-140, 168-183, 204-224, 247-267, 302-359, 366-381, 408-489, 507-566, 605-611, 650-654, 734-740, 765-787, 818-858, 866-870, 880-888, 899-901, 910-912, 917-924, 929-936, 943-946, 950, 984-1013, 1028-1030 /home/admin/.local/lib/python3.8/site-packages/pandas/core/nanops.py 626 505 19% 71, 83, 88-104, 130-160, 167-182, 186-195, 202-217, 253-261, 310-350, 354-356, 361-397, 415-429, 451-462, 474-494, 533-551, 588-606, 643-655, 664-674, 711-740, 768-821, 844-848, 880-895, 934-941, 981-1017, 1058-1070, 1083-1097, 1145-1149, 1191-1195, 1237-1285, 1327-1384, 1419-1427, 1439-1456, 1483-1497, 1513-1545, 1568-1576, 1581-1585, 1599-1614, 1620-1643, 1657-1671, 1675-1700, 1708-1720, 1747-1767 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/__init__.py 184 134 27% 78, 133-150, 163-173, 183-201, 233-334, 343-365, 384-409, 417-433, 446-473, 489-494 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/array_ops.py 191 155 19% 68-83, 99-139, 164-188, 217-234, 256-298, 302-339, 358-405, 424, 430, 433, 435, 448, 470-519, 537-542 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/common.py 56 34 39% 64, 69-81, 101-105, 128-151 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/dispatch.py 6 1 83% 26 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/docstrings.py 57 2 96% 50, 60 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/invalid.py 17 9 47% 30-37, 54-55 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/mask_ops.py 59 52 12% 42-73, 106-126, 156-184, 188-189 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/methods.py 34 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/missing.py 56 48 14% 49-73, 105-134, 158-180 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/api.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/concat.py 284 246 13% 54-58, 78, 95, 112, 129, 146, 364-385, 406-563, 570-623, 626-629, 632-633, 639-640, 653-699, 702-705, 709, 713-821 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/encoding.py 154 138 10% 147-224, 236-334, 447-533 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/melt.py 138 117 15% 33-35, 50-158, 214-248, 489-540 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/merge.py 919 816 11% 104-106, 128, 148-162, 176-217, 324-358, 616-633, 680-747, 760-803, 806-825, 830-831, 835-840, 847-869, 872-884, 906-927, 935-1034, 1038, 1046-1101, 1125-1135, 1155-1276, 1285-1440, 1461-1463, 1471-1558, 1562-1605, 1642-1690, 1737-1779, 1799-1800, 1816-1842, 1846-1847, 1859-1864, 1891-1898, 1914-1990, 1996-2063, 2068-2189, 2204-2234, 2240-2242, 2247, 2275-2279, 2285-2309, 2369-2468, 2475-2505, 2511-2517, 2527-2554, 2558-2560, 2564, 2568-2575, 2591-2645 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/pivot.py 366 333 9% 51, 71-110, 129-255, 269-339, 345-362, 368-436, 448-480, 484-494, 506-562, 671-734, 740-814, 818-831, 864-885 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/tile.py 181 158 13% 241-305, 369-389, 403-472, 481-505, 522-539, 557-561, 568-591, 602-608, 617-625, 632-640, 647-651 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/util.py 28 22 21% 33-60, 77-82 /home/admin/.local/lib/python3.8/site-packages/pandas/core/roperator.py 29 15 48% 11, 15, 19, 23, 27, 31, 38-42, 46, 50, 54, 58, 62 /home/admin/.local/lib/python3.8/site-packages/pandas/core/sample.py 58 48 17% 19, 31-76, 90-113, 144-151 /home/admin/.local/lib/python3.8/site-packages/pandas/core/series.py 1122 781 30% 179-187, 221-230, 377-519, 546-570, 576, 584-586, 591, 605, 619, 669, 673-674, 716, 750, 754-756, 762, 778-781, 787, 857-863, 916-921, 938, 945-960, 971, 985, 990, 993-1033, 1037-1073, 1077-1093, 1096-1097, 1112-1138, 1141-1219, 1222-1225, 1230-1248, 1251-1256, 1259-1263, 1280-1290, 1298, 1302-1305, 1311-1312, 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5987, 5998, 6016, 6028, 6040, 6051, 6087-6098, 6101-6108, 6111-6112 /home/admin/.local/lib/python3.8/site-packages/pandas/core/shared_docs.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/sorting.py 252 222 12% 47-49, 81-106, 144-200, 223-224, 228-232, 239-254, 276-287, 293-301, 329-366, 398-440, 458-473, 480-484, 516-533, 549-577, 587-594, 606-623, 656-670, 681-692, 710-725 /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/accessor.py 581 386 34% 49, 123-129, 140-141, 179-195, 220-236, 239-240, 251-393, 411-448, 593-683, 888-895, 912-913, 1008-1009, 1022-1023, 1085-1086, 1151-1152, 1281-1290, 1321-1322, 1354-1355, 1485-1508, 1551-1552, 1613-1625, 1650, 1655, 1660, 1723-1728, 1802-1803, 1878-1879, 1898-1907, 1925-1926, 2014-2015, 2023-2024, 2032-2033, 2088-2089, 2096-2097, 2155-2156, 2197-2198, 2224-2225, 2292-2293, 2359-2363, 2429-2433, 2525-2526, 2612-2654, 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/home/admin/.local/lib/python3.8/site-packages/pandas/core/window/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/common.py 81 72 11% 18-146, 150-161, 166-168 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/doc.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/ewm.py 227 170 25% 18-19, 70-94, 118-121, 347-394, 409, 415, 445, 492, 517-546, 569-600, 621-630, 651-663, 700-735, 771-812, 825-830, 843-847, 868-892, 898, 901, 904, 912, 921, 924, 970-1012 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/expanding.py 74 23 69% 17-18, 125, 137, 171, 186, 209, 239, 266, 293, 320, 347, 406, 466, 508, 526, 566, 602, 676, 719, 789, 812-816 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/numba_.py 146 129 12% 49-75, 111-173, 208-236, 244-257, 293-349 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/online.py 52 43 17% 32-86, 91-99, 102-114, 117-118 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/home/admin/.local/lib/python3.8/site-packages/pandas/io/_util.py 6 2 67% 9-10 /home/admin/.local/lib/python3.8/site-packages/pandas/io/api.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/clipboards.py 11 1 91% 20 /home/admin/.local/lib/python3.8/site-packages/pandas/io/common.py 444 337 24% 124-132, 135, 138, 154-156, 161, 166, 183-185, 189-214, 219, 226, 252-260, 268-270, 278, 319-458, 480-482, 522-531, 560-586, 598-600, 615, 630, 645, 702-913, 936, 939-951, 963-969, 977-984, 991-999, 1003-1006, 1017-1025, 1032-1037, 1041-1042, 1053, 1056, 1059-1061, 1064-1066, 1069-1071, 1078-1084, 1087, 1090-1100, 1107-1134, 1139-1148, 1154-1167, 1175-1187, 1209-1212, 1233-1253 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_base.py 413 301 27% 395, 434, 473-515, 523-545, 552, 556, 559-569, 574, 578, 582, 586, 589-591, 596-597, 620-626, 655-690, 716-897, 1103-1121, 1129, 1134, 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102 71 30% 24-27, 43, 65-86, 90-93, 115-125, 149-158, 163, 168, 173, 178, 197-209, 214, 219, 223-236, 260-271, 295-305, 327-332 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlrd.py 62 43 31% 33-35, 39-41, 44-50, 54, 57-58, 61-62, 67-126 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlsxwriter.py 83 63 24% 101-172, 192-210, 219, 223-224, 230, 241-275 /home/admin/.local/lib/python3.8/site-packages/pandas/io/feather_format.py 43 28 35% 54-96, 139-162 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/__init__.py 4 2 50% 5-8 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/console.py 33 28 15% 15-47, 63-76, 87-94 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/format.py 908 752 17% 112, 209-214, 217-231, 234, 243-261, 279-295, 298-322, 325-363, 366-374, 377, 386-421, 426, 429, 432, 435, 442-451, 457-460, 468-476, 480-484, 504-510, 537-549, 585-609, 615-621, 625, 631, 635, 639, 643, 647, 651, 655, 659, 663, 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/home/admin/.local/lib/python3.8/site-packages/pandas/io/json/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_json.py 501 386 23% 84, 107, 127, 146-204, 222-237, 240, 243-244, 266-269, 272-273, 281-286, 292-301, 327-379, 383, 408, 433, 458, 483, 743-784, 817-862, 872-878, 894-919, 925, 931, 935, 939, 946-981, 987-1008, 1017-1018, 1021, 1025, 1029, 1033, 1036-1061, 1064, 1072, 1099-1122, 1128-1131, 1134-1141, 1144, 1150-1160, 1163, 1176-1241, 1251-1280, 1283, 1291-1298, 1301-1307, 1315-1346, 1354-1374, 1377-1382, 1387-1415 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_normalize.py 142 126 11% 35-39, 86-120, 146-166, 184-191, 237-244, 388-536 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_table_schema.py 134 114 15% 42-43, 79-96, 101-120, 124-151, 195-224, 283-316, 355-382 /home/admin/.local/lib/python3.8/site-packages/pandas/io/orc.py 52 38 27% 80-97, 162-205 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895-975, 1044, 1136, 1222, 1289, 1351, 1459, 1534, 1583-1589, 1674, 1760-1765, 1786-1831, 1857-1864 /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/_misc.py 73 43 41% 12-16, 40-41, 64-65, 84-85, 159-160, 252-253, 321-322, 387-388, 455-456, 514-515, 549-550, 571-574, 577-578, 581-584, 587-588, 599, 602, 610-615 /home/admin/.local/lib/python3.8/site-packages/pandas/testing.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/__init__.py 4 2 50% 5-11 /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/api.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/frequencies.py 307 236 23% 98, 132-175, 184-210, 216, 222, 226, 230, 241-280, 284-285, 289-290, 294, 298, 301, 305-306, 310, 313-342, 345-353, 356-367, 370-381, 384-391, 395-405, 413-427, 432-433, 437, 441-446, 470-506, 525-564, 580-583, 587-589, 593-594, 598-599, 603-604, 608-609 /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/offsets.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/util/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/util/_decorators.py 135 79 41% 56-94, 164-214, 243-252, 257-260, 291-337, 368, 436, 448-449, 491 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_exceptions.py 48 36 25% 16-27, 36-51, 75-89 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_print_versions.py 48 34 29% 24-27, 34-36, 56-90, 108-134 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_str_methods.py 12 1 92% 23 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_tester.py 18 11 39% 25-35 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_validators.py 122 97 20% 33-41, 55-79, 117-123, 132-136, 161-163, 206-221, 251-263, 285-302, 326-336, 341, 346, 353-357, 377-390, 409-424, 435-442, 446-448 /home/admin/.local/lib/python3.8/site-packages/pandas/util/version/__init__.py 270 129 52% 28, 31, 34, 37, 40, 43, 46, 49, 52, 60, 63, 66, 69, 72, 75, 78, 81, 84, 123-126, 139, 146, 151-154, 157-160, 164, 169-172, 175-178, 183-186, 193, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 255-268, 276-294, 338, 363, 366-391, 395-396, 400-401, 405-406, 410, 418-421, 425, 429-438, 442, 446, 450, 454, 458, 462, 471-489, 493-495, 508, 537, 543, 550, 557, 570 /home/admin/.local/lib/python3.8/site-packages/paramiko/__init__.py 36 0 100% /home/admin/.local/lib/python3.8/site-packages/paramiko/_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/paramiko/agent.py 246 162 34% 82-95, 98-101, 104-108, 111-119, 128-130, 133-149, 152-173, 176-178, 188, 196-203, 212-213, 216, 226-232, 234-241, 261-264, 267, 273-276, 283-287, 309-315, 318, 321-325, 332-336, 345, 348, 374-377, 380, 383, 386-387, 415, 421, 439-453, 456, 460, 463, 467-469, 475-477, 481-482, 485-497 /home/admin/.local/lib/python3.8/site-packages/paramiko/auth_handler.py 627 439 30% 107-110, 113-120, 134-142, 148-157, 160-169, 172-179, 182-183, 194-200, 203-209, 219, 243-248, 251-252, 262, 267-283, 287-294, 298-305, 326-332, 339, 364-371, 375, 389-391, 406-503, 508, 514-534, 538-547, 550-724, 742-744, 746-753, 766-768, 772-790, 793-806, 811-818, 830, 855, 870-871, 874-875, 879, 883, 887, 891, 894, 897-920, 923-944, 947-948, 951-952, 965, 981-983, 999-1024, 1027, 1030-1049, 1052-1059, 1068-1077, 1081-1092 /home/admin/.local/lib/python3.8/site-packages/paramiko/auth_strategy.py 86 52 40% 25, 30-32, 35, 41, 50, 69-70, 75, 80-81, 102, 111-113, 118-121, 138-145, 148, 200-201, 208, 226, 229, 245-246, 258, 267-303 /home/admin/.local/lib/python3.8/site-packages/paramiko/ber.py 85 66 22% 35-36, 39, 42, 45, 48, 51-94, 98-105, 109-115, 118-130, 136-139 /home/admin/.local/lib/python3.8/site-packages/paramiko/buffered_pipe.py 88 34 61% 67-80, 92, 108-114, 145, 152-154, 161-164, 178-186, 198, 208-212 /home/admin/.local/lib/python3.8/site-packages/paramiko/channel.py 596 346 42% 69, 137-140, 146-159, 188-201, 221-228, 273-281, 298-307, 328-333, 353-360, 375, 400-402, 417-423, 472-490, 507-514, 520, 530, 536, 547, 570-582, 610, 630-633, 643, 652-669, 681, 698-699, 704-708, 725, 746-747, 752-756, 773-779, 796-799, 819-823, 843-846, 864-867, 931-942, 955-965, 977, 989, 995, 1030-1037, 1042, 1048-1058, 1065, 1080-1153, 1155-1161, 1171, 1190-1207, 1221-1224, 1236, 1241, 1252, 1265-1272, 1280, 1284-1290, 1303-1331, 1356, 1362-1363, 1377-1378, 1389-1390 /home/admin/.local/lib/python3.8/site-packages/paramiko/client.py 282 131 54% 99-107, 125-126, 141-147, 160, 169, 190, 214-215, 382-385, 389-401, 409, 428, 430, 432, 434, 439, 446-449, 464-468, 471, 475, 480-483, 511-518, 562, 565, 597-600, 608, 618, 632-633, 648-649, 684, 689-693, 700-705, 708-722, 728-741, 748-761, 781, 784, 799, 803-819, 843, 855, 871-877, 889 /home/admin/.local/lib/python3.8/site-packages/paramiko/common.py 93 3 97% 36-37, 43 /home/admin/.local/lib/python3.8/site-packages/paramiko/compress.py 12 4 67% 29, 32, 37, 40 /home/admin/.local/lib/python3.8/site-packages/paramiko/config.py 267 220 18% 91, 100, 109-110, 119-121, 131-181, 225-247, 251-282, 296-323, 330-333, 337-350, 355-406, 409, 422-471, 482, 494-503, 509-512, 520-556, 577-593, 602-604, 607-631, 682-685, 696 /home/admin/.local/lib/python3.8/site-packages/paramiko/dsskey.py 123 92 25% 57-83, 86-92, 95, 99, 103, 106, 109, 112-134, 137-163, 166-176, 184-194, 211-223, 228-229, 232-233, 236-258 /home/admin/.local/lib/python3.8/site-packages/paramiko/ecdsakey.py 178 124 30% 77, 80-82, 85-87, 90-92, 120-169, 173, 178, 181-198, 201, 205, 212, 215, 218, 221-228, 231-244, 247, 255, 271-278, 283-284, 287-288, 291-327, 330-333, 336-339 /home/admin/.local/lib/python3.8/site-packages/paramiko/ed25519key.py 112 30 73% 50, 61-62, 68, 84, 95-105, 108, 114, 121-136, 142, 147, 166, 181-185, 192, 205, 209-210 /home/admin/.local/lib/python3.8/site-packages/paramiko/file.py 245 139 43% 67, 77-79, 85-86, 93-95, 109-112, 122, 132, 142, 152-154, 173-213, 239, 241, 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/home/admin/.local/lib/python3.8/site-packages/paramiko/kex_group14.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/paramiko/kex_group16.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/paramiko/kex_gss.py 344 294 15% 90-95, 101-116, 130-141, 154-159, 168-172, 181-195, 205-240, 249-294, 309-313, 348-357, 363-376, 385-400, 406-420, 429-464, 472-492, 505-557, 568-572, 580-594, 602-644, 657-661, 680, 683, 686 /home/admin/.local/lib/python3.8/site-packages/paramiko/message.py 105 25 76% 58, 64, 85-88, 110, 122, 137-141, 155, 163, 245-250, 258-259, 297, 299, 301 /home/admin/.local/lib/python3.8/site-packages/paramiko/packet.py 389 124 68% 59-62, 138, 183-184, 221, 224, 227-228, 231, 234, 237, 240, 256-258, 261, 286, 314-316, 320, 324, 329-339, 342, 344, 355-367, 369-371, 379, 382, 385, 406-412, 424, 429, 432-436, 440, 447-450, 464, 478-484, 498-511, 516-524, 529, 535, 544, 556, 568, 573, 581, 587, 599-607, 614-621, 627, 629, 638-644, 654-657, 665, 667-673, 691, 696 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/home/admin/.local/lib/python3.8/site-packages/paramiko/ssh_gss.py 253 198 22% 51-57, 64-68, 100-107, 122-141, 151-152, 161, 175-183, 192-197, 208, 227-236, 253-263, 287-319, 334-346, 360-366, 378-392, 401-403, 416, 421, 438-448, 473-498, 514-526, 540-546, 558-572, 582-584, 596, 612-621, 643-675, 690-702, 715-724, 736-754, 763, 778 /home/admin/.local/lib/python3.8/site-packages/paramiko/transport.py 1423 712 50% 148-149, 473-478, 481-499, 529-530, 645-664, 676-677, 686, 714-720, 760-762, 771-774, 778, 822-844, 858-864, 881-885, 913-926, 932-937, 953, 1015, 1029, 1041, 1088, 1102-1105, 1107-1108, 1122-1125, 1129, 1133-1136, 1171-1190, 1201-1204, 1216, 1229-1234, 1249-1260, 1274-1277, 1296-1314, 1326-1339, 1401-1467, 1503-1507, 1518, 1532-1534, 1545-1547, 1570-1575, 1624-1660, 1699, 1703, 1708, 1754-1762, 1772-1785, 1803-1811, 1828-1834, 1847-1849, 1871, 1882, 1897, 1913-1916, 1919-1933, 1941-1942, 1948, 1954-1955, 1975-1978, 1982-1984, 2010, 2012, 2033-2034, 2040-2046, 2051, 2057, 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334-337 /home/admin/.local/lib/python3.8/site-packages/psutil/__init__.py 950 691 27% 37-38, 127-128, 131-132, 135-136, 139-140, 143-180, 248-259, 271-281, 290-292, 298-304, 346, 349-393, 396-411, 421-423, 426, 429-431, 436, 467-505, 518-549, 556-568, 574-579, 586-601, 617-618, 628-647, 654-686, 690, 694-697, 703-715, 722-724, 728, 732-737, 746, 752, 758, 764, 776, 793-798, 813-816, 829-837, 850, 858, 862-866, 872, 876, 886, 915-956, 993-1048, 1059, 1070, 1074, 1090, 1102-1119, 1133-1147, 1154, 1178, 1184-1202, 1211-1212, 1222-1223, 1233-1234, 1244-1245, 1254-1255, 1273-1275, 1322-1323, 1326, 1329-1331, 1334-1347, 1350-1356, 1360-1364, 1383-1385, 1393-1403, 1431-1482, 1521-1571, 1593-1599, 1632-1634, 1638-1640, 1647-1659, 1666-1675, 1679-1696, 1736-1783, 1808-1859, 1864, 1877-1907, 1918, 1981-1984, 2000, 2013, 2025, 2060-2072, 2111-2121, 2155, 2176-2203, 2218, 2237-2259, 2272, 2290, 2304, 2317, 2327-2337, 2406, 2409 /home/admin/.local/lib/python3.8/site-packages/psutil/_common.py 442 251 43% 29-30, 33-34, 39, 131-133, 144-145, 157, 161-162, 278-279, 282-283, 295-304, 307, 320-332, 340-350, 360-367, 377-384, 412, 447-457, 462, 466-469, 481-488, 496-503, 509-517, 524-545, 552-553, 565-566, 576-590, 605-606, 623-628, 634-639, 645-678, 682-690, 694-695, 703-704, 721-727, 739-747, 757-760, 770-780, 785-798, 803-832, 836-842, 846 /home/admin/.local/lib/python3.8/site-packages/psutil/_compat.py 243 215 12% 27, 30-41, 57-119, 132-272, 278-324, 330-345 /home/admin/.local/lib/python3.8/site-packages/psutil/_pslinux.py 1130 874 23% 56, 113, 121-124, 217-232, 239-245, 258-264, 298-305, 310-313, 344-371, 390-492, 498-546, 592-616, 622-652, 657-672, 683-727, 778-797, 800-813, 832-868, 873-908, 913-946, 949-977, 985, 992-1022, 1027-1043, 1058-1146, 1151-1182, 1203-1290, 1303-1322, 1332-1405, 1415-1428, 1434-1441, 1452, 1459-1484, 1491-1506, 1515-1526, 1536-1539, 1545, 1558-1580, 1589-1590, 1595-1597, 1600-1602, 1605-1607, 1611-1615, 1618-1632, 1636-1657, 1661-1663, 1667-1672, 1678-1704, 1709-1715, 1720, 1724, 1728-1735, 1750-1753, 1766-1788, 1801-1858, 1862-1869, 1874-1883, 1890-1891, 1895-1919, 1928, 1932, 1939, 1944-1949, 1953-1968, 1975-1978, 1982-1988, 1997-2017, 2021-2025, 2029-2069, 2073-2075, 2079, 2083, 2087-2089, 2093-2095 /home/admin/.local/lib/python3.8/site-packages/psutil/_psposix.py 89 67 25% 30-47, 61-110, 120-158, 167-175 /home/admin/.local/lib/python3.8/site-packages/pyarrow/__init__.py 170 121 29% 41-59, 80-93, 97-102, 106-116, 123-157, 312-315, 343, 347, 351-356, 360-367, 375, 393-416, 426-466 /home/admin/.local/lib/python3.8/site-packages/pyarrow/_compute_docstrings.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/_generated_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/compute.py 203 92 55% 114, 137-138, 175-179, 208-211, 215-232, 238-245, 248-262, 391-403, 425-441, 484-485, 539-544, 584-591, 631-638, 663-664, 700-713, 730 /home/admin/.local/lib/python3.8/site-packages/pyarrow/filesystem.py 225 139 38% 54-55, 66, 79, 94-105, 108, 118, 124, 130, 143, 156, 167, 178, 189, 196, 225-228, 235, 239, 260-261, 265-266, 270-274, 278-279, 283-284, 288, 292-293, 300-301, 305, 311-312, 321-325, 329, 333, 341, 345-346, 350-351, 355-359, 366-367, 370-371, 377-378, 385-393, 397-402, 411-433, 437-440, 444-459, 467-511 /home/admin/.local/lib/python3.8/site-packages/pyarrow/hdfs.py 82 52 37% 42-49, 52, 59, 63, 67, 71, 86, 90, 94, 111, 126-131, 135-149, 153-165, 169-172, 176-185, 223-227, 235-240 /home/admin/.local/lib/python3.8/site-packages/pyarrow/ipc.py 61 36 41% 51-52, 84-85, 109-110, 121-122, 126-141, 146-150, 154, 190, 195, 234, 259-264, 282-285 /home/admin/.local/lib/python3.8/site-packages/pyarrow/types.py 155 47 70% 56, 61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116, 121, 126, 131, 136, 141, 146, 151, 156, 161, 166, 171, 176, 181, 186, 191, 196, 201, 206, 211, 223, 228, 233, 238, 243, 248, 253, 258, 263, 268, 273, 278, 283, 288, 293 /home/admin/.local/lib/python3.8/site-packages/pyarrow/util.py 96 60 38% 64, 101-106, 114-123, 127-131, 135, 142-151, 158, 177-194, 198-202, 206-207, 213-230 /home/admin/.local/lib/python3.8/site-packages/pyarrow/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/vendored/docscrape.py 473 282 40% 24, 46, 62, 82, 101, 104, 109-112, 155-157, 164, 169, 172, 183, 187-188, 222, 224, 238-241, 297-341, 349-360, 365, 373-376, 395-396, 398-399, 406, 412-420, 424-428, 431-442, 447, 450, 453-455, 458-460, 463-465, 468-481, 484-489, 492-521, 524-538, 541-555, 565-574, 577-582, 585-599, 604-607, 616-666, 670-672, 680-682, 689-693, 697-716 /home/admin/.local/lib/python3.8/site-packages/pycparser/__init__.py 25 18 28% 32-48, 82-90 /home/admin/.local/lib/python3.8/site-packages/pycparser/ast_transforms.py 21 18 14% 64-96, 103-105 /home/admin/.local/lib/python3.8/site-packages/pycparser/c_ast.py 782 446 43% 25-28, 37-51, 56, 80-100, 149-158, 164-165, 176-179, 182-185, 192-194, 197-200, 203-206, 213-216, 219-222, 225-228, 235-238, 241-244, 247-250, 257, 260, 263-264, 271-273, 276-280, 283-286, 293-295, 298-301, 304-307, 314-315, 318-321, 324-325, 332-334, 337-340, 343-346, 358-359, 362-363, 370, 373, 376-377, 394-398, 401-406, 413-414, 417-420, 423-424, 431-432, 435-438, 441-442, 449-451, 454-457, 460-463, 470, 473, 476-477, 484, 487, 490-491, 498-500, 503-505, 508-509, 516-518, 521-523, 526-527, 534-535, 538-541, 544-545, 552-553, 556-559, 562-563, 574-577, 580-581, 588-592, 595-600, 603-610, 617-619, 622-625, 628-631, 643-646, 649-652, 659-662, 665-670, 673-678, 685-686, 689-690, 693-694, 701-702, 705-706, 709-710, 721-722, 725-726, 733-736, 739-743, 746-751, 758-759, 762-765, 768-769, 776-778, 781-783, 786-787, 794-796, 799-803, 806-809, 820-823, 826-827, 839-841, 844-845, 852-853, 856-858, 861-862, 874-877, 880-881, 888-891, 894-897, 900-903, 910-912, 915-918, 921-924, 931-934, 937-941, 944-949, 962-964, 967-968, 982-984, 987-988, 1001-1003, 1006-1007, 1014-1016, 1019-1021, 1024-1025, 1032-1034, 1037-1040, 1043-1044, 1051-1053, 1056-1059, 1062-1065, 1072-1073, 1076-1077, 1080-1081 /home/admin/.local/lib/python3.8/site-packages/pycparser/c_lexer.py 227 39 83% 83-84, 92-94, 97, 278-282, 290, 306, 317, 322, 329-330, 334, 340-341, 344, 442, 446, 450, 454, 458-459, 463, 474, 478, 482, 486-487, 491-492, 496, 502-503, 513-514 /home/admin/.local/lib/python3.8/site-packages/pycparser/c_parser.py 581 219 62% 167, 177, 193, 284, 327, 336-339, 387, 395-411, 422-423, 447, 467-474, 487, 518, 543, 554, 559, 564, 571-574, 583-590, 599-601, 616, 664-669, 693-712, 753, 773, 779, 784, 789, 819, 871, 877, 884, 889, 899-900, 909-911, 923, 940-948, 981-1002, 1011, 1016, 1036-1039, 1045, 1050, 1056, 1063-1069, 1075-1085, 1119, 1144-1154, 1162-1168, 1192-1195, 1223, 1233, 1240, 1270, 1281, 1290, 1311-1315, 1320, 1326-1329, 1335-1341, 1346, 1355, 1361, 1366-1372, 1385, 1399, 1404-1410, 1425-1431, 1436, 1445-1450, 1455, 1467, 1476, 1480, 1486, 1490, 1494, 1498, 1502, 1506, 1511, 1515, 1519, 1523, 1528, 1532, 1536, 1542, 1546-1549, 1555-1562, 1575, 1595, 1599, 1608, 1634, 1642, 1653, 1659, 1672, 1680, 1686, 1694-1695, 1701, 1707, 1711, 1721, 1725, 1730-1731, 1740-1747, 1753-1757, 1761, 1774, 1776, 1779, 1781, 1790-1800, 1807, 1819-1824, 1830-1835, 1857-1863 /home/admin/.local/lib/python3.8/site-packages/pycparser/lextab.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pycparser/ply/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pycparser/ply/lex.py 692 415 40% 59-60, 66, 69, 80, 83, 86, 95, 98, 145-169, 175-206, 213, 219, 244, 255, 265, 277-278, 284, 290, 296, 339-340, 360-361, 368-396, 399-407, 411, 416, 419-422, 440, 450-454, 463-469, 494-523, 546, 578-581, 587-589, 592-594, 597-599, 605-612, 622-630, 637-638, 642-644, 647-649, 651-653, 655-657, 679-681, 693-694, 696-699, 708, 711-712, 717-718, 730-822, 833-856, 867, 881-882, 896, 902-903, 909-910, 920-1047, 1056-1080, 1092 /home/admin/.local/lib/python3.8/site-packages/pycparser/ply/yacc.py 1916 1562 18% 97, 114, 119, 122, 129, 132, 140-146, 150-156, 176-177, 180-181, 184-185, 190-198, 219, 222, 242, 246, 252, 264-266, 272-274, 277, 295, 298-303, 321, 325-327, 329, 350-683, 697-989, 1015-1016, 1030, 1063, 1087-1088, 1123-1135, 1157-1168, 1176-1271, 1312-1339, 1342, 1345, 1348, 1351, 1354, 1358-1370, 1374-1375, 1392, 1395, 1429-1437, 1440-1444, 1447, 1455-1460, 1475-1507, 1511, 1514, 1525-1530, 1551-1624, 1634-1640, 1652-1662, 1673-1727, 1737-1745, 1754-1759, 1769-1774, 1786-1791, 1804-1829, 1837-1864, 1875-1914, 1933-1958, 1980, 1986, 1999-2023, 2056-2064, 2067-2087, 2101-2133, 2138-2154, 2165-2195, 2199-2224, 2255-2270, 2284-2292, 2304-2320, 2330-2341, 2372-2437, 2452-2455, 2474-2477, 2492-2500, 2511-2526, 2534-2718, 2727-2839, 2849-2866, 2884-2888, 2896-2927, 2947, 2961-2967, 2982-2983, 2999-3018, 3027-3029, 3037-3055, 3061-3063, 3066-3068, 3071-3073, 3080-3089, 3097-3124, 3149-3206, 3219, 3226, 3239, 3246-3256, 3262-3263, 3276, 3285, 3294-3494 /home/admin/.local/lib/python3.8/site-packages/pycparser/plyparser.py 55 7 87% 26-28, 67, 107-112 /home/admin/.local/lib/python3.8/site-packages/pycparser/yacctab.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/pygit2/__init__.py 92 43 53% 123-162, 203-225 /home/admin/.local/lib/python3.8/site-packages/pygit2/_build.py 18 12 33% 46-53, 58-67 /home/admin/.local/lib/python3.8/site-packages/pygit2/blame.py 69 36 48% 33-36, 44-47, 52, 58, 63, 68, 72, 77, 82, 86, 91-95, 102-105, 108, 111, 114-118, 130-137, 140 /home/admin/.local/lib/python3.8/site-packages/pygit2/callbacks.py 209 162 22% 84-86, 89-93, 111-115, 148, 170, 221-238, 243-263, 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, 348, 353, 356, 359-360, 365-369, 377-384, 388-396, 402, 405-417, 420-421, 424, 430-434, 437, 440, 443-457, 460 /home/admin/.local/lib/python3.8/site-packages/pygit2/packbuilder.py 40 23 42% 37-43, 47, 50, 53, 57-59, 62-64, 67-69, 72, 75-77, 81 /home/admin/.local/lib/python3.8/site-packages/pygit2/refspec.py 37 17 54% 37-38, 43, 48, 53, 58, 63, 69, 74, 77-84, 90, 96 /home/admin/.local/lib/python3.8/site-packages/pygit2/remote.py 155 112 28% 41-60, 69-71, 74, 80, 86, 92, 97-101, 106-107, 122-130, 138-164, 169-171, 177, 181-182, 188-192, 198-202, 220-224, 241-249, 252-265, 268-275, 284-296, 306-319, 326-327, 332-333, 338-339, 345-346, 352-353 /home/admin/.local/lib/python3.8/site-packages/pygit2/repository.py 552 422 24% 77, 85, 105-117, 121, 139-163, 169-174, 184-198, 204-205, 208-211, 214, 217, 223-224, 237-241, 250-254, 281-291, 308-316, 324-346, 353-354, 361-362, 369-373, 410-429, 444-453, 459-479, 543-568, 575, 615-636, 644-648, 690-698, 704-729, 743-762, 818-838, 894-919, 983-1039, 1074-1091, 1095-1104, 1128-1129, 1141, 1148-1149, 1187-1224, 1249-1262, 1288-1303, 1310-1316, 1326-1327, 1347-1363, 1372-1375, 1383-1393, 1396-1399, 1402-1404, 1407, 1410, 1413-1416, 1423-1424, 1427, 1439-1442, 1445-1446, 1449, 1452, 1455, 1459, 1462, 1491, 1493, 1497, 1501-1506 /home/admin/.local/lib/python3.8/site-packages/pygit2/settings.py 74 25 66% 40, 43, 65-71, 81, 86, 90, 98, 102, 110, 120, 128, 138, 148, 153, 158, 163, 168, 173, 178 /home/admin/.local/lib/python3.8/site-packages/pygit2/submodule.py 37 19 49% 35-40, 43, 47-51, 56-57, 62-63, 68-69, 74-75, 80-81 /home/admin/.local/lib/python3.8/site-packages/pygit2/utils.py 60 46 23% 33-36, 40-49, 53-62, 66-70, 85-102, 105, 108, 119-121, 124, 127-132 /home/admin/.local/lib/python3.8/site-packages/pytz/__init__.py 198 125 37% 56-75, 87-108, 113-124, 167-190, 195, 204-206, 226-228, 231, 234, 237, 240, 244-246, 250-254, 257, 260, 295, 307, 347, 350-366, 379-390, 403-406, 409, 412, 415, 418, 421, 425-427, 431-435, 491-502, 509-512, 516 /home/admin/.local/lib/python3.8/site-packages/pytz/exceptions.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pytz/lazy.py 100 59 41% 4-8, 21-28, 31-38, 41-48, 51-58, 61-68, 87, 98-106, 142, 151-160 /home/admin/.local/lib/python3.8/site-packages/pytz/tzfile.py 76 66 13% 21, 25-123, 126-133 /home/admin/.local/lib/python3.8/site-packages/pytz/tzinfo.py 178 126 29% 7-8, 34-41, 49-58, 66, 76, 87-89, 97, 105, 113, 117-119, 144-148, 151, 156, 183-194, 198-204, 251-259, 320-397, 422-428, 461-467, 499-505, 508-517, 524, 542-580 /home/admin/.local/lib/python3.8/site-packages/reportlab/__init__.py 28 2 93% 12, 18 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/PyFontify.py 67 46 31% 101-152, 156-161 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/abag.py 19 8 58% 27-30, 33-36 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/boxstuff.py 40 28 30% 9-19, 48, 50, 52-53, 55-56, 58-78, 80-83 /home/admin/.local/lib/python3.8/site-packages/reportlab/lib/colors.py 716 389 46% 60, 75, 81-86, 92-93, 97, 101, 104, 107, 110, 113, 116-117, 120-121, 125-126, 131-133, 136-144, 148, 153, 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/home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courierbold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courierboldoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courieroblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helvetica.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticabold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticaboldoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticaoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_symbol.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesbold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesbolditalic.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesitalic.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesroman.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_zapfdingbats.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/pdfdoc.py 1657 716 57% 32-35, 78, 89, 140, 165, 188-189, 194, 203, 206-213, 219, 223, 229-230, 236, 251-254, 273, 276-277, 283, 301, 304, 307-308, 311-314, 317-335, 342, 350, 359, 366, 373, 380, 385, 388-395, 410, 432-436, 444, 462, 468-469, 475-487, 516, 519, 526-529, 550, 554-555, 558, 565-572, 579-580, 593-595, 604-621, 627-633, 637-638, 642-643, 645, 647, 650, 653, 662, 676, 678, 680, 685-687, 701, 705-708, 714-717, 720, 725-726, 729-734, 766-769, 780, 783, 805, 832-839, 856, 861-864, 893-894, 913, 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/home/admin/.local/lib/python3.8/site-packages/requests/compat.py 30 5 83% 12-13, 36-37, 42 /home/admin/.local/lib/python3.8/site-packages/requests/cookies.py 239 149 38% 19-20, 41, 44, 47, 52-58, 70, 73, 76, 80, 85, 92, 96, 100, 121, 132, 156-167, 201-204, 212-223, 231-232, 240, 248-249, 257, 265-266, 275, 279-283, 287-291, 299-304, 313-319, 322-325, 334, 341, 347, 350-356, 362-364, 378-384, 398-413, 417-420, 424-426, 430-433, 437, 441-452, 461-489, 495-504, 536-537, 550, 553, 557-559 /home/admin/.local/lib/python3.8/site-packages/requests/exceptions.py 37 8 78% 19-24, 41-42 /home/admin/.local/lib/python3.8/site-packages/requests/hooks.py 14 6 57% 27-32 /home/admin/.local/lib/python3.8/site-packages/requests/models.py 455 182 60% 95, 118, 122-126, 134, 147, 149, 178-183, 189, 192-195, 211, 214, 223-227, 281, 294, 298-311, 381, 384-392, 402-408, 418, 429-430, 435-436, 439, 445, 452-455, 457, 462, 465, 469, 472, 476-479, 487-493, 508-516, 528-529, 537-542, 545, 550, 563, 587, 598-609, 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245-259, 263-312, 316-342, 346-366, 370-373, 389-391, 395, 399-413, 417-426 /home/admin/.local/lib/python3.8/site-packages/seaborn/algorithms.py 66 60 9% 36-86, 91-105, 114-129 /home/admin/.local/lib/python3.8/site-packages/seaborn/axisgrid.py 1011 915 9% 38-43, 47-50, 54-56, 60-64, 95-176, 180-183, 187-196, 200-226, 231-234, 309-474, 581-606, 632-688, 720-755, 759-763, 768-779, 783-785, 791-799, 803-804, 808-815, 819-826, 830-837, 841-853, 857-865, 890-957, 964, 969, 974-980, 991, 998-1011, 1016-1023, 1028-1035, 1040-1052, 1057-1069, 1145-1250, 1263-1267, 1280-1282, 1295-1297, 1310-1321, 1335-1406, 1411-1447, 1454-1469, 1473-1508, 1512-1553, 1557-1562, 1566-1570, 1591-1669, 1673-1676, 1700-1702, 1724-1738, 1761-1799, 1820-1822, 1830-1831, 1962-2049, 2064-2214 /home/admin/.local/lib/python3.8/site-packages/seaborn/categorical.py 1249 1116 11% 44-240, 245-265, 269-324, 329-337, 342-346, 350-388, 392-397, 406-415, 419-476, 481-505, 509-512, 522-546, 551-671, 675-686, 690-692, 702-714, 718-724, 728-747, 752-757, 761-935, 939-946, 954-979, 987-995, 1001-1010, 1015-1016, 1021-1039, 1043-1046, 1057-1082, 1086-1088, 1099-1112, 1116-1156, 1160-1164, 1172-1177, 1184-1192, 1196-1209, 1215-1242, 1249-1276, 1280-1298, 1303-1332, 1336-1416, 1420-1424, 1434-1438, 1442-1545, 1550-1571, 1584-1593, 1598-1630, 1639-1642, 1655-1687, 1692-1697, 1702-1781, 1788-1791, 1805-1839, 1844-1874, 1878-1883, 1887-1890, 1896-1999, 2003-2058, 2062-2065, 2240-2249, 2397-2406, 2631-2640, 2799-2820, 2996-3017, 3179-3188, 3372-3381, 3577-3608, 3708-3724, 3743-3862 /home/admin/.local/lib/python3.8/site-packages/seaborn/cm.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/crayons.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/colors/xkcd_rgb.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/distributions.py 923 859 7% 109, 118, 124-126, 132, 140-155, 164-176, 180-186, 192-200, 204-210, 215-268, 284-329, 353-713, 728-873, 889-1031, 1050-1215, 1221-1279, 1285-1322, 1328-1358, 1391-1456, 1618-1754, 1922-1956, 2044-2082, 2147-2302, 2395-2403, 2543-2661 /home/admin/.local/lib/python3.8/site-packages/seaborn/external/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/external/docscrape.py 455 272 40% 43, 64, 80, 100, 119, 122, 127-130, 173-175, 182, 187, 190, 201, 236, 238, 250-253, 308-350, 358-369, 374, 382-385, 391, 404-405, 407-408, 415, 421-429, 432-443, 448, 451-454, 457-460, 463-466, 469-472, 475-488, 491-496, 499-528, 531-546, 549-563, 567-571, 580, 585-609, 612-617, 620-634, 643-691, 695-697, 705-707, 714-718 /home/admin/.local/lib/python3.8/site-packages/seaborn/external/husl.py 194 151 22% 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72-91, 95-114, 118-119, 123, 127-130, 134-137, 141-144, 148-153, 157-175, 179-184, 188-189, 193-194, 198-199, 203-219, 223-235, 239-247, 251-257, 261-271, 275-285, 289-299, 303-313 /home/admin/.local/lib/python3.8/site-packages/seaborn/matrix.py 548 491 10% 25-28, 33-36, 41-49, 61-86, 98-182, 189-239, 243-255, 259-268, 272-284, 289-343, 535-549, 563-616, 619-621, 624-638, 643-651, 666, 672, 683-728, 774-780, 790-857, 861-899, 906-919, 939-949, 974-986, 990-1004, 1033-1055, 1058-1060, 1065-1085, 1100-1150, 1153-1207, 1214-1235, 1402-1408 /home/admin/.local/lib/python3.8/site-packages/seaborn/miscplot.py 27 20 26% 20-30, 35-48 /home/admin/.local/lib/python3.8/site-packages/seaborn/palettes.py 231 199 14% 64-67, 71-72, 76-77, 81-90, 145-226, 287-297, 359-371, 431-454, 459-467, 543-548, 624-629, 702-709, 729-735, 761-762, 789-790, 905-942, 948-977, 1021-1038 /home/admin/.local/lib/python3.8/site-packages/seaborn/rcmod.py 113 83 27% 113-117, 122, 127, 132-135, 174-298, 330-331, 378-443, 483-484, 489-491, 494, 497-501, 548-556 /home/admin/.local/lib/python3.8/site-packages/seaborn/regression.py 319 271 15% 13-14, 36-56, 60-66, 69, 86-136, 141-153, 158-188, 193-229, 233-248, 252-265, 269-290, 294-296, 300-317, 321-331, 335-340, 345-376, 385-408, 413-425, 576-636, 826-839, 1071-1096 /home/admin/.local/lib/python3.8/site-packages/seaborn/relational.py 349 310 11% 197-345, 363-377, 381-422, 436-563, 583-590, 603-667, 684-704, 800-822, 914-1042 /home/admin/.local/lib/python3.8/site-packages/seaborn/utils.py 281 240 15% 24-29, 49-59, 84-89, 109-124, 141, 161-168, 177-181, 198, 214-215, 243-317, 322-326, 350-368, 373-374, 383-394, 403-409, 418-424, 436-442, 477-530, 546-554, 570, 576-597, 613-619, 643-648, 653-668, 673-674, 688-695, 701-710 /home/admin/.local/lib/python3.8/site-packages/seaborn/widgets.py 184 168 9% 8-23, 38-42, 47-48, 53-58, 93-154, 188-239, 273-324, 355-383, 414-440 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/__init__.py 320 0 100% /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/__init__.py 23 0 100% /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/account_api.py 39 30 23% 32-34, 49-54, 70-109 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/companies_api.py 255 234 8% 32-34, 49-54, 70-109, 145-150, 173-226, 256-261, 278-323, 353-358, 375-420, 451-456, 474-525, 556-561, 579-630, 660-665, 682-727 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/contacts_api.py 1026 963 6% 32-34, 51-56, 74-125, 157-162, 181-238, 268-273, 290-335, 365-370, 387-432, 462-467, 484-529, 559-564, 581-626, 657-662, 680-731, 761-766, 783-828, 858-863, 880-925, 955-960, 977-1022, 1051-1056, 1072-1111, 1144-1149, 1169-1218, 1250-1255, 1274-1323, 1357-1362, 1383-1436, 1470-1475, 1496-1553, 1583-1588, 1605-1650, 1683-1688, 1708-1763, 1795-1800, 1819-1876, 1906-1911, 1928-1973, 2005-2010, 2029-2078, 2109-2114, 2132-2177, 2208-2213, 2231-2282, 2313-2318, 2336-2381, 2413-2418, 2437-2494, 2524-2529, 2546-2591, 2622-2627, 2645-2696, 2727-2732, 2750-2801, 2832-2837, 2855-2906 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/conversations_api.py 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1083-1088, 1106-1157, 1188-1193, 1211-1262, 1292-1297, 1314-1359 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/external_feeds_api.py 193 176 9% 32-34, 51-56, 74-119, 150-155, 173-218, 255-260, 284-341, 372-377, 395-440, 472-477, 496-547 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/files_api.py 191 174 9% 32-34, 56-61, 84-137, 167-172, 189-234, 264-269, 286-331, 361-366, 383-428, 461-466, 486-537 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/inbound_parsing_api.py 121 108 11% 32-34, 51-56, 74-119, 155-160, 183-238, 269-274, 292-337 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/master_account_api.py 278 255 8% 32-34, 50-55, 72-111, 143-148, 167-218, 248-253, 270-315, 346-351, 369-414, 446-451, 470-521, 552-557, 575-620, 651-656, 674-719, 750-755, 773-818 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/api/notes_api.py 189 172 9% 32-34, 56-61, 84-137, 167-172, 189-234, 264-269, 286-331, 362-367, 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115, 127, 131-154, 158, 162, 166-169, 173 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_smtp_email_to.py 53 32 40% 46-52, 63, 74-77, 88, 100, 104-127, 131, 135, 139-142, 146 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_smtp_email_to1.py 53 32 40% 46-52, 63, 74-77, 88, 100, 104-127, 131, 135, 139-142, 146 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_test_email.py 43 26 40% 44-48, 59, 71, 75-98, 102, 106, 110-113, 117 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_test_sms.py 43 26 40% 44-48, 59, 71, 75-98, 102, 106, 110-113, 117 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_transac_sms.py 114 69 39% 58-80, 91, 102-107, 118, 129-132, 143, 154-157, 168, 179-186, 197, 209, 220, 232, 243, 255, 266, 278, 282-305, 309, 313, 317-320, 324 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_whatsapp_message.py 72 43 40% 50-61, 72, 84, 95, 107, 118, 129-132, 143, 154-157, 161-184, 188, 192, 196-199, 203 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sso_token_request.py 65 40 38% 48-57, 68, 79-82, 93, 105, 116, 127-134, 138-161, 165, 169, 173-176, 180 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_details_response.py 70 41 41% 50-63, 74, 86, 97, 109, 120, 132, 142, 153, 157-180, 184, 188, 192-195, 199 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_details_response_plan_info.py 61 36 41% 48-58, 68, 79, 89, 100, 111, 123, 127-150, 154, 158, 162-165, 169 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_details_response_plan_info_credits.py 52 31 40% 46-53, 64, 76, 86, 97, 101-124, 128, 132, 136-139, 143 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_details_response_plan_info_credits_emails.py 52 31 40% 46-53, 64, 76, 87, 99, 103-126, 130, 134, 138-141, 145 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_details_response_plan_info_features.py 61 36 41% 48-58, 68, 79, 89, 100, 110, 121, 125-148, 152, 156, 160-163, 167 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_details_response_plan_info_features_inbox.py 52 31 40% 46-53, 64, 76, 87, 99, 103-126, 130, 134, 138-141, 145 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_details_response_plan_info_features_landing_page.py 52 31 40% 46-53, 64, 76, 87, 99, 103-126, 130, 134, 138-141, 145 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_details_response_plan_info_features_users.py 52 31 40% 46-53, 64, 76, 87, 99, 103-126, 130, 134, 138-141, 145 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_update_plan_request.py 52 31 40% 46-53, 63, 74, 84, 95, 99-122, 126, 130, 134-137, 141 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_update_plan_request_credits.py 43 26 40% 44-48, 59, 71, 75-98, 102, 106, 110-113, 117 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_account_update_plan_request_features.py 61 36 41% 48-58, 69, 81, 92, 104, 115, 127, 131-154, 158, 162, 166-169, 173 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_accounts_response.py 52 31 40% 46-53, 64, 76, 86, 97, 101-124, 128, 132, 136-139, 143 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/sub_accounts_response_sub_accounts.py 74 45 39% 50-59, 70, 81-84, 95, 106-109, 120, 131-134, 145, 156-159, 163-186, 190, 194, 198-201, 205 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/task.py 90 53 41% 54-71, 82, 94, 105, 116-119, 130, 141-144, 155, 167, 178, 190, 201, 213, 217-240, 244, 248, 252-255, 259 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/task_list.py 43 26 40% 44-48, 59, 71, 75-98, 102, 106, 110-113, 117 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/task_reminder.py 67 42 37% 48-55, 66, 77-80, 91, 102-111, 122, 133-136, 140-163, 167, 171, 175-178, 182 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/task_types.py 52 31 40% 46-53, 64, 76, 87, 99, 103-126, 130, 134, 138-141, 145 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_attribute.py 52 31 40% 46-53, 64, 76, 87, 99, 103-126, 130, 134, 138-141, 145 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_attribute_enumeration.py 54 33 39% 46-51, 62, 73-76, 87, 98-101, 105-128, 132, 136, 140-143, 147 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_batch_contacts.py 43 26 40% 44-48, 59, 71, 75-98, 102, 106, 110-113, 117 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_batch_contacts_contacts.py 124 71 43% 62-93, 104, 116, 127, 139, 150, 162, 173, 185, 196, 208, 219, 231, 242, 254, 265, 277, 288, 300, 311, 323, 327-350, 354, 358, 362-365, 369 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_batch_contacts_model.py 52 31 40% 46-53, 63, 74, 84, 95, 99-122, 126, 130, 134-137, 141 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_campaign_status.py 46 29 37% 44-48, 59, 70-77, 81-104, 108, 112, 116-119, 123 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_child.py 79 46 42% 52-68, 79, 91, 102, 114, 125, 137, 148, 160, 171, 183, 187-210, 214, 218, 222-225, 229 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_child_account_status.py 70 41 41% 50-63, 74, 86, 97, 109, 120, 132, 143, 155, 159-182, 186, 190, 194-197, 201 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_child_domain.py 43 26 40% 44-48, 59, 71, 75-98, 102, 106, 110-113, 117 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_contact.py 97 56 42% 56-78, 89, 101, 112, 124, 135, 147, 158, 170, 181, 193, 204, 216, 227, 239, 243-266, 270, 274, 278-281, 285 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_email_campaign.py 319 186 42% 102-193, 204, 216, 226, 237, 248, 260, 271, 283, 294, 306, 317, 329, 340, 352, 363, 375, 386, 398, 408, 419, 430, 442, 453, 465, 476, 488, 499, 511, 522, 534, 545, 557, 568, 580, 591, 603, 614, 626, 637, 649, 660, 672, 683, 695, 706, 717-722, 733, 744-751, 762, 773-778, 789, 801, 812, 824, 835, 846-851, 862, 874, 885, 897, 901-924, 928, 932, 936-939, 943 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_email_campaign_recipients.py 61 36 41% 48-58, 69, 81, 92, 104, 115, 127, 131-154, 158, 162, 166-169, 173 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_email_campaign_sender.py 61 36 41% 48-58, 69, 81, 92, 104, 115, 127, 131-154, 158, 162, 166-169, 173 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_external_feed.py 122 73 40% 60-88, 99, 111, 122, 134, 145, 156-163, 174, 186, 197, 209, 220, 232, 243, 255, 266, 277-282, 293, 305, 309-332, 336, 340, 344-347, 351 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_list.py 52 31 40% 46-53, 64, 76, 87, 99, 103-126, 130, 134, 138-141, 145 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_sender.py 61 36 41% 48-58, 69, 81, 92, 104, 115, 127, 131-154, 158, 162, 166-169, 173 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_sms_campaign.py 108 63 42% 58-83, 94, 106, 117, 128-131, 142, 154, 164, 175, 186, 198, 209, 221, 232, 244, 255, 267, 271-294, 298, 302, 306-309, 313 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_smtp_template.py 124 71 43% 62-93, 104, 116, 126, 137, 148, 160, 171, 183, 194, 206, 217, 229, 240, 252, 263, 275, 286, 298, 309, 321, 325-348, 352, 356, 360-363, 367 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_smtp_template_sender.py 61 36 41% 48-58, 69, 81, 92, 104, 115, 127, 131-154, 158, 162, 166-169, 173 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/update_webhook.py 73 44 40% 50-63, 74, 86, 97, 109, 120, 131-139, 150, 162, 166-189, 193, 197, 201-204, 208 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/upload_image_model.py 44 27 39% 44-47, 58, 69-72, 76-99, 103, 107, 111-114, 118 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/upload_image_to_gallery.py 53 32 40% 46-52, 63, 74-77, 88, 100, 104-127, 131, 135, 139-142, 146 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/variables_items.py 61 36 41% 48-58, 68, 79, 89, 100, 110, 121, 125-148, 152, 156, 160-163, 167 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/whatsapp_camp_stats.py 84 51 39% 52-63, 73, 83-86, 96, 106-109, 119, 129-132, 142, 152-155, 165, 175-178, 182-205, 209, 213, 217-220, 224 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/whatsapp_camp_template.py 133 76 43% 64-98, 109, 121, 132, 144, 155, 167, 177, 188, 198, 209, 220, 232, 243, 255, 266, 278, 289, 301, 311, 322, 332, 343, 347-370, 374, 378, 382-385, 389 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/rest.py 121 52 57% 29-30, 46, 50, 66, 70, 77, 83, 87, 134, 143-147, 151, 157, 168-214, 228, 234, 242, 250, 260, 279, 289, 301-310, 314-323 /home/admin/.local/lib/python3.8/site-packages/sklearn/__check_build/__init__.py 18 12 33% 19-31, 45-46 /home/admin/.local/lib/python3.8/site-packages/sklearn/__init__.py 29 9 69% 69, 103-112 /home/admin/.local/lib/python3.8/site-packages/sklearn/_config.py 21 13 38% 75-82, 144-150 /home/admin/.local/lib/python3.8/site-packages/sklearn/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/glm_distribution.py 86 54 37% 59-66, 132, 156, 175, 204, 208, 215-235, 246, 272-323, 329, 335, 341, 347 /home/admin/.local/lib/python3.8/site-packages/sklearn/base.py 259 142 45% 54-88, 108-138, 159, 169, 197-198, 221-244, 265-291, 304, 310, 322, 325-333, 362, 365, 413-419, 424-434, 449-453, 460, 464-467, 499-500, 503, 552-554, 557, 583-584, 587, 599, 619-621, 639-640, 662-665, 697-702, 724, 750, 761, 767, 784, 800, 816, 840-857 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_affinity_propagation.py 142 119 16% 22-32, 119-254, 370-377, 385, 388, 409-434, 450-463, 485 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_agglomerative.py 337 242 28% 42-80, 90-131, 221, 228, 240-241, 245-342, 423-603, 608-609, 613-614, 618-619, 658, 838, 842, 848, 852, 857, 864-866, 874-880, 888-889, 907, 910, 920-924, 946, 1077-1082, 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404-407, 414-416, 419 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py 207 160 23% 81-101, 124-179, 197-207, 218-221, 238, 245-249, 252, 282-293, 309-314, 323-330, 353-357, 385-388, 485-489, 514-575, 588-642 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_bayes.py 203 180 11% 162-174, 197-300, 324-332, 343-356, 361-386, 515-526, 546-633, 641-650, 656-661, 685-694 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py 492 409 17% 58-75, 124-168, 311, 440-551, 706-717, 751-874, 879, 893-898, 1031, 1085-1146, 1157-1171, 1200-1356, 1518, 1526, 1529, 1532, 1725-1740, 1743, 1746, 1749, 1880-1889, 1913-1958, 1961, 2078-2087, 2263-2276, 2279, 2282, 2285, 2444, 2452, 2455, 2458 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/__init__.py 2 0 100% 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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, 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/home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_locally_linear.py 225 201 11% 49-73, 108-114, 159-189, 292-521, 644-655, 658-665, 688-689, 705-706, 725-734 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_mds.py 111 89 20% 71-132, 234-276, 379-387, 390, 398, 418-419, 439-461 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_spectral_embedding.py 179 148 17% 52-72, 89-95, 120-141, 221-360, 463-469, 472, 481, 503-536, 560-579, 601-602 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py 258 224 13% 58-64, 94-121, 163-193, 255-272, 345-401, 453-477, 664-679, 684-841, 854-910, 932-934, 950-951 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/__init__.py 78 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_base.py 78 71 9% 67-131, 175-202, 234-251 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py 511 452 12% 48-52, 83-128, 132-137, 202-210, 296-355, 456-557, 618-639, 758-785, 852-875, 935-946, 1068, 1192-1200, 1214-1247, 1251-1261, 1269-1299, 1458-1540, 1653-1660, 1771-1778, 1846-1858, 1966-2060, 2135-2152, 2225-2281, 2365-2403, 2477-2506 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/base.py 37 33 11% 26-45, 81-114 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/confusion_matrix.py 61 47 23% 71-72, 107-162, 255-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/det_curve.py 44 36 18% 65-68, 88-129, 210-229 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/precision_recall_curve.py 47 35 26% 77-81, 107-140, 203-225 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/roc_curve.py 47 35 26% 73-77, 100-132, 210-230 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_ranking.py 336 292 13% 83-106, 199-224, 294-317, 326-349, 522-547, 595-648, 688-730, 811-823, 913-956, 1005-1046, 1090-1106, 1149-1191, 1238-1250, 1290-1299, 1303-1307, 1407-1411, 1458-1466, 1564-1569, 1646-1717 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_regression.py 168 137 18% 88-122, 182-194, 257-271, 335-351, 408-416, 477-492, 552-584, 676-723, 753-756, 808-821, 857, 896 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py 226 132 42% 52-60, 77, 81-92, 107-122, 133-134, 155-166, 169-171, 199, 204, 236-242, 276-288, 291, 326-362, 365, 383-392, 397, 426-459, 485-530, 614 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/__init__.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_bicluster.py 32 22 31% 12-17, 22-28, 38-45, 80-86 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_supervised.py 170 139 18% 43-69, 74-83, 127-149, 214-229, 289-299, 383-389, 453-473, 542, 611, 710, 768-798, 889-919, 998-1020, 1091-1100, 1115-1123 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_unsupervised.py 93 76 18% 33-34, 109-117, 135-149, 214-248, 281-298, 339-363 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/pairwise.py 412 339 18% 45-61, 135-164, 194-198, 272-323, 399-439, 451-508, 512-514, 587-601, 670-673, 722-723, 782-804, 833-841, 861-862, 880-886, 910-911, 967-978, 1004-1005, 1033-1041, 1067-1075, 1101-1108, 1136-1142, 1180-1191, 1241-1251, 1296-1298, 1342, 1347, 1354-1373, 1379-1405, 1422-1434, 1443-1470, 1594-1635, 1747-1790, 1845, 1937-1954 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/__init__.py 32 1 97% 37 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py 342 251 27% 96-116, 127-136, 141-142, 160-184, 244-265, 268, 274-306, 310-314, 380-386, 390-406, 421-429, 433, 437, 450, 473-489, 510-511, 514-523, 539-540, 556-557, 573-574, 590-591, 607-608, 624-625, 631-639, 643-644, 704, 708-721, 747-892, 896-968, 1278-1284, 1288, 1608-1611, 1619 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_split.py 467 353 24% 78-83, 92-95, 99, 106, 155-161, 183-185, 235, 238-245, 262-264, 273-298, 324-333, 354, 428, 432-444, 499, 502-536, 562, 636, 640-690, 693-695, 731-732, 831-834, 859-887, 934-944, 968-971, 997, 1057, 1060-1075, 1099-1102, 1128, 1156-1169, 1195-1202, 1226-1229, 1232, 1284, 1340, 1350-1354, 1386-1388, 1413, 1416, 1484-1489, 1492-1503, 1575-1580, 1583-1594, 1626, 1691-1696, 1699-1757, 1793-1794, 1803-1864, 1906-1909, 1933-1937, 1941-1945, 1966, 1972, 1993, 2017-2018, 2058-2073, 2168-2199, 2211-2241, 2250-2252 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py 380 335 12% 231-279, 288-304, 309-313, 438-446, 543-657, 666-709, 838-892, 937-964, 980-1021, 1039-1045, 1165-1182, 1189-1197, 1202-1209, 1353-1417, 1444-1476, 1483-1518, 1625-1645, 1671 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/__init__.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_base.py 443 394 11% 61-66, 86-111, 135-139, 160-197, 223-248, 274-296, 307-315, 318-358, 361-525, 529, 538, 547, 581-594, 649-765, 816-846, 855, 889-901, 978-1083, 1139-1170 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_classification.py 158 136 14% 153-159, 179, 195-221, 239-275, 414-421, 441-487, 504-529, 548-616 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_graph.py 60 35 42% 16-21, 29-36, 106-113, 187-194, 307-311, 327, 345-347, 371, 374, 490-494, 510, 528-529, 553, 556 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_kde.py 86 66 23% 101-119, 124-139, 164-179, 197-211, 233, 256-283, 286 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_lof.py 80 54 32% 184-190, 218-223, 246, 265-299, 320-327, 346-356, 384-393, 421, 450-458, 486-498, 521-526 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nca.py 157 129 18% 169-176, 197-244, 265-268, 304-377, 400-443, 453-456, 483-524, 527 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nearest_centroid.py 70 54 23% 91-92, 107-181, 202-205 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_regression.py 62 40 35% 152-157, 161, 170, 190, 206-229, 352-358, 378, 395-426 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_unsupervised.py 10 2 80% 118, 142 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/__init__.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_data.py 810 681 16% 71-80, 161-217, 323-325, 335-341, 362-363, 386-417, 432-441, 456-463, 466, 545-561, 683-685, 695-699, 726-727, 762-860, 877-897, 914-937, 940, 1007, 1017-1020, 1040-1041, 1065-1085, 1100-1110, 1125-1134, 1137, 1198-1215, 1319-1323, 1344-1387, 1402-1416, 1431-1444, 1447, 1536-1555, 1632-1635, 1639-1641, 1646-1651, 1668-1681, 1701-1708, 1739-1837, 1891-1937, 2001-2002, 2023-2024, 2043-2045, 2048, 2083-2098, 2157-2158, 2179-2180, 2200-2205, 2208, 2253, 2272-2282, 2299-2310, 2313, 2321, 2350-2378, 2484-2489, 2499-2519, 2531-2568, 2589-2625, 2630-2694, 2699-2717, 2737-2750, 2768-2771, 2789-2793, 2796, 2920-2931, 3020-3022, 3043-3044, 3047, 3050-3077, 3092-3106, 3139-3152, 3158-3163, 3169-3185, 3192-3207, 3217-3219, 3228-3243, 3267-3290, 3293, 3394-3395 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_discretization.py 117 102 13% 131-134, 153-237, 242-271, 288-318, 337-353 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py 272 241 11% 42-67, 70-74, 77-110, 113-156, 159, 318-322, 325-333, 339-397, 416-420, 442-443, 459-505, 524-600, 617-635, 721-724, 743-771, 787-793, 809-844 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_function_transformer.py 41 26 37% 11, 91-97, 100-102, 106-109, 128-132, 147, 162, 166-171, 174 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_label.py 274 229 16% 100-102, 116-118, 132-138, 152-163, 166, 262-274, 289-298, 321, 343-350, 387-403, 406, 471-569, 577-613, 619-657, 725-726, 742-754, 773-798, 816-824, 827-831, 847-864, 881-898, 902 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_base.py 362 291 20% 39-60, 81-104, 108, 117, 152-240, 249-250, 253-255, 262-287, 291-323, 342-344, 347-361, 370-378, 393-400, 417-430, 433-440, 449-457, 471-496, 500-514, 517, 521-532, 542-544, 552-564, 592-595, 614-625, 632-636, 666-667, 670-676, 706-707, 710, 713-727, 730-738, 752-764, 768, 772, 791-830, 930-995 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_bounds.py 21 14 33% 54-74 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_classes.py 124 65 48% 187-198, 224-246, 249, 382-391, 417-432, 435, 657, 667, 877, 887, 1042, 1054, 1062, 1065, 1211, 1218, 1346, 1376-1379, 1396-1397, 1412, 1431-1432, 1440, 1448, 1451 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_classes.py 291 224 23% 103-115, 128-129, 139-140, 145-397, 401-411, 436-463, 489-491, 515-516, 520-539, 576-578, 598-600, 846, 898-903, 929-951, 970-979, 1197, 1247-1252, 1271-1277, 1511, 1732 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_export.py 377 339 10% 44-70, 75, 181-194, 203-214, 218-237, 241-262, 266-366, 376-406, 412-427, 431-436, 439-463, 466-524, 534-560, 565-574, 577-625, 628-662, 769-795, 802-815, 876-972 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_reingold_tilford.py 131 110 16% 9-22, 25, 28, 31-38, 41-44, 48, 51, 54-56, 60-64, 68-70, 74-95, 99-132, 136-144, 148-154, 162-165, 169-178, 183-188 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/__init__.py 366 299 18% 84, 87, 90, 93-96, 107, 125-132, 165-167, 172-179, 184-193, 198-205, 224-268, 312-346, 355-409, 502-563, 631, 651-661, 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/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 223 184 17% 41-46, 69-78, 92-95, 111-115, 135-157, 211-242, 327-374, 424-448, 488-501, 543-546, 579-592, 619-626, 649-657, 687, 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/graph.py 22 17 23% 54-71 /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 397 261 34% 66-74, 80, 89, 99-103, 110-111, 124, 163-177, 182, 192, 195-198, 202, 209-212, 238, 241-242, 259-262, 277-283, 298-300, 348-394, 400, 511-540, 545, 552, 560, 563, 568, 578-584, 592-593, 610-614, 617-618, 630, 637, 645-655, 659, 669, 677, 683, 691-702, 811-833, 852-864, 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/home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/cost_graph_pb2.py 40 18 55% 69-86 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/debug_pb2.py 31 10 68% 55-64 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/event_pb2.py 90 36 60% 19-20, 115-148 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/full_type_pb2.py 55 6 89% 68-73 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/function_pb2.py 74 36 51% 121-156 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/graph_debug_info_pb2.py 50 26 48% 85-110 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/graph_pb2.py 22 6 73% 35-40 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/histogram_pb2.py 20 8 60% 31-38 /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/meta_graph_pb2.py 117 56 52% 198-253 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/home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/linear.py 128 92 28% 11-22, 25, 30-36, 42, 45, 48-50, 54-61, 66-67, 71-73, 76, 87-105, 109, 112, 117, 120, 123-125, 129-141, 146, 149, 152, 156-157, 167-207 /home/admin/.local/lib/python3.8/site-packages/torch/ao/nn/sparse/quantized/utils.py 25 12 52% 4, 21-26, 29, 32-34, 38 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/__init__.py 14 2 86% 20-21 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fake_quantize.py 172 100 42% 22, 25, 28, 31, 48-53, 57, 61, 65, 69, 73, 77, 118-152, 156, 159-178, 182, 192-194, 200-224, 235-241, 246, 250, 280-285, 289, 293, 309, 445-451, 461-462, 472-473, 483-484, 494-495 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fuse_modules.py 61 47 23% 16-20, 24-30, 45-67, 70-82, 85-95, 151, 162 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/fuser_method_mappings.py 96 68 29% 26-45, 63-91, 108-119, 135-141, 149, 175-181, 190, 195, 200-201, 244-252, 260-270 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/observer.py 535 396 26% 26-29, 32, 35, 38-40, 81-82, 102-103, 107, 111, 172-202, 216-223, 250-253, 272-327, 331, 422-439, 446-455, 460, 464, 469-470, 528-529, 540-554, 597-615, 620, 623-648, 652, 655, 667-703, 724, 737-738, 781-791, 794-817, 862-877, 890-893, 900-943, 953-1006, 1017-1032, 1045-1068, 1071-1127, 1131-1147, 1150-1154, 1166-1187, 1220-1226, 1229, 1233, 1253-1260, 1263, 1267, 1284-1285, 1288-1289, 1293, 1297, 1315-1317, 1320, 1324, 1340, 1343, 1347, 1351-1356, 1360, 1368-1374, 1382-1393, 1402-1422 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/qconfig.py 141 96 32% 66, 89-93, 197-213, 270-315, 339, 364-372, 375-383, 390-405, 427-451, 458-482, 489-495, 498 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quant_type.py 9 2 78% 13-19 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantization_mappings.py 83 38 54% 177-181, 186, 191, 196-199, 204, 213-222, 230-237, 242, 249-252, 257, 262, 268-274, 280-289, 293, 299-302, 311, 314 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantize.py 251 219 13% 32, 56-72, 92-96, 101, 106, 109-116, 135-203, 206, 224-229, 262-285, 290-306, 316-322, 341-350, 382-435, 452-463, 478-485, 516-524, 542-564, 577-614, 624-631 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/quantize_jit.py 82 64 22% 8-9, 12-13, 20, 29, 38-45, 48-63, 66-67, 70-71, 75-94, 97-98, 101-102, 107-119, 171-172, 212-213 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/stubs.py 30 17 43% 13-15, 18, 30-32, 35, 54-59, 62-64 /home/admin/.local/lib/python3.8/site-packages/torch/ao/quantization/utils.py 147 113 23% 100-103, 106-108, 111, 115, 123, 126-151, 165-170, 173-175, 178-180, 186, 193-195, 202, 208, 214, 220, 226-228, 239-243, 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22-39, 137-176, 181, 191-197, 200-201, 204-205, 208-220, 223-225, 228-230, 233-234, 237-239, 246-250, 254-257, 260-262, 266-268, 276-278, 283-399, 437-443, 446-447, 450-451, 472-479, 564-566, 569-586, 589-593, 602, 608, 611-613, 617-677, 684 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/profiler_legacy.py 152 125 18% 29-49, 52, 64-70, 73, 76-89, 92-94, 97-99, 102-103, 106-108, 115-117, 121-124, 127-129, 133-135, 143-145, 149-284 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/profiler_util.py 501 423 16% 17-24, 27-30, 33, 37-51, 69-117, 120-143, 147, 167, 185-230, 233, 236-249, 266-288, 296-301, 306-312, 316-319, 323-333, 353, 357, 362-363, 366, 379-401, 404-405, 413-416, 425-428, 434-436, 442-444, 450-452, 458-470, 474-481, 485-488, 492, 495, 524-544, 547-578, 581, 584, 605-606, 613-618, 621-624, 628-636, 642-651, 658-663, 675-910 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/variable.py 9 1 89% 7 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/home/admin/.local/lib/python3.8/site-packages/torch/cpu/amp/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/cpu/amp/autocast_mode.py 22 15 32% 10-15, 18-20, 24-26, 29-31 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/__init__.py 385 167 57% 29-30, 51-53, 56-58, 70, 80, 87-93, 96, 123, 125, 134, 137, 144-146, 156, 163, 165, 188, 202, 207, 211, 213, 224-225, 231-234, 241-242, 251-252, 256-257, 269-270, 273-279, 282-284, 298-299, 312-314, 362, 368-375, 392-399, 403-414, 418-427, 439, 450-452, 459, 464, 467, 472-476, 482-483, 494-496, 508-509, 521-522, 535-536, 542-543, 557-568, 573-574, 589-600, 615-626, 640-643, 654-655, 664, 668, 672, 677, 682, 687, 692, 697, 702, 707, 712, 717, 722, 727, 732 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/_utils.py 21 11 48% 24, 26-30, 33, 38-42 /home/admin/.local/lib/python3.8/site-packages/torch/cuda/amp/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/cuda/amp/autocast_mode.py 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121, 125-127, 148-149, 187, 189, 195, 200-201, 204-211, 225-228, 232-234, 243-245, 263, 320-322, 326, 329, 332, 336, 340, 344, 349, 357-359, 365, 371, 377, 391-412, 446-449, 453-458, 462, 466, 469-477, 485-488, 491, 494, 497, 500-502, 510-513, 522-527, 530-543, 546, 587-590, 593-599, 602-603, 611-615, 618-620, 623, 626, 629, 636, 639, 651-653, 658-659, 667-669, 675-680, 690, 699, 704, 709, 714, 729-751, 754-766, 769, 772-774, 777-779, 782-783 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/conv.py 331 168 49% 51, 84, 86, 89-94, 98, 115-122, 152-166, 169-171, 287-294, 299-303, 307, 450, 580-585, 590-602, 607, 616, 632-665, 774-780, 785-795, 940, 1080-1086, 1091-1102, 1124-1127, 1146-1150, 1155-1169, 1173-1180, 1185, 1233-1251, 1254, 1302-1320, 1323, 1371-1389, 1392, 1438-1457, 1460, 1506-1525, 1528, 1574-1593, 1596 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/distance.py 25 9 64% 37-40, 43, 72-74, 77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/dropout.py 33 12 64% 13-18, 21, 58, 100, 149, 191, 233, 282 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/flatten.py 48 27 44% 40-42, 45, 48, 106-116, 119-125, 129-135, 138, 141 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/fold.py 38 15 61% 136-141, 144, 148, 287-291, 294, 298 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/instancenorm.py 72 40 44% 18-19, 23, 26, 29, 32, 38-62, 67-72, 143, 146-147, 182, 185-186, 259, 262-263, 298, 301-302, 375, 378-379, 414, 417-418 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/lazy.py 79 49 38% 15, 18, 23, 26, 29, 33, 37, 41, 45, 49, 175-178, 185-194, 208-217, 224, 231-236, 248-256, 260 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/linear.py 91 36 60% 42, 45, 100, 117, 130, 180-191, 194-197, 200, 203, 243-250, 253-254, 257-263 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py 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1108-1109, 1112, 1140, 1179, 1218 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/rnn.py 473 382 19% 21, 43-124, 127-131, 140-175, 182-191, 194-196, 199-205, 210-221, 225-226, 229-232, 235-237, 241-254, 257-292, 296, 299-304, 411-420, 425, 430, 433-499, 673, 676-683, 692-695, 703-705, 712, 719, 722-784, 898-900, 905, 910, 913-967, 983-997, 1000-1005, 1008-1010, 1072-1074, 1077-1108, 1173-1174, 1177-1197, 1264-1265, 1268-1288 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/sparse.py 130 71 45% 129-133, 142-144, 154-155, 158, 163-174, 205-218, 318-341, 344-345, 348-350, 383, 390-400, 432-447 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/transformer.py 187 144 23% 57-83, 136-149, 156, 161-163, 186-190, 203-246, 267-270, 288-299, 361-388, 391-393, 410-466, 471-475, 479-480, 523-546, 549-551, 570-580, 585-589, 594-598, 602-603, 607, 611-616 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/upsampling.py 37 17 54% 141-150, 153, 157-162, 207, 253 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/utils.py 35 17 51% 32-38, 57-75 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/__init__.py 10 3 70% 11-14 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_functions.py 88 60 32% 14-30, 34, 41-45, 49, 56-75, 79-82, 89-104, 108, 118-124 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_replicated_tensor_ddp_utils.py 13 7 46% 18-23, 27, 31 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/comm.py 81 70 14% 29-38, 56-58, 76-104, 126-149, 186-199, 228-241 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py 94 76 19% 17-37, 122-145, 148-169, 172, 175, 178, 181, 199-232 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py 440 339 23% 46-53, 57-60, 67-79, 83-137, 159-168, 172-178, 186-193, 200-230, 237, 538-664, 667-672, 675-677, 691-766, 769-776, 780-794, 798-839, 850-860, 865-898, 905-916, 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/home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/activation.py 242 222 8% 64-83, 86, 90-150, 160-219, 224-247, 305, 321-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/rnn.py 230 196 15% 34-48, 51-73, 76-80, 83, 93-104, 108-115, 126-128, 131-136, 140-143, 151-157, 160-209, 218-241, 277-312, 315-361, 364, 368-379, 383 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/conv.py 108 58 46% 19-35, 51-54, 67-71, 74, 78, 87-90, 103-107, 110, 114, 123-126, 139-143, 146, 150, 160-177, 195-198, 211-221, 224, 228, 239-242, 254-266, 269, 273, 283-286, 299-309, 312, 316 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/home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/conv.py 111 64 42% 56-67, 72, 77-84, 120-131, 136, 141-147, 184-195, 200, 205-211, 253-259, 264, 269-271, 313-319, 324, 329-331, 373-379, 384, 389-391 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/linear.py 58 43 26% 34-39, 43-55, 58, 61-66, 70-72, 83-112, 122-126 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/rnn.py 506 403 20% 12, 16-33, 37-38, 41-42, 46-47, 59-123, 126, 129-140, 147-170, 173-179, 184-191, 197-198, 202-204, 208-210, 214-216, 221-254, 258-341, 346-364, 367, 370, 391, 394, 401-426, 432-440, 446-455, 461-463, 469-474, 479-482, 486, 490-504, 616, 619, 622-625, 633-670, 677-685, 691-699, 704-706, 710-713, 717, 725-763, 766, 769-774, 777-778, 783-789, 795-841, 845-873, 877-886, 889, 892, 897-901, 907-909, 913-915, 937-938, 941, 944-962, 966, 989, 992, 995-1001, 1008, 1029, 1032, 1035-1039, 1047 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/functional.py 143 114 20% 40-42, 70-72, 89-91, 106-109, 154-168, 213-227, 273-287, 325-327, 358-363, 374-378, 390-394, 409-411, 431-441, 446-450, 460-462, 475-481, 492-494, 499-503, 516-518, 570-571, 592-593, 614-615 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/__init__.py 38 13 66% 44-50, 53, 58-60, 63, 82, 85, 89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/activation.py 94 49 48% 28-29, 32, 35, 39, 49-51, 54, 58, 62-63, 67, 78-80, 83, 87, 91-92, 96, 108-111, 114, 118, 122-123, 127, 138-140, 143, 147-148, 159-162, 165-172, 176, 180-181, 185 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/batchnorm.py 64 38 41% 8-11, 15-26, 30-43, 52-53, 56, 61-62, 67, 73, 82-83, 86, 91-92, 97, 103 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/conv.py 381 265 30% 25-30, 37, 46-80, 83, 86, 89, 92-104, 119-124, 128-129, 152-160, 166-179, 182-186, 189, 195-212, 216-238, 249-265, 316-324, 329, 332-336, 341-342, 345, 348, 353-360, 370, 416-423, 428, 431-435, 439, 442, 445, 450-456, 467, 513-521, 526, 529-533, 537, 540, 543, 548-554, 565, 577-582, 588-592, 602-623, 634-651, 699-706, 711, 714, 719-720, 723-724, 727-728, 733-735, 740, 787-794, 799, 802, 807-808, 811-812, 815-816, 821-823, 828, 876-883, 888, 891, 896-897, 900-901, 904-905, 910-912, 917 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/dropout.py 13 4 69% 15, 18, 22, 26 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/embedding_ops.py 134 95 29% 12-22, 26-29, 34-37, 40, 48-50, 54-61, 65, 93-110, 113-116, 119, 122, 125-129, 132, 135, 145-175, 179-190, 220-225, 229-234, 239, 249-276, 280-292 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/functional_modules.py 105 69 34% 34-35, 38, 43-45, 49-52, 56-58, 62-65, 69-71, 75-78, 93, 98-99, 103-104, 108-109, 113-114, 118-119, 123-125, 154-157, 160-162, 167-169, 173, 176, 181, 186-188, 192-195, 199-201, 205-208, 212-214, 218-220, 224-230 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/linear.py 141 103 27% 16-22, 26-31, 36-41, 44, 63-65, 69-86, 91, 125-147, 150, 153, 158, 161, 194-196, 203-218, 225, 228, 231, 234, 244-276, 288-296 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/normalization.py 100 59 41% 15-22, 25, 30, 34-38, 42, 58-64, 67, 72, 76-80, 93-99, 102, 107, 111-115, 119, 134-140, 143, 148, 152-156, 160, 175-181, 184, 189, 193-197, 201 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/utils.py 50 35 30% 12, 15-31, 37-41, 48-71 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/clip_grad.py 34 26 24% 30-56, 68-70, 85-89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/convert_parameters.py 29 24 17% 16-24, 36-54, 73-84 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/fusion.py 33 27 18% 7-14, 17-33, 36-43, 46-53 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/init.py 10 7 30% 44-51 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/memory_format.py 8 6 25% 64-69 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/parametrizations.py 152 124 18% 13-17, 24-28, 45-65, 68-105, 109-168, 258-281, 292-316, 320-326, 362-370, 374-388, 393, 472-486 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/parametrize.py 223 198 11% 48-54, 58-61, 97-189, 209-254, 258-269, 281-299, 315-346, 471-552, 566-573, 601-656, 665-668, 686-728 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/rnn.py 126 90 29% 22-24, 64, 76, 82-85, 89-92, 95, 98, 101, 104, 107, 110, 113, 116, 134-142, 147, 151, 166-184, 193-195, 199-204, 244-261, 321-335, 379-396, 433-446, 481-482, 511-513 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/spectral_norm.py 138 111 20% 26-32, 35-41, 73-93, 96-102, 105, 111-112, 116-152, 160, 172-198, 206, 209-214, 273-281, 295-314 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/stateless.py 53 41 23% 12-36, 39-48, 52-55, 63-69, 81-84, 125-142 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py 52 36 31% 15-18, 22-24, 28-54, 57-61, 64, 108-109, 123-129 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/__init__.py 63 28 56% 42-47, 51-54, 348-350, 386-388, 392-394, 408-410, 414-416, 420-422, 430-432, 464-466, 473, 480, 487, 498, 509 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_constants.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_globals.py 20 6 70% 33, 37-41 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_patch_torch.py 112 90 20% 49-72, 77-78, 84-99, 103-116, 123, 132-133, 137, 145-167, 184-222, 230-231 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_caffe2.py 151 113 25% 11-35, 39-46, 50-51, 55-56, 63-65, 70-76, 83-85, 92-105, 112-125, 130-136, 141-149, 154-160, 165, 172, 178-191, 196-212, 226-249, 253-262, 267-285, 289-301, 306-318 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_helper.py 624 503 19% 67-112, 121-123, 127-130, 134-140, 144-146, 150-157, 163, 199-232, 284-315, 324-325, 335-343, 347, 351, 355, 362, 366, 370, 379-380, 388, 395-397, 401-409, 413-418, 422-430, 435-440, 444, 453, 462, 471-479, 483-488, 492-507, 511-518, 522-537, 552-555, 559-572, 578-591, 597-604, 608-611, 625-635, 639-659, 663-684, 688-710, 714-726, 730-737, 741-752, 756-785, 790-838, 855-933, 946-952, 956-961, 965-972, 976-1000, 1004-1008, 1012-1015, 1024-1043, 1052-1062, 1066-1113, 1117-1122, 1126-1135, 1149-1165, 1169-1176, 1180-1182, 1186-1193, 1207-1229, 1248-1277, 1286-1297, 1301-1304, 1309, 1313, 1317, 1323, 1432 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset10.py 268 194 28% 24-27, 32-35, 39-55, 60, 65, 74-118, 155-179, 191-202, 218-221, 225-248, 252-287, 300, 311, 327-393, 405-430, 439, 443-447, 451-456, 460, 467-506, 526-535, 539-544, 548-553, 557-561, 576-588, 603-614 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset11.py 585 467 20% 19-38, 44-71, 76-84, 89-97, 101-121, 127, 131-237, 242-245, 264, 271-275, 280-295, 302-311, 315-316, 320-333, 337-343, 347-353, 357-358, 362, 366-378, 382, 386, 390, 394-398, 402-406, 411-414, 429-450, 462-465, 470, 477, 481, 485-487, 492-524, 529, 534-543, 553-600, 604-608, 612-614, 618-620, 632-642, 646, 650, 654-709, 714-720, 724-726, 730-777, 781-784, 788, 792-808, 812-829, 833-839, 845-866, 872-893, 905-936, 943-944, 948-954, 968-1012, 1016-1017, 1025-1045, 1050-1066, 1082-1153, 1158-1185, 1195-1206, 1215-1216, 1224-1240 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset9.py 2398 1890 21% 58-60, 64, 68-70, 74, 78-79, 83-91, 96-98, 102, 106, 110-113, 118-119, 124-131, 137-165, 169-189, 194, 198, 212-225, 230-232, 237-238, 243-247, 251, 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126-132 /home/admin/.local/lib/python3.8/site-packages/torch/package/find_file_dependencies.py 70 55 21% 15-18, 21-23, 26-28, 31-32, 35-43, 46-49, 52-55, 59-99 /home/admin/.local/lib/python3.8/site-packages/torch/package/glob_group.py 31 19 39% 42-45, 48, 51, 54-55, 61-64, 70-82 /home/admin/.local/lib/python3.8/site-packages/torch/package/importer.py 101 75 26% 52, 73-133, 143-162, 169, 172, 185, 197-203, 206-224, 227-232 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_exporter.py 441 342 22% 74, 107-109, 128-152, 200-241, 256-290, 299-301, 320-350, 368-381, 393-396, 399-408, 411-415, 418-428, 434-485, 498-503, 513-559, 586-686, 696, 706-707, 724-726, 743-745, 762-764, 788, 829, 860, 874, 879-930, 933, 939-944, 947-961, 965-972, 977-979, 985-1035, 1039, 1048-1052, 1056-1058, 1061-1063, 1066-1067, 1078, 1083-1089, 1098, 1107, 1116, 1125, 1133-1136, 1146, 1161-1163 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_importer.py 346 274 21% 70-111, 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/home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuser_method_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/observer.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/qconfig.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quant_type.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantization_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize_jit.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/stubs.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quasirandom.py 66 54 18% 48-68, 85-104, 120-129, 135-137, 148-153, 156-171, 174-179 /home/admin/.local/lib/python3.8/site-packages/torch/random.py 46 33 28% 18, 23, 36-42, 49-55, 62, 85-129 /home/admin/.local/lib/python3.8/site-packages/torch/return_types.py 18 2 89% 11, 14 /home/admin/.local/lib/python3.8/site-packages/torch/serialization.py 527 356 32% 38-40, 93-114, 118-119, 123-124, 133-147, 151-157, 165-169, 178, 184, 188-190, 211, 214, 225, 230, 233, 237, 247, 250, 255-256, 259-260, 265-269, 273-277, 286-293, 299-305, 310-312, 323-325, 374-381, 385-525, 529-604, 707-711, 713, 721-726, 734-946, 957, 964-979, 987, 1013, 1039-1040 /home/admin/.local/lib/python3.8/site-packages/torch/sparse/__init__.py 24 9 62% 11-12, 209-218 /home/admin/.local/lib/python3.8/site-packages/torch/special/__init__.py 37 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/storage.py 475 260 45% 13-14, 31, 70-71, 76, 79, 82, 87, 93-95, 98, 106, 110-113, 116-121, 125, 129, 133, 137, 141, 145, 149, 153, 157, 161, 165, 169, 173-177, 188-195, 200-207, 210, 218, 259-278, 281-284, 292-293, 297, 303-357, 373, 379, 384, 389, 396, 403-424, 431, 442, 445, 448-470, 473-490, 497-516, 519-520, 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33-47, 50 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/callable.py 21 7 67% 15, 49-52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combinatorics.py 24 11 54% 44-49, 52-54, 59-60, 63 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combining.py 54 35 35% 35-42, 45-51, 54-56, 80-87, 90-96, 99-101 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/grouping.py 39 25 36% 40-46, 49-59, 62-70 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/utils.py 17 9 47% 32-42, 45, 48 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/common.py 116 91 22% 21-22, 30-39, 50-76, 80-90, 94-101, 138-173, 183, 186-187, 190-192, 195-198, 201-202, 205, 208, 211, 214 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/decoder.py 160 130 19% 33-60, 98-111, 140-141, 144-185, 188, 195-209, 216-230, 238-245, 248-251, 254, 262-266, 277-278, 283-285, 289-290, 293-307, 310-324, 327 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/dataset.py 94 53 44% 51, 54, 164, 167, 184-185, 188, 191, 207-212, 215-220, 223, 226-235, 239-241, 255-256, 259-262, 265-269, 284-285, 288-290, 293, 310-314 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/distributed.py 50 38 24% 64-94, 97-121, 124, 135 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/graph.py 63 51 19% 16, 21-70, 74-75, 80-90 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/graph_settings.py 45 37 18% 14-20, 24-35, 39-56, 60-68 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/sampler.py 111 74 33% 31, 34, 73, 76, 79, 97-107, 113-115, 118-133, 136, 149-150, 153-154, 157, 183-193, 196-197, 200, 223-232, 236-255, 262-265 /home/admin/.local/lib/python3.8/site-packages/torch/utils/dlpack.py 27 10 63% 98-119 /home/admin/.local/lib/python3.8/site-packages/torch/utils/hooks.py 121 96 21% 16-18, 21-23, 26, 29-35, 38, 41, 50-51, 55-59, 75-82, 85-89, 92-96, 99-118, 123-151, 154-161, 164-193 /home/admin/.local/lib/python3.8/site-packages/torch/utils/model_zoo.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/throughput_benchmark.py 51 33 35% 6-23, 28-29, 33, 37, 44, 48, 53, 96-99, 108, 117, 150-156 /home/admin/.local/lib/python3.8/site-packages/torch/version.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/__init__.py 36 16 56% 16-17, 23-28, 45-47, 54, 74-80, 91, 95 /home/admin/.local/lib/python3.8/site-packages/torchvision/_internally_replaced_utils.py 38 20 47% 12, 16, 21-22, 30-49 /home/admin/.local/lib/python3.8/site-packages/torchvision/_utils.py 20 11 45% 11-16, 24-32 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/__init__.py 45 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/_optical_flow.py 194 147 24% 34-38, 41-44, 49, 53-72, 75, 78, 123-141, 156, 159, 185-202, 217, 220, 250-270, 285, 288, 321-361, 376, 379, 406-426, 431, 446, 455-463, 468-474, 480-502 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/caltech.py 87 68 22% 43-74, 84-117, 121, 124, 127-137, 145, 170-191, 201-218, 222, 225, 228-232 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/celeba.py 92 73 21% 69-110, 117-130, 133-142, 145-152, 155-182, 185, 188-189 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/cifar.py 80 50 38% 60-92, 95-101, 111-123, 126, 129-135, 138-141, 144-145 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/cityscapes.py 80 64 20% 114-172, 183-199, 202, 205-206, 209-211, 214-221 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58 26% 16-26, 29-32, 35-38, 41-52, 74-83, 93-105, 108, 130-140, 150-163, 166 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/flowers102.py 55 40 27% 51-74, 77, 80-89, 92, 95-102, 105-114 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/folder.py 100 77 23% 21, 33, 41-46, 62-105, 144-154, 185-190, 219, 229-236, 239, 247-249, 254-260, 264-269, 310-318 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/food101.py 46 31 33% 43-65, 70, 73-82, 85, 88, 91-93 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/gtsrb.py 44 31 30% 35-60, 63, 67-76, 79, 82-99 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/hmdb51.py 58 40 31% 78-112, 116, 119-138, 141, 144-151 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/imagenet.py 108 87 19% 43-55, 58-65, 69, 72, 76-87, 91-96, 108-150, 164-176, 194-212 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/inaturalist.py 113 95 16% 75-109, 114-133, 139-168, 179-196, 199, 210-219, 222, 225-241 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kinetics.py 100 72 28% 20, 116-156, 160-167, 176-197, 211-229, 236, 239, 242-248, 312-323 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kitti.py 58 39 33% 61-84, 105-109, 112-128, 131, 135, 139-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lfw.py 125 96 23% 42-58, 61-63, 66-72, 75-82, 85, 88, 91, 123-126, 129-144, 147-151, 161-170, 173, 205-207, 210-235, 245-255 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lsun.py 94 75 20% 18-31, 34-50, 53, 79-93, 96-136, 146-161, 164, 167 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py 243 152 37% 65-66, 70-71, 75-76, 80-81, 91-104, 107-111, 118-119, 122-128, 138-150, 153, 157, 161, 165, 168, 176-195, 198-199, 294-298, 302, 306, 310, 314, 318, 321, 324, 329-339, 421-428, 432-433, 437-438, 441, 444-461, 467-474, 478-486, 489, 493, 511-531, 535-540, 544-549 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/omniglot.py 47 32 32% 42-60, 63, 73-83, 86-89, 92-99, 102 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/oxford_iiit_pet.py 63 47 25% 50-87, 90, 93-112, 115-119, 122-126 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/pcam.py 47 35 26% 77-95, 98-100, 103-116, 119-121, 124-130 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/phototour.py 99 73 26% 92-109, 119-129, 132, 135, 138, 141-161, 165-174, 177-178, 184-206, 213-215, 223-228 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/places365.py 83 55 34% 73-83, 86-92, 95, 99, 103-108, 111-123, 126-139, 142-143, 146-156, 159, 162, 165-170 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/rendered_sst2.py 42 27 36% 44-57, 60, 63-72, 75, 78-81, 84-86 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sbd.py 59 39 34% 61-96, 99-100, 103-104, 110-116, 119, 122-123 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sbu.py 59 45 24% 36-58, 68-77, 81, 85-89, 93-114 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/semeion.py 43 28 35% 37-51, 61-73, 76, 79-83, 86-91 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/stanford_cars.py 49 38 22% 41-74, 77, 81-88, 91-111, 118-121 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/stl10.py 104 79 24% 55-88, 91-100, 111-126, 129, 132-145, 148-154, 157-161, 164, 168-176 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sun397.py 36 23 36% 36-51, 56, 59-68, 71, 74-76 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/svhn.py 44 30 32% 61-91, 101-113, 116, 119-122, 125-126, 129 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/ucf101.py 45 32 29% 71-101, 105, 108-118, 121, 124-130 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/usps.py 34 23 32% 52-70, 80-92, 95 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/utils.py 235 185 21% 38-45, 49-50, 54-63, 70-74, 78, 82-86, 90-100, 106-115, 130-167, 178-182, 195-199, 203-215, 229-280, 288-289, 299-302, 333-361, 377-393, 411-429, 440-450, 454, 466-485 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/video_utils.py 199 164 18% 28-29, 41-49, 62, 65, 68, 75, 122-140, 143-166, 169-173, 177-182, 185-193, 212-232, 245-255, 258, 261, 267, 274-279, 283-291, 306-379, 382-407, 411-423 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/vision.py 60 42 30% 38-54, 64, 67, 70-78, 81-82, 85, 90-91, 94-98, 101-102, 105-111 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/voc.py 94 61 35% 9-10, 79-127, 130, 158, 168-174, 203, 213-219, 223-237 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/widerface.py 95 76 20% 65-81, 94-103, 106, 109-110, 113-158, 161-169, 173-180, 183-194 /home/admin/.local/lib/python3.8/site-packages/torchvision/extension.py 48 12 75% 15, 27-28, 33, 49, 56-57, 66, 84-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/__init__.py 12 2 83% 9-10 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/_load_gpu_decoder.py 6 1 83% 6 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/_video_opt.py 154 124 19% 13, 31-32, 58-65, 70-71, 87-105, 111-120, 188-217, 226-252, 259-262, 335-369, 380-408, 418-423, 432-484, 490-505 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/image.py 72 48 33% 12-13, 45-48, 60-62, 82-85, 103-106, 121-124, 157-164, 182-188, 202-205, 227-230, 249-252, 256-257 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/video.py 207 181 13% 21-31, 41-42, 46, 81-139, 151-219, 225-234, 263-347, 351-356, 360-364, 384-415 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/video_reader.py 48 33 31% 9-10, 17-18, 24, 94-111, 125-133, 136, 151-152, 160, 179-181 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/__init__.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_api.py 66 38 42% 53-60, 63, 66, 86-107, 121-142 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_meta.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_utils.py 104 67 36% 51-64, 67-73, 83-89, 125-126, 132-142, 174-228, 236-240, 244-247, 252-256 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/alexnet.py 37 17 54% 19-37, 48-52, 106-116 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/convnext.py 131 84 36% 32-35, 46-60, 63-66, 77-79, 82-87, 101-167, 170-173, 176, 186-194, 299-308, 331-340, 361-370, 393-402 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/densenet.py 158 98 38% 36-46, 49-51, 55-58, 62-65, 69, 73, 78-94, 109-118, 121-125, 130-134, 164-211, 214-219, 227-238, 249-257, 360-362, 385-387, 410-412, 435-437 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/_utils.py 231 193 16% 22-23, 41-71, 87-119, 136-137, 140-144, 155-160, 163-181, 193-224, 238, 253-271, 286-301, 341-346, 359-386, 397-415, 420, 423-431, 447-449, 464-479, 484, 506-510, 521-538 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/anchor_utils.py 125 105 16% 40-50, 65-74, 77, 80, 85-113, 116-133, 163-182, 187-202, 206, 212-236, 239-247, 250-268 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/backbone_utils.py 82 64 22% 42-54, 57-59, 112-113, 125-146, 158-174, 193-194, 205-242 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/faster_rcnn.py 188 134 29% 38-40, 201-280, 293-296, 299-304, 322-341, 355-357, 360-369, 547-569, 613-644, 656-685, 739-751, 812-820 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585-673, 794-819, 862-891 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/roi_heads.py 481 441 8% 28-49, 69-79, 91-94, 109-126, 131-162, 168-213, 220-231, 244-295, 300-326, 331-342, 347-360, 368-383, 389, 394-400, 405-425, 429-461, 466-471, 476-489, 522-548, 551-557, 560-566, 570-601, 605-610, 614-616, 620-628, 636-666, 676-725, 742-876 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/rpn.py 175 149 15% 29-41, 53-62, 73-79, 83-86, 90-111, 161-181, 184-186, 189-191, 197-229, 232-240, 250-297, 314-337, 363-391 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssd.py 273 223 18% 49-53, 58-60, 63, 71-73, 80-87, 90-103, 108-112, 117-121, 201-242, 248-251, 260-319, 327-410, 415-461, 466-535, 539-548, 552-568, 643-679 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssdlite.py 119 79 34% 33, 49-51, 74-78, 85-87, 90, 100-104, 109-113, 125-148, 152-161, 169-184, 262-328 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371-378, 382-395, 400-402, 405-407, 457-475 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mnasnet.py 119 73 39% 38-45, 60-63, 70-77, 84-87, 93-94, 113-156, 159-162, 174-209, 306-314, 339-341, 366-368, 393-395, 420-422 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenet.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv2.py 105 72 31% 23-32, 43-78, 81-84, 112-181, 186-191, 194, 265-275 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv3.py 138 97 30% 29-33, 54-61, 65, 76-124, 127-130, 155-223, 226-233, 236, 242-285, 295-303, 398-401, 428-431 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/_utils.py 25 18 28% 10-17, 21-23, 33-45 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/raft.py 288 219 24% 31-59, 62-68, 75-92, 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273-288, 300-308, 396-398, 432-434, 468-470 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/vision_transformer.py 223 155 30% 46-52, 64-75, 98-108, 111-119, 136-152, 155-157, 178-266, 269-287, 291-305, 318-337, 618-620, 651-653, 684-686, 717-719, 749-751, 784-838 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/__init__.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_box_convert.py 28 22 21% 17-25, 39-47, 61-63, 77-81 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_register_onnx_ops.py 34 16 53% 16-21, 25-44, 57-60 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/_utils.py 59 49 17% 13-15, 19-25, 29-38, 45-69, 74-77, 82-84, 92-106 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/boxes.py 162 123 24% 73, 90, 106-112, 128-133, 148-165, 189-216, 232-235, 241-252, 269-273, 292-304, 320-337, 356-362, 367-381, 398-415 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ciou_loss.py 25 20 20% 45-71 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/deform_conv.py 65 50 23% 63-92, 126-151, 154-159, 170, 182-195 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/diou_loss.py 32 26 19% 45-57, 66-87 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/drop_block.py 68 52 24% 28-52, 74-102, 114-119, 129, 132-133, 145, 155 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/feature_pyramid_network.py 100 80 20% 33, 84-110, 122-134, 149-156, 163-170, 184-204, 218-220, 229-235, 243-249 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/focal_loss.py 18 14 22% 35-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/giou_loss.py 24 20 17% 43-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/misc.py 93 67 28% 27-33, 45-49, 56-62, 65, 85-114, 153, 202, 237-243, 246-250, 253-254, 282-298, 309-310, 313 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/poolers.py 123 92 25% 20-34, 45, 68-72, 80-85, 89-96, 101-107, 114-135, 140-144, 169-228, 278-288, 291-292, 295-296, 303-304, 325-331, 341 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_align.py 29 18 38% 46-57, 71-75, 78, 81-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_pool.py 28 17 39% 40-49, 58-61, 64, 67-68 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_align.py 31 18 42% 53-61, 78-83, 86, 89-97 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_pool.py 30 17 43% 42-51, 60-63, 66, 69-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/stochastic_depth.py 34 24 29% 26-44, 56-59, 62, 65-66 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/_pil_constants.py 18 8 56% 17-25 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/_presets.py 119 91 24% 24-26, 29, 32, 48-53, 56-62, 65-72, 75, 93-98, 101-118, 121-128, 131, 149-153, 156-162, 165-171, 174, 184-199, 202, 205 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/autoaugment.py 207 167 19% 16-90, 127-131, 136-221, 224, 249-253, 262-281, 284, 314-319, 322, 347-365, 368-377, 402-405, 408, 433-453, 456-463, 497-507, 510-531, 535, 539, 543, 552-601, 604-615 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional.py 505 409 19% 39-47, 71-76, 88-93, 105-110, 115, 120, 135-174, 193-209, 234-239, 259, 262-271, 275, 279, 283, 287-289, 292, 295-307, 310-315, 318-323, 327, 332, 355-360, 417-421, 424, 428, 432, 476-481, 501-506, 523-547, 581-585, 600-605, 623-633, 668-688, 703-708, 730-753, 779-797, 814-819, 836-841, 858-863, 897-902, 930-935, 961-995, 1042-1081, 1132-1214, 1232-1237, 1259-1264, 1283-1288, 1317-1355, 1370-1375, 1391-1399, 1414-1419, 1436-1441, 1458-1463, 1481-1486 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional_pil.py 261 192 26% 19, 26-33, 38-40, 45-50, 55-58, 63-66, 71-76, 81-86, 91-96, 101-120, 130-142, 153-222, 234-237, 249, 251, 254, 256-275, 278, 293-311, 322-327, 340-344, 355-360, 365-378, 383-385, 390-392, 397-399, 404-409, 414-416, 421-423 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py 555 501 10% 10, 14-15, 19-21, 25-28, 33-34, 38-44, 48-59, 63-65, 69-117, 121-123, 127-129, 133-142, 146-162, 166-173, 177-190, 194-219, 223-233, 237-253, 257-259, 263-298, 302-319, 326-350, 354-370, 374-426, 436-503, 515-542, 546-558, 562-571, 576-603, 619-629, 635-642, 653-675, 685-693, 704-722, 728-745, 749-755, 761-764, 768-790, 795-803, 808-817, 822-832, 836-855, 859-869, 874-891, 899-912, 916, 921-933, 937-960, 964-970 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/transforms.py 745 571 23% 88-90, 93-95, 98-103, 124, 134, 137, 147, 161, 164, 187-189, 192, 214-215, 226, 229-233, 255-259, 269, 272, 321-339, 349, 352-353, 369-371, 381, 384, 426-444, 454, 457, 468-471, 474, 477, 488-491, 494, 497-502, 526-529, 532-536, 539-545, 552-556, 563-566, 569-570, 573, 631-642, 645-653, 663-678, 681, 695-697, 707-709, 712, 726-728, 738-740, 743, 765-785, 796-807, 822-842, 845, 878-899, 914-943, 953-954, 957-962, 994-996, 1006, 1009, 1043-1046, 1056, 1059, 1082-1102, 1112-1130, 1133-1138, 1166-1171, 1175-1191, 1216-1223, 1233-1247, 1250-1257, 1294-1326, 1335-1336, 1346-1355, 1358-1367, 1424-1481, 1496-1519, 1528-1540, 1543-1552, 1572-1574, 1584, 1587, 1607-1609, 1619-1622, 1625, 1657-1678, 1697-1720, 1730-1750, 1753-1761, 1782-1799, 1812, 1822-1823, 1826-1827, 1831-1840, 1844-1848, 1852-1859, 1873-1875, 1885-1887, 1890, 1905-1908, 1918-1920, 1923, 1938-1941, 1951-1953, 1956, 1971-1974, 1984-1986, 1989, 2003-2005, 2015-2017, 2020, 2034-2036, 2046-2048, 2051 /home/admin/.local/lib/python3.8/site-packages/torchvision/utils.py 276 245 11% 57-132, 154-160, 201-266, 295-346, 378-418, 436-453, 468-488, 501-535, 539-540, 562 /home/admin/.local/lib/python3.8/site-packages/torchvision/version.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_dist_ver.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_monitor.py 45 12 73% 42-45, 63, 72-86, 95 /home/admin/.local/lib/python3.8/site-packages/tqdm/_tqdm_pandas.py 10 6 40% 12-24 /home/admin/.local/lib/python3.8/site-packages/tqdm/asyncio.py 53 36 32% 24-34, 37, 40-52, 55, 62-67, 75-81, 88 /home/admin/.local/lib/python3.8/site-packages/tqdm/auto.py 14 3 79% 27-28, 37 /home/admin/.local/lib/python3.8/site-packages/tqdm/autonotebook.py 12 5 58% 16-20 /home/admin/.local/lib/python3.8/site-packages/tqdm/cli.py 188 173 8% 17-40, 54-97, 151-311 /home/admin/.local/lib/python3.8/site-packages/tqdm/gui.py 10 1 90% 181 /home/admin/.local/lib/python3.8/site-packages/tqdm/std.py 696 372 47% 46-49, 127-128, 154-161, 165, 169-184, 187-211, 240-241, 390-398, 418, 437-439, 538, 542-547, 565-566, 573-574, 581-582, 587-588, 615-646, 650-660, 702-705, 715-717, 722-726, 735-756, 761, 805-950, 969, 975, 984, 987-995, 998-1002, 1031, 1034, 1037, 1044, 1047, 1080-1083, 1107-1111, 1114, 1122-1130, 1133-1134, 1137, 1140-1146, 1170-1172, 1192-1194, 1225-1264, 1280, 1284, 1292-1294, 1307-1308, 1312-1324, 1340, 1344-1345, 1355-1359, 1371-1381, 1393-1395, 1399-1401, 1416-1432, 1438-1440, 1444-1445, 1451, 1454, 1483-1486, 1489, 1495, 1498, 1515-1520, 1525 /home/admin/.local/lib/python3.8/site-packages/tqdm/utils.py 175 92 47% 22, 28-31, 70, 81-96, 108-109, 112-113, 119, 122, 128, 131, 134, 142, 146-149, 169-170, 176, 179, 196-209, 231-248, 254-260, 268-269, 273-278, 389-398 /home/admin/.local/lib/python3.8/site-packages/tqdm/version.py 8 6 25% 4-9 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4200-4286, 4290, 4296-4379, 4387-4430, 4464-4534 /home/admin/.local/lib/python3.8/site-packages/uritemplate/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/uritemplate/api.py 11 3 73% 43, 66, 85 /home/admin/.local/lib/python3.8/site-packages/uritemplate/orderedset.py 59 40 32% 28-32, 35, 38, 42-47, 52-55, 59-63, 67-71, 74-78, 81-83, 86, 89-92 /home/admin/.local/lib/python3.8/site-packages/uritemplate/template.py 48 33 31% 30-34, 72-83, 86, 89, 92-94, 97, 102-120, 147, 169 /home/admin/.local/lib/python3.8/site-packages/uritemplate/variable.py 195 171 12% 56-71, 74, 77, 89-122, 130-143, 153-190, 204-240, 250-295, 304-325, 356-386, 392-399, 403, 407, 411-413, 417-419 /home/admin/.local/lib/python3.8/site-packages/zipp.py 123 73 41% 28, 47-50, 62, 73-75, 78-79, 82, 89-92, 100-111, 121-124, 127-130, 223-224, 232-242, 246, 250, 254, 258, 262, 265-266, 269-270, 273, 276, 279, 282, 285, 288-291, 294, 297, 300-301, 307-312 /home/admin/mtr/.credentials/credentials.py 1 0 100% /home/admin/workarea/git/Velours/python/dev/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/dev/angular_coefficients_to_crops.py 371 201 46% 12-41, 49-51, 66-68, 79-91, 97-104, 109-179, 185-186, 188-189, 194, 232, 237-238, 271, 290-304, 312-313, 316-317, 331, 346, 351-354, 365-367, 406, 417-504 /home/admin/workarea/git/Velours/python/dev/conditional_crop_copy.py 414 151 64% 14, 24, 29, 35-39, 51, 55-56, 83, 93-94, 103-104, 106, 113-114, 118, 121, 126-129, 136, 150, 157, 163, 167, 172-173, 177, 179, 182-231, 240-241, 270-274, 276-279, 289-295, 298-299, 304-306, 311-312, 321-323, 340, 343-344, 358-370, 400, 408, 418-419, 422-423, 435, 437, 442-445, 456-458, 471-473, 506, 509, 512, 524-540 /home/admin/workarea/git/Velours/python/dev/generate_new_image.py 477 245 49% 27-28, 31-36, 72-77, 83-84, 87-88, 94, 99-106, 135-147, 156-157, 162-175, 223-227, 245-248, 251, 254-261, 268, 279-280, 296-297, 302-312, 316-333, 337-357, 362-370, 404-405, 407-408, 411-412, 414, 420, 423-431, 437-438, 440, 459-466, 498-508, 543-578, 585-586, 593, 611, 655-656, 664-671, 678-771 /home/admin/workarea/git/Velours/python/dev/poly_crop_reduction.py 238 157 34% 9-20, 40, 45, 54-56, 58-59, 117, 119-120, 127-168, 172-226, 229-244, 260-310, 330-381 /home/admin/workarea/git/Velours/python/file_uploader.py 73 35 52% 14-15, 23-24, 28-30, 36-37, 54-56, 62-64, 70-80, 84-95, 98 /home/admin/workarea/git/Velours/python/misc/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/misc/split_time_score.py 955 738 23% 30-47, 51-60, 67-224, 275-307, 347, 350-365, 389-390, 404-412, 422-423, 448-449, 456-460, 468, 477-478, 483-484, 493-499, 504-535, 551, 564, 569-571, 596-630, 636-680, 810-831, 836, 847-849, 872, 874, 877, 895, 897, 918-979, 993-1841 /home/admin/workarea/git/Velours/python/mtr/Gan2/pre_ops.py 265 175 34% 14-16, 19-21, 24, 27, 30, 33-35, 51-52, 76-86, 89-105, 141-201, 215-293, 317-320, 322-325, 327-334, 337, 361-415 /home/admin/workarea/git/Velours/python/mtr/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/cnn/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/cnn/classifier_new.py 289 77 73% 24-39, 124, 184, 213-215, 221, 225-227, 240, 245-254, 263-270, 277, 291, 337-338, 354, 356-357, 365-369, 378-379, 395, 427-430, 456-457, 465, 485, 507, 522-523, 536-550 /home/admin/workarea/git/Velours/python/mtr/cnn/ordonner.py 73 39 47% 20-29, 36, 44, 54, 66-101, 104 /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelConfig.py 63 45 29% 15-18, 23-26, 30, 35-48, 54-68, 73-77, 81-85, 88-95, 98, 101 /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelData_queries.py 177 74 58% 19, 26, 35-42, 61, 66-82, 102, 111-134, 145, 149, 151, 155-160, 162-163, 165, 167, 169, 172-173, 206-207, 227-228, 232-233, 241-257, 294 /home/admin/workarea/git/Velours/python/mtr/database_queries/CachePhotoData_queries.py 385 178 54% 35-37, 56, 58, 63, 88, 101-110, 117, 119, 133, 139-141, 149-150, 163, 171-175, 177-181, 191, 195, 203-206, 208, 210-216, 236-240, 265, 268, 270-276, 309-311, 325-327, 349, 351-354, 366-373, 376-378, 383, 388-390, 395-396, 406, 408, 410-411, 418-427, 429, 432, 437, 449, 459-460, 462-483, 487-489, 494-497, 502, 509, 513, 517, 528-530, 532-533, 538-546, 549, 553, 556-557, 560, 563-568, 574-577, 580, 582, 585-593, 615, 620, 626, 628-631, 633-636, 641-643, 647-653, 659-660, 665-667, 672-734 /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 485 400 18% 35, 39, 43, 46-53, 58-64, 70-80, 85-101, 106, 110-113, 116-130, 134-149, 152-157, 160-162, 170-179, 182-191, 194-201, 206-220, 225-241, 246-264, 268-285, 289-305, 308-309, 312-313, 316-322, 340-345, 348-351, 354-363, 367-371, 374-381, 384-390, 393-403, 406-413, 416-418, 421-428, 431-444, 447-457, 460-482, 489, 498, 501-508, 511-517, 520-523, 527-531, 534-543, 546-564, 567-572, 575-580, 583-588, 592-601, 605-626, 628-637, 639-649, 651-661, 663-670, 675-702 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_admin_tools.py 89 55 38% 27-28, 30-34, 45, 61, 64, 76-92, 97-105, 110-137, 142, 147-163, 166-172 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_queries.py 294 201 32% 25, 29-49, 52-56, 59-78, 81-89, 92-98, 101-105, 108-113, 116-124, 131-141, 146-155, 159-166, 169-179, 183-204, 207-227, 230-240, 243-255, 260-268, 274-290, 308-370, 386, 389, 397, 418, 421, 430, 496-518, 521-535 /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 150 112 25% 32-53, 59-68, 73-80, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, 120, 123, 126, 129, 132-135, 141-142, 146-150, 155-174, 180-199, 203-205, 208-215, 241-278 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_queries.py 1520 811 47% 44, 58, 71, 83, 101, 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2209, 2234, 2238-2245, 2291-2315, 2327-2329, 2333-2342, 2347-2365, 2368-2376, 2398, 2400, 2403-2405, 2419-2420, 2428, 2447-2478, 2483-2511, 2515-2521, 2525-2544, 2548-2557, 2561-2565, 2574, 2581, 2604-2610, 2616-2643, 2648-2676, 2681-2682, 2688-2724, 2728-2740, 2747-2762, 2773-2809, 2818-2854, 2857-2871, 2874-2885 /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 214 96 55% 19-20, 24-25, 30-130, 145, 148-149, 152, 156, 162-163, 171, 219-220, 225-228, 240-241, 278-279, 303, 306-307, 312, 315-316, 330, 333-334, 338, 341-342 /home/admin/workarea/git/Velours/python/mtr/database_queries/descriptor_queries.py 354 238 33% 23-42, 56, 63, 67, 73, 77, 82-103, 106-145, 163, 166, 169, 184, 218, 226-264, 270-301, 304-321, 333, 338, 349-352, 360-387, 390-400, 404-407, 412-435, 444-471, 474-477, 480-495, 499-556 /home/admin/workarea/git/Velours/python/mtr/database_queries/general_queries.py 174 60 66% 17-22, 45-46, 48-49, 56-58, 83-84, 87, 92-99, 112, 121-133, 141-153, 167, 178-181, 192, 204-208, 223 /home/admin/workarea/git/Velours/python/mtr/database_queries/graph_nodes_queries.py 77 60 22% 28-34, 38-54, 59-130 /home/admin/workarea/git/Velours/python/mtr/database_queries/hashtag_queries.py 165 110 33% 46-50, 64-65, 72, 80-91, 94-110, 113-125, 128-133, 136-142, 145-163, 166-173, 176-191, 196, 204-215, 219-226, 229-234, 237-243 /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 324 189 42% 38, 42, 46, 53-94, 102, 107-114, 117-126, 129-136, 141-161, 164-168, 172-186, 272-273, 282-287, 292, 298, 301-302, 315, 330-340, 343-344, 359-360, 363-377, 392-393, 404, 412-489, 499-518, 529-544, 570, 577-588 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_retrieval_queries.py 567 403 29% 14, 54, 58, 65-75, 85-89, 110-115, 121-137, 143-156, 162-175, 194-195, 202, 213-214, 226, 231, 235, 238-240, 243-245, 248-251, 262, 268, 275-280, 283, 285, 288, 291-292, 302-340, 346-362, 365-439, 442-489, 495-506, 509-558, 561-562, 569-619, 622-645, 648-682, 689, 697, 709, 717, 725, 728, 733, 738-756, 764, 770-772, 775, 784, 788-790, 795-801, 804, 810-814, 819-840, 846-863, 866-878, 882-936, 960, 982, 989-1000, 1016 /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py 517 261 50% 51, 53, 68-84, 100, 109-126, 134, 142, 146, 152-170, 176, 182, 184-186, 188, 191, 200, 204, 210, 214-224, 227-234, 237-247, 252-267, 273, 282, 286-296, 299-311, 314-323, 333, 337, 351, 363, 366, 372-373, 377, 381-387, 390-395, 401, 405-409, 412-422, 437, 453, 459, 485-509, 528-529, 537, 560-583, 588-596, 601, 606, 610-620, 628, 632-634, 642, 649, 654-674, 696, 729-760, 762-771, 792-793, 795-798, 805-806, 808-811 /home/admin/workarea/git/Velours/python/mtr/datou/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/calcul_brightness_image.py 76 46 39% 4, 7-8, 22-24, 42-68, 71-78, 81-88, 91-98 /home/admin/workarea/git/Velours/python/mtr/datou/count_refus.py 64 7 89% 15, 61-69, 72-73, 94 /home/admin/workarea/git/Velours/python/mtr/datou/darker_image.py 39 4 90% 15, 19, 24, 60 /home/admin/workarea/git/Velours/python/mtr/datou/data_augmentation_imgaug.py 244 194 20% 16, 19-22, 25, 27-176, 188-193, 203, 241-303 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py 1765 1000 43% 44-45, 76-102, 109-110, 113-114, 118-119, 154, 159, 189-190, 213-214, 228-230, 242-244, 247-248, 278, 307-311, 316, 333-337, 340, 347, 382, 404-405, 469-470, 488-502, 516-523, 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2538, 2544, 2546, 2548, 2550, 2552, 2560, 2562, 2565, 2569, 2572, 2574, 2580, 2582, 2584, 2587, 2594, 2596, 2598, 2600, 2602, 2604, 2606, 2608, 2613, 2615, 2617, 2619, 2621, 2627, 2629, 2631, 2635, 2648, 2651, 2653, 2655, 2657, 2661, 2663, 2669, 2671, 2673, 2676-2682, 2716-2718, 2733-2735, 2751, 2757-2759, 2761, 2766, 2775-2777, 2786-2789, 2798-2802, 2818, 2823, 2826, 2834-2848, 2853, 2858, 2870-2888 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_object.py 478 150 69% 16-23, 35-37, 46, 51-62, 101-122, 178, 194, 212, 215, 222-246, 252-290, 317, 335, 363-369, 374, 376, 385, 389, 396, 400, 412, 495, 499-500, 512-513, 522-523, 570, 579, 589, 616, 635-652, 660, 675-679, 688-689, 694, 723-743, 747-771 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_data_increase.py 210 104 50% 35, 37-38, 97, 106, 129-166, 222-224, 229-302, 305-347 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_save.py 1287 808 37% 24, 33-38, 45, 49-96, 101-145, 150, 155, 160-178, 183, 200-260, 269-305, 316, 324-325, 339-344, 347, 349, 361, 368-369, 374-436, 445-486, 489-553, 569, 580, 582, 593, 595, 604-608, 613-619, 655, 669-670, 674, 676, 695, 712-717, 722, 726-728, 738-744, 747-761, 764-779, 783-809, 813-864, 883, 885, 902, 917, 919, 936, 957, 962-965, 971-976, 979, 981, 1006-1008, 1012-1014, 1017-1018, 1047-1078, 1086-1087, 1095, 1098, 1102, 1106-1116, 1138-1140, 1155-1157, 1172-1175, 1194-1198, 1223-1253, 1257-1279, 1295-1332, 1338-1357, 1362-1387, 1393-1457, 1472-1500, 1523-1534, 1538-1619, 1625, 1631-1632, 1654-1655, 1667, 1671, 1675, 1677, 1683, 1689-1690, 1694, 1696, 1698, 1703, 1705, 1708, 1710, 1712, 1716, 1719, 1721, 1730-1739, 1743-1745, 1749-1769, 1775-1783, 1786-1818, 1821-1836, 1850, 1854, 1858-1861 /home/admin/workarea/git/Velours/python/mtr/datou/datou_local_cache_db.py 157 117 25% 62-70, 73-84, 88-102, 105-113, 117-122, 126-136, 139-143, 167-175, 178-194, 197-201, 204-205, 214-218, 233-257, 287-301, 304-307, 311 /home/admin/workarea/git/Velours/python/mtr/datou/datou_step_finale.py 344 269 22% 9-77, 82-130, 135-351, 371-373, 376-377, 396, 414-427, 431, 436, 440-444, 469, 471-490, 497 /home/admin/workarea/git/Velours/python/mtr/datou/detect_blur_image.py 109 72 34% 12-15, 18-20, 24-34, 55-64, 77, 87-145 /home/admin/workarea/git/Velours/python/mtr/datou/image_blanchir.py 30 2 93% 27, 31 /home/admin/workarea/git/Velours/python/mtr/datou/image_temperature.py 22 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_deprecated.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_end_or_aggreg.py 484 469 3% 17, 19, 21, 24, 27, 34-274, 288-767, 908-918 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_initialisation.py 372 326 12% 15-244, 249-267, 284-289, 304, 313-314, 317-331, 337-346, 358-360, 376-558 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_post_processing.py 1092 513 53% 29-62, 90, 99-103, 110-115, 121-133, 137, 153-157, 180-181, 188-192, 200, 202, 218, 236-237, 267, 273-274, 295, 298-303, 312-318, 368-369, 397-404, 410, 412-490, 495-499, 522-523, 538-544, 547, 552-554, 579, 582-583, 588, 596, 609, 641-642, 645-646, 651-653, 656, 680-689, 695-697, 707, 717-723, 726-727, 731-736, 742-743, 746-747, 756-763, 766-774, 777-778, 794-799, 805-839, 844-857, 863-865, 885-887, 892-899, 916-966, 974-1048, 1053-1123, 1164-1166, 1170-1171, 1231, 1252-1273, 1285, 1291-1297, 2578-2580, 2583-2584, 2587-2594, 2604, 2611-2623, 2626, 2630-2634, 2641-2648, 2709-2711, 2804-2805, 2819-2820, 2831-2833, 2840-2850, 2858, 2890, 2901 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py 1409 788 44% 34-89, 129, 132, 138, 141-142, 155, 162-166, 177-182, 194-199, 239-241, 266-281, 287, 289, 295, 356, 360, 368-382, 397-405, 414-428, 431-432, 434-436, 438, 444, 446, 453, 491, 496-497, 503, 512, 516-702, 709-844, 849-891, 904-905, 914, 919-926, 934-935, 938-940, 962-966, 1009-1016, 1020-1027, 1030-1032, 1040, 1085-1086, 1096, 1104-1114, 1118-1120, 1128-1142, 1151-1152, 1165, 1183-1185, 1189, 1210-1211, 1215, 1232-1234, 1239, 1253, 1256-1257, 1262, 1266, 1270-1318, 1334-1335, 1356-1357, 1374-1471, 1497, 1514, 1520-1563, 1592-1593, 1598, 1608, 1632, 1635-1636, 1675-1678, 1682, 1689-1690, 1695, 1698, 1705-1711, 1715-1720, 1731-1745, 1792, 1826-1829, 1868-1870, 1945-1946, 1949-1950, 1955, 1964, 1970, 1975-1976, 1996-2001, 2005-2193 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py 2059 1345 35% 40, 47-59, 75, 84-86, 89, 98, 104, 139, 143, 149-150, 162-174, 211, 232, 253-257, 280-308, 325-326, 331, 334-335, 338, 350, 386-387, 392-393, 401, 410-411, 415, 427-556, 560-628, 637, 651-652, 663-664, 669, 697, 702, 710, 724, 729, 736, 745, 749, 755-761, 764, 803-807, 816, 819-823, 827, 830, 832-845, 855-889, 917-918, 924, 927-929, 937-943, 970, 982, 992-1000, 1005, 1009, 1016-1026, 1032-1077, 1098, 1103-1106, 1109-1111, 1118-1137, 1139-1161, 1185, 1187, 1190-1211, 1220-1296, 1303-1490, 1494-1523, 1527-1603, 1610-1698, 1702-1879, 1891, 1938, 1945-1948, 1955-1957, 1960, 1991-1992, 1996, 2001-2011, 2018-2019, 2040-2041, 2055-2102, 2157, 2190-2193, 2238-2240, 2247-2261, 2264-2273, 2278-2279, 2282-2291, 2294-2315, 2317-2318, 2382-2394, 2398-2443, 2465-2466, 2481, 2483, 2485, 2487, 2493, 2501, 2504, 2520, 2531, 2535, 2540-2644, 2651-2839, 3047-3058, 3063, 3066-3067, 3072-3074, 3077, 3101-3103, 3112, 3123-3135, 3146-3147, 3160, 3190, 3206, 3211-3218, 3477-3580, 3584-3623, 3627-3700 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_send_or_copy.py 554 378 32% 19-195, 200-268, 273-332, 336-379, 397, 415, 424, 427-428, 430, 437, 444-449, 456, 462, 485-486, 493-623, 665-666, 671, 675-676, 680, 689-692, 700-716, 719-720, 728-741, 749, 751-754, 770, 809, 814-816, 833-834, 839 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_sort.py 193 163 16% 12-115, 125-127, 143-144, 161, 178-183, 189-287, 291-305 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_util.py 298 134 55% 16-17, 21, 26, 40, 47-48, 70, 97-98, 106, 125-126, 143-155, 179-183, 194-196, 198-201, 214, 219, 224-231, 241-257, 261-284, 289, 293-294, 297-300, 303-305, 307-309, 319-324, 327-333, 366-371, 395, 405, 409-411, 423-467 /home/admin/workarea/git/Velours/python/mtr/datou/merge_rubbia.py 50 46 8% 12-36, 40-86 /home/admin/workarea/git/Velours/python/mtr/datou/send_mail_dechet.py 227 129 43% 19, 21, 27-28, 60, 66-119, 126-131, 146, 155, 157, 164-170, 175-179, 183-188, 191-193, 195-197, 207-221, 230, 234, 249-251, 255-259, 263, 269, 282-340, 344-345, 350, 354-355 /home/admin/workarea/git/Velours/python/mtr/lib/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/fotonower_connect.py 321 216 33% 72, 77-84, 87-89, 95-118, 122-183, 186-212, 219-220, 224, 229-230, 232-239, 251-252, 257-260, 266-278, 281, 284, 287-289, 306, 317, 322-324, 328, 331-334, 337-383, 388-411, 414-432, 435-460 /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 302 238 21% 35-43, 49-298, 304-342, 359, 372-373, 386-390, 405, 426-432, 447-552 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_segment.py 70 16 77% 45, 94, 114-124, 180, 197-198, 203-204, 229 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/prepare_maskdata.py 359 81 77% 28, 51, 62, 78, 83, 120-121, 140-141, 145-146, 148-149, 154-155, 164, 176-177, 187, 195, 198, 202, 218-220, 241-243, 246, 269-270, 290-293, 315, 317-318, 321, 326-332, 335-342, 347-348, 360-369, 391-393, 400-403, 411-414, 424-427, 429, 460-462, 502-504, 513-514, 545 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/svm_subroutines.py 69 63 9% 21-43, 50-99, 104-136 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/class_split_time_score.py 548 194 65% 48-49, 54, 73, 77, 85, 97, 102-114, 117-123, 142, 166-167, 173-174, 183, 265, 289, 295, 298, 311-320, 323, 326, 350, 353, 356, 359-370, 380-384, 395, 408-413, 440, 470, 472, 514, 526, 529, 541, 589-594, 598, 607, 623-627, 632-638, 643-649, 654-670, 673, 676, 679-695, 698-701, 704-707, 710-713, 716-730, 733, 737-771, 775-796 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/lib_split_time_score.py 1956 940 52% 19-36, 42, 78, 84-85, 96-104, 108-115, 125-136, 139-141, 182-185, 207-231, 236-411, 416-647, 654-729, 745-747, 752-776, 784-864, 868-886, 897-1001, 1006-1088, 1103, 1108, 1115, 1121-1134, 1139-1206, 1209-1250, 1255-1264, 1267-1351, 1354-1372, 1377-1414, 1419-1443, 1448-1467, 1695-1700, 1703-1721, 1768-1772, 1821, 1844-1864, 1948-1949, 1999, 2028, 2050-2105, 2175, 2178-2182, 2194, 2217, 2230, 2251-2252, 2271-2275, 2357, 2423, 2428, 2454-2458, 2544-2546, 2581-2582, 2613-2614, 2628-2630, 2647-2674, 2756-2757, 2774, 2821-2823, 2840-2842, 2876-2877, 2885-2898, 2913-2914, 2921, 2943-2944, 2951, 2959-2963, 2969-2973, 2983, 2996-3000, 3011, 3055-3107, 3115-3123, 3147-3149, 3168, 3174-3175, 3184-3186, 3188, 3215-3227, 3231, 3235-3237, 3243, 3255-3256, 3264-3285, 3292, 3338, 3344-3413, 3422-3480, 3511-3512, 3558-3560, 3594, 3649, 3662, 3695-3740, 3772-3778, 3846-3847, 3872, 3878-3879, 3885, 3898-3899, 3913, 3920, 3935-3953, 3963-3981, 3988, 3992-4026 /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 59 55% 40, 44, 47-50, 52, 59, 61, 65-68, 94, 96, 98, 102, 104, 108, 110, 112, 114, 116, 124, 131, 141, 143-144, 146-148, 162, 164-167, 170-194 /home/admin/workarea/git/Velours/python/mtr/ses_mailer.py 45 25 44% 34-36, 39-83 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/flip_images.py 241 88 63% 20-91, 98-105, 111-114, 138-139, 155, 164-171, 177-180, 204-205, 220, 237, 244, 247, 254-255, 264, 269, 309-310, 314, 336, 341, 352, 381 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/image_utils.py 328 225 31% 21-28, 37-52, 88, 91-113, 121, 129, 142, 144-162, 181-191, 194-236, 242-253, 265-298, 301-314, 343, 348-354, 363-365, 368-381, 385-397, 401-441, 446-465, 470-473, 476-484 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/rotate_crop_and_images.py 894 306 66% 62, 65, 80-81, 97, 120, 139-140, 150-151, 176, 208, 212, 217-218, 223, 227-230, 239-241, 262, 268, 275, 297-310, 318-319, 333-334, 359-360, 370, 400-401, 412-413, 443, 458, 461, 482-483, 495, 498-499, 502, 522-525, 529, 532, 535, 544-547, 554-555, 573-574, 581-582, 586, 605, 608, 611, 620, 626, 636, 681-683, 692, 714-727, 735, 753, 796-833, 837, 886, 906-908, 913, 915-916, 922-926, 959-963, 966-969, 991, 1007-1015, 1064-1072, 1076, 1095, 1100, 1104-1111, 1142, 1182-1192, 1200, 1202, 1206, 1209-1214, 1237, 1261, 1276, 1284-1285, 1302-1306, 1310, 1325-1385, 1396-1514 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/simple_image_editor.py 2091 1596 24% 24-25, 43-51, 60-81, 86-126, 131-134, 140-324, 329-332, 335-359, 365-387, 391-422, 429-446, 451-469, 475-485, 492-598, 605-613, 619-793, 798-815, 821-853, 859-907, 910-911, 916-936, 942-972, 979-1100, 1109-1145, 1151-1183, 1189-1227, 1232-1251, 1259-1567, 1575-1639, 1643-1654, 1660-1683, 1690-1756, 1762-1828, 1832-1907, 1913-1990, 2023-2024, 2035, 2041-2042, 2044-2045, 2057-2064, 2077, 2081, 2097, 2102, 2113-2120, 2127-2128, 2137, 2145-2146, 2172, 2176, 2181-2194, 2216, 2223, 2233-2234, 2239, 2244, 2250, 2262, 2303, 2315, 2328-2331, 2335, 2349-2355, 2379-2384, 2395-2421, 2431-2465, 2479-2741, 2852-2854, 2868, 2939, 2944, 2949, 2952-2953, 2959, 2961, 2964, 2979, 3007-3008, 3041-3042, 3080, 3102, 3140-3156, 3164-3189, 3200-3304, 3339, 3359-3360, 3362-3363, 3387, 3409-3417, 3508-3540, 3562, 3579-3590, 3594-3600, 3603-3682, 3685-3688, 3691-3723, 3726-3752, 3757-3819, 3825-3877 /home/admin/workarea/git/Velours/python/mtr/split_time_gps_score.py 723 514 29% 14-26, 36-68, 78-89, 106-107, 119-141, 151-182, 232, 236, 244-256, 299-300, 311, 314, 360-379, 399-401, 451-484, 488-509, 526-554, 558-596, 614, 631-634, 646, 649-650, 658, 665-671, 691-700, 713-721, 732-799, 812-865, 869-877, 881-893, 903-938, 948-987, 999-1034, 1046-1070, 1079, 1092-1093, 1097-1310 /home/admin/workarea/git/Velours/python/mtr/tfhub2/data_ops.py 228 196 14% 18-26, 29-37, 41-49, 52-60, 63-68, 71-82, 85-93, 96-117, 120-129, 132-149, 152-183, 187-189, 193-211, 215-229, 232-249, 271-302 /home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py 162 51 69% 42, 44-52, 86, 99, 110-111, 115, 128-131, 139, 173-181, 186-231, 246, 281-282, 293, 295 /home/admin/workarea/git/Velours/python/mtr/tfhub2/foto_datasets.py 242 131 46% 23-25, 41-51, 64-66, 82, 85, 95-108, 117-119, 122-123, 131, 136-137, 139-140, 153-168, 173-240, 246, 251, 256, 264, 271, 287-322, 341, 343, 366, 379-381, 386, 391, 395-401 /home/admin/workarea/git/Velours/python/mtr/tfhub2/fotonower_data_ops.py 111 94 15% 19-23, 26-30, 33-38, 41-44, 48-84, 91-144, 148-182, 186-192 /home/admin/workarea/git/Velours/python/mtr/tfhub2/ops.py 201 170 15% 29-31, 40, 44, 48, 60, 65-72, 76-129, 139-151, 155-167, 171-177, 182-186, 191-202, 207-210, 219-244, 254-280, 290-319, 324-326, 334-343, 346-348, 351-358, 362-373, 377-394 /home/admin/workarea/git/Velours/python/mtr/utils/MTRMongoClient.py 99 87 12% 21-92, 97-208, 213-241 /home/admin/workarea/git/Velours/python/mtr/utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cd.py 11 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cdn/copy_to_ovh.py 14 2 86% 16, 27 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/s3_bucket_manager.py 116 91 22% 16-18, 37-44, 47-52, 55-58, 61-73, 79-88, 101-108, 123-129, 132-136, 144-163, 166-170, 173-181, 183-187, 189-191, 193 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/swift_upload_manager.py 169 117 31% 17, 20-21, 28, 30, 33, 40, 53-55, 59-60, 63, 72-74, 76-79, 82-90, 102-105, 110-113, 115-117, 127-128, 130-131, 133-143, 145-163, 172, 175-178, 180-195, 197-210, 213-214, 216-219, 222-275 /home/admin/workarea/git/Velours/python/mtr/utils/general_util.py 57 32 44% 11-12, 20-27, 30, 33-57, 61-63, 69-70, 75, 86-90 /home/admin/workarea/git/Velours/python/mtr/utils/kmean_cloud_storage.py 15 5 67% 19-20, 23, 26, 29 /home/admin/workarea/git/Velours/python/mtr/utils/load_caffe.py 61 26 57% 23, 29, 43, 48, 55, 61-62, 65, 70, 76-94 /home/admin/workarea/git/Velours/python/mtr/utils/prepare_photo_learning.py 201 125 38% 14-15, 62-81, 89, 94-97, 103, 114, 123-124, 131, 137, 140, 154, 156-158, 176, 189-232, 238-365 /home/admin/workarea/git/Velours/python/mtr/utils/upload_batch.py 58 19 67% 39-41, 48-49, 57-60, 73-83 /home/admin/workarea/git/Velours/python/mtr/utils/utils_timer.py 11 3 73% 13-15 /home/admin/workarea/git/Velours/python/prod/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/prod/caffe_vision.py 1390 1154 17% 47, 68-72, 77-81, 110-112, 118-121, 131-132, 143, 149, 156-170, 173-175, 181, 184, 189, 192, 213-214, 220-274, 285, 313-315, 321, 326, 333-337, 340-344, 347-351, 360-367, 381, 394-395, 400, 410-416, 419-421, 431-573, 577-592, 595-602, 606-759, 763-782, 787-810, 814-877, 883-889, 894-901, 907-926, 934-947, 954-958, 964-981, 986-1001, 1008-1028, 1039, 1052, 1074-1076, 1082, 1085, 1095, 1099, 1103, 1116-1121, 1126-1333, 1401-2359 /home/admin/workarea/git/Velours/python/prod/cod/main_cod.py 559 276 51% 16-24, 27-43, 46, 49-50, 54, 60-90, 95-96, 107-108, 129, 134-137, 147-150, 156, 165-167, 169-171, 176-178, 184, 198, 206-209, 215, 219-220, 258-259, 269, 316-336, 342-359, 364, 370-372, 375-378, 383-413, 437-447, 468, 480-502, 521-522, 524-526, 530-535, 538-539, 566, 577, 593-594, 604, 616-622, 627-644, 653-654, 664-665, 668-669, 694-799 /home/admin/workarea/git/Velours/python/prod/memo/Rubbia_Report.py 377 354 6% 91-130, 137-192, 201-215, 224-238, 246-287, 336-342, 350-365, 379-406, 414-421, 424-456, 464-473, 477-492, 498-503, 508-554, 558-590, 593-684 /home/admin/workarea/git/Velours/python/prod/memo/SLA_RUBBIA.py 546 510 7% 13-14, 109-110, 115-116, 125-135, 139-168, 173-240, 244-301, 305-322, 327-337, 342-363, 366-427, 431-454, 458-470, 475-509, 512-528, 534-585, 598-599, 614-807 /home/admin/workarea/git/Velours/python/prod/memo/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/prod/memo/example_of_unwanted_materials.py 103 76 26% 13-15, 42-47, 50, 52, 54-85, 93-95, 97-113, 115-135, 141-176, 178-182, 185-195 /home/admin/workarea/git/Velours/python/prod/memo/lib_sla.py 343 109 68% 19, 49, 56-60, 68, 75, 107-109, 113, 132, 135, 185, 208-209, 212, 230-231, 234, 240, 249-250, 257-258, 261-262, 272-295, 306-308, 340-344, 368, 372, 375-383, 387-401, 404-415, 432, 449-467, 500, 515, 532-536, 555-558, 561-565, 576-600, 607-612 /home/admin/workarea/git/Velours/python/prod/memo/memo.py 837 529 37% 44, 52, 64-65, 70-101, 105-106, 124-187, 192-199, 201-216, 231-234, 238-242, 247-249, 251-254, 257-260, 263-264, 273-281, 288-289, 294, 302, 306-307, 310, 313-314, 318-321, 324-331, 335-339, 344-345, 351-352, 360-363, 369-370, 375-426, 429-430, 433, 436, 443, 445-446, 451-542, 556, 562-563, 571, 574, 583, 595-596, 601-634, 648, 651, 655, 661, 670-674, 680-724, 739, 745-746, 759-793, 808, 811, 815-816, 825, 828, 831-834, 840, 843-884, 907, 910, 920, 924, 927, 931-934, 937, 946, 957, 960-966, 987-988, 997-998, 1025-1030, 1033, 1041, 1044, 1056, 1062, 1075, 1077-1078, 1085-1093, 1096, 1101, 1106, 1111, 1113-1116, 1122-1176, 1183, 1191, 1201-1262, 1284, 1287, 1293-1294, 1297, 1308, 1313, 1318-1322, 1325-1339 /home/admin/workarea/git/Velours/python/prod/non_supervised_algorithm.py 131 59 55% 40, 58, 72-73, 155, 160, 165-166, 175-264 /home/admin/workarea/git/Velours/python/prod/vision_faster_rcnn.py 244 171 30% 13-16, 61-89, 98, 133-137, 145-172, 178-219, 223-360, 378-380, 398, 401, 415-458 /home/admin/workarea/git/Velours/python/tests/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/tests/cache_photo_data_test.py 74 20 73% 41, 49-62, 84, 90-94 /home/admin/workarea/git/Velours/python/tests/cod_main_test.py 75 12 84% 32, 57, 92, 98-101, 122, 128, 131, 134, 138 /home/admin/workarea/git/Velours/python/tests/datou_test.py 1923 635 67% 37, 43-45, 51-52, 58-60, 66-70, 101-104, 108, 122-126, 157-159, 186-188, 194-197, 203-205, 213-215, 221-224, 230-232, 241, 271-276, 283, 302-304, 317, 333, 352-354, 360-364, 378-416, 448-450, 457, 464-466, 499-501, 510-513, 540-542, 546, 549, 553, 568-569, 579, 582-583, 588, 626-628, 631, 652-657, 679, 707-709, 729, 731, 737-739, 743-745, 777-779, 790-791, 814, 816, 822-824, 828-830, 866-868, 886-887, 915-917, 935-936, 962, 968-970, 983-987, 998, 1025, 1031-1033, 1046-1050, 1066, 1071, 1099-1101, 1111, 1118, 1174, 1291, 1298-1301, 1317, 1323, 1333, 1374, 1380-1383, 1807-1809, 1846, 1852-1855, 1874-1878, 1920, 1926-1929, 1935, 1948, 1959-2067, 2100-2102, 2119-2123, 2128-2131, 2163-2165, 2171-2172, 2192, 2194, 2200-2202, 2206-2208, 2246, 2255-2257, 2267, 2287, 2293, 2295, 2301-2302, 2305-2306, 2311-2312, 2315-2320, 2326-2330, 2357-2361, 2367-2369, 2382-2386, 2399, 2427, 2433-2435, 2444-2446, 2448-2450, 2459, 2517-2520, 2542-2546, 2552-2553, 2556-2558, 2583, 2594-2596, 2601-2602, 2610-2611, 2636, 2656-2659, 2663-2665, 2669-2671, 2675-2677, 2711-2713, 2717, 2723, 2731-2733, 2743, 2777-2779, 2782-2784, 2794-2796, 2798-2800, 2803-2805, 2837-2839, 2842-2844, 2854-2856, 2858-2860, 2863-2865, 2881-2935, 2960, 2966-2968, 2981-2983, 2985-2987, 2996, 3042-3044, 3051, 3066-3121, 3149-3151, 3154-3156, 3166-3168, 3170-3172, 3175-3177, 3180-3182, 3211-3213, 3247, 3253-3255, 3261, 3264-3266, 3303-3304, 3311-3313, 3319, 3322-3324, 3360-3362, 3365-3367, 3407-3409, 3412, 3415-3417, 3423-3425, 3428, 3431-3433, 3460-3462, 3465, 3472, 3476-3477, 3483-3490, 3517, 3521-3525, 3532, 3537-3540, 3567, 3571-3575, 3582, 3587-3590, 3682-3684, 3700-3701 /home/admin/workarea/git/Velours/python/tests/mask_test.py 167 43 74% 27-29, 35-36, 63-68, 79-80, 85-87, 103, 106, 109, 112-116, 120-130, 167-169, 219-227, 249-250, 260, 268, 276, 280-281, 288 /home/admin/workarea/git/Velours/python/tests/python_tests.py 221 45 80% 37-39, 94-95, 99, 105, 107, 112, 124, 128, 130, 134, 141, 143, 147, 149, 151, 153, 155, 157, 163, 165, 167, 172, 188, 202, 208, 222-225, 247-254, 271-272, 288-289, 370 /home/admin/workarea/git/raspi-fotonower-x/python/to_upload_status.py 2 0 100% /usr/lib/python3/dist-packages/babel/__init__.py 3 0 100% /usr/lib/python3/dist-packages/babel/_compat.py 51 26 49% 34-56, 63-67, 77-80 /usr/lib/python3/dist-packages/babel/core.py 329 213 35% 27, 70-77, 102-105, 157-168, 192-193, 216-219, 261-331, 334-337, 343, 346, 349-355, 358, 363-365, 378-393, 418-421, 432-435, 446-449, 468, 482, 494, 506, 515, 531, 542, 554, 566, 580, 592, 604, 615-618, 626, 632, 641, 650, 659, 673, 687, 704, 720, 731, 740, 749, 759, 773, 787, 801, 814, 836, 851, 867, 884, 896, 907, 918, 932, 962-977, 1026-1040, 1083-1115, 1131-1133 /usr/lib/python3/dist-packages/babel/localedata.py 112 86 23% 34-39, 49-55, 66, 96-123, 138-156, 167, 170, 181-189, 198-201, 204, 207, 210-221, 224, 227, 230 /usr/lib/python3/dist-packages/babel/plural.py 280 181 35% 43-73, 112-119, 122-123, 137-139, 149-150, 158, 161, 164-166, 184-189, 211-229, 242-252, 272, 292, 306-315, 334-349, 353, 358-359, 363, 367, 371, 375, 413-421, 425-432, 435-438, 441-444, 447-461, 464-471, 474-478, 481-484, 487-495, 498, 520-521, 538, 550-553, 567-583, 598-603, 620, 623-629 /usr/lib/python3/dist-packages/certifi/__init__.py 2 0 100% /usr/lib/python3/dist-packages/certifi/core.py 5 0 100% /usr/lib/python3/dist-packages/chardet/__init__.py 11 7 36% 31-39 /usr/lib/python3/dist-packages/chardet/big5freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/big5prober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/chardistribution.py 117 83 29% 49-59, 65-68, 72-82, 88-98, 103, 110, 115-118, 125-129, 134-137, 144-148, 153-156, 163-167, 172-175, 182-189, 194-197, 204-214, 219-222, 229-233 /usr/lib/python3/dist-packages/chardet/charsetgroupprober.py 72 61 15% 34-37, 40-47, 51-55, 59-63, 66-83, 86-106 /usr/lib/python3/dist-packages/chardet/charsetprober.py 55 36 35% 40-42, 45, 49, 52, 56, 59, 63-64, 81-101, 115-145 /usr/lib/python3/dist-packages/chardet/codingstatemachine.py 28 18 36% 56-61, 64, 69-78, 81, 84, 88 /usr/lib/python3/dist-packages/chardet/compat.py 10 4 60% 26-29 /usr/lib/python3/dist-packages/chardet/cp949prober.py 16 6 62% 36-41, 45, 49 /usr/lib/python3/dist-packages/chardet/enums.py 35 1 97% 62 /usr/lib/python3/dist-packages/chardet/escprober.py 58 45 22% 43-56, 59-67, 71, 75, 78-81, 84-101 /usr/lib/python3/dist-packages/chardet/escsm.py 17 0 100% /usr/lib/python3/dist-packages/chardet/eucjpprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/euckrfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euckrprober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/euctwfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euctwprober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/gb2312freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/gb2312prober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/hebrewprober.py 77 48 38% 155-162, 165-171, 175-176, 179, 193, 223-253, 259-280, 284, 289-292 /usr/lib/python3/dist-packages/chardet/jisfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/jpcntx.py 81 61 25% 124-129, 132-141, 144-168, 171, 175-178, 181, 185-186, 190, 193-210, 214-231 /usr/lib/python3/dist-packages/chardet/langbulgarianmodel.py 5 0 100% /usr/lib/python3/dist-packages/chardet/langcyrillicmodel.py 13 0 100% /usr/lib/python3/dist-packages/chardet/langgreekmodel.py 5 0 100% /usr/lib/python3/dist-packages/chardet/langhebrewmodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/langthaimodel.py 3 0 100% /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 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/usr/lib/python3/dist-packages/pkg_resources/py31compat.py 12 5 58% 9-13 /usr/lib/python3/dist-packages/rfc3986/__init__.py 16 0 100% /usr/lib/python3/dist-packages/rfc3986/_mixin.py 112 86 23% 28-51, 54, 59-63, 68-72, 77-81, 91, 113-123, 139-147, 165-168, 182-185, 199-202, 216-219, 229, 247-299, 308-319, 341-353 /usr/lib/python3/dist-packages/rfc3986/abnf_regexp.py 63 0 100% /usr/lib/python3/dist-packages/rfc3986/api.py 15 6 60% 38, 52, 77, 92-93, 106 /usr/lib/python3/dist-packages/rfc3986/compat.py 23 10 57% 20-21, 25-26, 45-47, 52-54 /usr/lib/python3/dist-packages/rfc3986/exceptions.py 45 24 47% 18, 28, 37, 52-59, 71-81, 89-95, 103-110 /usr/lib/python3/dist-packages/rfc3986/iri.py 50 34 32% 49-57, 61-73, 76, 86-89, 111-142 /usr/lib/python3/dist-packages/rfc3986/misc.py 31 5 84% 116-121 /usr/lib/python3/dist-packages/rfc3986/normalizers.py 79 63 20% 24, 29-37, 42, 47, 52-67, 72-76, 81-83, 88-90, 101-105, 114-139, 144-167 /usr/lib/python3/dist-packages/rfc3986/parseresult.py 166 124 25% 34-46, 50, 55, 60, 65, 81-92, 98-112, 134-139, 152, 159-172, 176-181, 193-198, 208-220, 227-244, 267-273, 287, 294-314, 327-337, 342-364, 368-385 /usr/lib/python3/dist-packages/rfc3986/uri.py 30 17 43% 88-96, 102-115, 128, 144-147 /usr/lib/python3/dist-packages/rfc3986/validators.py 129 99 23% 60-73, 87-89, 103-105, 119-123, 135-136, 148-149, 165-174, 190-199, 220-240, 245-251, 256-258, 265-271, 284-289, 306-309, 324-329, 344, 359, 374, 389, 396, 411-430, 435-450 /usr/lib/python3/dist-packages/secretstorage/__init__.py 21 18 14% 17-53 /usr/lib/python3/dist-packages/secretstorage/collection.py 104 99 5% 22-201 /usr/lib/python3/dist-packages/secretstorage/defines.py 11 0 100% /usr/lib/python3/dist-packages/secretstorage/dhcrypto.py 28 22 21% 18-59 /usr/lib/python3/dist-packages/secretstorage/exceptions.py 5 0 100% /usr/lib/python3/dist-packages/secretstorage/item.py 73 68 7% 17-145 /usr/lib/python3/dist-packages/secretstorage/util.py 112 107 4% 15-180 /usr/lib/python3/dist-packages/simplejson/__init__.py 80 55 31% 120-122, 126-130, 248-279, 372-385, 457, 519-535, 539-562, 577 /usr/lib/python3/dist-packages/simplejson/compat.py 29 16 45% 5-18, 24, 26 /usr/lib/python3/dist-packages/simplejson/decoder.py 225 166 26% 14-15, 26-27, 60-133, 145-234, 237-270, 369, 373, 390, 392, 397, 399 /usr/lib/python3/dist-packages/simplejson/encoder.py 394 341 13% 13-14, 42-62, 69-103, 244, 247, 249, 251, 272, 284-302, 314-380, 400-404, 407-417, 441-722 /usr/lib/python3/dist-packages/simplejson/errors.py 29 23 21% 7-12, 16-23, 41-50, 53 /usr/lib/python3/dist-packages/simplejson/raw_json.py 3 1 67% 9 /usr/lib/python3/dist-packages/simplejson/scanner.py 64 53 17% 9-10, 21-83 /usr/lib/python3/dist-packages/six.py 491 216 56% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 308, 488, 496, 501-507, 519-525, 530-532, 538-540, 545, 550, 554-568, 583, 592, 600-616, 631, 645-647, 653-673, 679, 683, 687, 691, 698-721, 737-738, 743-795, 797-804, 814-834, 853-855, 871, 873, 893-898, 913, 915, 933, 937, 948-955, 976-977 /usr/lib/python3/dist-packages/stevedore/__init__.py 9 0 100% /usr/lib/python3/dist-packages/stevedore/driver.py 29 17 41% 51-53, 66, 100-105, 108-118, 139-141, 147-148 /usr/lib/python3/dist-packages/stevedore/enabled.py 13 7 46% 64-65, 77-84 /usr/lib/python3/dist-packages/stevedore/exception.py 3 0 100% /usr/lib/python3/dist-packages/stevedore/extension.py 104 71 32% 46-49, 58, 99-107, 141-146, 150-152, 155-156, 160-165, 176-179, 183, 187-214, 220-230, 237, 259-265, 269, 290, 294-301, 309, 317, 326, 331 /usr/lib/python3/dist-packages/stevedore/hook.py 11 6 45% 59, 74-78, 87-89 /usr/lib/python3/dist-packages/stevedore/named.py 34 24 29% 74-89, 123-129, 134-140, 143-146, 154-156 /usr/lib/python3/dist-packages/swiftclient/__init__.py 7 2 71% 31-32 /usr/lib/python3/dist-packages/swiftclient/client.py 959 551 43% 53-63, 71-72, 75-76, 89, 128-135, 146-156, 164-190, 195, 197-208, 214-217, 220-221, 232, 242, 257, 275-276, 279, 282, 285-288, 291, 294, 320-328, 331-368, 411, 417, 421-424, 427-430, 441, 459, 490, 495, 524-526, 536-567, 573-574, 594-600, 604, 611-612, 615, 639-646, 658-659, 694-700, 702, 715, 720, 725-727, 738, 743, 796, 798, 801, 803-813, 817, 819, 821, 823, 825, 836, 838, 857-875, 896-921, 953, 955-970, 975, 977, 981, 983, 985, 987, 990, 992, 1004, 1007, 1027-1048, 1068-1092, 1111-1133, 1155-1180, 1213, 1216, 1221, 1228, 1231-1234, 1236, 1262-1285, 1332, 1339, 1341, 1347, 1349, 1351, 1355, 1357, 1362, 1364, 1367, 1375, 1379-1387, 1397, 1420-1439, 1465-1503, 1529-1557, 1568-1578, 1656, 1660, 1677-1679, 1694-1702, 1722-1727, 1741-1742, 1748, 1753-1791, 1795, 1812, 1818, 1836, 1842, 1848, 1855, 1873, 1886, 1892, 1903-1905, 1914, 1920, 1927, 1933-1944, 1947-1950 /usr/lib/python3/dist-packages/swiftclient/exceptions.py 51 45 12% 25-36, 40-43, 48-81 /usr/lib/python3/dist-packages/swiftclient/utils.py 229 158 31% 41, 51-68, 100-197, 202-204, 220-239, 248-253, 264, 290, 304-305, 310, 332-339, 342, 345, 348, 351-363, 367-369, 373-378, 382-387, 391-392, 396-397, 401-405, 410-416, 419, 424-427 /usr/lib/python3/dist-packages/swiftclient/version.py 6 3 50% 24-28 /usr/lib/python3/dist-packages/urllib3/__init__.py 33 8 76% 56-62, 86 /usr/lib/python3/dist-packages/urllib3/_collections.py 187 87 53% 5-6, 9-16, 70, 73, 76-80, 83-84, 87, 102-103, 145, 149, 152-153, 160, 166-170, 175, 178-179, 184, 198-206, 209-212, 228, 236, 243-244, 249-250, 256, 261-268, 275-286, 297, 300-305, 308-310, 321-323, 334-354 /usr/lib/python3/dist-packages/urllib3/connection.py 173 40 77% 17-21, 27-30, 153, 163-171, 178-184, 187-188, 194, 215, 221, 224, 226, 298-301, 319-327, 331, 335, 364, 378, 391, 411-420, 428 /usr/lib/python3/dist-packages/urllib3/connectionpool.py 318 124 61% 76, 83, 86, 89-91, 97, 215, 221-236, 254-263, 267-273, 294-303, 313, 318, 330-348, 378-381, 401, 405, 418-425, 443-444, 454, 461, 479-493, 602, 612, 618, 637-638, 663, 697-726, 734-735, 745-748, 765-777, 782-804, 823-838, 954-955, 970, 977-978, 1004, 1035-1040, 1057 /usr/lib/python3/dist-packages/urllib3/contrib/__init__.py 0 0 100% /usr/lib/python3/dist-packages/urllib3/contrib/_appengine_environ.py 11 1 91% 36 /usr/lib/python3/dist-packages/urllib3/contrib/pyopenssl.py 248 246 1% 47-498 /usr/lib/python3/dist-packages/urllib3/contrib/socks.py 75 66 12% 55-210 /usr/lib/python3/dist-packages/urllib3/exceptions.py 96 21 78% 21-22, 26, 33-34, 38, 79-83, 90-92, 147-150, 222, 225, 241-242, 249-250 /usr/lib/python3/dist-packages/urllib3/fields.py 90 29 68% 18-20, 38-61, 83, 114, 155, 177-181, 221, 242-243 /usr/lib/python3/dist-packages/urllib3/filepost.py 43 6 86% 34, 57-60, 85, 88 /usr/lib/python3/dist-packages/urllib3/packages/__init__.py 8 2 75% 10-11 /usr/lib/python3/dist-packages/urllib3/packages/ssl_match_hostname/__init__.py 11 6 45% 7, 10-16 /usr/lib/python3/dist-packages/urllib3/poolmanager.py 172 70 59% 96, 102, 170, 173-175, 189, 199-200, 225, 299-306, 325, 328, 337-372, 411-431, 434-439, 448-456, 460-469, 473 /usr/lib/python3/dist-packages/urllib3/request.py 39 17 56% 54, 75, 88-97, 145, 150-166 /usr/lib/python3/dist-packages/urllib3/response.py 399 211 47% 34-36, 39, 42-61, 73-74, 77, 80-98, 103-118, 131, 134, 137-139, 143-152, 190, 217, 236, 251, 269, 286-287, 291, 294, 302, 315-322, 332, 337-338, 341, 347-348, 352, 365, 367-373, 377, 388-391, 407-408, 427-443, 455-462, 495, 518-529, 539, 560-561, 585, 603, 610, 615, 618, 621, 626, 628, 631-634, 637-642, 648-653, 657, 661-666, 675, 680-689, 692-711, 727-781, 789-792, 795-809 /usr/lib/python3/dist-packages/urllib3/util/__init__.py 10 0 100% /usr/lib/python3/dist-packages/urllib3/util/connection.py 66 18 73% 19, 21, 25-26, 53, 73, 77-86, 91, 118, 130-131 /usr/lib/python3/dist-packages/urllib3/util/queue.py 14 1 93% 7 /usr/lib/python3/dist-packages/urllib3/util/request.py 50 22 56% 13, 63, 65, 71, 74, 77, 80, 85, 96, 98-103, 119-133 /usr/lib/python3/dist-packages/urllib3/util/response.py 35 17 51% 19-35, 55, 71, 83-86 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262-283, 290, 298, 306, 316-322, 331-337, 345-350, 359-364, 373, 382, 391-397, 418, 425 /usr/lib/python3/dist-packages/yaml/composer.py 92 31 66% 18-22, 26-27, 40-41, 65-70, 74-75, 82, 96, 100-115, 126 /usr/lib/python3/dist-packages/yaml/constructor.py 479 295 38% 32, 38-39, 44-45, 52, 61, 69, 71-72, 74, 82-98, 102, 107-108, 114, 119, 125-129, 134, 141, 148-157, 175-177, 186-204, 208-209, 213, 221-222, 234-235, 242, 244, 246, 248, 250, 252, 254-261, 271-292, 295-307, 323-350, 356-373, 377-394, 397-400, 406-408, 417-424, 427, 487, 490-492, 495, 498, 501-513, 517, 520, 523, 526-538, 541-563, 566-570, 573-577, 581-593, 596-612, 617-621, 635-656, 659, 716, 719, 722, 726 /usr/lib/python3/dist-packages/yaml/cyaml.py 46 24 48% 19-21, 26-28, 33-35, 40-42, 47-49, 59-66, 76-83, 93-100 /usr/lib/python3/dist-packages/yaml/dumper.py 23 12 48% 17-25, 35-43, 53-61 /usr/lib/python3/dist-packages/yaml/emitter.py 838 769 8% 22-29, 42-104, 108-109, 112-116, 121-131, 134-144, 147-154, 161-167, 171, 176, 179-211, 215-223, 227-228, 234-258, 261-264, 267-270, 275-278, 281-290, 293-306, 311-314, 317-331, 334-352, 355-357, 360-364, 369-371, 374, 377-384, 389-390, 393, 396-407, 410-412, 415-418, 423, 427, 431-434, 438-452, 460-467, 470-492, 495-513, 516-535, 540-543, 546-555, 558-578, 581-614, 617-624, 629-778, 789-790, 794-795, 798, 802-812, 815-825, 828-836, 839-843, 846-850, 855-906, 927-978, 981-989, 992-1043, 1046-1078, 1081-1137 /usr/lib/python3/dist-packages/yaml/error.py 58 42 28% 15-34, 38-43, 52-56, 59-74 /usr/lib/python3/dist-packages/yaml/events.py 61 6 90% 9-13, 17-19 /usr/lib/python3/dist-packages/yaml/loader.py 47 24 49% 14-19, 34-39, 44-49, 58-63 /usr/lib/python3/dist-packages/yaml/nodes.py 29 7 76% 4-7, 9-23 /usr/lib/python3/dist-packages/yaml/parser.py 352 210 40% 101, 110-111, 157, 163, 167-180, 197-199, 209-215, 218-246, 268, 275-277, 283-291, 293-300, 302-310, 320-323, 333, 338-341, 343-346, 348-351, 357-369, 377-379, 382-398, 403-415, 434-435, 437-438, 453-458, 472-474, 477-500, 503-510, 513-524, 527-529, 538-540, 543-567, 570-581, 584-585, 588 /usr/lib/python3/dist-packages/yaml/reader.py 122 69 43% 27-31, 34-40, 76-85, 90-92, 96, 108-109, 119, 123-135, 141-143, 149-175, 178-185 /usr/lib/python3/dist-packages/yaml/representer.py 248 176 29% 19-24, 27-31, 34-63, 78-83, 86-101, 104-129, 132, 137-142, 145, 148, 151-155, 158-162, 165, 172-189, 199, 207, 210-213, 216-217, 220-221, 224-228, 231, 275-283, 286, 289-290, 293, 313-356, 360-364 /usr/lib/python3/dist-packages/yaml/resolver.py 135 78 42% 30, 33, 51-89, 94-112, 117-118, 122-141, 146, 153, 155-159, 163 /usr/lib/python3/dist-packages/yaml/scanner.py 753 487 35% 119, 129, 133, 138, 177, 181, 185, 195, 199, 203, 207, 211, 215, 219, 227, 231, 235, 239, 243, 251, 258, 290-293, 315-321, 341, 355, 393-400, 403, 406, 411-422, 425, 428, 433-445, 448, 451, 456-468, 473-482, 487-515, 520-543, 572-593, 604-610, 615-621, 626-632, 635, 638, 643-649, 655, 687-688, 693-696, 701-704, 709, 714-719, 725, 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584-591, 611-714, 718-721 /usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py 1477 1139 23% 95-97, 116-117, 123-124, 139-163, 210-222, 238-248, 252, 260-262, 272-274, 295-298, 301-304, 320, 324, 328-331, 334-335, 388, 392-403, 455-456, 466-467, 475, 487, 501-505, 553-554, 609-611, 630-703, 707, 714, 720-723, 726, 733-737, 740-743, 747-771, 775, 781-784, 790-791, 795, 799-801, 804, 810-815, 824-825, 833-836, 841, 853-856, 861-863, 866-867, 870-871, 874-878, 882-890, 903-945, 956-961, 965-972, 980-985, 988-990, 998-1010, 1024-1056, 1063-1065, 1072-1074, 1080-1081, 1107-1123, 1145, 1154, 1157, 1161-1168, 1184-1191, 1235-1271, 1278, 1304-1322, 1341-1342, 1373-1400, 1409, 1419-1464, 1479-1509, 1521-1531, 1549-1579, 1594-1597, 1610-1691, 1708-1737, 1741, 1748-1764, 1768-1775, 1779, 1787-1794, 1802-1803, 1813-1833, 1875-1933, 1957, 1967-1989, 1996, 2003-2005, 2020-2059, 2067, 2081-2093, 2103, 2114-2116, 2127, 2131, 2145-2166, 2211-2212, 2220-2221, 2225-2230, 2237-2268, 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659-661, 675-683, 687-703 /usr/local/lib/python3.8/dist-packages/IPython/core/magic_arguments.py 102 16 84% 130, 135-136, 153, 164, 172, 190, 203-204, 206, 232, 262, 269, 272-274 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/magics/auto.py 38 27 29% 29-31, 51-60, 107-128 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/basic.py 249 188 24% 22-23, 27-38, 41, 44, 51-64, 67, 130-174, 181, 185-193, 210-275, 293-302, 307-309, 326-359, 369-379, 384-386, 415-465, 493-500, 544-546, 567-582, 622-651 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/code.py 304 260 14% 64-81, 100-134, 141-164, 170, 178-179, 209-243, 260-275, 285, 325-377, 383-507, 511-517, 664-730 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/config.py 46 36 22% 32-33, 107-158 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/display.py 29 10 66% 40, 45, 56, 61, 72-77, 82 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218, 222-256, 265-286, 290-305, 309-324, 327-354, 358-384, 388-397, 401-427, 431-440 /usr/local/lib/python3.8/dist-packages/IPython/core/splitinput.py 34 23 32% 58-78, 111-119, 134, 137 /usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py 709 610 14% 152, 170-234, 247-300, 311-319, 330-343, 348-374, 399-423, 426-435, 450-475, 488-509, 522, 525-526, 531-542, 545-552, 558-563, 568-574, 578, 585-587, 595, 619, 623-625, 628-631, 662-704, 719-741, 756-813, 823, 835-838, 842-845, 867-881, 885-894, 898-1069, 1074-1093, 1096-1106, 1116-1141, 1144-1165, 1171-1205, 1227-1248, 1251-1257, 1263-1270, 1293-1307, 1310-1324, 1330, 1338-1350, 1354, 1357, 1360, 1363, 1392-1403, 1407-1414, 1425, 1433-1434, 1437-1439, 1446-1454, 1459-1461, 1465, 1472-1495, 1499, 1503 /usr/local/lib/python3.8/dist-packages/IPython/core/usage.py 9 0 100% /usr/local/lib/python3.8/dist-packages/IPython/display.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/extensions/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/IPython/extensions/storemagic.py 114 98 14% 24-31, 35-46, 50, 54-56, 72-75, 121-227, 232 /usr/local/lib/python3.8/dist-packages/IPython/lib/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/lib/backgroundjobs.py 209 164 22% 76-89, 93-94, 98-99, 103-104, 171-197, 200-201, 209, 223-242, 249-254, 261-266, 276-282, 287-293, 298-310, 322-330, 334-337, 340-344, 347-354, 386, 391-417, 420, 423, 426, 429-442, 455-462, 465, 477-488, 491 /usr/local/lib/python3.8/dist-packages/IPython/lib/clipboard.py 41 33 20% 17-33, 38-44, 53-67 /usr/local/lib/python3.8/dist-packages/IPython/lib/display.py 254 202 20% 99-115, 119-128, 133-151, 155-173, 178-194, 198-200, 204-210, 213-219, 223-231, 234-237, 240-243, 261-264, 268-276, 308-310, 314-319, 327-328, 343-344, 381-387, 390-391, 399-405, 410, 471-488, 512-544, 550-566, 575-579, 584-592, 597-605, 627-628, 631-639, 642, 645-649, 652-654 /usr/local/lib/python3.8/dist-packages/IPython/lib/pretty.py 500 393 21% 108-111, 116-117, 127-134, 140-144, 151-155, 162-166, 171-175, 186-197, 200-207, 210-214, 218-229, 237-248, 254-261, 271-276, 280-286, 290-295, 299-302, 309-321, 343-354, 358-399, 409-417, 423, 429-430, 433-435, 438-439, 445-450, 453-461, 467-469, 475-477, 480-483, 486-494, 497-500, 508-538, 547-559, 568-586, 596-608, 614-623, 628-648, 659-678, 684-690, 695-703, 708-718, 724-725, 760-761, 778-782, 803-811, 814-819, 822-827, 831-836, 844-856 /usr/local/lib/python3.8/dist-packages/IPython/lib/security.py 33 25 24% 54-70, 99-114 /usr/local/lib/python3.8/dist-packages/IPython/paths.py 68 52 24% 22-70, 75-84, 101-106, 113-119 /usr/local/lib/python3.8/dist-packages/IPython/terminal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/terminal/debugger.py 77 53 31% 30-32, 35-62, 71-120, 129, 133-141 /usr/local/lib/python3.8/dist-packages/IPython/terminal/embed.py 173 130 25% 66-93, 105-106, 130, 135, 139-145, 149-163, 171, 174-175, 196-237, 267-334, 362-399 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/usr/local/lib/python3.8/dist-packages/IPython/terminal/ptutils.py 86 64 26% 38-54, 58-61, 67-70, 74-77, 80-92, 99-133, 140-144, 155-168 /usr/local/lib/python3.8/dist-packages/IPython/terminal/shortcuts.py 140 111 21% 27-28, 34-95, 99-104, 109-152, 161, 170, 174-176, 180-184, 188-191, 194, 200, 203, 215-226, 238-249, 253-254, 258-274 /usr/local/lib/python3.8/dist-packages/IPython/testing/__init__.py 9 5 44% 33-37 /usr/local/lib/python3.8/dist-packages/IPython/testing/skipdoctest.py 3 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/PyColorize.py 110 82 25% 143, 186-196, 200-205, 217-281, 286-325, 330 /usr/local/lib/python3.8/dist-packages/IPython/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/_process_common.py 68 57 16% 34-40, 69-111, 130-133, 151, 171-175, 190-212 /usr/local/lib/python3.8/dist-packages/IPython/utils/_process_posix.py 82 57 30% 37-39, 62-67, 94-97, 115-118, 133-203, 214-224 /usr/local/lib/python3.8/dist-packages/IPython/utils/_sysinfo.py 1 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/capture.py 103 70 32% 18-21, 24-25, 29-35, 38, 41, 44, 47, 50, 53, 56, 59, 76-80, 83, 88-90, 95-97, 110, 114-119, 131-134, 137-163, 166-170 /usr/local/lib/python3.8/dist-packages/IPython/utils/colorable.py 7 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/coloransi.py 67 12 82% 93-94, 122-124, 149, 156, 161, 172-173, 179-180 /usr/local/lib/python3.8/dist-packages/IPython/utils/contexts.py 26 16 38% 37-38, 42-53, 56-60, 70 /usr/local/lib/python3.8/dist-packages/IPython/utils/data.py 6 3 50% 22-23, 28 /usr/local/lib/python3.8/dist-packages/IPython/utils/decorators.py 14 10 29% 38-49 /usr/local/lib/python3.8/dist-packages/IPython/utils/dir2.py 34 28 18% 16-20, 38-51, 64-84 /usr/local/lib/python3.8/dist-packages/IPython/utils/encoding.py 23 7 70% 30, 53-58, 64-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/frame.py 17 11 35% 50-53, 66-67, 81-82, 91-94 /usr/local/lib/python3.8/dist-packages/IPython/utils/generics.py 9 2 78% 12, 30 /usr/local/lib/python3.8/dist-packages/IPython/utils/importstring.py 12 10 17% 27-39 /usr/local/lib/python3.8/dist-packages/IPython/utils/io.py 130 76 42% 28-31, 41-42, 47-49, 52-64, 68-73, 84, 122-132, 136-139, 143-145, 149-150, 153-154, 170-188, 207-211, 216-218, 223-227, 232-236, 246-248 /usr/local/lib/python3.8/dist-packages/IPython/utils/ipstruct.py 76 56 26% 85-88, 111-123, 146-151, 165-166, 180-182, 196-198, 212-215, 223-229, 232, 245, 263, 271, 360-390 /usr/local/lib/python3.8/dist-packages/IPython/utils/module_paths.py 11 8 27% 61-70 /usr/local/lib/python3.8/dist-packages/IPython/utils/openpy.py 45 35 22% 24-40, 46-58, 75-79, 100-103 /usr/local/lib/python3.8/dist-packages/IPython/utils/path.py 191 148 23% 27, 30-53, 57, 67, 76-81, 87-90, 99-109, 146-162, 190-211, 220-230, 239-249, 254-256, 260-262, 266-268, 272-274, 278-280, 297-302, 307-311, 321-327, 341-351, 362-363, 375-382, 395-419, 429-436 /usr/local/lib/python3.8/dist-packages/IPython/utils/process.py 29 17 41% 15, 17, 47-50, 55-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/py3compat.py 108 72 33% 18-19, 22-23, 27-29, 32-34, 38-40, 45-59, 66-76, 93-140, 147, 156-158, 165-168, 179, 189 /usr/local/lib/python3.8/dist-packages/IPython/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/IPython/utils/strdispatch.py 33 23 30% 25-26, 31-33, 38-40, 44-52, 55, 58-61, 65-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/sysinfo.py 40 24 40% 54-65, 81-82, 97-99, 119, 123, 128-129, 134, 152-165 /usr/local/lib/python3.8/dist-packages/IPython/utils/syspathcontext.py 32 22 31% 24, 27-31, 34-40, 46, 49-53, 56-62 /usr/local/lib/python3.8/dist-packages/IPython/utils/tempdir.py 23 12 48% 24-26, 29-30, 35, 38, 51-53, 56-57 /usr/local/lib/python3.8/dist-packages/IPython/utils/terminal.py 59 37 37% 27-33, 53, 58, 62, 68-69, 73, 81-105, 110-112, 117-119, 123-125, 129 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299, 333-337, 345-349, 352-356, 377-378, 388-393, 396-406, 410-420, 424, 427-432, 444-448, 454-458, 462-466 /usr/local/lib/python3.8/dist-packages/absl/flags/_flagvalues.py 525 404 23% 135-136, 139, 169, 201-206, 217-227, 238-247, 260-263, 276-288, 303-314, 329-340, 349, 365-375, 383-393, 403-404, 410, 414, 416, 418, 420, 423-430, 438, 440, 446, 449, 459, 467, 471-491, 495-503, 506-510, 525-531, 554-561, 578-583, 587, 590, 593, 613-637, 640, 643, 647-649, 661, 684-796, 800, 809, 813-819, 823, 827, 840-857, 870-879, 883-886, 890-892, 903-905, 916-918, 926, 929-963, 976-980, 984-995, 1013-1018, 1041-1086, 1126-1168, 1183-1190, 1203-1204, 1219-1255, 1259, 1264 /usr/local/lib/python3.8/dist-packages/absl/flags/_helpers.py 164 117 29% 29-30, 34-35, 127, 132, 157-158, 174-186, 191-204, 210-233, 238-261, 284-318, 339-356, 371-398, 407-424, 428-431 /usr/local/lib/python3.8/dist-packages/absl/flags/_validators.py 94 61 35% 64-68, 80-82, 90, 93, 104, 128-129, 132, 135, 145, 171-172, 183, 186-190, 193, 222-223, 252-257, 286-288, 320-327, 355-360, 383-384, 405-420, 435-449, 462-463 /usr/local/lib/python3.8/dist-packages/absl/logging/__init__.py 406 221 46% 96, 169, 181-194, 208, 215-216, 220, 223, 266, 280-284, 298-303, 312, 317, 322, 326, 331-333, 338, 343, 348, 382-383, 408-414, 429-430, 473, 476, 499, 515-524, 529, 534, 539, 544, 552, 568-575, 596-620, 633-647, 656-668, 704-718, 722, 727, 744-772, 776-779, 783-790, 803-808, 827-850, 854-870, 883, 886, 889, 892, 895-896, 899-902, 906, 913, 916, 931-938, 989, 993-996, 998, 1002, 1006, 1010, 1014-1017, 1021, 1025, 1029, 1046-1047, 1064-1065, 1083-1086, 1099-1100, 1110, 1119-1121, 1133-1148, 1156 /usr/local/lib/python3.8/dist-packages/absl/logging/converter.py 62 32 48% 107-114, 129-135, 151, 153, 157, 169, 184-200, 215 /usr/local/lib/python3.8/dist-packages/absl/testing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/testing/_pretty_print_reporter.py 50 28 44% 31-32, 35-40, 43-44, 47-48, 51-52, 55-56, 59-60, 63-64, 67-68, 84-87, 91-93, 96 /usr/local/lib/python3.8/dist-packages/absl/testing/absltest.py 885 696 21% 52-54, 75-76, 82-88, 94-97, 131-135, 210-223, 270, 276-278, 300-303, 315, 321, 348-350, 363-371, 383, 391-417, 423, 428-429, 434-435, 449-452, 463-464, 482-488, 504-510, 518-520, 538-542, 545-549, 575-590, 634-639, 661-665, 670, 674, 678-680, 684-692, 696-700, 704-707, 722-732, 742-743, 753-754, 764-765, 775-776, 792-817, 827-834, 843-850, 860-865, 890-911, 915-919, 924-928, 942-946, 951-978, 1004-1032, 1038-1065, 1069-1073, 1078-1085, 1119-1146, 1164-1182, 1210-1224, 1240-1243, 1246, 1249-1255, 1275-1283, 1311-1323, 1336-1350, 1364-1376, 1391-1405, 1449-1529, 1542-1607, 1611-1619, 1647-1658, 1671-1685, 1690-1700, 1704, 1724-1754, 1759, 1764, 1771, 1776, 1782-1836, 1847-1858, 1876-1892, 1905-1910, 1920-1921, 1928, 1947-1948, 1954-1959, 1966-1968, 1971-1972, 1978-1984, 2028-2065, 2071-2077, 2097-2104, 2108-2117, 2122-2127, 2145-2149, 2179-2220, 2230-2312, 2333-2335, 2340-2344, 2349-2352, 2357-2365, 2370-2371 /usr/local/lib/python3.8/dist-packages/absl/testing/parameterized.py 151 108 28% 188, 197, 201, 207-213, 238-244, 247, 255-327, 331-345, 361-387, 406, 428, 449-458, 469-473, 492-509, 516, 529, 559-563 /usr/local/lib/python3.8/dist-packages/absl/testing/xml_reporter.py 247 180 27% 64, 78-80, 92-99, 112-117, 127, 151-186, 189, 192, 206-223, 226-231, 239-243, 246-269, 272-304, 312-316, 324, 332, 346-353, 356-357, 361-373, 376-377, 380-398, 413-415, 439-447, 450-452, 455-456, 459-460, 463-465, 468-470, 473-474, 477-480, 483-488, 491-501, 504-506, 531-533, 548, 551-554 /usr/local/lib/python3.8/dist-packages/absl/third_party/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/case.py 187 177 5% 15-63, 70-235, 239-273 /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/result.py 16 9 44% 16-25 /usr/local/lib/python3.8/dist-packages/appdirs.py 257 211 18% 29-39, 77-97, 131-163, 195-203, 236-254, 291-300, 302-304, 310, 345-353, 388-404, 411-415, 419, 424, 429, 434, 439, 444, 449, 460-476, 480-503, 507-530, 533-556, 559-571, 577-608 /usr/local/lib/python3.8/dist-packages/astunparse/__init__.py 13 3 77% 18-20 /usr/local/lib/python3.8/dist-packages/astunparse/printer.py 37 28 24% 10-12, 16, 19, 23-51 /usr/local/lib/python3.8/dist-packages/astunparse/unparser.py 718 440 39% 77-78, 81-82, 85, 93-97, 100-101, 123-126, 129-139, 151, 154, 157-158, 161-165, 168-175, 178-189, 192-193, 196-197, 200-205, 208-213, 216-221, 232-243, 253-256, 258-261, 264-275, 278-290, 295-296, 298-302, 308-344, 350, 362-363, 369, 372, 375-386, 389-408, 411-420, 427-430, 439, 443, 446-459, 463-479, 483-486, 489-491, 494-495, 498-500, 503-518, 524, 527-529, 534, 541-547, 549, 552, 556-568, 576-580, 583-587, 590-594, 597-603, 606-615, 618-624, 627-630, 633-649, 663-677, 701-704, 712, 729-738, 748-749, 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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, 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29% 58-59, 63, 67-75, 80-82, 87-88, 91-93, 96-97, 100-107, 110-115, 118-126, 132-135, 138-141, 144-149, 152-155, 158-164, 172-175, 178, 181-205, 208-216, 219-223, 243-250, 254-264, 267-271, 274-284, 287-308 /usr/local/lib/python3.8/dist-packages/boto/gs/user.py 26 20 23% 25-29, 32, 35, 38-43, 46-54 /usr/local/lib/python3.8/dist-packages/boto/handler.py 29 19 34% 30-32, 35-38, 41-46, 49, 54-57, 60 /usr/local/lib/python3.8/dist-packages/boto/https_connection.py 49 34 31% 41-44, 47, 59-62, 75-83, 105-114, 118-135 /usr/local/lib/python3.8/dist-packages/boto/jsonresponse.py 108 89 18% 30-32, 35-41, 44-47, 50, 53-55, 64-74, 77-86, 89-91, 94-109, 112-119, 127-132, 135-137, 140-155, 158-168 /usr/local/lib/python3.8/dist-packages/boto/plugin.py 38 22 42% 53-56, 60-66, 70-78, 86, 91-93 /usr/local/lib/python3.8/dist-packages/boto/provider.py 247 178 28% 183-214, 217-219, 222, 227-229, 232, 237-239, 242, 247-263, 267-378, 383-402, 413-434, 437-441, 444-465, 468-473, 476, 479, 484 /usr/local/lib/python3.8/dist-packages/boto/pyami/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/pyami/config.py 158 108 32% 42, 47-49, 59, 61, 65-69, 78, 88-93, 96-103, 111-121, 124, 127, 130-134, 137-141, 144-148, 151, 166-169, 172-180, 183-186, 189-191, 194-202, 205-217, 220-235 /usr/local/lib/python3.8/dist-packages/boto/regioninfo.py 89 66 26% 44, 56-57, 77-82, 100-115, 121-134, 161-182, 208-220, 226-229, 235-239, 247-249, 259-262, 265, 268, 271-276, 289-290 /usr/local/lib/python3.8/dist-packages/boto/resultset.py 111 98 12% 47-62, 65-76, 79-82, 85-134, 140-142, 145-148, 151, 154, 157-160, 163-176 /usr/local/lib/python3.8/dist-packages/boto/s3/__init__.py 17 11 35% 43-44, 54-55, 63-74 /usr/local/lib/python3.8/dist-packages/boto/s3/acl.py 111 89 20% 34-36, 39-51, 54-64, 67-72, 75-82, 88-89, 92, 95-97, 100-101, 104-108, 111-114, 117-121, 130-135, 138-140, 143-156, 159-171 /usr/local/lib/python3.8/dist-packages/boto/s3/bucket.py 700 576 18% 71, 95-97, 100, 103, 106, 109, 112-117, 131, 143, 175-194, 197-231, 282, 328, 363, 369-390, 394-411, 424-426, 469-472, 521-522, 535, 606-609, 623-625, 630, 662-730, 757-759, 766-788, 847-889, 894-908, 912-922, 926-936, 940-944, 948-963, 989-1002, 1028-1040, 1043-1046, 1072-1080, 1110-1119, 1123-1124, 1134-1146, 1162-1171, 1193-1197, 1206-1207, 1216-1227, 1235-1240, 1243-1249, 1253-1260, 1288-1308, 1323-1339, 1350-1366, 1377-1389, 1396-1404, 1438-1441, 1448, 1454-1461, 1483, 1489-1493, 1522-1526, 1530-1538, 1544-1551, 1560-1563, 1570-1576, 1586-1594, 1598-1606, 1618-1632, 1644, 1651-1658, 1669-1673, 1679-1687, 1736-1767, 1775-1806, 1815-1822, 1826, 1829-1835, 1838-1845, 1849-1864, 1867, 1870-1878 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlistresultset.py 67 54 19% 29-41, 54-59, 62, 73-86, 99-105, 108, 121-134, 147-151, 154 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlogging.py 50 41 18% 28-33, 36-47, 50, 53-57, 60-65, 69-83 /usr/local/lib/python3.8/dist-packages/boto/s3/connection.py 282 212 25% 58-62, 67-69, 76, 79-82, 85-88, 91-95, 98-99, 106, 113, 119, 122-126, 132-135, 176-199, 205-208, 211-212, 215, 226, 232-237, 298-355, 361-380, 386-438, 443-453, 468-469, 508-511, 528-555, 577-581, 606-627, 644-647, 653-667 /usr/local/lib/python3.8/dist-packages/boto/s3/cors.py 80 69 14% 65-78, 81, 84, 87-100, 103-117, 126-130, 133, 140-144, 194-210 /usr/local/lib/python3.8/dist-packages/boto/s3/deletemarker.py 28 23 18% 26-31, 34-38, 41-55 /usr/local/lib/python3.8/dist-packages/boto/s3/key.py 690 590 14% 106-135, 138-147, 150, 154-157, 160, 163, 168-169, 172-175, 180-184, 187-192, 197-207, 210, 219-223, 226-231, 234-242, 246-259, 262-273, 280, 304-334, 348, 352-361, 381-385, 397-402, 408-415, 441-451, 501-507, 515-519, 522-542, 551, 557, 561, 566-573, 576, 580-581, 584-585, 588-589, 592-593, 596, 605-610, 624-635, 639, 695-713, 760, 767-966, 969-1021, 1036-1045, 1108-1132, 1214-1311, 1374-1375, 1437-1444, 1493, 1503-1575, 1602, 1658-1664, 1722-1739, 1795-1804, 1829-1831, 1853-1856, 1859-1867, 1875-1889, 1893-1912, 1929-1935 /usr/local/lib/python3.8/dist-packages/boto/s3/keyfile.py 74 53 28% 35-44, 47-49, 52-85, 88-89, 92-94, 97, 102, 107, 110, 113, 116, 119, 122, 125, 128, 131, 134 /usr/local/lib/python3.8/dist-packages/boto/s3/lifecycle.py 148 113 24% 48-65, 68, 71-76, 79-86, 89-99, 111-112, 115, 118-121, 124-128, 131-137, 152-154, 157-161, 164-171, 178-182, 185, 188-203, 210-213, 230-231, 234-236, 242, 246, 250, 259-263, 266, 273-278, 310-311 /usr/local/lib/python3.8/dist-packages/boto/s3/multidelete.py 64 48 25% 41-44, 47-50, 53, 56-66, 82-85, 88-92, 95, 98-107, 121-123, 126-134, 137 /usr/local/lib/python3.8/dist-packages/boto/s3/multipart.py 160 133 17% 46-52, 55, 59, 62-71, 86-90, 93-96, 99, 102-111, 118-125, 134-146, 149, 152, 155-162, 165-175, 178-200, 211-226, 253-261, 290-301, 317-318, 330 /usr/local/lib/python3.8/dist-packages/boto/s3/prefix.py 16 10 38% 24-25, 28, 31-34, 38-41 /usr/local/lib/python3.8/dist-packages/boto/s3/tagging.py 52 34 35% 7-8, 11, 14-17, 20, 24, 29-33, 36, 39-40, 43-47, 54-58, 61, 64-68, 71 /usr/local/lib/python3.8/dist-packages/boto/s3/user.py 23 18 22% 24-28, 31, 34-39, 42-49 /usr/local/lib/python3.8/dist-packages/boto/s3/website.py 122 85 30% 24-26, 57-63, 66-72, 75, 78-89, 94-98, 101, 104-106, 109-114, 131-133, 136, 152-153, 156-159, 162, 165, 168-171, 191-192, 195-198, 201, 204-209, 213, 218-223, 245-247, 250, 283-288, 291 /usr/local/lib/python3.8/dist-packages/boto/storage_uri.py 488 377 23% 57, 62, 66, 69-70, 77-78, 82-83, 87-89, 103-149, 152, 158-160, 165-172, 176-177, 180-184, 187-191, 194-196, 199-203, 209-218, 224-227, 231-234, 237-240, 288-299, 302-318, 321, 328-332, 335-344, 348-355, 365-366, 379-390, 402-413, 417-421, 425-429, 433-438, 441-443, 446-453, 457-464, 468-470, 475-491, 496-504, 508-515, 518-520, 524, 528, 536, 540, 544, 548, 552, 556, 560, 564, 568-576, 579-581, 584-585, 588-591, 596-605, 610-619, 624-625, 630-631, 636-640, 646-649, 653-655, 661-675, 680-696, 700-706, 713-724, 733-735, 738-740, 743-745, 749-754, 757-759, 762-764, 767-769, 773, 779-787, 791-795, 800-802, 805-811, 816-822, 826-833, 838-840, 845-849, 875-880, 889, 893, 897, 901, 905, 909-911, 915, 919, 923, 927, 932, 937, 944 /usr/local/lib/python3.8/dist-packages/boto/utils.py 575 467 19% 108-111, 119-169, 173-183, 187-202, 212-237, 241, 246-266, 269-270, 273-337, 340-343, 346-347, 350-351, 354-355, 358-359, 383, 399-405, 413-427, 432-441, 454-460, 464-466, 470-481, 485-498, 505-508, 518-543, 549-554, 557-576, 579, 582, 588, 616-619, 629-646, 689-691, 694, 697-700, 703, 706-709, 712, 715-717, 720-728, 731, 734-741, 744-750, 753-765, 780-782, 785-787, 790, 793-797, 800-803, 808-859, 863-872, 876-881, 897-899, 920-944, 959-972, 1000, 1004-1029, 1038, 1048-1049, 1060, 1070-1083, 1093-1098 /usr/local/lib/python3.8/dist-packages/boto/vendored/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/exceptions.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/regions.py 81 60 26% 58, 65, 85, 94-96, 99-102, 106-116, 120-124, 128-152, 156-160, 163-177, 180-182, 186 /usr/local/lib/python3.8/dist-packages/boto/vendored/six.py 444 208 53% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /usr/local/lib/python3.8/dist-packages/cachetools/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/cachetools/abc.py 25 14 44% 21-24, 29-36, 39-43 /usr/local/lib/python3.8/dist-packages/cachetools/cache.py 60 38 37% 6, 9, 12, 21-27, 30, 38-41, 44-57, 60-62, 65, 68, 71, 74, 79, 84, 89 /usr/local/lib/python3.8/dist-packages/cachetools/decorators.py 73 69 5% 11-44, 52-88 /usr/local/lib/python3.8/dist-packages/cachetools/keys.py 24 14 42% 17-20, 23, 26, 29, 40-43, 49-52 /usr/local/lib/python3.8/dist-packages/cachetools/lfu.py 22 14 36% 10-11, 14-16, 19-20, 23-24, 28-33 /usr/local/lib/python3.8/dist-packages/cachetools/lru.py 27 18 33% 10-11, 14-16, 19-20, 23-24, 28-33, 36-39 /usr/local/lib/python3.8/dist-packages/cachetools/rr.py 19 11 42% 8, 15-20, 25, 29-34 /usr/local/lib/python3.8/dist-packages/cachetools/ttl.py 156 118 24% 12-13, 16, 19-22, 28-29, 32-35, 38-43, 46, 49, 52, 59-64, 67-72, 75-84, 87-99, 102-106, 109-116, 119-126, 129-136, 139-141, 145-147, 152, 157, 161-172, 175-177, 180-181, 184-185, 188-189, 196-203, 206-208 /usr/local/lib/python3.8/dist-packages/cycler.py 177 107 40% 73, 110, 118, 122, 127, 131, 157-178, 185-189, 218-223, 229, 240-243, 255-262, 265, 268-273, 284-292, 303-311, 317-322, 325-333, 337-347, 396-397, 425, 454-465, 509, 513-516, 519-526, 548-556 /usr/local/lib/python3.8/dist-packages/cython.py 10 6 40% 9-17 /usr/local/lib/python3.8/dist-packages/decorator.py 270 120 56% 49-60, 64-67, 70-73, 100, 114-115, 117, 119-120, 133, 139, 143, 155-156, 168, 174, 186-189, 217, 233-235, 241, 246, 271, 279, 282-283, 291, 299-300, 306, 314-316, 318, 321, 329, 339-348, 357-454 /usr/local/lib/python3.8/dist-packages/defusedxml/ElementTree.py 64 25 61% 21-23, 52, 79-105, 108, 113, 120 /usr/local/lib/python3.8/dist-packages/defusedxml/__init__.py 21 15 29% 25-51 /usr/local/lib/python3.8/dist-packages/defusedxml/common.py 65 42 35% 23, 31-34, 37-38, 46-52, 55-56, 64-68, 71-72, 81-90, 98-105, 115-122, 125-132 /usr/local/lib/python3.8/dist-packages/gast/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/gast/ast3.py 192 145 24% 10-160, 174, 189-194, 200-207, 214-232, 239, 260-264, 270-306, 310-376, 391 /usr/local/lib/python3.8/dist-packages/gast/astn.py 24 1 96% 21 /usr/local/lib/python3.8/dist-packages/gast/gast.py 78 46 41% 9-11, 292, 302-304, 308-316, 327-331, 342-366, 375-380 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400, 423-438, 447-450, 458-462, 470-474, 488-582, 592-708, 720, 724, 730, 743-758, 770, 777-790, 800, 811, 817, 821-824, 841 /usr/local/lib/python3.8/dist-packages/h5py/_hl/datatype.py 24 11 54% 37, 43-45, 49-56 /usr/local/lib/python3.8/dist-packages/h5py/_hl/files.py 238 113 53% 46-48, 52, 78, 89, 95, 103-107, 115, 117, 119, 125-127, 130-137, 143-146, 158-167, 172, 174-206, 210-215, 239, 245-253, 259, 266-268, 274-275, 281-282, 287-298, 304, 310-314, 379, 382-383, 386-393, 400-402, 411, 418, 438-439, 451-452, 465-478 /usr/local/lib/python3.8/dist-packages/h5py/_hl/filters.py 175 141 19% 78-84, 88-101, 113-244, 263-283, 302-342 /usr/local/lib/python3.8/dist-packages/h5py/_hl/group.py 234 168 28% 41, 62-69, 132-140, 158-174, 189-206, 223-238, 247-253, 260-262, 271-274, 301-342, 368-393, 399, 404, 409-410, 415, 455-494, 504-507, 530-534, 560-565, 569-579, 603, 606, 609, 622, 627, 630-631, 634 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections.py 295 221 25% 58-95, 111, 117-118, 150-151, 171, 176, 180-182, 185, 197-207, 211-218, 222, 226, 230, 252-269, 279-283, 293, 299, 304, 315-320, 337, 340-341, 345-414, 417-419, 424-442, 453-475, 482-488, 495-508, 519-598 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections2.py 43 33 23% 26-45, 57-74, 83-91, 94-95, 102-105 /usr/local/lib/python3.8/dist-packages/h5py/_hl/vds.py 52 33 37% 56-90, 94, 97-99, 120-123, 126-127 /usr/local/lib/python3.8/dist-packages/h5py/h5py_warnings.py 19 6 68% 16-17, 34-36, 39 /usr/local/lib/python3.8/dist-packages/h5py/version.py 21 3 86% 32, 34, 36 /usr/local/lib/python3.8/dist-packages/imagecodecs/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/imagecodecs/imagecodecs.py 300 226 25% 330, 350-351, 356, 371, 375-383, 387, 390, 395, 401-402, 408-479, 484, 489-510, 515-603, 608-641, 646-666, 675, 684, 689, 698, 703-706, 711-722, 727-734, 745-788, 796-802, 814 /usr/local/lib/python3.8/dist-packages/imageio/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/fetching.py 105 88 16% 62-114, 150-184, 210-225, 230-232, 237-247 /usr/local/lib/python3.8/dist-packages/imageio/core/findlib.py 74 65 12% 23-29, 37-81, 99-160 /usr/local/lib/python3.8/dist-packages/imageio/core/format.py 276 178 36% 94, 103, 108, 111, 115, 118, 126, 142, 149, 155, 164-170, 179-185, 192, 199, 216-221, 228, 235, 238-239, 242-244, 247-248, 256-261, 267, 272-275, 289, 300, 331, 343-349, 359, 367-370, 386-392, 401-414, 419, 422-425, 437, 447, 458, 484-502, 517-521, 527, 531, 551, 557, 560-565, 569-609, 633, 635, 655, 657, 659-665, 678-697, 705-725, 735 /usr/local/lib/python3.8/dist-packages/imageio/core/functions.py 157 124 21% 100-121, 139-142, 172-186, 213-231, 259-267, 291-308, 354-374, 397-427, 453-455, 479-496, 542-561, 586-615 /usr/local/lib/python3.8/dist-packages/imageio/core/request.py 319 274 14% 19-20, 89-128, 133-262, 271, 279, 289, 295, 310-350, 359-372, 381-421, 427-428, 435-437, 440-467, 476-482, 492-496, 501-530, 533, 539-561, 564-565, 568, 571 /usr/local/lib/python3.8/dist-packages/imageio/core/util.py 263 210 20% 30-34, 38-42, 55-108, 122-134, 139-143, 149, 155-158, 164-169, 180-185, 205-211, 214-225, 228-230, 252-257, 265-273, 281, 290-309, 316, 324-328, 335-339, 346-350, 355, 358, 361, 364, 376-383, 387-395, 398-400, 404-408, 423-466, 479-491, 506-520, 531-542, 548-555 /usr/local/lib/python3.8/dist-packages/imageio/plugins/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/imageio/plugins/_freeimage.py 603 405 33% 63-67, 74-87, 422, 429-432, 439-447, 450-454, 461-480, 485-514, 518-521, 526-527, 530-531, 537, 544-549, 555-557, 564, 573-603, 609, 615, 620-625, 628, 631-640, 645-652, 660-723, 728-797, 807-839, 842-856, 876-895, 941-975, 980-1031, 1041-1083, 1089-1110, 1113-1163, 1170-1187, 1220-1245, 1271-1295, 1298-1299, 1305-1321, 1326-1328 /usr/local/lib/python3.8/dist-packages/imageio/plugins/bsdf.py 140 113 19% 14-52, 62-63, 66-73, 76, 126-130, 133-135, 143-180, 185, 188-194, 198-231, 240-249, 256-257, 261-289 /usr/local/lib/python3.8/dist-packages/imageio/plugins/dicom.py 147 122 17% 31-33, 41-56, 89-100, 105, 111-155, 159-161, 165-168, 171-199, 202-231, 239-266 /usr/local/lib/python3.8/dist-packages/imageio/plugins/example.py 43 23 47% 54-56, 67-69, 82-84, 89, 93, 97-106, 111, 124, 129, 133, 138 /usr/local/lib/python3.8/dist-packages/imageio/plugins/feisem.py 41 32 22% 27, 38-41, 55-84 /usr/local/lib/python3.8/dist-packages/imageio/plugins/ffmpeg.py 304 258 15% 27, 30-31, 57-66, 178-189, 192-194, 204-239, 257-327, 339-344, 357-358, 361, 370-386, 389, 394-468, 472-474, 478-502, 523-527, 530-532, 537-572, 575, 582-617, 629-636, 639-641, 646-649, 653-661, 667-698 /usr/local/lib/python3.8/dist-packages/imageio/plugins/fits.py 40 24 40% 15-23, 80, 84, 90-102, 105, 108, 112-116, 120 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimage.py 177 122 31% 48, 52-59, 63-70, 76, 79-80, 83, 86-88, 91-93, 99-102, 106-109, 113-132, 135, 168-174, 177-178, 215-219, 225-240, 243-259, 303-309, 312-314, 323-340, 349-362, 365-368, 393-397, 485-510 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimagemulti.py 144 104 28% 27-32, 35, 38, 41-45, 48-55, 62-67, 71, 75-86, 90-94, 97, 137-140, 197-200, 203-205, 225-254, 263-299, 308-322 /usr/local/lib/python3.8/dist-packages/imageio/plugins/gdal.py 35 19 46% 15-23, 40-43, 46, 52-54, 57, 60, 63-65, 68 /usr/local/lib/python3.8/dist-packages/imageio/plugins/grab.py 63 37 41% 25, 28-40, 44, 47, 50, 66-70, 73-79, 95-99, 102-111 /usr/local/lib/python3.8/dist-packages/imageio/plugins/lytro.py 304 225 26% 63, 69, 74, 78, 83, 101-103, 109-136, 142-143, 148, 152, 157-171, 177-195, 212-214, 220-280, 285, 289, 295-302, 308-312, 319-325, 348-371, 375-383, 388-391, 410-412, 418-435, 441-442, 447, 451, 456-470, 476-494, 511-513, 519-559, 564, 568, 574-582, 588-592, 599-607, 630-653, 657-665, 670-673 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/usr/local/lib/python3.8/dist-packages/imageio/plugins/spe.py 126 96 24% 255, 260, 264-295, 298-303, 307, 310-370, 373-399, 402-405, 408-411, 414-438, 456-465 /usr/local/lib/python3.8/dist-packages/imageio/plugins/swf.py 179 147 18% 25-27, 68-71, 74-76, 82-138, 141, 144, 147-150, 154-179, 188-215, 218, 224-239, 242-267, 270-288, 292-298, 302-320, 323 /usr/local/lib/python3.8/dist-packages/imageio/plugins/tifffile.py 91 65 29% 19-23, 208, 212, 218-229, 232-234, 237-240, 243-256, 259-276, 281-297, 300, 303-311, 314-322 /usr/local/lib/python3.8/dist-packages/ipykernel/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipykernel/_version.py 11 5 55% 7-11, 13 /usr/local/lib/python3.8/dist-packages/ipykernel/comm/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipykernel/comm/comm.py 86 52 40% 21-22, 28, 40, 51-59, 63-66, 76, 82-99, 103-118, 122, 135, 144, 150-152, 156-163 /usr/local/lib/python3.8/dist-packages/ipykernel/comm/manager.py 72 52 28% 37-40, 44, 48-51, 56, 66-72, 77-99, 104-113, 117-129 /usr/local/lib/python3.8/dist-packages/ipykernel/connect.py 58 41 29% 31-37, 58-81, 91-103, 128-137, 163-178 /usr/local/lib/python3.8/dist-packages/ipykernel/jsonutil.py 81 59 27% 73-106, 133-197 /usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py 441 316 28% 20-22, 61-63, 80, 163-171, 176-209, 216-223, 228-282, 287, 291, 295-331, 343-349, 358-365, 377-383, 393, 397-408, 412-441, 450, 459, 466, 482-483, 495, 505, 514, 520-569, 575, 579-585, 591, 599-611, 616, 620-627, 633, 636-643, 647, 657-661, 664-679, 683-693, 699, 703-710, 715, 723-742, 747, 755-767, 771-773, 778, 786-788, 794-798, 805-807, 811-817, 825, 835-843, 856-860, 868-904, 909-912 /usr/local/lib/python3.8/dist-packages/ipython_genutils/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/ipython_genutils/_version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipython_genutils/encoding.py 23 7 70% 30, 53-58, 64-68 /usr/local/lib/python3.8/dist-packages/ipython_genutils/importstring.py 12 10 17% 27-39 /usr/local/lib/python3.8/dist-packages/ipython_genutils/path.py 70 55 21% 55-71, 90-95, 100-101, 110-117, 130-154, 165-172 /usr/local/lib/python3.8/dist-packages/ipython_genutils/py3compat.py 196 141 28% 16-17, 20-21, 25-27, 30-32, 36-40, 45-57, 64-79, 96-143, 158-167, 175, 183-185, 195-198, 203-204, 212, 220, 224-293, 298-307 /usr/local/lib/python3.8/dist-packages/ipython_genutils/text.py 68 51 25% 19, 49-60, 74-87, 101-113, 125, 134-135, 140-146, 155-158, 215-217, 238-243 /usr/local/lib/python3.8/dist-packages/ipywidgets/__init__.py 21 7 67% 31-33, 38-40, 50 /usr/local/lib/python3.8/dist-packages/ipywidgets/_version.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/docutils.py 6 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/domwidget.py 27 14 48% 26-28, 36-38, 41-50 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/interaction.py 289 226 22% 11-12, 17-18, 33-34, 51-62, 73-85, 90-93, 99-128, 132-152, 177-232, 245-268, 272, 278-290, 295-307, 312-343, 348-359, 364-380, 388-393, 444, 507-538, 555-556, 570, 576 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/trait_types.py 78 33 58% 45-52, 84-87, 100-103, 125-128, 137-140, 162-165, 168, 195-206, 220 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/util.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/valuewidget.py 12 5 58% 20, 24-27 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget.py 403 283 30% 13-15, 32-39, 42-49, 59, 65-71, 80-116, 129-131, 141-162, 173-174, 183-195, 208-211, 216-223, 243-256, 259-265, 281-283, 302-303, 312, 317-318, 323-339, 349-354, 357-369, 372, 398, 411-415, 419, 427-438, 443-448, 455, 467-471, 481-489, 506-523, 526, 529-533, 540-545, 557, 571, 586, 590-594, 600-606, 609, 624-628, 633-642, 646-662, 668-689, 693, 697, 702, 707, 712-732, 736-737, 740-755, 758-763 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_bool.py 31 3 90% 22-24 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_box.py 37 5 86% 63-65, 68-69 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_button.py 41 12 71% 61-63, 68-73, 86, 94, 104-105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_color.py 14 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_controller.py 29 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_core.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_date.py 13 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_description.py 23 6 74% 28-34 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_float.py 168 62 63% 24-26, 36-39, 44-49, 54-59, 70-73, 78-83, 88-93, 262, 266, 270, 274, 278-281, 290-294, 298-308, 312-315 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_int.py 185 67 64% 42-44, 53-61, 73-75, 85-93, 98-101, 106-111, 116-121, 202, 206, 210, 214, 218-221, 243-247, 251-261, 265-268 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_layout.py 61 7 89% 82-85, 93-96 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_link.py 36 15 58% 24-34, 50-52, 56, 75, 105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_media.py 102 46 55% 44-51, 71-75, 86-88, 92-96, 101-111, 116-133, 159, 163, 166, 194, 197, 223, 226 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_output.py 64 33 48% 76-77, 97-105, 109-113, 117-127, 131-132, 136, 142, 146, 157-159 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selection.py 295 148 50% 11-12, 119-132, 136-139, 175-191, 196-200, 205-218, 222-225, 230-235, 239-243, 247-254, 258-260, 264-271, 274-280, 316-327, 331-335, 340-344, 349-352, 357-363, 368-371, 375-377, 381-383, 387-389, 392-395, 529-531, 535-540, 544-547, 553-555, 559-565, 615-619, 624-629 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selectioncontainer.py 36 14 61% 27-30, 44-46, 57-61, 65-68 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_string.py 68 15 78% 30-32, 79-81, 91-92, 106-108, 120-123 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_style.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_templates.py 198 138 30% 80-85, 89-91, 95-96, 100-106, 157-158, 162-167, 172, 176-242, 247, 281-289, 293-295, 300-312, 315-330, 333-345, 349-355, 397-398, 403-450, 454 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_upload.py 39 11 72% 24-26, 58-64, 68 /usr/local/lib/python3.8/dist-packages/jedi/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jedi/_compatibility.py 339 252 26% 18-19, 34-45, 52-53, 56, 59, 63-88, 92-111, 115-150, 158-162, 169-205, 223-224, 230-233, 246-248, 266, 273-275, 285, 311-312, 319, 324-325, 329-330, 334-335, 339-340, 350-352, 360-370, 376, 387-396, 404-410, 414-426, 432-440, 445-451, 456-466, 483-530, 536-582, 591-624 /usr/local/lib/python3.8/dist-packages/jedi/api/__init__.py 341 241 29% 58-62, 127-203, 209-248, 251, 254, 275, 278-283, 286-291, 310-311, 314-319, 322-342, 345-350, 370-371, 375-408, 422, 425, 429-431, 454, 471-480, 483-488, 502-514, 517-522, 541-558, 569-600, 603-634, 648-649, 657, 661-671, 683, 686-687, 715, 719-727, 765, 769-777, 800-801, 830-845, 849-855, 863-869, 884-886, 898-901 /usr/local/lib/python3.8/dist-packages/jedi/api/classes.py 297 193 35% 37, 47-49, 53, 81-86, 93, 98-104, 115, 175-187, 202, 208-211, 216-219, 224-227, 260-270, 273, 276, 311-329, 355-368, 374-377, 384-387, 402-403, 406-411, 416-427, 447-448, 451-466, 472-489, 497-522, 525, 542-552, 555-561, 570, 582, 594, 604-613, 616-624, 647-649, 664, 670-675, 678-684, 687-693, 696-697, 709-716, 719, 728, 732-738, 747-748, 758-761, 764, 770, 773, 782-783, 793, 803, 812-814, 824, 836, 839, 853, 861, 870, 879-883 /usr/local/lib/python3.8/dist-packages/jedi/api/completion.py 348 296 15% 30, 35-40, 44-68, 72-73, 80-81, 85-89, 96, 102-114, 117-151, 174-282, 285-295, 298-317, 320-323, 326-337, 340-355, 358-360, 363-365, 371-389, 401-421, 424-431, 442-448, 455-499, 503-516, 539-577, 583-619 /usr/local/lib/python3.8/dist-packages/jedi/api/completion_cache.py 19 11 42% 5-9, 14-19 /usr/local/lib/python3.8/dist-packages/jedi/api/environment.py 219 166 24% 36-37, 41-45, 49, 65-67, 70-107, 110-111, 114, 129, 134-136, 145, 148, 157-169, 173-177, 190-198, 202-238, 242-252, 257-264, 284-321, 334-338, 351-363, 374-377, 385-393, 398-420, 424-425, 432-454, 458-473, 477-480 /usr/local/lib/python3.8/dist-packages/jedi/api/errors.py 19 7 63% 8, 16, 21, 26, 31, 36, 39 /usr/local/lib/python3.8/dist-packages/jedi/api/exceptions.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/api/file_name.py 115 103 10% 17-54, 64-81, 85-99, 103-156 /usr/local/lib/python3.8/dist-packages/jedi/api/helpers.py 319 272 15% 26, 30-35, 39-42, 47, 51-61, 66-71, 77, 83-117, 124-156, 163-179, 183-201, 206-209, 213, 217, 220-268, 272-336, 343-357, 361-371, 375-421, 427-439, 449-465, 474-493, 497-500 /usr/local/lib/python3.8/dist-packages/jedi/api/interpreter.py 23 12 48% 12, 19, 24-25, 28, 34-41 /usr/local/lib/python3.8/dist-packages/jedi/api/keywords.py 34 25 26% 8-15, 22, 30-57 /usr/local/lib/python3.8/dist-packages/jedi/api/project.py 214 164 23% 40-51, 56-61, 65, 78, 82, 92-98, 106-118, 139-151, 156-161, 169-205, 208-213, 236, 248, 251, 256-343, 346, 350-353, 358-362, 375-411, 415 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/__init__.py 133 110 17% 19-23, 26-36, 39, 42-48, 51, 56-58, 64-73, 89, 92-98, 104-108, 112-118, 122-138, 142-218, 225 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/extract.py 239 210 12% 20-29, 36-41, 49-93, 100-126, 130, 134-138, 147-149, 153, 160-164, 172-200, 204, 209-292, 296-306, 310-316, 320-337, 341-353, 357-363, 371-379, 383-386 /usr/local/lib/python3.8/dist-packages/jedi/api/strings.py 64 47 27% 27-50, 54-58, 68-77, 81-86, 90-93, 97-98, 102-109 /usr/local/lib/python3.8/dist-packages/jedi/cache.py 65 40 38% 32-43, 60-73, 84-93, 105-113 /usr/local/lib/python3.8/dist-packages/jedi/common/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jedi/common/utils.py 24 18 25% 6-13, 21-26, 31-36 /usr/local/lib/python3.8/dist-packages/jedi/common/value.py 54 31 43% 3-4, 7-11, 48, 55, 59-61, 68-74, 77, 80, 83-84, 87, 90, 93, 96, 99-104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/jedi/debug.py 80 50 38% 22, 36-56, 74-75, 81-82, 89-97, 103-109, 113-120, 124-127, 136-140 /usr/local/lib/python3.8/dist-packages/jedi/file_io.py 54 30 44% 8, 11, 14, 17, 20, 23, 28, 31, 34, 37, 40-57, 62, 68-69, 72-75 /usr/local/lib/python3.8/dist-packages/jedi/inference/__init__.py 107 76 29% 86-108, 111, 117-121, 126-130, 135-136, 139-140, 144, 147-179, 183-194, 197 /usr/local/lib/python3.8/dist-packages/jedi/inference/analysis.py 126 101 20% 32-37, 41, 45, 50-51, 54, 58, 61, 65, 68, 71, 81-91, 98-109, 116-127, 138-217 /usr/local/lib/python3.8/dist-packages/jedi/inference/arguments.py 228 156 32% 19-31, 53-68, 76-108, 135, 138, 148-172, 180-183, 188, 191-231, 234-239, 242-247, 250, 253-279, 284, 287-288, 291, 296, 300, 304, 308, 311, 314, 317, 321-333, 337-349 /usr/local/lib/python3.8/dist-packages/jedi/inference/base_value.py 285 179 37% 27-34, 39, 42, 45-47, 50, 53, 56, 62-70, 73-77, 84-98, 101-104, 107-120, 123-126, 130-132, 136, 152, 155-163, 166, 169-176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 210, 213-218, 221-223, 226-227, 230-231, 234-235, 241, 244-245, 249, 253, 256, 260, 263, 266, 274, 283-289, 294, 297-298, 305-306, 309, 312, 317, 320, 325-326, 329, 334-335, 338, 341, 344, 349-371, 376, 379-382, 387, 390, 393, 396, 399, 402-410, 413, 416, 419-436, 440-448, 456 /usr/local/lib/python3.8/dist-packages/jedi/inference/cache.py 72 44 39% 25-45, 80, 90-121 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/__init__.py 38 26 32% 8-16, 25-26, 29-32, 35-37, 40, 48-53, 57, 63-68 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/access.py 327 243 26% 82-98, 109-112, 117, 121-140, 145, 151, 154, 158-159, 167-175, 180-181, 184, 187, 190, 193, 196-199, 202, 205-219, 222, 225-227, 230-234, 237-250, 253, 256, 259-265, 270-285, 288, 291, 294, 297, 300, 303-313, 316, 319-323, 327-351, 354-396, 399-401, 404, 407-409, 412-421, 424-425, 428-456, 459-461, 467-482, 485, 488-490, 493, 507-516, 519-538, 541, 548-552, 557-564 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/getattr_static.py 97 81 16% 16-21, 25-31, 35-39, 43-54, 58-65, 73-127, 131, 135, 152-184 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/mixed.py 155 108 30% 47-49, 52, 58, 63-66, 69-72, 75-78, 81-83, 86, 96, 108-109, 113-117, 121-129, 134-135, 138, 146, 156-175, 179-249, 256-291 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433-434, 437, 442-444, 447-448, 460-478, 482-485, 488-507, 510, 517, 537-587, 591-600, 606, 611-618, 624-629 /usr/local/lib/python3.8/dist-packages/jedi/inference/context.py 294 199 32% 20-21, 25, 28-34, 41-86, 89-106, 109-112, 115, 118, 121, 124, 127, 130, 133, 137, 140, 144, 147, 150, 154-159, 167-168, 172, 176, 179, 182, 185, 188, 191, 194, 197, 200, 204, 207, 210, 213, 216, 221-222, 225-248, 251-287, 290-297, 302, 312, 315-327, 330, 334, 338, 346, 351, 354, 358, 361, 366, 369, 378-380, 383, 386, 389, 392, 397, 404, 408, 411, 418-432, 483-500 /usr/local/lib/python3.8/dist-packages/jedi/inference/docstrings.py 144 116 19% 52-56, 61-75, 82-100, 108-133, 154-161, 179-183, 187-235, 244-245, 256-268, 273-290, 296-307 /usr/local/lib/python3.8/dist-packages/jedi/inference/filters.py 215 115 47% 26-28, 32, 36, 43, 46, 49, 52, 56-68, 75-78, 81, 86, 89, 98, 110-115, 118-120, 123-127, 130-141, 146-151, 154, 158-166, 171-172, 175, 180, 185-189, 193-195, 198, 206, 209-214, 217-223, 226, 229-230, 235, 238, 241, 244, 252-254, 258, 270-278, 281-291, 296-299, 307, 331-334, 340 /usr/local/lib/python3.8/dist-packages/jedi/inference/flow_analysis.py 84 65 23% 15-20, 23-26, 29, 39-42, 46-83, 87-110, 114-123 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/annotation.py 228 193 15% 34-45, 49-59, 63-76, 88-107, 112-132, 139-182, 186-195, 204-230, 247-272, 276-282, 295-304, 308-313, 351-370, 374, 378-381, 385, 389-408, 414-425, 429-433, 437-443 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/base.py 217 149 31% 18-20, 23-32, 35, 38, 53-54, 57-63, 68, 73-81, 86, 89, 93, 96-117, 122-137, 147, 162-163, 166, 169-178, 181, 184-185, 188, 192-193, 196, 199-201, 205-242, 247-248, 252-261, 264-279, 284-293, 296, 310-314, 318, 322-332, 338, 342, 345, 348, 353-355, 359, 362, 365, 370-373, 376, 379 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/conversion.py 138 122 12% 11-47, 51-59, 64-92, 96-98, 109-142, 146-153, 158-167, 175-208 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/generics.py 67 38 43% 15-23, 28-32, 35, 40-41, 45, 48, 53-63, 70-71, 74-78, 81, 86, 89, 92, 95, 98, 101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/stub_value.py 69 43 38% 13-14, 17, 25-34, 37, 43-50, 53, 60, 65-70, 73, 78-81, 88-101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/type_var.py 84 65 23% 9-18, 27-48, 53-71, 74, 77, 80-85, 89, 93, 98-105, 108, 111-114, 117 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typeshed.py 158 130 18% 23-26, 33-53, 57-69, 81-89, 95-124, 130-143, 154-229, 233-249, 258-264, 271, 281-295 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typing.py 236 154 35% 40, 43-96, 105-130, 137, 140, 151, 159, 174, 179, 183, 189-228, 237-240, 244, 247, 250, 253-266, 269, 278-286, 293, 296-303, 306-310, 313-316, 321-323, 327-355, 368-369, 376, 381-386, 397-399, 402, 406-407, 413, 426-429, 433, 436-442, 445-449, 455-457 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/utils.py 17 14 18% 11-30 /usr/local/lib/python3.8/dist-packages/jedi/inference/helpers.py 122 97 20% 17-21, 29-43, 65-109, 113-121, 125-129, 133, 137-139, 143, 147, 151, 160-163, 167-192, 196, 200-207 /usr/local/lib/python3.8/dist-packages/jedi/inference/imports.py 284 237 17% 39, 42-43, 46, 53-70, 75-96, 100-118, 122-125, 134-152, 169-224, 229, 235-238, 247-260, 269-273, 284-326, 331-356, 365-427, 432-440, 450-464, 473-510, 514-519, 523-548, 558-563 /usr/local/lib/python3.8/dist-packages/jedi/inference/lazy_value.py 37 18 51% 7-9, 12, 15, 21, 27, 32, 35, 40-44, 47-48, 52-55, 61 /usr/local/lib/python3.8/dist-packages/jedi/inference/names.py 450 314 30% 16-23, 38, 44, 47-54, 58, 61, 64, 67-69, 73, 76, 80, 87, 99-101, 104, 109-110, 113-126, 129-132, 135-139, 142-211, 214-215, 219, 223, 228, 231-237, 240, 243-245, 248-251, 255, 260-261, 264, 278-279, 287-290, 307-331, 334-346, 351-354, 357-360, 363-368, 371, 378, 381, 392, 396-401, 409-416, 419-424, 427, 432-434, 437, 441, 444-450, 453-456, 460, 463-488, 491-496, 502, 506-525, 530-531, 534-538, 541-543, 548, 551, 554, 562-563, 566-574, 578-584, 588-590, 593, 597, 600, 609, 612, 615, 620-634, 640-644, 651-652, 656 /usr/local/lib/python3.8/dist-packages/jedi/inference/param.py 130 113 13% 14-18, 23-26, 29, 32-47, 50, 73-225, 246, 250-257 /usr/local/lib/python3.8/dist-packages/jedi/inference/parser_cache.py 4 1 75% 6 /usr/local/lib/python3.8/dist-packages/jedi/inference/recursion.py 67 47 30% 55, 64-75, 81-90, 100-105, 108-109, 112-153 /usr/local/lib/python3.8/dist-packages/jedi/inference/references.py 182 157 14% 29-42, 46, 53-69, 73-77, 81-96, 100-113, 117-160, 164-175, 179-193, 197-218, 222-224, 228-245, 257-270, 274-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/signature.py 108 72 33% 9-34, 39-40, 44, 48, 51-54, 57, 60, 63-65, 70-71, 74, 80-82, 86-89, 93-97, 100-117, 122-124, 128, 132-134, 137, 146, 149 /usr/local/lib/python3.8/dist-packages/jedi/inference/syntax_tree.py 545 485 11% 45-63, 69-137, 144-150, 155, 161-237, 241-261, 270-352, 357-366, 385-444, 448-469, 477-487, 493-502, 506-518, 526-538, 542, 546, 550, 554-567, 571-642, 647-730, 741-782, 789-809, 814, 821-846 /usr/local/lib/python3.8/dist-packages/jedi/inference/sys_path.py 148 125 16% 18-29, 43-72, 79-97, 105-135, 139-147, 151-170, 174-177, 189-207, 211-215, 230-271 /usr/local/lib/python3.8/dist-packages/jedi/inference/utils.py 55 23 58% 13, 20, 26, 32, 74-78, 84-86, 89, 92, 96, 99-103, 112-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/value/decorator.py 7 3 57% 11-12, 15 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/dynamic_arrays.py 114 87 24% 36-40, 52-123, 128-130, 144-145, 148-149, 152-165, 168, 173-175, 178, 181-187, 192-194, 197, 202-204 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/function.py 305 223 27% 32-33, 37, 40, 45-54, 61-64, 67-71, 74, 79-81, 84, 87, 90-114, 117-118, 121-123, 126, 132-161, 164-165, 168, 171, 176-177, 180, 185-186, 189, 194-197, 201, 206, 211-248, 251-263, 268-312, 315, 321, 327-360, 365-366, 369, 377-378, 381, 391, 394, 401, 406-407, 410-421, 424, 427, 431-470 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/instance.py 353 227 36% 28-30, 33, 36, 41-42, 45-58, 63-64, 67, 74-81, 86-87, 94-97, 100, 103, 106, 109, 113, 117, 120-121, 125, 128, 134, 137, 144-146, 149-155, 159, 162, 168-172, 176, 179-199, 203-215, 223-239, 242-250, 253-275, 278-283, 291-297, 303-307, 314-322, 328-349, 352, 355-370, 373-393, 396, 406-414, 418-422, 427-428, 431, 434, 437-438, 446-448, 451, 455, 461-462, 465-466, 469-473, 476-480, 483, 489, 492, 497, 500, 508-510, 514, 517, 522-523, 527-529, 532, 535, 545-546, 549, 552, 555, 561, 569-575, 578-580, 583-592, 595-597, 605, 608, 613-614, 617-619 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/iterable.py 404 270 33% 27, 37-40, 49-52, 55, 58, 62, 68, 71, 75, 81-82, 85, 88, 91, 95-122, 133, 136-163, 168-170, 173-174, 177, 182, 190, 193, 196-203, 206, 210, 213-215, 220-224, 231-237, 251, 255, 262-267, 270-271, 274-280, 283, 286, 290-291, 295-306, 311, 321-329, 332-334, 338-343, 350-358, 362, 365-407, 414-417, 420, 427-429, 433-439, 447-453, 457-458, 462-470, 473, 479, 490-491, 494-499, 502, 505, 508, 523-524, 527-528, 531-548, 552, 558, 561, 564, 567, 572-574, 577-579, 582, 589-620, 625-630, 633-635, 642-658 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/klass.py 204 151 26% 59-61, 66-75, 81-87, 90, 100-105, 112-126, 130-131, 136, 139-144, 147, 151, 154, 158-187, 190-221, 227-229, 232, 235-237, 243-267, 275-289, 292-296, 300-309, 314-317, 329-330, 336-356, 360-361, 365-379 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/module.py 129 85 34% 22-24, 27-35, 45-56, 63-73, 76-77, 80, 83, 88, 92-98, 101-104, 111-128, 136, 144-156, 159-164, 167-169, 175-178, 181, 184-186, 194-219, 222, 225 /usr/local/lib/python3.8/dist-packages/jedi/parser_utils.py 194 159 18% 26-56, 60-69, 79, 83-94, 99-109, 113-124, 128-142, 158-175, 182-188, 196-219, 223-228, 235-244, 252-269, 280, 287-292, 299-311, 322-326 /usr/local/lib/python3.8/dist-packages/jedi/plugins/__init__.py 31 1 97% 21 /usr/local/lib/python3.8/dist-packages/jedi/plugins/flask.py 11 8 27% 7-20 /usr/local/lib/python3.8/dist-packages/jedi/plugins/pytest.py 99 71 28% 21-25, 31-41, 44-61, 67-77, 83-90, 95-98, 108-112, 117, 123-142, 147-153, 156-164 /usr/local/lib/python3.8/dist-packages/jedi/plugins/registry.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/plugins/stdlib.py 440 292 34% 106-132, 138-143, 157-173, 181-188, 194, 200-208, 213-217, 223-224, 227, 230-235, 238-241, 246-253, 258-259, 263, 268, 278-289, 294-328, 336, 341, 346-347, 350, 358-360, 363, 366, 371-372, 375-377, 382, 391-392, 395-397, 403, 408, 423-470, 475-478, 481-485, 488-502, 505-509, 514, 519, 524-526, 529-530, 535-537, 540-549, 553, 560, 568, 573, 581-586, 591-612, 617-618, 621, 626-628, 631, 634-637, 642-643, 647-661, 666, 675, 680-681, 685, 688, 693, 702-711, 717-734, 801-808, 814-817, 821, 824-825, 828-833, 838-842 /usr/local/lib/python3.8/dist-packages/jedi/settings.py 19 2 89% 72, 75 /usr/local/lib/python3.8/dist-packages/joblib/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/joblib/_compat.py 15 3 80% 11, 24-25 /usr/local/lib/python3.8/dist-packages/joblib/_memmapping_reducer.py 180 132 27% 28, 38-39, 70, 73-78, 81-95, 98, 107-119, 152-178, 183, 189-202, 213-236, 243-252, 281-286, 293-298, 301-361, 374-434 /usr/local/lib/python3.8/dist-packages/joblib/_memory_helpers.py 65 63 3% 5-105 /usr/local/lib/python3.8/dist-packages/joblib/_multiprocessing_helpers.py 34 11 68% 20-21, 34-37, 51-53, 61-64 /usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py 271 174 36% 38-39, 78-79, 92, 99, 123, 132-136, 153, 163-184, 188, 203-205, 209-212, 216-220, 230-238, 242-245, 249, 253, 258-260, 282-284, 288-344, 348-360, 367-368, 392-399, 407-409, 432-462, 467-489, 493-497, 509-519, 523-547, 551-555, 561-564, 567-574, 579-583, 590, 593, 604, 607-624, 631 /usr/local/lib/python3.8/dist-packages/joblib/_store_backends.py 196 137 30% 26-31, 152-174, 179-193, 198-200, 205-208, 212, 217-223, 227-238, 242-243, 247-249, 253-260, 264-270, 274, 278, 282-294, 298-322, 326-328, 332, 345-348, 352, 356-388, 395-415 /usr/local/lib/python3.8/dist-packages/joblib/backports.py 48 37 23% 22-30, 37-76, 80-81 /usr/local/lib/python3.8/dist-packages/joblib/compressor.py 315 209 34% 12-13, 17-18, 22-23, 27-28, 61, 65, 73, 78, 107-110, 115, 127, 130-131, 136-140, 145-152, 164, 168-172, 178-186, 202, 206-209, 220, 225-235, 239-243, 248-249, 289-321, 330-348, 353, 357-358, 362, 366-367, 371-372, 377-383, 386-388, 391-393, 396-401, 406-424, 430-440, 446-470, 478-485, 492-493, 502-511, 515-520, 537-562, 566-568 /usr/local/lib/python3.8/dist-packages/joblib/disk.py 59 42 29% 27-38, 44-52, 59-63, 90-101, 106-124 /usr/local/lib/python3.8/dist-packages/joblib/executor.py 32 21 34% 28-50, 58-59, 63-65, 68-69, 72-73 /usr/local/lib/python3.8/dist-packages/joblib/externals/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle.py 623 480 23% 65-66, 85, 88-95, 109-115, 119-124, 128-139, 151-164, 169-202, 209-227, 257-274, 334-339, 343-384, 388, 406, 410-432, 440-443, 448-465, 473-477, 480-488, 491, 496-499, 505-509, 517-541, 551-556, 579-582, 591-608, 617-692, 706-760, 767-812, 820-847, 850-851, 854, 865-879, 886-892, 900-941, 944, 948, 953-954, 961-967, 974-989, 996-1033, 1036, 1039, 1050, 1055, 1060, 1066, 1073, 1083-1089, 1093-1094, 1109, 1122-1128, 1138-1139, 1143-1145, 1149, 1153, 1157-1161, 1186, 1194-1251, 1260, 1271-1281, 1296-1297, 1305-1315, 1335-1348, 1357-1397 /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle_fast.py 227 169 26% 47, 60-63, 70-78, 83-84, 91, 103-132, 136-158, 162-174, 190-198, 203-208, 212-213, 218-260, 264, 268, 272, 276-279, 283, 287, 291, 295, 306-312, 320-330, 346-370, 374-385, 419-431, 463-475, 481-483, 498-501, 504-534, 537-547 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/_base.py 287 271 6% 34-615, 623-627 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/_posix_reduction.py 41 21 49% 20, 29-31, 36-43, 51-52, 55-58, 67-68, 71-74 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat.py 18 8 56% 14, 19, 23, 29-38 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat_posix.py 4 1 75% 11 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/context.py 135 95 30% 37-85, 91-97, 101, 118-153, 164-165, 170-171, 175-206, 214-215, 219-220, 224-225, 229-230, 234-235, 239-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/process.py 57 42 26% 20-31, 35-39, 42-64, 67-81, 89, 100-108 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/queues.py 131 102 22% 36-62, 66-67, 72-75, 79-111, 121-175, 182-183, 187-189, 195-210, 214-215, 219, 225-229, 234-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/reduction.py 126 59 53% 27-30, 63-66, 79-82, 87, 91, 101, 109, 113, 121, 127-129, 146, 149, 154-165, 176-177, 185-197, 202-210, 218, 223, 232-234, 240, 246-250, 256 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/utils.py 94 75 20% 11-12, 20-21, 25-28, 32-46, 52-60, 67-116, 125-138, 143-145, 149-172 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/cloudpickle_wrapper.py 60 44 27% 7-8, 16-17, 20-24, 29-31, 38, 42-44, 48-49, 55-83, 95-113 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py 507 411 19% 88-89, 94, 132-138, 143, 146-147, 150-155, 158-159, 171-174, 177-179, 182-184, 189-197, 214, 217, 223-229, 232, 236-237, 245-248, 254-256, 262-268, 271-272, 275, 283-286, 289-312, 317-325, 337, 342-347, 372-465, 487-505, 542-757, 766-785, 794-797, 803-810, 882-933, 939-951, 955-994, 998-1014, 1019-1022, 1025-1048, 1073-1081, 1084-1117 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/reusable_executor.py 91 70 23% 34-37, 84-142, 150-155, 158-159, 163-194, 199-207, 212-213 /usr/local/lib/python3.8/dist-packages/joblib/format_stack.py 209 188 10% 34-35, 45-68, 72, 88-94, 104-116, 120-147, 151-176, 181-322, 337-365, 371-401 /usr/local/lib/python3.8/dist-packages/joblib/func_inspect.py 176 154 12% 47-79, 84-93, 110-162, 173-177, 190-193, 198-203, 228-318, 322-325, 330-349, 356-359 /usr/local/lib/python3.8/dist-packages/joblib/hashing.py 117 85 27% 24, 33-42, 49, 58-64, 67-75, 78-94, 101-103, 111-127, 141-150, 155, 174-182, 189-242, 258-267 /usr/local/lib/python3.8/dist-packages/joblib/logger.py 76 55 28% 28-31, 35-36, 40-44, 48-57, 77, 81, 85, 96-124, 136-156 /usr/local/lib/python3.8/dist-packages/joblib/memory.py 374 270 28% 57-63, 92-100, 106, 109, 112, 114-133, 135, 146-148, 153-160, 166-182, 225-244, 248-253, 257-277, 281, 284, 293-295, 306-307, 310-313, 316-317, 320-325, 329, 332-333, 358, 361, 365, 415-453, 483-545, 562-563, 568, 574-576, 583, 588-590, 594-595, 605-625, 636-713, 717-724, 730-745, 764-795, 804, 884, 887-901, 905, 914-921, 952, 956-962, 971-974, 978-979, 990-992, 999, 1008-1010 /usr/local/lib/python3.8/dist-packages/joblib/my_exceptions.py 53 20 62% 24, 27-33, 46-48, 51-59, 75, 80, 84, 89, 94-99, 112-113 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle.py 204 161 21% 13-14, 78-82, 91-104, 112-161, 165-178, 195-209, 234-249, 253-260, 272-295, 320-332, 342-355, 361, 415-515, 526-548, 588-607 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_compat.py 105 75 29% 21-25, 38-61, 71-75, 90-92, 96-120, 140-142, 148-154, 164-173, 176, 185-192, 198, 227-247 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_utils.py 92 65 29% 22-23, 27-28, 36-37, 45-49, 54-56, 73-90, 95-100, 105-112, 144-182, 187-197, 229-245 /usr/local/lib/python3.8/dist-packages/joblib/parallel.py 362 293 19% 40-41, 65-73, 83-124, 181-209, 212, 215, 218-222, 234, 241-249, 254-255, 259, 267-270, 282-291, 297-311, 327-329, 332-340, 360-363, 388-389, 620-696, 699-701, 704-705, 709-725, 728-730, 733-734, 744-759, 769-771, 783-836, 842-849, 855-887, 895-940, 943-1032, 1035 /usr/local/lib/python3.8/dist-packages/joblib/pool.py 116 83 28% 42-43, 75-87, 91-99, 120-127, 130-131, 135-137, 140, 143-177, 199-207, 210-216, 296-313, 316-329 /usr/local/lib/python3.8/dist-packages/jupyter_client/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/adapter.py 256 210 18% 17-25, 38-52, 64, 67, 70-74, 81, 84-96, 103-109, 125-127, 132-146, 149-151, 154-157, 160-170, 173-179, 182-190, 194-195, 200-202, 205-214, 219-220, 231-234, 239-258, 261-266, 269-282, 285-290, 297-307, 310-317, 321-339, 344-346, 349-358, 363-364, 386-398 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/channels.py 45 28 38% 29-32, 35-37, 41-48, 52-58, 62, 65-70, 74, 79, 82 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/client.py 188 138 27% 23-29, 34, 69, 77, 81, 85, 89, 94-101, 112-150, 162-177, 194-212, 216-224, 231-235, 239-253, 311-388 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/channels.py 50 32 36% 11-12, 35-38, 41-43, 47-57, 61-67, 71, 74-79, 83, 88, 91 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/client.py 161 121 25% 25-31, 36, 77-115, 127-142, 159-179, 183-191, 198-202, 262-339 /usr/local/lib/python3.8/dist-packages/jupyter_client/channels.py 119 82 31% 61-80, 87-88, 91-99, 109-132, 136-170, 174, 178, 182-185, 189-192, 195-200, 210 /usr/local/lib/python3.8/dist-packages/jupyter_client/channelsabc.py 25 7 72% 16, 20, 24, 37, 41, 45, 49 /usr/local/lib/python3.8/dist-packages/jupyter_client/client.py 172 120 30% 26-30, 53, 78, 82, 86, 90, 105-118, 125-134, 139, 148-155, 160-167, 172-179, 184-190, 195-202, 206-218, 258-277, 295-300, 322-329, 363-370, 379-381, 390-396, 404-406, 410-412, 420-422, 441-443 /usr/local/lib/python3.8/dist-packages/jupyter_client/clientabc.py 45 14 69% 32, 36, 40, 44, 48, 52, 60, 64, 68, 72, 76, 80, 84, 88 /usr/local/lib/python3.8/dist-packages/jupyter_client/connect.py 247 186 25% 79-167, 191-224, 253-274, 296, 319-325, 329-330, 350, 355-356, 377-396, 402-406, 413-419, 423-430, 438-446, 455-461, 465-481, 492-497, 511-523, 531-538, 542-551, 555-557, 561, 565, 569, 573 /usr/local/lib/python3.8/dist-packages/jupyter_client/jsonutil.py 50 34 32% 38-44, 53-59, 63-72, 76-84, 88-92 /usr/local/lib/python3.8/dist-packages/jupyter_client/kernelspec.py 196 139 29% 44-47, 50-58, 65, 73, 82, 90-103, 108, 111, 130, 134, 146-159, 163-183, 190-200, 204-222, 229-237, 252-270, 277-289, 292-297, 315-347, 351-354, 359, 366, 370, 376 /usr/local/lib/python3.8/dist-packages/jupyter_client/launcher.py 59 51 14% 56-158 /usr/local/lib/python3.8/dist-packages/jupyter_client/localinterfaces.py 170 129 24% 29-30, 34-42, 48-52, 58-59, 68-89, 96-108, 113-121, 127-135, 140-164, 173-196, 201-203, 216-248, 254, 259, 264, 269, 274 /usr/local/lib/python3.8/dist-packages/jupyter_client/manager.py 349 261 25% 42, 48, 52, 61, 64, 75-77, 83-85, 102, 109, 113, 126-127, 134, 137, 141-143, 147-149, 157-166, 174-205, 212, 217-219, 222-225, 239-267, 274-283, 286-287, 301-306, 311-315, 323-338, 342-347, 368-379, 406-418, 423, 430-456, 464-478, 488-498, 502-509, 523-524, 538-543, 551-562, 583-594, 621-634, 641-675, 683-697, 707-717, 721-728, 735-736, 744-755, 760-771, 784-789 /usr/local/lib/python3.8/dist-packages/jupyter_client/managerabc.py 27 8 70% 21, 29, 33, 37, 41, 45, 49, 53 /usr/local/lib/python3.8/dist-packages/jupyter_client/multikernelmanager.py 185 104 44% 31-39, 60-63, 67, 73, 76-90, 94-105, 109, 119, 123, 126, 130-147, 157-160, 174-188, 198, 212, 216-222, 233, 247, 258, 275-276, 286-287, 402, 422-428, 442-456, 461-463, 473-475, 488-490, 500-502, 506-512 /usr/local/lib/python3.8/dist-packages/jupyter_client/session.py 442 311 30% 27, 65-75, 148-153, 157, 172, 177, 184, 187-191, 201-205, 209, 212, 215, 218, 221, 226-228, 232-247, 305-315, 323-333, 338-340, 344, 364, 368, 376-384, 388, 393-396, 411-412, 423-425, 432-434, 488-496, 508-514, 519-521, 525-568, 571, 580-590, 600-605, 630-664, 711-766, 784-794, 810-828, 853-865, 869-876, 883-889, 916-954, 957-961, 965-978 /usr/local/lib/python3.8/dist-packages/jupyter_core/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_core/paths.py 194 155 20% 33-38, 47-51, 59-68, 78-98, 109-114, 118-122, 147-166, 170-174, 186-202, 209-213, 232-247, 266-287, 291, 316-344, 361-377, 394, 415-448, 452-456 /usr/local/lib/python3.8/dist-packages/jupyter_core/version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/__init__.py 18 5 72% 22, 37-40 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/affine_transformations.py 107 77 28% 16-17, 22-24, 28-31, 55-59, 84-90, 114-118, 145-156, 171-180, 194-195, 212-219, 236-242, 246-251, 281, 285-289, 292-298, 301-308, 311-317, 320-335 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py 120 97 19% 93-99, 124-173, 180-223, 227-233, 237-261, 273-284, 288, 292-295, 299 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171-176 /usr/local/lib/python3.8/dist-packages/pickleshare.py 194 152 22% 43-45, 50-51, 62, 65, 73-86, 91-107, 111-123, 127-133, 139-154, 158-177, 186-198, 204-211, 215, 220-224, 227, 230, 240-243, 259-275, 279, 282, 297, 300, 302, 304-306, 311-347, 350 /usr/local/lib/python3.8/dist-packages/pooch/__init__.py 20 14 30% 36-50 /usr/local/lib/python3.8/dist-packages/pooch/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/pooch/core.py 120 84 30% 197-230, 410, 413, 424, 545-568, 575-576, 589-590, 610-637, 658-673, 702-711, 725-733 /usr/local/lib/python3.8/dist-packages/pooch/downloaders.py 82 69 16% 14-15, 47-60, 139-143, 161-203, 253-261, 277-310 /usr/local/lib/python3.8/dist-packages/pooch/processors.py 75 50 33% 38, 64-81, 88, 117-133, 162-182, 213, 240-252, 262-279 /usr/local/lib/python3.8/dist-packages/pooch/utils.py 101 54 47% 33, 96, 150, 173-193, 214-216, 246, 248, 255-256, 261-271, 308-315, 346-362, 386-393, 430-436 /usr/local/lib/python3.8/dist-packages/pooch/version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/application.py 442 346 22% 93-94, 220-336, 343-353, 366-374, 385, 395-399, 410-431, 437-477, 482, 492-523, 530-537, 546-559, 569, 579-581, 585-591, 614-778, 801-811, 826-839, 849-851, 862-869, 875-884, 916-929, 938-939, 959-981, 993-1006, 1019, 1030, 1034-1036, 1043-1053, 1064-1065, 1073, 1078, 1087-1132, 1136-1140, 1145, 1148, 1158-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/current.py 64 39 39% 7-8, 11-13, 50, 54-58, 62-66, 75, 97-103, 111-112, 127-134, 148-165 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/dummy.py 17 5 71% 21, 28, 35, 44, 47 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/run_in_terminal.py 44 33 25% 13-14, 50-57, 73-116 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26% 54-60, 65-68, 71-75, 81-116, 131-159, 180-188, 193 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/nested.py 40 28 30% 32-33, 36, 63-75, 81-109 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/word_completer.py 33 26 21% 41-49, 55-83 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/data_structures.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/document.py 525 407 22% 52, 68-71, 98-121, 130, 133-136, 145, 150, 155, 160, 165, 169, 173, 178-179, 184-185, 193-196, 205-224, 231, 237, 243, 248-250, 256-259, 266, 273, 280-281, 291-292, 301-304, 312-315, 324-340, 345, 350, 356, 372-397, 404-405, 420-434, 446-453, 458-461, 475-496, 510-536, 546-547, 556-572, 581-604, 613-625, 634-652, 661-671, 680-690, 696-699, 705-708, 720-725, 742-747, 761-783, 794-816, 828-834, 838, 842, 846-854, 858, 864, 872-876, 890-897, 908-947, 958-997, 1005-1033, 1048-1098, 1104-1111, 1118-1129, 1136-1145, 1154, 1165-1174 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88, 90, 101-103, 117-123, 126, 129, 142, 147, 150, 159, 162, 165, 174, 177, 186, 189, 210, 213 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/cli.py 27 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/utils.py 14 2 86% 32, 41 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/ansi.py 152 133 12% 30-47, 53-109, 115-187, 193-211, 214, 217, 225-228, 246 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/base.py 66 49 26% 6, 30-37, 68-99, 108-114, 126, 129, 144-145, 148-160, 168-174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/html.py 70 59 16% 30-96, 99, 102, 110-113, 119-123, 129-132 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/pygments.py 14 6 57% 8, 22, 25-30 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/utils.py 24 16 33% 28-29, 40-41, 56-57, 68-85 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102-103, 111-112, 118-119, 128-132, 141-145, 153, 162, 176, 184, 192, 200, 208-210, 219-223, 236, 244-246, 254-262, 270, 281-289, 297-302, 310-315, 323-328, 337, 354-364, 373-382, 391-411, 420, 428-436, 444-452, 460, 471-472, 481-482, 491-498, 511, 520, 528, 541, 550, 566-569, 578-586, 599, 609-627, 635, 643, 657, 666-678, 686-687 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/open_in_editor.py 14 7 50% 20, 29-35, 42-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/page_navigation.py 26 19 27% 38, 51-59, 67-79 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/scroll.py 80 69 14% 26-50, 57-80, 87, 94, 101-113, 120-144, 151-160, 169-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/vi.py 949 904 5% 74-76, 80-85, 91-94, 105-126, 133-141, 147-166, 181-289, 304-369, 388-2161, 2165-2210 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/defaults.py 11 2 82% 35-49 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/digraphs.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/emacs_state.py 17 7 59% 18-19, 22, 27, 31, 35-36 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_bindings.py 215 138 36% 57, 101, 104, 126, 137, 149, 158, 189-196, 199-201, 205, 209, 238-281, 297-325, 340-364, 376-389, 397-417, 461-462, 469, 475-476, 480-481, 484-485, 488-489, 514-517, 521-541, 555-556, 563-572, 583, 596-599, 602-607, 617-618, 625-635 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_processor.py 238 184 23% 23-24, 44-47, 53, 56-58, 91-98, 101-117, 124-127, 134-145, 152-204, 213-216, 222-225, 236-292, 298-303, 306-354, 362-375, 382-387, 397-420, 443-451, 454, 462, 466-469, 476, 483, 490-499, 506, 514-525, 530 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/vi_state.py 47 27 43% 7-8, 28-29, 40-76, 81, 86-91, 98-106 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/keys.py 165 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/containers.py 956 776 19% 62-64, 140, 148, 155, 160-166, 174, 215-227, 230, 233, 236, 289-308, 311-318, 321-327, 330-331, 339-364, 381-414, 428-474, 526-545, 548-555, 558-579, 582-583, 591-616, 623-667, 684-730, 769-775, 778-781, 784, 792, 803-841, 867-986, 1000-1011, 1014, 1017, 1020-1022, 1069-1090, 1093-1095, 1098-1100, 1103, 1149-1162, 1166, 1177-1185, 1195-1202, 1221, 1230-1236, 1243-1246, 1252-1255, 1263, 1277, 1284, 1294, 1301, 1309-1312, 1319-1324, 1342-1345, 1349, 1353, 1357, 1361, 1364, 1378-1379, 1490-1524, 1527, 1530-1543, 1551-1558, 1564, 1573-1593, 1604-1620, 1637-1671, 1680-1684, 1688-1695, 1710-1726, 1738-1898, 1924-2140, 2150-2162, 2169-2181, 2188-2191, 2203-2214, 2224-2262, 2277-2281, 2287-2292, 2303-2413, 2424-2507, 2525-2528, 2532-2541, 2545-2558, 2561, 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306-307, 313-323, 329-339, 345-346, 355-360, 366-375, 382-384, 391-394, 406-417 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/margins.py 119 90 24% 18, 43, 63, 80-81, 84-85, 90-132, 141-142, 145-148, 153-156, 173-175, 178, 183-243, 276-277, 282-283, 288-305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/menus.py 281 228 19% 43-45, 71, 74-81, 91-95, 101-132, 138, 144, 159-167, 173-181, 190-205, 218-232, 245-265, 281-284, 327-340, 343, 346, 353-371, 383-390, 396-507, 513, 519-557, 564-607, 626-664, 680-685, 694, 697-702, 705-720 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/mouse_handlers.py 12 4 67% 18-24, 39-40 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/processors.py 378 291 23% 38, 80, 109-115, 122, 155-157, 168, 187, 193-243, 264-267, 278-312, 323, 326-334, 354-357, 368-398, 404-439, 451-489, 502-503, 509-522, 529, 541, 544-550, 557, 570-571, 575-580, 583, 593, 597-607, 622-629, 632-645, 660-667, 670-684, 707-710, 713-764, 790-798, 803-855, 858-903, 930-931, 937-940, 943, 958, 961-962, 969-975, 985, 988-1029 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/screen.py 100 71 29% 9, 107-120, 123, 128, 131, 152-192, 198, 204, 211-214, 221-227, 234, 242-249, 256-263, 272-293, 300-307, 310 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/utils.py 32 17 47% 23, 26, 29, 35, 39, 48-53, 67-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/base.py 31 15 52% 38, 50, 53-62, 73-74, 77-78, 81-82 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/pygments.py 112 79 29% 29, 69, 86, 94-110, 117-129, 141-143, 193-202, 214-222, 229-335 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/mouse_events.py 14 3 79% 41-42, 45 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/base.py 130 33 75% 164, 174, 177, 180, 183, 186, 189, 192, 195, 198, 201, 204, 207, 210, 213, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/color_depth.py 29 12 59% 52-74 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/defaults.py 27 20 26% 31-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/vt100.py 289 192 34% 123-145, 169-175, 181-190, 237-256, 273, 276-300, 304-312, 321-369, 383-396, 420-432, 449-473, 476, 480, 484, 490, 497, 503-507, 512, 519, 522, 525, 528-534, 540-542, 548, 555, 558, 567-570, 573, 576, 579, 582, 588, 591-596, 599-606, 609-614, 617-622, 625, 628, 634-673, 679-680, 684-685 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/patch_stdout.py 61 43 30% 52-68, 81-93, 100-116, 128-139, 142-144, 147-150, 156-157, 164, 167 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/renderer.py 299 255 15% 27-28, 70-243, 262-263, 266-270, 303-326, 331-369, 377, 386, 395-402, 417-457, 464-480, 488, 494-514, 525-637, 648-657, 664-673, 686-716 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/search.py 86 66 23% 16-17, 56-58, 61, 73-78, 94-119, 126-150, 157-181, 190-217, 226 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/selection.py 19 5 74% 48-50, 53, 56 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/dialogs.py 89 60 33% 53-69, 86-99, 116-140, 152-159, 176-196, 213-231, 246-285, 290-294, 305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/base.py 177 119 33% 58-59, 74-85, 128-148, 152-233, 237-247, 265-269, 272, 281-283, 286-300, 303, 306, 326-342, 345-358, 366-367, 379, 383-387, 405, 409-415, 419-422, 429-432, 439-444 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/formatters.py 158 91 42% 22, 52, 55, 64, 72, 75, 89-90, 93-94, 103-113, 116-124, 141, 144, 164-172, 180-206, 211, 228, 233-237, 244-247, 262-263, 268-273, 291-297, 300-306, 325-326, 329-335, 352-353, 358, 391, 402-413, 416, 423 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/prompt.py 449 368 18% 133, 159-184, 193, 406-458, 470-475, 481-490, 516, 522-685, 693-761, 767-824, 912-994, 1010-1040, 1091-1173, 1178-1189, 1193, 1197, 1202-1219, 1222, 1235-1249, 1262-1270, 1274-1282, 1286-1292, 1303, 1307, 1359-1361, 1412-1435, 1442-1443 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/utils.py 59 41 31% 27, 96-147, 160-173, 180-183, 190-191, 198 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/base.py 37 10 73% 129, 148, 151, 155, 166-167, 172-174, 177, 181 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/defaults.py 15 2 87% 213, 223 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/named_colors.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/pygments.py 18 11 39% 14-15, 39-43, 51-56, 66-67 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/style.py 165 126 24% 39-74, 97-103, 112-167, 201, 227-242, 246, 256-264, 272-313, 316, 329-336, 352-353, 373-374, 380-383, 387-390, 395, 398 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/style_transformation.py 124 76 39% 54, 80-83, 94, 111-112, 115-121, 124, 159-160, 163-189, 196-205, 220, 223, 236, 240, 256, 259-262, 265-268, 280-281, 284-286, 289, 294, 297-299, 302, 311, 349-375 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/utils.py 115 73 37% 64-68, 72-73, 77, 86, 92-93, 99-100, 106-107, 116, 119, 141-160, 170, 178, 185, 193-195, 202, 209, 214, 235-271, 276-279, 284-287, 295-298, 308-311 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/validation.py 66 34 48% 32-34, 37, 65, 73-76, 100, 112-114, 117, 120-126, 137, 140, 147-150, 159, 169-170, 174-175, 186, 189-190, 193-194 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/base.py 292 180 38% 192-256, 274, 278, 285, 289, 296, 300, 303, 326-342, 351, 367-382, 393-402, 411-419, 422, 448-486, 516, 529, 552, 590-604, 624, 644-702, 710-717, 720-763, 766, 808-809, 813, 822, 827, 836, 841, 846-849, 880, 884-885, 888 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/dialogs.py 34 21 38% 49-103, 106 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/menus.py 179 152 15% 47-168, 214-223, 226-258, 261-328, 332, 335, 348-353, 357-360 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/toolbars.py 154 115 25% 62, 83-103, 108, 115-178, 187, 192-204, 207, 226-254, 260, 265-328, 333, 341, 346-369, 374 /usr/local/lib/python3.8/dist-packages/ptyprocess/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ptyprocess/ptyprocess.py 410 335 18% 16-17, 32-33, 41-47, 57-89, 95-116, 123-126, 140-148, 157-176, 202-338, 341-352, 356-358, 362, 371-379, 385, 393-402, 409, 420, 438-447, 456-465, 499-501, 515-528, 536-549, 552-555, 562, 573-590, 602, 608, 614, 622-654, 663-683, 692-760, 771-772, 777-780, 790, 802, 805-808, 818-819, 827-828, 835-836 /usr/local/lib/python3.8/dist-packages/ptyprocess/util.py 35 32 9% 3-67 /usr/local/lib/python3.8/dist-packages/pyasn1/__init__.py 4 1 75% 7 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/decoder.py 817 685 16% 33, 39, 45, 48-55, 65-79, 85-98, 112-122, 129, 141-190, 197-226, 237-263, 269-293, 304-314, 324-371, 381-478, 490, 493, 496-534, 540-737, 743-946, 983-1023, 1029-1074, 1084-1109, 1115-1171, 1312-1626 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/ber/eoo.py 12 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/cer/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/cer/decoder.py 32 11 66% 22-35, 57 /usr/local/lib/python3.8/dist-packages/pyasn1/codec/der/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/codec/der/decoder.py 19 1 95% 37 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/compat/binary.py 18 15 17% 10-31 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/calling.py 7 3 57% 13-16 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/dateandtime.py 9 3 67% 16-17, 22 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/integer.py 68 58 15% 14-15, 20-94, 99, 102-107 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/octets.py 22 10 55% 10-27 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/string.py 11 8 27% 11-21, 26 /usr/local/lib/python3.8/dist-packages/pyasn1/debug.py 85 31 64% 52, 55, 63-65, 72-101, 104, 107, 110, 113, 122, 138, 151, 154 /usr/local/lib/python3.8/dist-packages/pyasn1/error.py 10 3 70% 47-49 /usr/local/lib/python3.8/dist-packages/pyasn1/type/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/type/base.py 249 74 70% 65, 70, 80, 108, 132, 138-141, 144, 149, 152, 155, 158, 162, 214, 233-236, 239, 271-273, 288, 294, 297, 300, 303, 306, 309, 312-313, 316, 367-376, 427, 447, 450, 456, 544, 550, 553, 556, 559, 562, 565, 568-569, 572, 576, 579, 604-614, 676, 681, 684, 687-691, 696, 699, 703 /usr/local/lib/python3.8/dist-packages/pyasn1/type/char.py 131 53 60% 58-97, 102, 105-109, 115-129, 135, 138, 145, 149-154, 157 /usr/local/lib/python3.8/dist-packages/pyasn1/type/constraint.py 177 81 54% 34-35, 49, 52, 55, 58, 61, 64, 67-68, 80, 88, 94, 143, 148, 151, 154, 157, 160, 200-204, 245, 249, 254, 318-320, 399-400, 403-404, 429-432, 435-436, 462-465, 468-469, 545-546, 549, 557-565, 568-575, 616-623, 626, 632, 635, 641, 644, 740-748 /usr/local/lib/python3.8/dist-packages/pyasn1/type/error.py 3 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/type/namedtype.py 232 68 71% 19-20, 58, 61, 64, 67, 70, 73, 76, 79, 82, 99, 102, 181, 184, 187, 190, 193, 196, 199, 202-206, 209, 212, 215-216, 227, 230, 233, 236, 243, 250, 252, 264, 301-305, 325-329, 349-353, 373-377, 402-406, 435-439, 470, 479, 488-489, 515, 549, 553, 561 /usr/local/lib/python3.8/dist-packages/pyasn1/type/namedval.py 89 41 54% 66-71, 74, 77, 83-90, 94-104, 116, 119, 122, 125, 128, 131, 134, 149, 152, 155, 158, 167, 172-173, 178-179, 182-183, 186-190 /usr/local/lib/python3.8/dist-packages/pyasn1/type/opentype.py 22 8 64% 78, 84, 89, 92, 95, 98, 101, 104 /usr/local/lib/python3.8/dist-packages/pyasn1/type/tag.py 123 34 72% 59, 67-69, 73, 76, 79, 82, 85, 88, 91, 94-101, 104-106, 109, 114, 210, 216, 222, 228, 234, 251, 262, 282, 325-327, 332 /usr/local/lib/python3.8/dist-packages/pyasn1/type/tagmap.py 44 20 55% 41, 45-53, 59-70, 90, 93, 96 /usr/local/lib/python3.8/dist-packages/pyasn1/type/univ.py 1274 794 38% 110, 113, 116, 119, 122, 125, 128, 131, 134, 137, 140, 143, 146, 149, 152, 155, 158, 161, 164, 167, 170-180, 183, 186, 189, 192, 197, 200-201, 204, 207, 210, 213, 216, 219, 222-226, 229, 232, 236, 242, 245, 248, 254, 264-265, 279, 346, 360-363, 468, 471, 479, 482-483, 486-487, 490-491, 494-495, 498-499, 502-503, 508, 511-517, 520-523, 526, 531-532, 535-536, 539-544, 547, 550, 553, 556, 559, 562-563, 571, 579, 584, 589-590, 601-615, 629-630, 633, 638, 651-661, 665, 667-705, 710-717, 726-732, 829, 832, 840-885, 890, 896-912, 915-920, 927, 930, 933, 954, 958-971, 982-1002, 1013-1024, 1029, 1032-1035, 1038, 1041, 1044, 1047, 1050, 1053, 1056, 1059, 1062, 1112, 1117, 1182, 1185, 1188, 1193, 1196-1199, 1202, 1205, 1208, 1224-1228, 1232, 1235, 1240-1241, 1248-1249, 1256, 1259, 1312-1315, 1334-1338, 1341-1376, 1381-1385, 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/usr/local/lib/python3.8/dist-packages/scipy/fft/_realtransforms.py 28 8 71% 63, 121, 179, 237, 387, 451, 568, 618 /usr/local/lib/python3.8/dist-packages/scipy/integrate/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_bvp.py 378 352 7% 29-57, 75-116, 124-142, 152-158, 243-276, 310-317, 322-347, 418-502, 506, 513, 556-577, 590-602, 632, 642-710, 1001-1159 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/base.py 99 80 19% 7-23, 118-151, 155-158, 170-191, 201-209, 212, 215, 231-234, 250-253, 256, 266-267, 270-275 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/bdf.py 245 222 9% 21-26, 31-34, 39-70, 188-242, 245-295, 298-438, 441, 447-451, 454-467 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/common.py 214 189 12% 13-17, 22-24, 39-40, 46-57, 62, 100-120, 153-176, 181-190, 206-238, 292-320, 325-363, 367-432 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/dop853_coefficients.py 153 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/ivp.py 162 143 12% 31-51, 77-78, 109-128, 146-154, 504-662 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/lsoda.py 57 46 19% 108-138, 141-161, 164-172, 177-180, 183-188 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/radau.py 262 230 12% 88-137, 169-177, 286-334, 337-387, 390-529, 532-533, 536, 541-545, 548-562 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/rk.py 190 127 33% 62-72, 89-104, 107, 110, 113-177, 180-181, 480-485, 488-494, 497-503, 506-524, 529-533, 536-549, 554-557, 560-576 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ode.py 507 401 21% 348-353, 357, 361-369, 382-394, 422-437, 441-445, 532-536, 540-541, 545-546, 562-567, 584-587, 620-625, 628-633, 637-656, 660, 673-686, 690-694, 722-723, 739-742, 751-754, 765-769, 786-787, 790-791, 804, 809, 814, 826-831, 862-881, 912-939, 942-987, 990-1014, 1017-1021, 1024-1028, 1044-1100, 1132-1143, 1146-1151, 1154-1168, 1171-1179, 1182-1187, 1210, 1216-1230, 1267-1283, 1287-1333, 1336-1353, 1356-1360, 1363-1367 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_quad_vec.py 262 227 13% 16, 19-24, 28, 36-41, 44-48, 51-56, 64-67, 70-71, 74-79, 83, 87-93, 98-99, 102, 202-400, 404-429, 436-449, 461-504, 512-569, 580-622 /usr/local/lib/python3.8/dist-packages/scipy/integrate/odepack.py 32 22 31% 229-260 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadpack.py 199 172 14% 41, 334-432, 436-465, 469-516, 581-585, 665-674, 799-810, 815, 823, 828, 832, 837-844, 847-883 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadrature.py 312 274 12% 45-49, 117-123, 151-168, 235-252, 256-258, 315-350, 354-381, 454-506, 568-623, 650-660, 668-669, 674-686, 773-806, 934-975 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_bsplines.py 324 291 10% 20-22, 27-30, 38-43, 182-226, 235-239, 245, 303-308, 332-355, 358, 367-370, 391-397, 425-437, 488-571, 581-588, 593, 597-604, 608-617, 735-861, 969-1022 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_cubic.py 259 230 11% 28-72, 142-158, 235-240, 245-255, 268-304, 343-350, 405-435, 438, 445, 450, 619-770, 784-837 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_fitpack_impl.py 413 388 6% 44-48, 215-311, 443-524, 580-607, 654-668, 710-733, 773-791, 890-988, 1039-1057, 1079-1080, 1128-1146, 1196-1229, 1288-1311 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_pade.py 27 22 19% 46-67 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack.py 65 49 25% 156-158, 289-290, 353-368, 417-429, 477-491, 531-534, 586-601, 654-657, 719-722 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack2.py 366 304 17% 171-196, 201-208, 211-232, 235-241, 244-256, 265-276, 303-317, 324-326, 330-332, 342, 380, 405-408, 415-421, 468-471, 524-525, 602-618, 741-770, 793, 801, 805, 841-880, 955-961, 983, 1002-1004, 1048-1069, 1110-1136, 1172-1198, 1273-1281, 1304, 1385-1396, 1486-1504, 1670-1727 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/interpolate.py 920 819 11% 32-34, 84-94, 201-252, 279-313, 318-328, 335, 431-540, 547, 552-581, 585, 591-614, 623-631, 637-646, 649, 652-654, 660-669, 687-700, 708-751, 754-758, 770-777, 784-787, 808-870, 902-923, 986, 1013-1031, 1063-1085, 1108-1167, 1217-1239, 1268, 1284-1296, 1313-1332, 1422, 1443-1477, 1502-1533, 1558-1597, 1600-1603, 1621-1639, 1705-1757, 1814-1843, 1873-1883, 1946-1965, 1978-1984, 1987-1991, 1994-1997, 2028-2062, 2069-2096, 2103-2136, 2163-2169, 2197-2203, 2236-2264, 2287-2312, 2415-2452, 2468-2501, 2505-2516, 2519-2521, 2525-2541, 2601-2676, 2689-2704, 2707, 2710-2712, 2717-2724 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/ndgriddata.py 47 38 19% 59-65, 77-81, 193-228 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/polyint.py 203 169 17% 18, 56-60, 78-80, 86, 90-92, 96-103, 106-111, 114-131, 134-139, 177-188, 216-218, 293-316, 319-326, 329-355, 400-406, 448-461, 502-513, 532-537, 559-577, 599, 602-617, 666 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/rbf.py 105 83 21% 145, 148, 151, 154, 157, 160, 163, 167-216, 223-268, 274-275, 278, 281-290 /usr/local/lib/python3.8/dist-packages/scipy/io/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/_fortran.py 79 61 23% 111-126, 129-136, 161-169, 245-294, 317, 340, 349, 352, 355 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/_fortran_format_parser.py 165 124 25% 34, 62-66, 69-71, 74-80, 84-90, 94, 123-132, 141-144, 147-153, 157-163, 167, 172-174, 177, 180, 185-186, 189-191, 194-205, 234, 237-250, 253-257, 260-261, 264-305, 308-312 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/hb.py 265 215 19% 45, 70-120, 141-205, 217-281, 285-299, 303-308, 313-332, 336-360, 391-400, 403-412, 416, 421, 437-443, 447, 451, 455, 459, 463, 466, 469, 496-504, 534-547 /usr/local/lib/python3.8/dist-packages/scipy/io/idl.py 429 382 11% 81-84, 89-90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 152-153, 163-170, 175-182, 187-224, 233-269, 278-316, 322-425, 431-445, 451-492, 498-546, 552-565, 570-631, 652, 655, 658, 705-873 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/byteordercodes.py 19 12 37% 57-69 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio.py 65 47 28% 19-22, 32-47, 71-80, 215-225, 266-279, 314-317 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio4.py 281 222 21% 91-95, 102-106, 110-127, 135-149, 168-185, 202-207, 221-223, 254-267, 273-301, 313-314, 317-326, 333-334, 351-357, 375, 386-406, 410-422, 442-446, 451-452, 455, 458, 478-490, 504-519, 522-541, 544-560, 567-584, 590-594, 616-618 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5.py 371 312 16% 151-167, 172-175, 179-186, 196-198, 217-233, 252, 261-307, 311-331, 375-400, 422-455, 471-477, 480, 483, 487-496, 500-504, 508-516, 536-559, 562-570, 587-590, 601-630, 633-655, 660-698, 703-718, 721-726, 729-733, 736-738, 742-755, 761-765, 789-798, 802-809, 829-849 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5_params.py 77 11 86% 195-197, 227-231, 235, 242-243, 249-250 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/miobase.py 108 70 35% 20, 179-184, 221-241, 305-318, 324, 328, 332, 363-377, 381-383, 387, 390-393, 398, 403-415 /usr/local/lib/python3.8/dist-packages/scipy/io/mmio.py 444 375 16% 54, 75, 101, 115, 119, 123, 127, 131, 135, 139, 150-151, 164-165, 178-179, 191, 196, 226-269, 294-327, 332-384, 389, 398, 417-425, 451-460, 469-479, 483-485, 490-657, 662-802, 812-828, 833-839 /usr/local/lib/python3.8/dist-packages/scipy/io/netcdf.py 486 393 19% 239-284, 289-293, 297-319, 323, 326, 348-352, 385-397, 408-409, 413-421, 425-428, 431-439, 442, 445-452, 455-479, 482-512, 515-553, 556-558, 561-602, 606-616, 619, 622-631, 634-635, 638-647, 650-733, 736-758, 761-776, 779-782, 785, 789, 793, 796, 799-802, 805-808, 867-876, 881-885, 896, 905, 919, 938-946, 958, 970, 973-988, 991-1021, 1027-1028, 1037-1045, 1058-1065, 1077-1093 /usr/local/lib/python3.8/dist-packages/scipy/linalg/__init__.py 36 4 89% 222-223, 227-228 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_ldl.py 85 74 13% 123-156, 207-241, 268-297, 335-354 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_polar.py 16 11 31% 98-112 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_qz.py 127 110 13% 19-34, 38-43, 47-52, 56-61, 65-71, 76-145, 262-265, 358-405 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_expm_frechet.py 153 138 10% 91-114, 122-127, 166-173, 177-187, 191-203, 207-222, 226-278, 298, 334-350, 393-411 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_matfuncs_sqrtm.py 85 74 13% 52-116, 163-196 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_procrustes.py 18 13 28% 76-91 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_sketches.py 15 8 47% 49-54, 167-168 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_solvers.py 216 194 10% 86-107, 159-199, 214-218, 228-233, 306-324, 446-528, 652-736, 778-844 /usr/local/lib/python3.8/dist-packages/scipy/linalg/basic.py 385 346 10% 27-37, 137-258, 330-359, 433-472, 568-596, 669-702, 706-711, 862-907, 952, 979, 981, 1034-1043, 1157-1246, 1304-1318, 1373-1391, 1451-1470, 1575-1619 /usr/local/lib/python3.8/dist-packages/scipy/linalg/blas.py 86 12 86% 301-310, 341, 352, 381-384 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp.py 364 338 7% 41-47, 51-73, 78-115, 214-267, 374-489, 503-530, 640-695, 767, 858, 951, 1031, 1124-1194, 1199-1203, 1252-1283, 1368-1431 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_cholesky.py 72 61 15% 19-44, 90-92, 154-156, 194-213, 274-286, 334-353 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_lu.py 48 38 21% 71-86, 135-148, 209-223 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_qr.py 130 121 7% 16-25, 121-173, 251-320, 386-424 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_schur.py 104 85 18% 119-178, 191-197, 201-210, 266-295 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_svd.py 88 60 32% 111, 116, 118, 132, 134, 139, 225-232, 273-281, 323-330, 384-391, 459-496 /usr/local/lib/python3.8/dist-packages/scipy/linalg/flinalg.py 30 23 23% 14-19, 23, 32-58 /usr/local/lib/python3.8/dist-packages/scipy/linalg/lapack.py 45 4 91% 808, 814, 826, 830 /usr/local/lib/python3.8/dist-packages/scipy/linalg/linalg_version.py 5 0 100% /usr/local/lib/python3.8/dist-packages/scipy/linalg/matfuncs.py 130 103 21% 52-55, 84-89, 136-138, 195-208, 255-256, 291-295, 330-334, 371-372, 409-410, 447-448, 485-486, 551-590, 626-670 /usr/local/lib/python3.8/dist-packages/scipy/linalg/misc.py 42 30 29% 141-181, 192-194 /usr/local/lib/python3.8/dist-packages/scipy/linalg/special_matrices.py 223 197 12% 62-73, 104-106, 138-140, 193-203, 239-244, 290-301, 344-358, 413-430, 465-471, 535-553, 600-617, 654-662, 698-700, 759-777, 840-863, 938-973, 1033-1043, 1109-1119, 1172-1196 /usr/local/lib/python3.8/dist-packages/scipy/misc/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/scipy/misc/common.py 73 65 11% 35-47, 85-120, 154-159, 195-204, 297-303 /usr/local/lib/python3.8/dist-packages/scipy/misc/doccer.py 29 8 72% 15, 21, 27, 33, 39, 44, 49, 54 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_docstrings.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_support.py 43 13 70% 42, 44, 46, 48, 52, 64-65, 78, 80-81, 83, 89, 91 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/filters.py 399 298 25% 81, 85, 90, 129-133, 141, 156-164, 332-340, 369-377, 404-419, 447-449, 487-495, 525-544, 583-591, 598-623, 648, 754, 783-795, 842-857, 897-909, 954-966, 971-1033, 1069, 1106, 1113-1162, 1201-1202, 1240, 1280, 1348-1364, 1421-1448 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/fourier.py 69 58 16% 42-55, 59-70, 120-129, 179-187, 241-249, 298-306 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/interpolation.py 210 182 13% 93-105, 127-139, 245-263, 329-351, 433-487, 520-539, 589-616, 677-746 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/measurements.py 315 288 9% 178-236, 298-305, 377-458, 463-466, 500-573, 617-618, 669-670, 721-722, 773, 782-884, 946, 1025, 1086, 1155-1164, 1210-1219, 1275-1293, 1355-1364, 1419-1424, 1459-1498 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/morphology.py 403 368 9% 50-53, 106-122, 208, 210, 218-285, 381, 501-513, 629-636, 775-782, 871-892, 1022, 1097-1107, 1213-1216, 1338-1364, 1443-1447, 1526-1530, 1636-1643, 1681-1695, 1739-1752, 1797-1810, 1872-1947, 1986-2063, 2175-2230 /usr/local/lib/python3.8/dist-packages/scipy/optimize/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py 219 182 17% 20, 23-24, 27-31, 34, 61-92, 102-146, 151-173, 177-178, 208-216, 219, 222-232, 238-242, 246-247, 264-265, 268-270, 278-280, 283-286, 304-305, 313-315, 321, 626-701, 705-707, 711-716, 720-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_constraints.py 180 157 13% 94-101, 136-139, 168-170, 173-176, 215-251, 267-274, 284-294, 304-307, 312-317, 324-411, 419-450 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentiable_functions.py 351 317 10% 31-160, 163-165, 168-170, 173-175, 178-181, 184-187, 190-193, 196-200, 223-420, 423-425, 428-429, 432-434, 437-439, 442-444, 447-449, 452-454, 458-461, 472-489, 492-494, 497-501, 504-505, 508-510, 521-528 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentialevolution.py 364 310 15% 294-308, 475-599, 607-637, 644-652, 666-687, 694, 702-704, 711, 730-849, 869-889, 894-908, 928, 948-959, 962, 965, 969-970, 974-975, 1011-1020, 1035-1144, 1150, 1154, 1158-1159, 1163-1195, 1199-1200, 1205-1206, 1211-1216, 1220-1224, 1228-1233, 1237-1242, 1249-1253, 1261-1262, 1265, 1294-1322, 1325, 1341-1346 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_dual_annealing.py 285 250 12% 55-71, 79-111, 115-127, 153-159, 166-195, 199-205, 209-210, 240-256, 259-277, 280-306, 315-355, 362-370, 373-374, 388-405, 409-425, 602-689 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_hessian_update_strategy.py 134 94 30% 52, 70, 87, 100, 136-145, 151-159, 162, 180-201, 217-220, 231-237, 279, 282-288, 311-312, 329-330, 334-375, 407-408, 412-430 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog.py 78 65 17% 78-110, 155-161, 510-581 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_ip.py 247 217 12% 84-119, 189-329, 345-349, 368-375, 399-412, 427-432, 447-453, 471-502, 534-544, 698-822, 1083-1127 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_rs.py 190 171 10% 47-99, 108-134, 161-237, 249-269, 277, 285-288, 295-310, 327-402, 522-558 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_simplex.py 107 98 8% 89-95, 154-166, 212-229, 355-435, 591-659 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_util.py 489 470 4% 57-68, 92-99, 118-121, 183-386, 497-778, 873-881, 971-1085, 1092, 1100-1130, 1138-1145, 1170-1173, 1241-1293, 1350-1396, 1473-1485 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsap.py 20 17 15% 79-105 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/bvls.py 116 109 6% 13-16, 20-177 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/common.py 295 261 12% 36-56, 108-170, 196-221, 235-247, 284-301, 316-324, 350-363, 371, 392-400, 418-439, 448-466, 499-510, 515-541, 548, 555-565, 571, 578-588, 597-600, 605-615, 620-631, 637-648, 660-671, 679-689, 697-707, 712-722, 730-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/dogbox.py 149 138 7% 65-77, 94-106, 125-149, 154-330 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/least_squares.py 255 229 10% 43-92, 98-105, 109-126, 130-149, 153-162, 169-177, 181-186, 190-195, 199-204, 212-237, 748-940 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/lsq_linear.py 82 70 15% 16-24, 218-317 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf.py 290 278 4% 121-126, 133-205, 210-402, 410-564 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf_linear.py 144 132 8% 53-69, 74-90, 95-142, 147-248 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_minimize.py 176 155 12% 479-636, 756-794, 799-806, 811-829 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_numdiff.py 254 237 7% 46-91, 100-103, 107-114, 147-175, 330-398, 404-441, 445-481, 486-561, 625-639 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_remove_redundancy.py 147 136 7% 30-31, 53-54, 83-92, 96-104, 139-230, 266-357, 393-449 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root.py 89 69 22% 153-203, 207-208, 246-257, 266-305, 369, 434, 476, 513, 550, 590, 654 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root_scalar.py 124 104 16% 30-33, 38-43, 47-49, 53-55, 58, 181-287, 306, 325, 343, 366, 392, 423, 442, 461 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo.py 657 595 9% 417-447, 456-655, 673-707, 721-737, 744-757, 762-778, 782-789, 793-795, 799-800, 803-804, 814-835, 838-845, 855-868, 871-879, 888-899, 907-910, 917-955, 974-1020, 1024-1028, 1031-1034, 1044-1052, 1070-1086, 1102-1104, 1124-1182, 1190-1199, 1203-1206, 1225-1240, 1245-1282, 1290-1292, 1302-1357, 1366-1375, 1384-1390, 1395-1403, 1407-1409, 1415-1421, 1431-1458, 1466-1472, 1476-1508, 1514-1531, 1534-1542, 1550, 1555-1568, 1574-1596, 1601-1605, 1610-1617, 1620-1628, 1631-1645, 1651-1671 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/sobol_seq.py 122 114 7% 30-40, 53-58, 97-102, 140-145, 197-372 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/triangulation.py 359 321 11% 8-46, 49, 56-85, 89-115, 119-141, 146-159, 163-174, 182, 195-225, 231-243, 258-298, 315-362, 372-451, 456-464, 467, 470-471, 477-480, 487-490, 497, 503-504, 513-517, 528-530, 536-569, 572, 575-585, 588-592, 596-600, 603-609, 616-626, 629-661 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_spectral.py 106 93 12% 66-164, 207-238, 245-251, 255, 259 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trlib/__init__.py 6 3 50% 7-12 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion.py 135 114 16% 25-35, 38, 43-45, 50-52, 57-59, 62-65, 70-72, 80-95, 98, 133-266 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/canonical_constraint.py 253 234 8% 43-48, 53-69, 78-91, 101-149, 153-181, 185-221, 225-261, 265-327, 337-390 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py 105 96 9% 14-15, 50-218 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py 175 152 13% 29-30, 33-36, 46-48, 51-57, 62-100, 107-112, 314-544 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/projections.py 164 145 12% 10, 41-55, 62-90, 96-172, 179-233, 240-287, 364-406 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/qp_subproblem.py 215 201 7% 45-63, 99-149, 189-234, 286-303, 308, 313, 364-409, 492-638 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/report.py 32 10 69% 11-16, 23-28, 32 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py 148 124 16% 38-57, 60-61, 64, 67, 80-86, 93-96, 100, 108-114, 130-136, 140, 143-163, 179-195, 199-205, 209-221, 226-240, 251-264, 287-347 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_dogleg.py 40 30 25% 31-35, 47-51, 57-62, 98-124 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_exact.py 139 124 11% 35-41, 80-122, 137-143, 173-185, 218-254, 264-285, 290-432 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_krylov.py 11 7 36% 22-59 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_ncg.py 51 42 18% 33-39, 71-128 /usr/local/lib/python3.8/dist-packages/scipy/optimize/cobyla.py 72 60 17% 139-170, 196-258 /usr/local/lib/python3.8/dist-packages/scipy/optimize/lbfgsb.py 136 123 10% 174-208, 265-380, 412-422, 440-456, 468-478 /usr/local/lib/python3.8/dist-packages/scipy/optimize/linesearch.py 320 299 7% 69-103, 150-186, 267-322, 384-468, 481-502, 512-523, 532-603, 644-659, 666-668, 685-726, 773-802, 852-883 /usr/local/lib/python3.8/dist-packages/scipy/optimize/minpack.py 284 255 10% 26-45, 139-165, 206-259, 384-457, 461-478, 482-491, 495-508, 683-813, 821-844, 848, 852, 856-870, 914-916 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nnls.py 21 16 24% 60-82 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nonlin.py 628 491 22% 139, 144-147, 152-154, 158-160, 269-367, 375-415, 434-453, 456-472, 521-530, 533, 536, 539, 542-547, 552-558, 562, 566, 573-658, 667-678, 681, 684-688, 703-708, 712-718, 723-746, 750-752, 756-758, 762-764, 768-770, 773-781, 784-790, 794-797, 803-808, 814-819, 850-884, 951-973, 977-978, 981, 984-988, 991, 994, 997, 1000-1006, 1046-1051, 1115-1121, 1124-1144, 1147-1167, 1170-1192, 1225-1226, 1229-1230, 1233, 1236, 1239, 1242, 1245, 1248, 1275-1276, 1279, 1282, 1285, 1288, 1291, 1294, 1325-1328, 1331-1332, 1335, 1338, 1341, 1344, 1347, 1350-1353, 1438-1476, 1479-1481, 1484-1491, 1494-1498, 1501-1508, 1511-1524 /usr/local/lib/python3.8/dist-packages/scipy/optimize/optimize.py 1239 1166 6% 59-61, 64-67, 70-74, 115-118, 124-129, 132, 140-145, 152, 159-164, 197-200, 229-238, 270-277, 310-317, 321-329, 435-453, 498-682, 690-707, 765, 809-813, 818-820, 841-869, 945-964, 990-1098, 1253-1271, 1297-1406, 1500-1519, 1545-1672, 1742-1750, 1771-1894, 1901-1910, 1914, 1918-1945, 1949-2053, 2056-2059, 2132-2138, 2158-2171, 2234-2239, 2253-2315, 2352-2417, 2426-2430, 2550-2570, 2599-2712, 2716-2728, 2903-2985, 2993-2994, 2998, 3069-3166, 3170-3242, 3246 /usr/local/lib/python3.8/dist-packages/scipy/optimize/slsqp.py 190 178 6% 57-66, 181-212, 237-475, 483-531 /usr/local/lib/python3.8/dist-packages/scipy/optimize/tnc.py 102 76 25% 243-277, 342-413, 420-441 /usr/local/lib/python3.8/dist-packages/scipy/optimize/zeros.py 471 417 11% 54-62, 65-68, 73-81, 86-93, 266-365, 377-469, 546-554, 643-651, 773-781, 878-886, 896-899, 904-908, 919-927, 932-937, 949-972, 980-992, 1002, 1013-1037, 1048-1064, 1067-1077, 1081-1085, 1089, 1092, 1096-1123, 1127-1132, 1139-1213, 1218-1244, 1353-1375 /usr/local/lib/python3.8/dist-packages/scipy/signal/__init__.py 44 2 95% 330, 343 /usr/local/lib/python3.8/dist-packages/scipy/signal/_arraytools.py 49 41 16% 43-46, 54, 92-106, 143-155, 196-209, 237-243 /usr/local/lib/python3.8/dist-packages/scipy/signal/_max_len_seq.py 31 25 19% 104-137 /usr/local/lib/python3.8/dist-packages/scipy/signal/_peak_finding.py 225 201 11% 66-81, 138, 194, 248-250, 264-267, 281-293, 307-319, 459-462, 584-590, 625-640, 673-678, 713-723, 935-1006, 1055-1126, 1164-1190, 1283-1299 /usr/local/lib/python3.8/dist-packages/scipy/signal/_savitzky_golay.py 81 70 14% 98-141, 153-165, 179-209, 219-223, 328-353 /usr/local/lib/python3.8/dist-packages/scipy/signal/_upfirdn.py 40 30 25% 59-63, 67-69, 75-86, 90-100, 207-210 /usr/local/lib/python3.8/dist-packages/scipy/signal/bsplines.py 202 177 12% 19, 28-43, 60-114, 125-129, 148-149, 157-167, 175-185, 189-193, 197, 202-206, 210-237, 241-252, 256-267, 292-295, 319-322, 337-358, 373-394 /usr/local/lib/python3.8/dist-packages/scipy/signal/filter_design.py 1094 1014 7% 47-56, 94-117, 180-193, 255-272, 424-477, 562-583, 662-693, 698-706, 821-828, 881-932, 987-1000, 1064-1071, 1095-1130, 1168, 1193-1200, 1230-1240, 1245-1250, 1421-1515, 1539-1561, 1592-1632, 1691-1704, 1765-1787, 1851-1876, 1938-1965, 2017-2046, 2155-2175, 2294-2386, 2393-2398, 2457-2474, 2520-2534, 2583-2600, 2652-2679, 2731-2759, 2866, 2983, 3094, 3218, 3380, 3385, 3389, 3421-3445, 3525-3615, 3693-3753, 3833-3915, 3993-4054, 4067-4074, 4091-4112, 4129-4154, 4161-4163, 4167-4179, 4201-4263, 4285-4288, 4318-4332, 4340-4357, 4371-4396, 4404-4435, 4449-4463, 4540-4568, 4648, 4728, 4764-4803 /usr/local/lib/python3.8/dist-packages/scipy/signal/fir_filter_design.py 272 250 8% 26-32, 79-85, 127-128, 250-261, 389-482, 593-684, 836-855, 968-1068, 1082-1090, 1215-1265 /usr/local/lib/python3.8/dist-packages/scipy/signal/lti_conversion.py 160 142 11% 76-114, 118-121, 125-126, 130-133, 137-139, 143-148, 176-195, 256-284, 304, 334, 405-504 /usr/local/lib/python3.8/dist-packages/scipy/signal/ltisys.py 919 757 18% 54-58, 66-70, 75, 79-82, 87, 92, 103-106, 117-120, 131-134, 206-221, 229, 236, 243, 250, 274, 284, 295, 383-398, 406-409, 414, 418, 425, 432, 439, 468, 479, 561-578, 583-592, 596, 606, 610-617, 622, 626, 638-639, 651, 663, 676, 697-702, 722-727, 800, 940-958, 963-972, 976, 987, 991-998, 1003, 1007, 1012, 1016, 1028-1030, 1042, 1055, 1067, 1131, 1207-1208, 1304-1317, 1322-1333, 1337, 1347, 1365-1404, 1411-1421, 1428, 1434-1473, 1479-1482, 1485-1488, 1491-1494, 1501-1508, 1513, 1517, 1522, 1526-1527, 1532, 1536-1537, 1542, 1546, 1558-1561, 1578, 1596, 1609, 1677, 1799-1853, 1861-1867, 1926-2032, 2058-2064, 2114-2133, 2200-2222, 2277-2292, 2358-2373, 2432-2437, 2494-2522, 2529, 2540-2580, 2590-2600, 2613-2636, 2655-2710, 2720-2763, 2777-2886, 2896-2912, 3095-3263, 3322-3379, 3431-3465, 3516-3550, 3615-3648, 3712-3722 /usr/local/lib/python3.8/dist-packages/scipy/signal/signaltools.py 1148 1072 7% 27, 47-50, 55-58, 77-93, 197-259, 265-270, 305-331, 365-391, 419-428, 523-544, 572-655, 736-860, 876-881, 889-892, 904-935, 963-977, 984-985, 996-1002, 1019-1037, 1146-1174, 1267-1296, 1353-1359, 1394-1409, 1437-1460, 1537-1549, 1627-1644, 1681-1692, 1817-1885, 1928-1953, 1997-2009, 2098-2120, 2145-2180, 2208-2210, 2265-2297, 2356-2372, 2377-2397, 2401-2424, 2503-2539, 2598-2644, 2648-2675, 2733-2749, 2832-2924, 3054-3134, 3184-3212, 3254-3297, 3391-3433, 3489-3505, 3557-3684, 3844-3885, 3890-3920, 3924-3927, 4000-4039, 4130-4151, 4202-4245 /usr/local/lib/python3.8/dist-packages/scipy/signal/spectral.py 363 339 7% 142-158, 268-289, 452-457, 584-601, 734-771, 870-895, 996-1022, 1172-1178, 1348-1456, 1566-1576, 1669-1870, 1896-1920, 1959-1981, 2001-2002 /usr/local/lib/python3.8/dist-packages/scipy/signal/waveforms.py 120 107 11% 58-88, 139-162, 224-262, 427-430, 440-483, 577-580, 591-593, 669-681 /usr/local/lib/python3.8/dist-packages/scipy/signal/wavelets.py 136 123 10% 29-76, 90-92, 127-198, 253-261, 301-308, 384-388, 462-473 /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/windows.py 289 243 16% 21-23, 28-31, 36-39, 112-121, 168-174, 226-238, 290-302, 349-357, 442, 501, 548, 609-610, 700-708, 790, 795, 856-878, 925-933, 1020, 1098, 1207-1216, 1271-1279, 1342-1349, 1438-1476, 1545-1563, 1616-1622, 1698-1710, 1876-1970, 1975-1982, 2095-2124 /usr/local/lib/python3.8/dist-packages/scipy/sparse/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/_index.py 221 190 14% 24-27, 35-75, 78-126, 129-150, 157-180, 185-191, 196-202, 205, 208, 211, 214, 217, 220, 223, 226, 229, 232, 235, 238, 242-244, 252-283, 288-322, 326-328 /usr/local/lib/python3.8/dist-packages/scipy/sparse/_matrix_io.py 42 32 24% 16, 63-80, 131-156 /usr/local/lib/python3.8/dist-packages/scipy/sparse/base.py 455 344 24% 71-75, 81-82, 86, 123-131, 157, 182-189, 194-203, 207-208, 212, 223, 239, 250, 254, 257-258, 263-281, 284-287, 295, 313-328, 340, 344, 348, 363, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 407, 410-424, 427, 430-443, 446-455, 466-530, 534, 537, 540, 543, 546-554, 561-564, 567-570, 577-617, 620, 624, 628, 632, 635, 638, 641, 644, 647, 650, 653-675, 678-691, 718, 736-741, 744, 756, 759, 762, 780-782, 791-799, 808-816, 851, 883, 894, 902, 910, 918, 926, 937, 945, 953, 993-1025, 1064-1097, 1124, 1146-1149, 1152-1176, 1179-1189 /usr/local/lib/python3.8/dist-packages/scipy/sparse/bsr.py 315 266 16% 123-214, 225-271, 279, 283-287, 292-293, 299-308, 317, 320, 330, 336, 339, 342-351, 354-364, 367-421, 436-441, 444-463, 468, 479-505, 508, 513-534, 546-561, 568-594, 599-607, 613-627, 635-676, 684-688, 722 /usr/local/lib/python3.8/dist-packages/scipy/sparse/compressed.py 737 654 11% 31-108, 111-123, 130-136, 148-195, 212-215, 219-248, 252-279, 283-313, 316, 322, 328, 334, 344-352, 355, 358, 365-458, 465-475, 478-489, 492-528, 531-539, 548-568, 571, 577, 592-611, 630-635, 642-647, 650-653, 657-668, 672-673, 678-698, 704-730, 736-758, 764-775, 782-793, 797-798, 801-802, 806-823, 826-853, 856-872, 880-911, 918-931, 942-1003, 1010-1017, 1023-1037, 1050-1053, 1068-1074, 1077-1079, 1089-1098, 1110-1113, 1116, 1123-1125, 1135-1138, 1143-1153, 1156-1187, 1201-1207, 1212-1242, 1248-1269, 1273-1290 /usr/local/lib/python3.8/dist-packages/scipy/sparse/construct.py 232 206 11% 62, 138-188, 218, 252-273, 311-355, 384-398, 406-431, 465, 499, 545-623, 668-677, 750-793, 842 /usr/local/lib/python3.8/dist-packages/scipy/sparse/coo.py 294 251 15% 129-198, 201-236, 242-263, 271-291, 294-300, 306-317, 323-330, 352-372, 394-414, 417-420, 425-443, 448-454, 459-475, 480-513, 521-525, 533-537, 541-554, 561-564, 571-579, 583-586, 589-593, 619 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csc.py 87 58 33% 111-119, 125-126, 129-132, 137-155, 164-180, 188-194, 200-206, 209, 212-214, 217-219, 222, 225, 228, 235, 261 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_laplacian.py 51 44 14% 69-81, 85, 89-111, 115-128 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_validation.py 31 25 19% 17-58 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csr.py 135 103 24% 129-137, 143-156, 161-164, 169-186, 191-222, 231, 234-242, 248-256, 263-271, 275, 278-307, 311-313, 316, 319, 322-325, 351 /usr/local/lib/python3.8/dist-packages/scipy/sparse/data.py 184 138 25% 23, 26, 29, 33-35, 38, 41, 44, 47, 50-53, 56-60, 63-68, 71-79, 84-89, 94, 99, 113-119, 126, 135-136, 148-156, 166-187, 191-215, 218-252, 255-289, 321, 353, 376, 399 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dia.py 224 188 16% 79-146, 149-150, 158-164, 167-168, 171-181, 187-225, 230-241, 244, 247-276, 279-282, 287-305, 311-318, 323-343, 349-365, 374-377, 380-392, 420 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dok.py 275 213 23% 23, 79-111, 115, 122, 125-128, 133-136, 139, 145, 151-158, 161, 164, 167, 170-191, 194, 197, 200-201, 204-205, 209-216, 220-227, 230-234, 237-246, 249-277, 280-301, 304-309, 312-316, 320-323, 327-332, 335-338, 341-346, 349-352, 358, 365-374, 380-384, 387-389, 394-404, 409-411, 416, 421-429, 457 /usr/local/lib/python3.8/dist-packages/scipy/sparse/extract.py 22 14 36% 38-42, 101-103, 162-164, 168-171 /usr/local/lib/python3.8/dist-packages/scipy/sparse/lil.py 291 232 20% 89-132, 135-136, 139-140, 143-147, 150-154, 160-172, 175, 181-185, 190-193, 198-206, 210-216, 220-226, 229-231, 234-235, 238, 241, 244-245, 248, 251-252, 255-256, 260-261, 265-271, 289-300, 303, 307-308, 314-324, 328-336, 339-350, 353-360, 363-369, 374-401, 406-426, 431-435, 440, 445-448, 454-484, 512-527, 553 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_expm_multiply.py 255 225 12% 18-23, 28-33, 38-43, 48-55, 140-144, 172-197, 204-223, 313, 342-346, 352, 358-360, 366-369, 375, 399, 414-417, 455-475, 506-511, 556-629, 639-648, 655-677, 684-713 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_norm.py 70 63 10% 15-19, 110-184 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_onenormest.py 199 177 11% 86-119, 130-139, 154-157, 162, 166-174, 178-180, 187-190, 194-197, 204-211, 215, 219, 261-322, 366-468 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/_add_newdocs.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py 197 173 12% 56-59, 63-82, 132-233, 302-324, 386-410, 442-469, 528-607 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py 726 642 12% 280-281, 297-299, 307, 313-364, 367-377, 435-533, 536-573, 576-595, 636-719, 722-759, 762-896, 900-904, 914-917, 922-927, 937-939, 942, 949-951, 961-974, 977-982, 992-1020, 1023-1028, 1032, 1037-1044, 1048-1054, 1058-1089, 1251-1349, 1554-1689, 1694-1713, 1717, 1721, 1804-1910 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py 323 310 4% 34-44, 52-57, 64-72, 77-79, 84-114, 119-125, 287-711 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/interface.py 346 245 29% 140-152, 160-168, 173-175, 184, 196, 222-243, 269-290, 294-298, 324-339, 364-377, 381-384, 387, 390, 407-419, 423-426, 429-432, 435-438, 441-444, 447-450, 453, 456, 459-465, 481, 491, 497, 501, 509-518, 521-524, 527, 530-533, 536-539, 542, 553-556, 559, 562, 565, 568, 573-576, 580, 583, 587, 590, 593-598, 603-610, 613, 616, 619, 622, 625-626, 631-639, 642, 645, 648, 651, 654-655, 660-666, 669, 672, 675, 678, 681-682, 687-695, 698-701, 704, 707, 710, 713, 716-717, 722-725, 728, 731-733, 737-740, 744, 747, 752, 755, 758, 761, 764, 767, 795-823 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/_gcrotmk.py 192 182 5% 66-182, 267-490 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/iterative.py 421 386 8% 73-77, 97-118, 137-198, 209-265, 276-337, 347-414, 514-647, 717-802 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lgmres.py 69 59 14% 128-235 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsmr.py 185 177 4% 197-482 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsqr.py 200 192 4% 81-95, 311-570 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/minres.py 203 196 3% 71-343, 347-363 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/utils.py 56 46 18% 23-27, 31, 65-123 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/matfuncs.py 354 279 21% 77-82, 103-115, 120-130, 155-173, 179-188, 191-193, 196-200, 203-205, 209, 218-235, 238-240, 243-246, 249-251, 255-256, 296, 338, 367-383, 387-390, 394-397, 401-404, 408-411, 415-418, 422-424, 428-430, 434-436, 440-442, 446-454, 458-466, 470-478, 482-490, 493-498, 501-506, 509-514, 517-525, 528-547, 595, 603-677, 700-709, 736-740, 762-764, 783-813, 833-855 /usr/local/lib/python3.8/dist-packages/scipy/sparse/sputils.py 176 146 17% 42-53, 58-63, 70, 80-90, 94, 105-118, 143-171, 176-180, 185, 194-207, 215-225, 229, 235, 241, 245-264, 269-314, 326-331, 338-339, 346-349, 353-356, 360-363 /usr/local/lib/python3.8/dist-packages/scipy/spatial/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/_plotutils.py 78 65 17% 11-27, 31-35, 81-90, 136-148, 212-264 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_procrustes.py 25 20 20% 101-132 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_spherical_voronoi.py 68 57 16% 133-166, 171-206, 217-243, 273-277 /usr/local/lib/python3.8/dist-packages/scipy/spatial/distance.py 633 503 21% 135-165, 170-172, 176-178, 182-183, 194-195, 200-220, 224-240, 244-259, 263-269, 273-286, 290-292, 307-309, 314-329, 334-339, 343-346, 350-357, 451-457, 508-524, 580-581, 620, 661-674, 708-721, 766, 813-820, 883-894, 941-950, 985-991, 1031-1037, 1079-1084, 1122-1130, 1170-1178, 1221-1236, 1286-1296, 1337-1342, 1353, 1397-1412, 1457-1462, 1507-1519, 1565-1578, 1624-1638, 1718, 1722, 1724-1731, 1999, 2006-2012, 2017, 2023, 2027, 2031, 2039-2050, 2069-2094, 2149-2212, 2254-2302, 2334-2339, 2344-2358, 2378-2380, 2403, 2407, 2708-2793 /usr/local/lib/python3.8/dist-packages/scipy/spatial/kdtree.py 419 379 10% 39-55, 78-83, 93-95, 98, 102, 120-126, 140, 154, 168, 182, 243-251, 256, 259, 262, 265, 268, 272-273, 277-281, 284-323, 329-407, 492-547, 550-572, 625-636, 663-705, 731-812, 842-889, 912-942, 978-996 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_groups.py 56 48 14% 6-58, 62-76, 80-90, 94-99, 103-105, 109-140 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_spline.py 176 159 10% 18-25, 30, 48-65, 83-104, 124-151, 168, 188, 220-248, 331-361, 364-404, 424-456 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/rotation.py 470 416 11% 15-17, 29-142, 146-150, 154-158, 162-173, 369-394, 407, 474-479, 568-612, 618, 674-707, 803-860, 907-910, 964-999, 1004, 1052-1073, 1159-1179, 1298-1329, 1397-1406, 1439-1443, 1467-1475, 1510-1527, 1560-1617, 1655, 1703, 1722-1727, 1768-1775, 1783-1838, 1915-1968, 2049-2070, 2090-2114 /usr/local/lib/python3.8/dist-packages/scipy/special/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/_basic.py 524 449 14% 100-112, 179-213, 250-254, 279-285, 305, 325, 345, 365, 397-401, 433-437, 469-473, 481-487, 515-519, 547-551, 602-606, 635-639, 667-671, 699-703, 747-755, 799-807, 888, 926-928, 941-943, 956-958, 971-973, 988, 1032-1034, 1072-1093, 1129-1150, 1201-1226, 1284-1307, 1343-1360, 1373-1380, 1424-1431, 1449-1460, 1476-1487, 1535-1538, 1586-1589, 1624-1639, 1666-1676, 1703-1713, 1740-1749, 1762-1764, 1777-1779, 1792-1794, 1800-1802, 1815-1817, 1830-1832, 1845-1847, 1860-1862, 1878-1880, 1904-1911, 1928-1935, 1984-1996, 2035-2050, 2065-2070, 2120-2158, 2195-2215, 2260-2270, 2333-2336 /usr/local/lib/python3.8/dist-packages/scipy/special/_ellip_harm.py 16 6 62% 97, 155-156, 160, 208-209 /usr/local/lib/python3.8/dist-packages/scipy/special/_logsumexp.py 34 12 65% 96-99, 105-106, 109-110, 118-119, 123, 127 /usr/local/lib/python3.8/dist-packages/scipy/special/_spherical_bessel.py 18 12 33% 53-56, 104-107, 153-156, 202-205 /usr/local/lib/python3.8/dist-packages/scipy/special/lambertw.py 4 1 75% 107 /usr/local/lib/python3.8/dist-packages/scipy/special/orthogonal.py 525 464 12% 128-151, 154-157, 160-173, 191-216, 264-290, 331-346, 394-404, 442-457, 503-524, 569-586, 630, 662-676, 744-760, 789-796, 825-831, 860-871, 898-910, 947-1020, 1050-1067, 1113-1124, 1157-1172, 1229-1248, 1282-1297, 1345-1364, 1400-1408, 1455-1463, 1499-1512, 1557-1566, 1603-1608, 1652-1659, 1696-1714, 1758-1765, 1802-1821, 1865-1866, 1894-1902, 1945-1952, 1980-1985, 2029-2039, 2080-2094, 2137-2143, 2170-2182 /usr/local/lib/python3.8/dist-packages/scipy/special/sf_error.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/spfun_stats.py 14 8 43% 86-95 /usr/local/lib/python3.8/dist-packages/scipy/stats/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_binned_statistic.py 159 145 9% 167-182, 336-349, 514-634, 640-675, 681-706 /usr/local/lib/python3.8/dist-packages/scipy/stats/_constants.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_continuous_distns.py 2994 1970 34% 35-36, 50-54, 97, 100, 103, 106, 109, 152, 155, 158, 161, 164, 180, 184, 188, 192, 196, 200, 204, 208, 235, 239, 242, 245, 248, 251, 254, 257, 260, 263, 266, 273-300, 342, 345, 348, 351, 354, 382, 385, 388, 391, 394, 422, 425, 428, 431-435, 438, 449, 461-463, 470-472, 482-486, 514, 520, 523-525, 528, 531, 534-539, 542-553, 563-658, 690-693, 697, 700, 703, 706-724, 754, 757, 760, 763-775, 778-779, 834-840, 843-852, 855, 858, 861, 864, 867, 870-887, 890-894, 948, 951, 954, 957, 960, 963, 969, 972-973, 1011, 1014, 1017, 1021, 1024, 1027, 1030, 1033, 1036, 1039, 1068, 1071, 1074, 1077, 1080, 1083, 1086, 1090-1091, 1130-1131, 1137, 1140-1141, 1144, 1147, 1150-1155, 1187, 1191, 1194, 1197, 1200, 1203, 1206, 1209-1213, 1242, 1245, 1248, 1251, 1281-1284, 1288-1289, 1292-1293, 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7383-7455, 7493, 7497, 7500, 7503, 7506, 7538, 7542, 7545, 7548, 7551, 7580, 7584, 7587-7604, 7607-7610, 7613, 7655, 7658, 7661-7663, 7666-7668, 7671, 7674, 7677-7678, 7681, 7728, 7731, 7734, 7737, 7740, 7743, 7746, 7799-7809, 7815-7824, 7830-7841, 7844-7861, 7867-7885, 7893, 7904, 7952-7953, 7959, 7965, 8053-8070, 8076, 8082, 8088, 8092-8093, 8097-8101, 8107-8109 /usr/local/lib/python3.8/dist-packages/scipy/stats/_discrete_distns.py 446 296 34% 43, 46, 49, 52-54, 58, 61-63, 66-67, 70-73, 76-84, 87-89, 119, 122, 126, 129, 134, 137, 140, 143, 146, 149, 193-194, 197, 200, 203-205, 208, 211-230, 263, 266, 270, 273-274, 277-278, 282-283, 286-289, 292-298, 331, 334, 337, 340, 343-344, 347, 350-351, 354-356, 359-364, 433, 436, 439-441, 444-449, 454, 459-471, 474-476, 483-489, 492-501, 504-513, 545, 548, 552, 555-567, 598, 601, 604-605, 609, 612-613, 616-617, 620-623, 626-631, 666, 669, 672-673, 676, 679-680, 683-686, 690-691, 694-698, 701-702, 731, 734, 739-740, 743-744, 747-751, 754-764, 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/usr/local/lib/python3.8/dist-packages/skimage/feature/texture.py 115 104 10% 106-155, 217-278, 326-337, 381-383, 444-493 /usr/local/lib/python3.8/dist-packages/skimage/feature/util.py 64 52 19% 10, 21, 27, 40, 77-135, 141-143, 166-174 /usr/local/lib/python3.8/dist-packages/skimage/filters/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/skimage/filters/_gabor.py 28 21 25% 10-12, 76-95, 170-177 /usr/local/lib/python3.8/dist-packages/skimage/filters/_gaussian.py 60 51 15% 100-126, 148-157, 258-290 /usr/local/lib/python3.8/dist-packages/skimage/filters/_median.py 14 9 36% 84-97 /usr/local/lib/python3.8/dist-packages/skimage/filters/_rank_order.py 14 12 14% 48-59 /usr/local/lib/python3.8/dist-packages/skimage/filters/_unsharp_mask.py 24 19 21% 9-16, 117-135 /usr/local/lib/python3.8/dist-packages/skimage/filters/_window.py 29 23 21% 99-129 /usr/local/lib/python3.8/dist-packages/skimage/filters/edges.py 126 82 35% 60-64, 91-93, 123-124, 162-185, 237-241, 270-271, 300-301, 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/usr/local/lib/python3.8/dist-packages/skimage/filters/thresholding.py 270 240 11% 52-81, 118-139, 204-234, 274-302, 340-356, 412-466, 511-521, 581-645, 694-730, 761, 799-838, 853-858, 891-915, 977-978, 1034-1038, 1076-1085, 1148-1179 /usr/local/lib/python3.8/dist-packages/skimage/io/__init__.py 33 1 97% 45 /usr/local/lib/python3.8/dist-packages/skimage/io/_image_stack.py 9 4 56% 20-23, 35 /usr/local/lib/python3.8/dist-packages/skimage/io/_io.py 44 22 50% 45, 51, 55-56, 59-61, 92, 126-136, 157-159, 179, 201 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/imageio_plugin.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/matplotlib_plugin.py 86 67 22% 44-58, 70-78, 97-112, 148-165, 176-200, 207-208 /usr/local/lib/python3.8/dist-packages/skimage/io/collection.py 152 119 22% 47-52, 77-78, 85-95, 175-205, 209, 213, 216-239, 258-313, 317-323, 327-328, 332, 335, 347, 366, 390, 435-443, 448 /usr/local/lib/python3.8/dist-packages/skimage/io/manage_plugins.py 136 22 84% 78, 115-116, 123, 188, 192-196, 202-206, 257, 293, 304-306, 330-331, 344-347 /usr/local/lib/python3.8/dist-packages/skimage/io/sift.py 27 20 26% 41-69, 73, 77 /usr/local/lib/python3.8/dist-packages/skimage/io/util.py 27 13 52% 24-41 /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 /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, 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/usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/microfrontend/python/ops/audio_microfrontend_op.py 21 6 71% 102-108 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/tensorboard/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/tensorboard/ops_util.py 12 2 83% 49-50 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/convert.py 171 125 27% 50, 58, 68-76, 98, 103, 142-236, 338-404, 437-460, 488-497, 525-526 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/convert_saved_model.py 67 50 25% 32-42, 59-60, 76-85, 97-109, 135-152, 187-207 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/interpreter.py 138 98 29% 46-50, 86-120, 125-128, 138, 160-165, 199-228, 237-238, 241-242, 254, 266-267, 283-295, 320-349, 359, 372-378, 386, 404, 417-421, 429, 446, 496, 510-511, 514, 547-548 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/slices.py 35 22 37% 37-45, 49-56, 59-80, 85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/ag_ctx.py 37 3 92% 58, 70, 73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config_lib.py 28 3 89% 48, 60, 64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/converter.py 125 24 81% 131, 136-138, 171, 187, 310-335, 340 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/function_wrappers.py 59 20 66% 55, 60-61, 66-68, 74, 76, 83, 85, 90-101, 107-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/naming.py 64 19 70% 62, 72-73, 79-88, 98-99, 110-113, 117-118 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/unsupported_features_checker.py 25 9 64% 35, 40-43, 46-49, 54, 57 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py 290 117 60% 55, 78-120, 146-147, 189, 206, 212-214, 218, 263-267, 280-287, 305, 308-309, 319-333, 343, 355, 362, 374, 413, 421-422, 425-426, 448, 450, 452, 459-464, 467-468, 481-482, 488-489, 499, 511, 514-522, 526-528, 532-534, 541-580, 588-590, 655-665, 732-734, 785-788, 831-836 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/conversion.py 322 94 71% 87, 117, 134, 151, 210, 308-310, 345, 350, 355, 388, 397-398, 406-410, 418-419, 440, 443-444, 451-453, 459-465, 474-476, 482-484, 509, 514-521, 526, 528-529, 538-632, 638-639, 683-690, 700, 704, 715, 724, 751, 758-759 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/directives.py 16 7 56% 44-46, 95-98 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/special_functions.py 33 20 39% 33-45, 53, 83-88, 113-119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/__init__.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/control_flow.py 449 379 16% 109-119, 128-135, 142-191, 204-234, 241-263, 272-297, 341-372, 377-401, 407-439, 452-485, 497-526, 538-586, 606, 621-677, 684-704, 735-751, 765-769, 772, 775-776, 779-781, 785-804, 808-811, 815-825, 830-846, 851-856, 861-886, 923, 932-966, 991-999, 1005-1030 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/data_structures.py 150 118 21% 45-54, 59-104, 109-163, 168, 189-199, 204-215, 220, 226-227, 257-269, 274-286, 291-295, 321-332, 337, 342-345, 351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/exceptions.py 24 14 42% 50-59, 77-80, 85-86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/logical.py 43 16 63% 29, 35, 47, 54, 64-67, 73, 78, 83-85, 90, 95, 100 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/py_builtins.py 253 176 30% 62, 68-84, 92-99, 120-161, 165-169, 173, 177-180, 184, 188-190, 195-197, 201, 205-207, 211-217, 221-223, 227-233, 237, 241, 247-273, 278, 284-294, 298, 303-319, 324-326, 336-343, 347-351, 355-361, 365, 369, 373-375, 379, 383, 387-389, 393, 397, 401-403, 407, 411, 415-417, 428-436, 440, 444-446, 454-462, 466, 470-472, 477-497, 501-507, 514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/slices.py 55 31 44% 55-67, 72, 77-81, 86, 91-92, 97, 117-125, 130, 135, 140, 145-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/special_values.py 27 10 63% 52, 55, 58-63, 66, 81, 93, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/anno.py 59 7 88% 40, 107, 134-138 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/ast_util.py 214 117 45% 101-105, 108-109, 112-113, 130-132, 137-142, 149-151, 154-157, 160-161, 164-170, 173-206, 222-227, 262-275, 294, 299, 309, 315-316, 318-319, 325, 330, 341, 350-351, 354-359, 362-378, 381-388, 392-394 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/cfg.py 416 121 71% 86-93, 132, 136-143, 188, 200, 334-335, 340, 356, 418, 420-422, 455-456, 468-471, 507-511, 515-526, 566, 571-576, 580-586, 662, 665, 671, 695-700, 714-728, 758, 761, 770, 773, 779, 782, 785, 794, 797, 822-841, 844-871, 874, 877, 884, 886, 906-912, 928-931 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/error_utils.py 80 60 25% 84-123, 144-145, 148, 165-175, 179-209, 212-219, 222-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/errors.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/inspect_utils.py 144 65 55% 57, 64, 72-79, 89, 91, 171-173, 195-237, 246, 261-262, 292-293, 299, 304-336, 349 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/loader.py 34 2 94% 80, 88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/origin_info.py 114 17 85% 77, 81-85, 117, 139-155, 180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/parser.py 116 28 76% 63-67, 75-78, 81, 93, 149-178, 197-198, 213, 239, 258, 279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/pretty_printer.py 87 69 21% 30-33, 36-38, 41, 44, 47, 50, 53, 56-57, 62-125, 129-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/qual_names.py 141 45 68% 48, 51, 58, 61, 70, 77, 80, 88, 96, 104, 107, 120-122, 134-138, 153-161, 173, 180, 187-191, 198, 203, 210-215, 244, 252, 265-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/activity.py 341 103 70% 121-124, 134-135, 189, 204-205, 244-249, 261-264, 269-274, 279, 281, 290-300, 324, 327, 330-332, 347, 352-361, 364, 372-382, 387, 400-404, 407, 445-456, 461-463, 466, 469, 472, 475, 481-497, 554-568, 571-579, 586 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/annos.py 17 1 94% 30 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py 121 9 93% 78, 191-194, 204-206, 209-211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py 166 26 84% 56, 78, 109, 159-174, 249, 279-292, 295-296 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/templates.py 147 28 81% 69-70, 86-87, 90-93, 98-99, 158-167, 175, 179, 190-195, 260, 283, 289-292 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/transformer.py 134 27 80% 131, 258-259, 264-265, 322, 329, 382-395, 398-406, 415-418, 421, 443 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/ag_logging.py 47 19 60% 37, 87-88, 111, 117, 126-128, 132-135, 140-142, 146-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/compat_util.py 14 3 79% 31, 37-38 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/context_managers.py 18 10 44% 38-49 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/misc.py 26 15 42% 42-52, 57-59, 63-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/py_func.py 48 37 23% 64-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/tensor_list.py 32 16 50% 28-40, 47-49, 52, 55-56, 59, 62, 65, 68 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/tensors.py 16 3 81% 39, 47, 53 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/testing.py 17 8 53% 30-37 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/type_check.py 7 1 86% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/client_lib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/device_lib.py 15 8 47% 34-42 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/pywrap_tf_session.py 31 11 65% 53-59, 66-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py 620 490 21% 57, 62, 66, 70, 74, 78, 84, 141, 192, 201, 206-207, 231, 245, 261-278, 301-316, 319, 322-326, 349-362, 374-379, 382, 386-396, 408-413, 416, 419-422, 434-437, 440, 443-446, 476-498, 501-502, 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92-113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/mlir.py 9 1 89% 41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/trt_convert.py 455 365 20% 58, 80-81, 86-88, 93-95, 105-108, 199-245, 258-284, 309-371, 384, 389-395, 487-542, 547-559, 562-569, 573-580, 587-592, 596-641, 649-654, 683-747, 763-830, 834-835, 842-844, 847-849, 861-867, 870, 873, 995-1022, 1033-1036, 1041-1047, 1051-1056, 1075-1124, 1146-1187, 1195-1266, 1340-1357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/jit.py 36 21 42% 36-37, 90-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/xla/xla.py 240 189 21% 115-122, 153-157, 160-168, 173-193, 198-266, 270-283, 286-288, 296, 301-303, 329-406, 430-441, 460-493, 507-528, 542-548, 555, 558-563, 566, 571-581, 598-629 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/__init__.py 64 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/batching.py 107 64 40% 81-86, 133-136, 179-192, 243-255, 282-285, 293-310, 315, 323-361, 364, 368, 394-422, 426 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/cardinality.py 27 8 70% 66, 95-98, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/counter.py 23 6 74% 51-54, 60, 66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute.py 70 45 36% 48-67, 71, 75, 88-119, 123, 130-133, 137, 150-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute_options.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/enumerate_ops.py 12 3 75% 55-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/error_ops.py 17 6 65% 50-53, 61-66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/get_single_element.py 13 3 77% 62-66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/grouping.py 177 125 29% 60-64, 106-124, 177-244, 252-268, 272-276, 283, 293-349, 353, 359, 362, 367, 375-388, 393-401, 407-413, 418-428, 433, 436, 439, 453-455, 459, 463, 467 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/interleave_ops.py 86 46 47% 95-101, 108-135, 138, 142, 169-226, 231, 272-276, 281, 290-291 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/iterator_ops.py 64 37 42% 32-38, 94-95, 184-221, 227-238, 243, 266-277, 281-284, 287, 290, 301-302, 313 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/optimization_options.py 76 10 87% 57, 63-66, 191, 209, 211, 232, 256, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/parsing_ops.py 61 41 33% 37-117, 121, 161-180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/prefetching_ops.py 103 71 31% 52-56, 72-80, 97-212, 220-225, 233-247, 250, 254, 257, 278-281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/random_ops.py 30 7 77% 37-40, 44, 53-54, 60 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/readers.py 300 228 24% 50-55, 59-63, 67-70, 87-107, 113-127, 134-151, 159-179, 184-203, 209-213, 268-311, 424-558, 584, 677-720, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/threading_options.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/unique.py 20 8 60% 45-48, 56-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/writers.py 22 7 68% 77-79, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/dataset_ops.py 1381 743 46% 203, 227-240, 252-279, 285, 289, 293, 306-318, 329, 354, 357, 367, 385, 406, 420, 423-427, 475-484, 640, 658-665, 668-673, 676, 734, 736, 744, 785-823, 840-846, 913, 958, 987, 1057-1084, 1105, 1137-1138, 1198, 1248, 1267, 1286, 1354, 1387, 1489-1497, 1620-1623, 1748-1751, 1780, 1804-1810, 1889-1891, 1919-1996, 2023-2024, 2051, 2064-2078, 2087, 2103, 2106-2150, 2186, 2189-2204, 2218, 2232, 2246, 2254, 2260, 2265, 2278, 2283, 2289, 2294, 2298, 2302, 2307, 2311, 2315, 2320, 2324, 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195, 198, 201 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/readers.py 198 115 42% 46-64, 80-90, 114-127, 131, 160-172, 176, 188-190, 196, 200, 216-229, 233, 242-291, 295, 299, 302, 336-349, 356, 362, 366, 378-380, 390, 398, 402, 430-447, 451, 488-505, 509, 524-527, 533, 537, 545-547 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/convert.py 23 13 43% 30-34, 49-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/nest.py 101 35 65% 47-50, 69-70, 75, 88-89, 91, 176, 180, 186, 225-244, 297, 302, 308, 314-320, 462 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/options.py 59 30 49% 23, 38-43, 46-49, 52-55, 81-84, 115, 120, 124, 131-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/random_seed.py 21 10 52% 42-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/structure.py 178 72 60% 45, 51, 57, 64, 100-114, 146-172, 198, 252, 350, 394-404, 435-439, 445-450, 456-462, 476, 486, 489, 493, 496, 499, 502, 506, 509, 512, 515, 518, 521, 524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/traverse.py 21 14 33% 39-56 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/__init__.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/analyzer_cli.py 581 520 10% 84-127, 151-158, 173-418, 444-458, 473-476, 479, 502-598, 616-639, 656-674, 690-738, 757-823, 834, 850-872, 891-908, 925-1050, 1069-1086, 1089-1095, 1107, 1113-1168, 1183-1240, 1244-1271, 1301-1361, 1404-1469, 1495-1520, 1533-1544, 1556-1579, 1601-1659 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_config.py 81 60 26% 41-50, 53-55, 71-97, 114-118, 121, 124-128, 139-147, 150-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_shared.py 177 136 23% 69-82, 99-110, 126-140, 144-147, 185-208, 229, 248-258, 264-273, 301-383, 405-431, 445-493 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/command_parser.py 207 177 14% 37-40, 43-47, 50, 72-101, 118-148, 164-171, 187, 204-216, 234-240, 259-281, 299-310, 328-339, 359-403, 426-439, 454-468, 486-491, 506-550 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/debugger_cli_common.py 451 353 22% 44-45, 49, 73-77, 94-107, 110, 123-132, 145-151, 198-212, 217, 221, 225, 228, 246-267, 285-300, 310-332, 343-345, 348, 359-362, 373-375, 403-431, 456-527, 563-585, 625-655, 682-716, 727, 741-757, 767, 783-790, 793-795, 807-812, 825-843, 850, 883-896, 909-915, 929-934, 948-953, 974-981, 992-1001, 1019-1023, 1026-1040, 1043-1047, 1051, 1063-1075, 1088, 1103-1105, 1125-1127, 1131, 1135, 1139, 1142, 1145, 1148, 1161-1162, 1170, 1173, 1176, 1179, 1194-1199, 1221-1248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/evaluator.py 52 37 29% 69-103, 115-116, 131-152 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/profile_analyzer_cli.py 302 252 17% 58-77, 103-131, 134, 137, 140, 143, 173-195, 209-220, 236-380, 396-439, 447-474, 504-575, 586-592, 613-733, 737-742, 756-762, 765, 786-802 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/tensor_format.py 240 215 10% 67-69, 103-199, 233-279, 321-403, 407-426, 449-481, 485, 503-568 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/ui_factory.py 23 16 30% 51-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/check_numerics_callback.py 122 89 27% 103-106, 114-119, 156-212, 216, 226-232, 242-289, 314-328, 414-419, 437-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/common.py 22 11 50% 44, 59-71, 86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_data.py 530 403 24% 53-56, 70-71, 74-76, 80, 99-102, 121-140, 144-148, 152-156, 160, 164, 168, 182, 199, 219-231, 241-242, 252-254, 266-268, 307-330, 334, 341, 350, 361, 375, 385, 395, 405, 415, 425, 435, 442, 455, 485-498, 502-522, 555-576, 580-581, 584-590, 594-598, 602-606, 621-623, 636-655, 669-673, 684, 717-718, 723-730, 739, 748, 771-799, 802-809, 812-817, 834-886, 906-918, 922, 933-935, 953-956, 970-971, 985-986, 1007-1020, 1036-1046, 1062-1066, 1085-1093, 1121-1137, 1141-1151, 1193-1229, 1249-1258, 1266, 1284-1295, 1313-1322, 1339-1344, 1363-1378, 1397-1415, 1448-1464, 1490-1497, 1522-1528, 1560-1567, 1594-1601, 1618-1625 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_events_writer.py 52 29 44% 51-54, 64-66, 76-79, 89-92, 102-104, 113-115, 125-128, 132, 137, 146, 150, 154, 157-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_gradients.py 120 82 32% 38-39, 53-65, 85-98, 102, 106, 109, 112, 157-169, 215-222, 267-284, 287-290, 304-306, 324-329, 338, 341-346, 353, 360-363, 369, 403-417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_graphs.py 237 183 23% 40-46, 50-51, 66-67, 83, 98, 116-139, 170-178, 191-214, 217, 220, 225-234, 241-266, 277-309, 320-329, 333-337, 346-354, 358-365, 372-393, 401-405, 413-431, 435, 440, 445-446, 450, 454, 458, 462, 466, 470, 474, 478, 503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_utils.py 69 60 13% 61-79, 137-197, 252-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/dumping_callback.py 301 237 21% 61, 67-68, 73, 77, 89-115, 125-133, 137, 141-143, 147, 151, 155-159, 176-189, 202-205, 216-232, 242-267, 288-301, 334-421, 453-514, 526-571, 590-608, 611-617, 632-643, 740-807, 819-826 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/op_callbacks_common.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/profiling.py 41 25 39% 44-56, 62, 76-80, 90-100, 104, 108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/source_utils.py 135 110 19% 44, 48-49, 53-54, 58, 78-84, 112-125, 145-158, 192-225, 262-325, 353-383 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/dumping_wrapper.py 42 28 33% 69-90, 110-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/framework.py 279 191 32% 130-131, 148-149, 177-178, 204-209, 259-270, 298-305, 314, 346-379, 383, 387, 391, 395, 428-517, 530-588, 605-634, 640-641, 645, 650, 654, 658, 661, 667-676, 679-684, 688, 691, 703, 733-734, 753, 806, 809-811, 814, 818-819, 822, 828-831, 867-874, 878, 915-923, 928, 948-951, 975-984, 989 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/grpc_wrapper.py 58 38 34% 57-66, 100-118, 137, 140, 146-151, 155-159, 193-210, 220-224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/hooks.py 95 67 29% 60-65, 83-86, 89, 92-141, 146-148, 176-180, 183, 186-217, 220, 256-264, 277-301, 335-349, 352-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/local_cli_wrapper.py 237 197 17% 80-131, 136, 139-206, 218, 230, 242-277, 280-285, 289-304, 320-374, 377-378, 399-446, 449-453, 462-468, 471-485, 488-513, 525-576, 579-603, 613, 633-642 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/all_reduce.py 392 355 9% 44-57, 71-75, 95-128, 145-157, 176-190, 223-251, 277-294, 317-356, 370-374, 394-423, 469-477, 496-518, 532-555, 582-589, 608-626, 640-645, 666-682, 701-711, 730-762, 767-776, 781-785, 790-791, 797-800, 820-842, 848-853, 860-865 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/central_storage_strategy.py 34 11 68% 56-70, 75, 103, 144, 162, 180, 246, 255-260 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py 173 108 38% 36-39, 44-56, 99, 117, 144-154, 172, 183-198, 202, 218-223, 227, 231, 235, 239, 243, 262-265, 269, 273, 305-321, 347-395, 411-415, 419, 423, 427, 431, 435, 441, 446, 450 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py 77 49 36% 30-31, 83-104, 116-149, 152-162, 166, 170, 174, 180, 184, 188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py 52 34 35% 29, 76-94, 112-120, 135-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py 160 132 18% 39-86, 96-107, 121-125, 135, 146-155, 164, 233-275, 280, 284, 288, 298-301, 321-358, 372, 386-394, 401-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py 78 42 46% 36-39, 43, 47-48, 76-79, 83-87, 91-95, 99, 103, 107, 111-114, 118, 124-126, 135-138, 160-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py 106 67 37% 40, 76-85, 90-95, 150-167, 170, 173, 201-214, 217, 220, 246-256, 278-300, 305, 308-319, 324 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py 228 165 28% 99-110, 116-118, 135, 148-158, 169-176, 180-183, 187-242, 247-344, 356-391, 399-408, 411-412, 421-422, 434-435, 456-470, 473-508, 511-530, 537-540, 545, 549, 553, 557, 561, 565, 577 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_util.py 11 3 73% 62-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_ops.py 452 356 21% 56-59, 64-73, 79-102, 107-116, 122-138, 144-151, 157-161, 165, 169-174, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/input_lib.py 673 488 27% 83-91, 124-131, 148-150, 155, 159, 162, 165-169, 172, 175, 180-216, 231-240, 245-256, 263-289, 292, 295-298, 301, 305-377, 388-391, 400, 405, 410, 415, 420, 424-427, 432, 444-450, 454, 459, 463, 469-481, 495-508, 513-521, 524, 527, 536, 546-571, 576, 580, 589-590, 593, 597-621, 650-705, 709-729, 734, 746-747, 763, 770-773, 785, 791-794, 797-803, 806-810, 830-845, 849-873, 879-885, 893-899, 906-909, 912-920, 923-927, 953-975, 1007-1019, 1025-1065, 1082-1086, 1089, 1093-1095, 1110-1112, 1130-1156, 1165-1167, 1171, 1174, 1178-1181, 1184, 1187, 1197, 1223-1239, 1244-1249, 1254, 1258, 1271, 1283, 1295, 1305-1306, 1319-1323, 1327, 1331, 1335, 1342-1344, 1348-1350, 1354-1357, 1361-1364, 1368, 1374-1388, 1396-1415, 1422-1431, 1440-1456, 1467-1478, 1498-1500, 1515, 1519-1521, 1543-1561, 1567, 1571-1579, 1595-1608 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/input_ops.py 41 27 34% 46-53, 58-61, 65, 79-101 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/mirrored_strategy.py 526 409 22% 65-74, 78-80, 113-203, 220-236, 241-252, 265-281, 285, 305-325, 329-332, 336-341, 426-429, 439-442, 451-476, 481-488, 492-505, 510-545, 553-569, 573-607, 613, 616, 626-633, 638, 645, 649-657, 664-669, 674-725, 733-739, 742-769, 777-788, 791-793, 796, 799-810, 818, 824-835, 838-846, 850-853, 856-858, 861, 865, 869, 873, 877, 881, 885, 889, 893, 896-898, 910, 914, 922-975, 978-1001, 1006-1010, 1016-1020, 1024-1026, 1030-1032, 1048-1086, 1090-1092 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/multi_worker_util.py 74 57 23% 40-46, 72-93, 120-134, 150-168, 173-188, 211-227, 243, 256, 261, 266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/numpy_dataset.py 45 29 36% 34-73, 79-91, 98 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/one_device_strategy.py 155 82 47% 80, 107, 146, 164, 182, 215, 231, 242, 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166, 204-216, 221-249, 268, 273, 284-292 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/boston_housing.py 25 16 36% 58-79 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar.py 19 12 37% 37-51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar10.py 31 19 39% 50-82 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar100.py 27 15 44% 59-84 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/fashion_mnist.py 26 15 42% 66-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/imdb.py 56 42 25% 99-158, 171-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/mnist.py 15 6 60% 57-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/reuters.py 47 33 30% 106-151, 164-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/distributed_training_utils.py 473 386 18% 68-80, 113-137, 142-166, 193-213, 236, 253-272, 308-322, 343-359, 363-366, 372-375, 381-399, 404-410, 424-426, 435, 447-449, 459-469, 502-573, 577-581, 585-588, 592-595, 600-620, 636-669, 684-689, 694-703, 732-773, 779-783, 790-825, 830-835, 840-849, 854-880, 885-902, 907-913, 918-947, 953-983, 993-1025, 1033-1040, 1045-1049, 1054-1062, 1066-1071, 1075-1076, 1080-1081, 1085-1086, 1090-1091, 1095-1096, 1101-1102, 1121-1133, 1137, 1151-1169, 1176-1192, 1197-1201 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/multi_worker_training_state.py 85 54 36% 41-45, 50-53, 57, 61-62, 66-69, 81-108, 118-141, 150-164, 172-177, 197-206, 219-221, 226-227 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer.py 1150 508 56% 367, 370, 433-438, 494, 499, 509, 513, 520, 529, 538-542, 548-558, 587-592, 617-637, 655, 673-699, 723-737, 754-758, 789, 796-799, 813-820, 830-831, 840, 848, 869, 889, 929-942, 945, 952, 960-972, 998, 1011-1013, 1018, 1023, 1067, 1119-1135, 1148-1166, 1237-1247, 1258-1268, 1274, 1282, 1285-1289, 1303-1308, 1327-1378, 1407-1464, 1510-1544, 1581-1588, 1600-1608, 1620-1628, 1644-1648, 1664-1668, 1686-1690, 1708-1712, 1731, 1751, 1770, 1789, 1807, 1827, 1847-1855, 1874-1878, 1901, 1908, 1948, 1955, 1972, 1977, 1986, 1988, 1999-2000, 2046-2048, 2052, 2062-2082, 2096-2097, 2111-2118, 2124-2147, 2150-2184, 2191-2193, 2196-2197, 2206-2216, 2243, 2248-2250, 2256, 2261, 2264, 2270, 2280, 2286-2291, 2302-2303, 2382, 2385, 2401-2406, 2424-2431, 2434-2444, 2463-2467, 2524-2527, 2529-2532, 2541, 2550-2551, 2586, 2597, 2606, 2610-2612, 2628, 2635-2642, 2646-2652, 2663, 2691, 2734, 2738, 2742, 2745, 2749, 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481-484, 488-490, 495, 500, 528-545, 575-592, 597-602, 607-617, 623-632 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/data_adapter.py 639 339 47% 56-57, 61-62, 115, 140, 156, 172, 185, 198, 203, 211, 216, 220-225, 229, 241, 249, 264-371, 388-408, 411, 414, 417, 420, 423, 427, 452, 467, 470-476, 493-523, 533, 539, 542, 546-548, 562-593, 596, 599, 602, 605, 608, 611, 622, 628, 630, 641-650, 660, 663, 666, 669, 672, 675, 691-699, 702, 705, 708, 711, 714, 720, 726-735, 762, 765, 779-780, 789, 801-802, 823, 830-831, 836-849, 855, 858, 861, 864, 867, 870, 891, 894, 917-922, 925-932, 944, 960, 965, 975-984, 1009-1011, 1028-1059, 1068, 1117, 1126, 1138-1147, 1162, 1184, 1190-1208, 1212, 1229-1268, 1301-1342, 1348, 1353-1356, 1366, 1375-1387, 1392-1399 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/input_layer.py 92 29 68% 100-104, 107-113, 115, 125-127, 138, 140, 146, 159-165, 180-187, 191, 268, 277, 280-281, 287, 290, 303 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/input_spec.py 100 45 55% 66-67, 76-77, 80-83, 87-93, 96, 106, 121-129, 155, 161, 167, 174-176, 182-184, 191, 199-200, 210, 212, 219-224, 232-236 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/network.py 939 481 49% 73-74, 78-79, 224, 254, 358-360, 383, 392, 405-408, 417-421, 426, 431-435, 447-451, 463, 468, 471-473, 486-491, 518-525, 551-556, 559, 563, 622-693, 714, 722-798, 830, 863, 869-874, 881-882, 910-913, 921-926, 945, 947, 953-954, 966-968, 986-991, 1051, 1113-1169, 1223, 1230-1235, 1237-1249, 1251, 1254, 1261, 1263, 1274-1283, 1298-1299, 1322-1325, 1346, 1360, 1368-1369, 1378-1379, 1390, 1400-1405, 1409-1410, 1433-1497, 1522-1525, 1553, 1559, 1565-1573, 1576-1582, 1587, 1591, 1603-1606, 1612, 1678, 1741-1744, 1787, 1802, 1818-1821, 1827, 1833-1847, 1852-1857, 1863-1877, 1883-1893, 1912-2056, 2069-2157 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/node.py 51 9 82% 76, 120-122, 158-159, 176-177, 182-184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/partial_batch_padding_handler.py 62 46 26% 34-36, 40-53, 58-62, 66-87, 91-111 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/sequential.py 196 102 48% 126, 167-169, 172, 190-199, 204, 215, 226-227, 241-255, 262-266, 270-299, 302-305, 311-312, 329-335, 359-363, 366-383, 387-403, 407-409, 413, 417-422 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py 459 256 44% 65-72, 86, 171, 196-197, 246-248, 347, 398-410, 450, 472, 480-494, 498, 527-544, 566-582, 785-881, 906-915, 936-952, 1039-1098, 1143, 1146-1151, 1273, 1290-1291, 1340-1359, 1400-1417, 1434-1439, 1464-1465, 1497-1500, 1525-1526, 1542, 1544, 1548-1549, 1559, 1568, 1572, 1576, 1581-1584, 1600, 1624-1627, 1634-1635, 1641, 1645, 1649-1660, 1663-1669, 1677-1688, 1691, 1701, 1721-1729, 1737, 1739, 1744, 1750-1760, 1785-1814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_arrays.py 259 222 14% 42-43, 126-458, 462-467, 471-477, 482-484, 501-535, 539-542, 546-550, 555-557, 564-583, 621-649, 678-687, 705-708 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_distributed.py 322 282 12% 46-47, 52-56, 73-120, 164-290, 315-420, 444-574, 599-672, 698-720, 737-754, 766-779, 786, 789, 793, 798 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_eager.py 119 98 18% 37-39, 55-82, 114-219, 250-283, 308-322, 349-366 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_generator.py 239 198 17% 123-336, 349-362, 398-418, 449-484, 493-509, 514-530, 535-538, 571-574, 604-606, 626-627, 659-666, 692-695, 706-707, 738-766, 791-800, 816-819 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_utils.py 834 682 18% 79-83, 92, 105, 110, 124, 131, 135-140, 143-145, 156-157, 161, 168-174, 178-192, 209-212, 241-244, 253-258, 262-286, 289-300, 303-310, 321-342, 345-347, 350-353, 358-364, 385-400, 428-436, 441-457, 486-583, 601-626, 633, 638, 657-697, 713-750, 771-808, 844-891, 907-915, 944-1042, 1046-1048, 1052-1056, 1069-1091, 1106-1133, 1142-1157, 1162-1186, 1210-1213, 1216-1228, 1233-1235, 1256-1270, 1278-1289, 1297-1306, 1333-1355, 1359-1364, 1380-1391, 1405, 1420-1421, 1435-1461, 1482-1503, 1525-1544, 1552, 1556, 1584-1613, 1639-1670, 1682-1689, 1694, 1700-1705, 1709-1711, 1723-1725, 1737-1759, 1787-1817, 1827-1840, 1850, 1857-1887, 1891-1892, 1896, 1923-1925, 1929, 1947, 1951, 1965-1970, 1975-1977, 1997-2007, 2012-2030, 2049-2078, 2110, 2123, 2133 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_v1.py 1067 853 20% 71-72, 143-163, 167, 175-180, 228-233, 301-463, 475-476, 481-486, 494-506, 522-545, 549, 556-586, 754-766, 874-879, 953-957, 970-976, 1025-1070, 1110-1146, 1166-1192, 1217, 1249-1251, 1276, 1288-1299, 1313-1337, 1348-1350, 1362-1380, 1386-1423, 1427-1437, 1460-1468, 1471-1478, 1494-1515, 1528, 1532, 1546-1627, 1632-1642, 1646-1650, 1673-1742, 1747-1756, 1760-1768, 1791-1799, 1805, 1818-1827, 1831-1856, 1876-1882, 1914-1938, 1949-1953, 1959-2003, 2006-2030, 2033-2041, 2049-2057, 2099-2170, 2242-2302, 2312-2436, 2440-2498, 2501-2534, 2572-2591, 2596-2636, 2643-2647, 2652, 2660, 2668, 2676, 2684, 2692, 2696-2702, 2706, 2710, 2714, 2732-2735, 2743-2746, 2753-2754, 2774-2775, 2781-2787, 2791, 2794-2802, 2806, 2813-2814, 2817, 2820, 2825-2829, 2832-2835, 2842-2846, 2886-2893, 2897, 2901, 2905, 2909, 2913, 2917, 2921, 2925, 2942-2972, 2979, 2983, 2987, 2991, 2995, 2999, 3002, 3005, 3009, 3012, 3016-3018, 3023-3042, 3048, 3054-3075, 3097-3099, 3103, 3107, 3111, 3115, 3135-3151, 3165-3175, 3179-3180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/estimator/__init__.py 23 12 48% 115-122, 212-219 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/initializers.py 89 8 91% 93, 118, 142, 166, 181, 194, 202, 207 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/__init__.py 180 21 88% 54-59, 157-159, 214-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/advanced_activations.py 135 78 42% 68-70, 73, 76-78, 82, 126-136, 140-157, 160-162, 165-172, 176, 203-205, 208, 211-213, 217, 244-246, 249-250, 253-255, 259, 279-281, 284, 287-289, 293, 345-358, 363, 369-375, 379 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py 730 587 20% 121-149, 152-191, 194-222, 225-250, 254-273, 277-282, 285-288, 291-295, 298-304, 319-325, 429, 582, 726, 874-898, 903-934, 937-994, 997-1027, 1030-1032, 1167-1190, 1195-1226, 1229-1295, 1298-1332, 1335-1338, 1430-1453, 1456-1496, 1499, 1502-1543, 1656, 1680-1719, 1841, 1866-1887, 1985-2001, 2004-2036, 2039-2056, 2060-2078, 2081-2093, 2134-2136, 2139-2141, 2144-2145, 2148-2150, 2216-2223, 2226-2239, 2243, 2248-2254, 2302-2305, 2308-2325, 2329, 2333-2335, 2384-2386, 2389-2393, 2396, 2399-2401, 2472-2492, 2495-2516, 2520, 2524-2526, 2584-2610, 2613-2642, 2646, 2650-2652, 2692-2694, 2697-2702, 2705-2708, 2711-2713, 2768-2788, 2791-2802, 2813-2833, 2838-2840, 2899-2925, 2928-2958, 2964-3014, 3020-3022 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional_recurrent.py 345 261 24% 165-181, 185-222, 228-275, 279-292, 302-350, 353-420, 510-537, 541-584, 587-644, 647-654, 657-660, 663-692, 842-869, 872-873, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 963-995, 999 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/core.py 458 303 34% 101-104, 107, 110-115, 118, 121-123, 179-183, 189-196, 199-212, 215, 218-224, 260-261, 264-266, 311-318, 321-325, 369-376, 379-383, 415-417, 420, 423, 426-428, 472-473, 495-515, 518-527, 530, 534-536, 571-578, 581-586, 589, 592-594, 630-632, 635-667, 670-679, 682-684, 714-716, 719-720, 723, 726-728, 825-841, 845-870, 874-890, 893-932, 937, 940-942, 945-966, 969-985, 989-1016, 1022-1048, 1129, 1150, 1154, 1176, 1183-1188, 1192, 1199, 1202-1208, 1211-1225, 1246-1250, 1253, 1256-1258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/cudnn_recurrent.py 189 143 24% 65-81, 84-121, 124-133, 137, 141-143, 147-149, 153, 156, 215-236, 240, 243-269, 272-316, 319-337, 400-422, 426, 429-465, 468-518, 521-540 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/dense_attention.py 132 95 28% 77-80, 92, 119-134, 138-167, 170-176, 180-196, 201-206, 307-308, 312-321, 332-335, 338-340, 443-444, 447-460, 473-480, 484-486, 491-495, 499-503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/embeddings.py 71 46 35% 101-123, 133-148, 151-154, 158-178, 181-185, 188-203 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/kernelized.py 80 56 30% 138-154, 157-200, 203-207, 210-216, 219-228, 234-251, 255-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/local.py 209 172 18% 151-171, 175-259, 263-274, 277-298, 301-334, 465-485, 489-581, 585-600, 603-625, 628-661, 704-724, 770-778, 807-816, 834-841 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/merge.py 342 244 29% 49, 67-87, 92-118, 122, 124-181, 187-202, 205-217, 251-254, 284-286, 290-293, 320-323, 357-360, 387-390, 417-420, 493, 496, 504-519, 526-536, 541-564, 567-571, 639-654, 659-674, 680-700, 704-722, 725, 728-733, 767, 796, 810, 846, 878, 892, 927, 947 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/noise.py 80 47 41% 60-62, 66-73, 76-78, 82, 111-113, 116-127, 130-132, 136, 170-174, 177, 180-203, 206-208, 212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization.py 511 442 14% 199-247, 258-271, 275-279, 283, 287-289, 292-295, 300-303, 306, 311-503, 506-516, 519-521, 525-635, 640-693, 696, 699-708, 711-722, 725-890, 893, 896-928, 1010-1034, 1042-1055, 1058-1105, 1109-1193, 1196, 1199-1212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization_v2.py 43 23 47% 136, 161-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/pooling.py 256 162 37% 60-70, 73-81, 84-98, 101-108, 193, 235, 272-282, 285-297, 300-315, 319-326, 458, 508, 544-554, 557-574, 577-596, 600-607, 654, 704, 714-717, 720-724, 727, 730-732, 775-777, 780-789, 792, 840-841, 849-852, 855-859, 862, 865-867, 906-909, 947-950, 957-960, 963-967, 970, 973-975, 1008-1011, 1043-1046 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding.py 218 164 25% 83-129, 136, 139-145, 163-170, 173-178, 181-187, 190-193, 196-205, 208-217, 220-234, 237-292, 317-318, 322-341, 345-364, 379, 394-412, 416, 421-429, 433-447, 452-458 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding_v1.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py 485 384 21% 78-83, 86-89, 92-96, 99-100, 104-110, 135-138, 141-144, 147-171, 174-175, 179-184, 214-219, 222-277, 280-281, 285-291, 315-316, 319-320, 323, 326-330, 367-383, 386-402, 405, 408-413, 467-506, 509-541, 544, 547-555, 570-578, 647-670, 696-704, 768-790, 793-816, 819, 822-829, 879-918, 921-952, 955, 958-966, 987-997, 1043-1054, 1057-1067, 1070, 1073-1078, 1118-1132, 1135-1157, 1160-1161, 1165-1171, 1213-1227, 1230-1252, 1255-1256, 1260-1266, 1270-1273, 1277-1282 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup.py 230 173 25% 104-190, 193-208, 211-212, 215, 218-222, 225-231, 234-235, 239-241, 245-246, 250-257, 260, 263-268, 283-285, 288-294, 297-305, 312, 333-353, 356-358, 361-389, 392-401, 404-429, 451, 455-465, 469-477, 489-495, 499, 504-507, 511-518, 523-524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup_v1.py 38 22 42% 63-66, 69, 72-76, 79-81, 84-85, 89-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization.py 95 56 41% 63-71, 75-104, 109-111, 114, 117, 120-122, 126-128, 149, 155-173, 178-197, 202, 212-220, 225-230, 234-235, 241 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization_v1.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py 309 235 24% 214-320, 325, 328-329, 332, 336-338, 342-343, 347, 350-359, 362-364, 379-404, 407, 410-420, 427, 461-521, 528-534, 537-543, 546-592, 595-633, 663-665, 669-694, 698-715, 730, 745-762, 766, 771-781, 785-802, 807-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization_v1.py 26 9 65% 84, 87, 91-97 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent.py 1046 802 23% 85-104, 108, 113-118, 121-131, 135-160, 165-180, 183-191, 195-200, 397-439, 444-447, 454, 457-505, 513-519, 522-592, 606-621, 625-645, 648-707, 717-814, 820-854, 857-866, 871-873, 890-943, 946-965, 969-974, 978, 1052, 1061, 1066, 1069, 1096-1099, 1110, 1121, 1124, 1131, 1153-1156, 1174-1177, 1266-1290, 1294-1319, 1322-1340, 1343, 1346-1378, 1489-1526, 1529-1530, 1535, 1539, 1543, 1547, 1551, 1555, 1559, 1563, 1567, 1571, 1575, 1579, 1583, 1587, 1590-1625, 1629-1631, 1709-1740, 1744-1778, 1781-1879, 1882-1907, 1910, 2032-2071, 2074-2075, 2080, 2084, 2088, 2092, 2096, 2100, 2104, 2108, 2112, 2116, 2120, 2124, 2128, 2132, 2136, 2140, 2144, 2147-2188, 2192-2194, 2275-2312, 2316-2353, 2357-2367, 2371-2376, 2379-2436, 2439-2477, 2480, 2518-2530, 2536-2549, 2552-2559, 2678-2717, 2720-2721, 2726, 2730, 2734, 2738, 2742, 2746, 2750, 2754, 2758, 2762, 2766, 2770, 2774, 2778, 2782, 2786, 2790, 2793-2834, 2838-2840, 2844-2852, 2877-2913, 2918, 2923-2926, 2931-2944, 2964-2991, 3008-3011 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent_v2.py 321 259 19% 165, 345-385, 388-398, 403-454, 462-503, 547-587, 593-673, 713-791, 895, 1059-1102, 1107-1207, 1233-1238, 1288-1321, 1359-1444, 1485-1569, 1596-1602, 1625-1626, 1631-1635, 1642-1645, 1649-1650 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/rnn_cell_wrapper_v2.py 43 17 60% 42-43, 66, 71-72, 75-82, 86-89, 98-100, 113, 124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/serialization.py 57 18 68% 68, 84-105 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/wrappers.py 370 318 14% 52-54, 57-60, 64-67, 70-77, 81-86, 126-137, 163-172, 175-184, 187-195, 199-251, 290-327, 400-456, 460-469, 480-495, 499-518, 522-593, 602-677, 680-681, 684-688, 691-707, 711-715, 718-728, 733-745 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/losses.py 266 127 52% 105, 107, 140-144, 157, 161, 177, 181-201, 243-246, 249-253, 312, 372, 433, 494, 570-576, 729, 793, 855, 916, 974, 1032, 1093, 1162, 1196-1198, 1228-1230, 1262-1266, 1300-1304, 1309-1319, 1347-1350, 1379-1382, 1411-1415, 1438-1444, 1481-1487, 1517-1527, 1555-1557, 1585-1594, 1632-1636, 1667-1669, 1714-1716, 1782, 1797-1803, 1816, 1831, 1853-1863 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/metrics.py 759 457 40% 151, 164-167, 186-207, 212, 216, 224, 244, 253, 268, 274, 290, 328-374, 377-385, 425, 516-518, 533-547, 551-554, 595-604, 608-618, 624-628, 665, 710, 765, 809, 889, 913-918, 936, 944-948, 951-952, 956-958, 1004, 1054, 1104, 1154, 1225-1237, 1256, 1269-1271, 1274-1275, 1279-1285, 1351-1363, 1382, 1395-1397, 1400-1401, 1405-1411, 1423-1450, 1465, 1478-1479, 1537-1541, 1546-1556, 1561-1566, 1623-1627, 1632-1642, 1647-1652, 1701-1705, 1713-1723, 1728-1730, 1782-1786, 1796-1807, 1810-1813, 1920-1981, 1985-2023, 2038-2067, 2129-2164, 2167-2214, 2219-2223, 2228-2244, 2295, 2326, 2359, 2390, 2423, 2456, 2491, 2523, 2555, 2570-2575, 2579, 2610, 2641, 2672, 2729-2734, 2754-2776, 2780-2799, 2803, 2806-2808, 2840-2844, 2847-2857, 2861, 2865, 2877-2908, 2911-2916, 2919-2920, 2973, 3112, 3136, 3155-3157, 3160-3166, 3170-3174, 3178-3184, 3200-3202, 3219, 3239-3252, 3268, 3285-3294, 3310-3312, 3327-3330, 3335, 3340, 3345, 3355-3364, 3368 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py 243 135 44% 63-69, 73-75, 82-85, 90, 93-96, 99-100, 104-105, 109-110, 113, 117-127, 131, 134-143, 161, 165, 169, 173, 176, 179, 183, 187, 190-191, 194-195, 198-199, 202-203, 206-207, 210-211, 214-215, 218-219, 222-223, 226-227, 230-231, 234-235, 238-239, 242-243, 246, 250, 254, 258, 262, 266, 270, 274, 277, 284, 289, 292, 304, 308, 312, 316, 325, 328, 331, 334, 337, 340, 343, 346, 349, 352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388-392, 395-399, 402-406, 409-413, 439-462, 481-483 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/device_compatibility_check.py 66 48 27% 54-61, 73-128, 154-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/get_layer_policy.py 11 3 73% 38-41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale.py 15 4 73% 29, 33-38, 48 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale_optimizer.py 140 81 42% 48, 123-155, 160, 182-189, 212-214, 218-224, 227-229, 232, 238-245, 251-272, 279, 284-286, 293-298, 301-303, 313, 317, 320, 323, 327, 330, 333, 336, 345, 349, 353, 357, 366, 372, 395-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/policy.py 133 60 55% 328, 331, 339, 341, 348, 361-371, 374, 376, 378, 382-388, 460, 463-471, 475-479, 508-509, 519-520, 549-558, 573-578, 582-586, 606, 612-617, 621-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/models.py 277 231 17% 57, 61, 67-74, 90-129, 162-215, 234-247, 265-276, 307-381, 419-426, 452-533, 550-557, 570-589, 638-723 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adadelta.py 49 28 43% 100-104, 108-111, 114-115, 121-127, 130-136, 147-153, 165-172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adagrad.py 60 35 42% 91-100, 103-107, 110-111, 118-124, 143-147, 150-155, 164-169, 179-186 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adam.py 84 61 27% 144-150, 155-161, 164-173, 185-192, 195-217, 232-269, 272-281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adamax.py 63 41 35% 103-108, 112-115, 118-126, 137-144, 157-181, 184-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/ftrl.py 60 42 30% 108-136, 141-146, 149-150, 162-181, 194-214, 228-245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/gradient_descent.py 53 24 55% 112, 118-120, 123-124, 128-143, 148-156, 161-166, 177-184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/learning_rate_schedule.py 249 184 26% 44, 48, 60, 135-140, 143-154, 158, 225-233, 236-256, 259, 360-367, 370-392, 399, 480-486, 489-502, 505, 573-578, 581-594, 597, 668-675, 678-716, 719, 803-810, 813-831, 835, 925-934, 937-965, 969, 983, 988 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/nadam.py 96 72 25% 92-105, 108-124, 127-146, 164-165, 168-188, 191-228, 231-239 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/regularizers.py 65 23 65% 152, 172, 192, 215-222, 225, 244, 280, 285, 290, 302, 304-311, 315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/hdf5_format.py 354 269 24% 43-44, 82-130, 158-217, 251-258, 272, 287-310, 316, 318, 320, 323-393, 397-404, 446, 468-473, 483-519, 527-572, 585-598, 610-613, 623-644, 661, 665, 682, 699, 729-789, 809-831, 851-856, 880 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/model_config.py 28 14 50% 29-30, 50-55, 86-90, 114-116 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/save.py 45 21 53% 39-40, 113-137, 181-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/base_serialization.py 33 10 70% 34, 43, 54, 74, 87-95, 106, 122, 172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/constants.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/json_utils.py 32 19 41% 38-41, 44, 48-56, 60, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/layer_serialization.py 74 40 46% 36, 41, 48-69, 72, 76, 81-96, 100-105, 111-119, 127, 131, 141, 144, 152, 155-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/load.py 460 367 20% 116-137, 143-148, 175-202, 209-215, 219, 223-234, 239-291, 295-313, 317-344, 348-363, 367-393, 401-431, 434-450, 454-466, 470-472, 486-510, 514-520, 523-539, 544-572, 577-579, 609-623, 627-646, 650-654, 662-687, 694-719, 723-725, 730-738, 745-753, 758-778, 786-791, 802-831, 835, 838-841, 848-863, 872-882, 885, 890-902, 915-922, 931-948, 953-958, 966-968, 972 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183-186, 190-203, 207-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/utils.py 112 88 21% 56-96, 101-113, 117-120, 125-128, 150-199, 214-220, 224-230, 234-239, 243-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model_experimental.py 135 89 34% 133-145, 151-155, 160-163, 168-221, 226-227, 231, 255-326, 331-361, 371, 416-430 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saving_utils.py 138 110 20% 48-53, 77-87, 91, 112-142, 147-191, 197-199, 204-233, 245-264, 270-275, 280-285, 291-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/all_utils.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/conv_utils.py 172 152 12% 29-48, 68-87, 103-113, 128-137, 160-186, 190-197, 201-208, 227-233, 279-309, 358-400, 439-456, 475-482 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/data_utils.py 425 321 24% 56-57, 62-64, 69-104, 111-114, 133-160, 214-282, 286-294, 316-325, 342-350, 357-367, 370, 373, 376-384, 389-393, 453, 462, 467, 471-472, 485-486, 506-514, 519-525, 531-533, 538, 559-645, 662, 689-715, 718, 728-738, 743, 753-759, 762-763, 768, 780, 791, 807-808, 819-826, 831-833, 837-860, 872-880, 894-907, 923, 946-947, 958-964, 968-974, 987-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/generic_utils.py 363 275 24% 72-73, 76-79, 82-83, 114, 135, 140, 167-189, 207-210, 216-221, 251-257, 263-296, 301-305, 314-347, 356, 360-382, 386, 388, 392, 397-402, 415-426, 441-474, 490-493, 518-540, 553-675, 678, 691-692, 717-739, 754-756, 766, 774, 779-781, 792, 797 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/io_utils.py 84 52 38% 32-33, 84-99, 102, 105-134, 143, 152, 161, 170, 182-185, 201-209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/layer_utils.py 202 85 58% 50, 55, 62-69, 79-90, 136, 152-162, 205-208, 222-223, 228, 262, 287-294, 310-326, 345-357, 382-396, 400-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/losses_utils.py 58 39 33% 48-49, 54-55, 61-67, 91-112, 117-121, 137-148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/metrics_utils.py 216 159 26% 75-93, 119-149, 157-166, 170-173, 179-182, 199-204, 227-234, 296-456, 472-475, 495-539 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/mode_keys.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/multi_gpu_utils.py 82 65 21% 31, 35-36, 157-266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/np_utils.py 26 16 38% 49-61, 76-78 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/tf_utils.py 203 105 48% 61-64, 83-91, 95-99, 116-152, 178, 181, 226, 231, 248, 251, 266-295, 319, 345, 352, 355-356, 359, 392, 397-404, 408, 427-428, 452, 458-462, 467-472, 478, 481, 485, 490-496, 521 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/version_utils.py 34 4 88% 69, 79-85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/vis_utils.py 150 125 17% 45-53, 57-59, 64-65, 98-249, 278-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/scikit_learn.py 106 77 27% 75-77, 88-106, 117-119, 130-132, 150-168, 181-187, 214-223, 240-242, 263-270, 293-308, 332-333, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/base.py 218 171 22% 105-110, 148, 153, 195-234, 244-246, 250-258, 262-269, 273-279, 282-294, 300-302, 305-314, 376-481, 507-552, 555-569, 573, 578, 582-593 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/file_io.py 257 108 58% 58, 66, 71, 76, 84, 120, 139-165, 169-170, 174-181, 189-191, 202, 205-208, 211, 220-221, 232, 316-320, 333-334, 350, 366-374, 396, 412, 458, 474, 526-535, 589-590, 610-614, 653, 681, 701-729, 782-790, 808-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/python_io.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/tf_record.py 81 48 41% 90-99, 114-125, 129-149, 170-171, 212, 294-298, 313, 317, 321 /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/module.py 96 58 40% 107-121, 130, 135-139, 154, 169, 193, 249-252, 287-291, 295, 299, 303, 310, 314, 326-378 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_grad.py 573 409 29% 44, 50, 72-212, 218, 228, 247-260, 266-285, 301-305, 319, 324-329, 337, 342, 347, 352, 358, 364-368, 374-382, 389-399, 407-424, 430-460, 466-500, 505-507, 516, 525, 531-538, 552-564, 569-585, 593-615, 627-687, 692-700, 705-713, 719, 728, 737, 742, 747, 755, 767, 773, 778, 784-785, 791-792, 810-832, 842-854, 864-865, 876-877, 882-883, 889-890, 898, 906-907, 915, 923-928, 934-939, 947-948, 953-954, 959, 964, 970, 975-1027, 1032-1090, 1095-1097, 1102-1107, 1112-1115, 1120-1123, 1128-1130, 1135-1150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_ops.py 1121 797 29% 193-195, 277, 341-344, 426, 475, 503, 530, 576, 602, 620-630, 649, 683, 715, 732-753, 787, 802-811, 825-836, 840, 902-973, 1037, 1137-1178, 1224, 1270-1277, 1328-1342, 1357-1393, 1407-1414, 1419-1425, 1446-1455, 1501-1511, 1594, 1601-1605, 1659-1693, 1746, 1786-1790, 1832, 1882, 1943-1961, 2042, 2112-2129, 2190-2210, 2368-2371, 2514-2517, 2653, 2661-2667, 2710, 2712, 2721-2723, 2728-2730, 2732, 2774, 2819, 2825-2850, 2883, 2918, 2923-2931, 2959-2981, 3023, 3092-3139, 3199, 3260-3294, 3311-3316, 3362-3392, 3404-3438, 3443-3456, 3519-3527, 3541, 3553, 3565, 3607-3650, 3661-3670, 3678, 3687, 3695, 3704, 3712, 3720-3729, 3845-3848, 3966-4009, 4016-4025, 4064-4090, 4141-4145, 4197, 4243-4251, 4340-4348, 4406-4410, 4424, 4512-4524, 4535, 4554-4560, 4589-4682, 4835-4846, 4852, 4860-4934, 4956-4975, 5007-5019, 5043-5060, 5106-5113, 5172-5184, 5307, 5350-5352, 5405, 5410-5416, 5441-5455, 5499-5565, 5571-5578, 5583-5591, 5641-5644 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/batch_ops.py 25 13 48% 79-111 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bitwise_ops.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/boosted_trees_ops.py 144 76 47% 63-66, 74-89, 93-95, 111-126, 129, 133, 138-140, 143, 147, 150, 153-156, 159, 162, 176-190, 203-204, 214-227, 234, 238, 245-247, 250, 254-255, 259-262, 271-276, 290, 303 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/candidate_sampling_ops.py 37 12 68% 83-84, 148-149, 208-209, 299-300, 337-338, 386-387 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/check_ops.py 590 425 28% 73-78, 83-85, 91, 238-271, 285-297, 327-372, 392-402, 435, 442-455, 487, 494-506, 539, 547-560, 593, 601-614, 648, 654-658, 696, 705, 758, 810-838, 873, 879, 915, 923, 959, 966, 1003, 1012, 1037-1061, 1094, 1124-1156, 1189, 1222-1255, 1259, 1263-1269, 1294-1321, 1353, 1386-1416, 1436, 1462-1475, 1494, 1515-1525, 1542-1555, 1561-1564, 1568-1574, 1578, 1648, 1715-1866, 1872-1883, 1910, 1947-1951, 1989-1993, 2012-2040, 2068-2074, 2096, 2120-2130, 2175-2178, 2183-2184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/clip_ops.py 107 77 28% 105-122, 132-151, 193-213, 239-263, 316-357, 391-404 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/clustering_ops.py 263 210 20% 134-149, 165-172, 186-199, 216-225, 241-263, 267, 289-315, 321-325, 354-392, 397-433, 450-499, 516-535, 584-597, 601-607, 612-615, 634-701, 708, 716-718, 721-733, 737-750, 753-760, 767 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/collective_ops.py 25 16 36% 50-52, 83-85, 127-136, 167-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/cond_v2.py 435 374 14% 60-94, 109-188, 220-297, 311-337, 358-375, 380, 406-435, 440-451, 470-478, 483-493, 513-538, 548-563, 587-614, 621-679, 684-689, 707-715, 720-721, 734-735, 749-750, 755-756, 763-801, 805-811, 820-826, 845-860, 865, 870, 873-942, 947-976, 986-1064, 1088-1124, 1138-1148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/confusion_matrix.py 62 42 32% 59-92, 152-201, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_grad.py 121 89 26% 42-88, 98-136, 143, 149-182, 194, 199, 209-232, 237, 243 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_ops.py 1318 1123 15% 86-104, 148-176, 189-198, 202-211, 240-259, 274-283, 305-317, 341-362, 390-424, 431-434, 438-442, 450-457, 473-492, 506-529, 545-562, 578-591, 596-618, 641-654, 663-679, 684, 689, 693, 697, 707, 718-724, 727, 732-734, 738-740, 744-745, 749-750, 754-758, 762-764, 768-785, 791-792, 796, 799, 802, 805, 808, 833-849, 858-867, 872, 876, 880, 884-886, 890-892, 895, 906-922, 927-933, 936, 940-972, 975, 979-1032, 1036-1050, 1053-1059, 1063-1083, 1086, 1090-1093, 1175-1296, 1310-1314, 1392, 1397-1401, 1433-1440, 1456-1475, 1484-1529, 1534, 1539, 1544, 1549, 1554, 1559, 1564, 1569, 1580-1609, 1612, 1625-1630, 1633, 1636-1638, 1642-1694, 1708-1719, 1727-1768, 1773-1788, 1810-1838, 1860-1904, 1928-1986, 2001-2086, 2093-2098, 2103-2223, 2230-2265, 2268-2296, 2300, 2481, 2668-2770, 2789-2803, 2807-2810, 2838-2848, 2854-2859, 2883-2921, 2957, 2993-3027, 3038-3048, 3064-3081, 3105-3144, 3177-3196, 3220-3257, 3282-3291, 3388, 3496, 3579, 3586-3587, 3592, 3596, 3599, 3602, 3615-3621 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_state.py 402 338 16% 58-105, 124-206, 211, 216, 221, 230-235, 240, 245, 254-261, 266, 271, 276, 281, 286, 291, 296, 332-386, 404-435, 451-491, 498, 502-511, 538-551, 555-557, 561-563, 573-590, 607-645, 660-716, 727-757, 771-781, 786-807, 812-831, 836-839 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_util.py 156 108 31% 47, 54, 59-65, 69-70, 75-79, 88-89, 94, 99, 104, 109, 114-130, 135-147, 152-155, 160-163, 168, 173-177, 187, 208-209, 226-229, 244-247, 252-255, 259, 263, 287-341, 344-363, 368-371 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_util_v2.py 98 66 33% 44-50, 62-65, 78, 82, 105-109, 124-126, 155-199, 215-218, 223, 228, 251-261, 266-287 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_v2_func_graphs.py 11 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_v2_toggles.py 20 3 85% 58, 67, 94 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/critical_section_ops.py 137 98 28% 55-62, 66, 71-74, 81-85, 109-120, 198-205, 209-228, 232, 264-337, 342-374, 379-381, 406-415 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ctc_ops.py 417 343 18% 57-60, 64-68, 184, 211-236, 246-253, 268, 283, 328-331, 383-391, 437, 460-502, 519-542, 548-556, 564-576, 583-621, 648-682, 686-689, 694-702, 712-720, 729, 787-816, 881-921, 997-1055, 1077-1117, 1132-1146, 1178-1187, 1204-1214, 1238-1301, 1345-1417, 1422 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/cudnn_rnn_grad.py 21 9 57% 27-30, 52-55, 79-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/custom_gradient.py 189 135 29% 64, 206, 211-214, 251-255, 258, 264-281, 288-298, 304-402, 408-454, 480-507, 551-560 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/data_flow_grad.py 51 20 61% 33-45, 53-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/data_flow_ops.py 622 469 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py 3989 3627 9% 39-47, 52, 64-78, 84, 101-110, 115, 138-154, 160, 195-236, 242, 265-277, 282, 294-308, 314, 338-347, 352, 364-378, 384, 425-462, 468, 497-528, 534, 547-566, 571-577, 634-678, 684-695, 774-827, 833-857, 885-926, 932, 959-1015, 1021-1051, 1064-1078, 1084, 1096-1122, 1128-1137, 1150-1176, 1182-1191, 1206-1233, 1239-1249, 1266-1306, 1311-1334, 1351-1401, 1407-1433, 1455-1508, 1514-1541, 1558-1608, 1614-1640, 1666-1708, 1713-1740, 1762-1815, 1821-1848, 1876-1930, 1936-1965, 1982-2022, 2027-2050, 2067-2119, 2125-2152, 2175-2229, 2235-2263, 2280-2330, 2336-2362, 2390-2433, 2438-2465, 2487-2541, 2547-2575, 2603-2659, 2665-2694, 2730-2771, 2777, 2812-2868, 2874-2904, 2980-3020, 3026-3050, 3084-3125, 3131, 3164-3220, 3226-3256, 3278-3290, 3295, 3316-3342, 3347-3356, 3382-3405, 3411, 3444-3468, 3474, 3507-3547, 3553-3571, 3609-3633, 3639, 3676-3716, 3722-3740, 3766-3805, 3811-3828, 3853-3864, 3869, 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7032-7044, 7064-7091, 7097-7109, 7123-7137, 7143, 7156-7182, 7188-7198, 7214-7240, 7246-7256, 7272-7287, 7293, 7308-7335, 7341-7353, 7390-7417, 7423-7435, 7450-7465, 7471, 7488-7538, 7544-7568, 7613-7672, 7678-7706, 7722-7737, 7743, 7758-7785, 7791-7803, 7821-7848, 7854-7866, 7886-7937, 7943-7968 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_dataset_ops.py 3330 3013 10% 34-74, 80-98, 129-160, 166-184, 203-230, 236-244, 266-315, 321-346, 367-394, 400-411, 429-470, 476-496, 517-566, 572-597, 620-661, 667-687, 704-745, 751-772, 788-830, 836-856, 875-926, 932-955, 973-1015, 1021-1037, 1053-1094, 1100-1119, 1141-1152, 1157-1164, 1178-1197, 1202-1209, 1226-1252, 1257-1271, 1285-1305, 1310-1318, 1335-1355, 1360-1367, 1379-1405, 1411-1419, 1434-1476, 1482-1502, 1528-1574, 1580-1601, 1626-1658, 1664-1678, 1696-1730, 1736-1751, 1784-1819, 1825-1846, 1869-1888, 1891, 1896, 1910-1919, 1928-1952, 1980-2028, 2034-2057, 2073-2119, 2125-2146, 2166-2211, 2217-2241, 2256-2301, 2307-2331, 2349-2363, 2366, 2371, 2380, 2382-2385, 2393-2412, 2427-2468, 2474-2494, 2514-2555, 2561-2580, 2594-2620, 2626-2636, 2652-2698, 2704-2725, 2750-2761, 2766-2773, 2792-2850, 2856-2884, 2919-2970, 2976-3001, 3031-3073, 3079-3105, 3129-3182, 3188-3214, 3232-3279, 3285-3309, 3331-3378, 3384-3405, 3422-3452, 3458-3470, 3484-3512, 3518-3528, 3565-3618, 3624-3650, 3681-3725, 3731-3759, 3772-3798, 3804-3813, 3828-3867, 3873-3892, 3905-3931, 3937-3946, 3958-3984, 3990-3998, 4023-4066, 4072-4095, 4124-4175, 4181-4208, 4256-4310, 4316-4344, 4398-4454, 4460-4489, 4549-4608, 4614-4645, 4671-4736, 4742-4775, 4801-4867, 4873-4906, 4936-4979, 4985-5012, 5032-5073, 5079-5100, 5125-5180, 5186-5212, 5230-5271, 5277-5297, 5312-5346, 5352-5364, 5384-5432, 5438-5463, 5493-5537, 5543-5567, 5598-5650, 5656-5682, 5699-5743, 5749-5770, 5788-5829, 5835-5855, 5870-5897, 5903-5915, 5936-5964, 5970-5981, 6000-6041, 6047-6067, 6089-6114, 6120-6135, 6149-6182, 6188-6203, 6223-6251, 6257-6268, 6281-6307, 6313-6322, 6393-6436, 6442-6465, 6478-6504, 6510-6519, 6541-6587, 6593-6618 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_debug_ops.py 422 389 8% 50-90, 96-116, 144-185, 191-211, 240-290, 296-322, 358-415, 421-451, 479-529, 535-561, 627-696, 702-739, 815-858, 864-883 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_decode_proto_ops.py 80 64 20% 102-165, 171-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_encode_proto_ops.py 59 44 25% 81-123, 129-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_experimental_dataset_ops.py 3926 3668 7% 36-78, 84-105, 134-176, 182-202, 231-280, 286-311, 327-368, 374-395, 417-459, 465-488, 509-578, 584-620, 636-687, 693-719, 735-761, 767-776, 792-818, 824-833, 852-872, 877-885, 908-951, 957-979, 999-1047, 1053-1080, 1096-1140, 1146-1167, 1196-1246, 1252-1278, 1294-1338, 1344-1365, 1387-1430, 1436-1460, 1476-1529, 1535-1562, 1578-1606, 1612-1622, 1641-1664, 1669-1678, 1701-1746, 1752-1774, 1794-1844, 1850-1877, 1918-1987, 1993-2023, 2048-2110, 2116-2144, 2159-2202, 2208-2228, 2241-2267, 2273-2283, 2298-2339, 2345-2365, 2381-2424, 2430-2451, 2490-2547, 2553-2582, 2601-2661, 2667-2696, 2709-2735, 2741-2751, 2768-2813, 2819-2840, 2855-2898, 2904-2924, 2957-3012, 3018-3045, 3087-3169, 3175-3223, 3240-3284, 3290-3311, 3331-3372, 3378-3399, 3423-3472, 3478-3503, 3522-3575, 3581-3608, 3626-3673, 3679-3702, 3718-3760, 3766-3787, 3811-3858, 3864-3887, 3906-3948, 3954-3976, 3990-4026, 4032-4047, 4060-4087, 4093-4103, 4129-4177, 4183-4205, 4222-4266, 4272-4293, 4315-4366, 4372-4394, 4409-4450, 4456-4476, 4491-4532, 4538-4558, 4599-4665, 4671-4700, 4725-4785, 4791-4818, 4833-4874, 4880-4899, 4912-4938, 4944-4953, 4981-5021, 5027-5046, 5062-5103, 5109-5129, 5162-5221, 5227-5257, 5296-5353, 5359-5387, 5400-5426, 5432-5441, 5458-5502, 5508-5529, 5544-5585, 5591-5611, 5668-5719, 5725-5752, 5797-5908, 5914-5983, 6033-6146, 6152-6222, 6239-6281, 6287-6308, 6339-6379, 6385-6405, 6429-6478, 6484-6508, 6536-6577, 6583-6605, 6625-6683, 6689-6719, 6737-6782, 6788-6811, 6827-6869, 6875-6895, 6919-6965, 6971-6993, 7030-7157, 7163-7233, 7252-7294, 7300-7321, 7335-7369, 7375-7389, 7403-7438, 7444-7459, 7473-7494, 7499-7507, 7520-7546, 7552-7562, 7588-7634, 7640-7661, 7678-7720, 7726-7746, 7768-7819, 7825-7846, 7861-7902, 7908-7927, 7942-7983, 7989-8008 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_functional_ops.py 540 492 9% 58-106, 112-138, 157-186, 192-202, 226-252, 258-269, 297-344, 350-373, 395-441, 447-471, 489-522, 528-542, 565-613, 619-643, 674-721, 727-750, 781-823, 829-849, 883-914, 920-934, 960-986, 992-1001, 1029-1073, 1079-1098 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_image_ops.py 1972 1820 8% 36-64, 70-82, 108-135, 141-151, 173-199, 205-215, 238-264, 270-280, 343-387, 393-414, 470-507, 513-532, 566-596, 602-618, 654-688, 694-711, 764-825, 831-861, 884-913, 919-931, 953-979, 985-994, 1045-1105, 1111-1140, 1172-1206, 1212-1227, 1256-1282, 1288-1298, 1329-1356, 1362-1373, 1423-1498, 1504-1543, 1560-1587, 1593-1604, 1631-1661, 1667-1679, 1736-1782, 1788-1812, 1830-1860, 1866-1878, 1926-1967, 1973-1992, 2014-2053, 2059-2068, 2097-2134, 2140-2157, 2197-2230, 2236-2250, 2292-2322, 2328-2340, 2384-2416, 2422-2435, 2489-2529, 2535-2552, 2616-2657, 2663-2677, 2719-2751, 2757-2771, 2803-2843, 2849-2869, 2904-2943, 2949-2958, 2987-3021, 3027-3043, 3074-3105, 3111-3124, 3146-3184, 3190-3207, 3228-3266, 3272-3289, 3311-3349, 3355-3372, 3393-3432, 3438-3455, 3475-3514, 3520-3537, 3557-3596, 3602-3620, 3709-3789, 3795-3838, 3927-4002, 4008-4048, 4066-4104, 4110-4128, 4146-4185, 4191-4209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_io_ops.py 1163 1009 13% 46-85, 91, 119-181, 187-217, 238-260, 266, 286-320, 326-340, 358-379, 385, 405-444, 450-459, 486-512, 517-527, 543-582, 588-597, 613-627, 633, 648-674, 680-690, 703-717, 723, 735-763, 769-779, 805-820, 826, 854-870, 876, 904-933, 939-950, 976-1004, 1010-1020, 1033-1040, 1045, 1057-1076, 1081-1087, 1106-1114, 1119, 1137-1157, 1162-1169, 1185-1199, 1205, 1220-1246, 1252-1262, 1301-1335, 1341-1355, 1387-1422, 1428-1443, 1487, 1493-1515, 1522, 1534, 1559-1579, 1584-1592, 1635-1657, 1662-1671, 1696-1718, 1723-1732, 1749-1776, 1782-1793, 1807-1834, 1840-1850, 1869-1895, 1901, 1919-1959, 1965-1983, 2003-2030, 2036, 2055-2096, 2102-2120, 2141-2163, 2169, 2189-2223, 2229-2243, 2264-2296, 2301-2308 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_linalg_ops.py 1226 1097 11% 33-59, 65-74, 88-114, 120-130, 143-169, 175-185, 199-229, 235-247, 262-293, 299-312, 328-360, 366-380, 396-431, 437-454, 467-493, 499-508, 529-560, 566-578, 601-639, 645-661, 692-731, 737-746, 769-795, 801-811, 848-880, 886-899, 991-1024, 1030-1045, 1076-1103, 1109-1118, 1161-1205, 1211-1223, 1244-1283, 1289-1298, 1311-1337, 1343-1352, 1382-1425, 1431-1443, 1473-1499, 1505-1514, 1543-1587, 1593-1606, 1663-1694, 1700-1714, 1746-1785, 1791-1800, 1873-1907, 1913-1929, 1968-2012, 2018-2030, 2052-2078, 2084-2093, 2129-2160, 2166-2178, 2221-2258, 2264-2280, 2306-2333, 2339-2349, 2379-2412, 2418-2431 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_list_ops.py 716 641 10% 42-74, 80-91, 119-155, 161-174, 189-218, 224-235, 270-304, 310-322, 339-367, 373-384, 403-431, 437-448, 471-502, 508-520, 540-571, 577-589, 605-631, 637-646, 675-707, 713-724, 744-771, 777-787, 801-828, 834-845, 865-897, 903-914, 932-958, 964-974, 998-1026, 1032-1044, 1067-1096, 1102-1114, 1142-1171, 1177-1190, 1210-1237, 1243-1254, 1277-1305, 1311-1323, 1345-1381, 1387-1401 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py 492 436 11% 48-63, 68-78, 108-142, 148-162, 193-225, 231-245, 267-293, 299-309, 364-400, 406-422, 445-476, 482-496, 519-560, 566-586, 604-630, 635-647, 665-691, 697-707, 729-773, 779-802, 820-848, 854-865, 884-923, 929-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_lookup_ops.py 714 631 12% 47-78, 84, 110-157, 163-184, 200-208, 213, 247-266, 271, 305-341, 346-363, 379-399, 404-412, 434-452, 458, 479-510, 516-527, 548-564, 570, 590-618, 624-635, 653-661, 666, 683-703, 708-716, 734-742, 747, 764-784, 789-797, 814-834, 839-846, 859-873, 879, 891-917, 923-932, 970-1015, 1021, 1059-1128, 1134-1168, 1195-1227, 1233, 1258-1296, 1302, 1327-1383, 1389-1414, 1441-1489, 1495-1516 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_manip_ops.py 46 31 33% 64-92, 98-109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py 5995 5342 11% 37-63, 69-78, 103-136, 142-157, 172-211, 217-226, 249-288, 294-303, 330-361, 367-377, 397-428, 434-448, 465-491, 497-507, 530-561, 567-580, 612-642, 648-660, 683-714, 720-733, 748-778, 784-797, 827-859, 865-878, 908-940, 946-959, 990-1029, 1035-1044, 1068-1107, 1113-1122, 1153-1192, 1198-1207, 1229-1268, 1274-1284, 1310-1349, 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4858-4897, 4903-4913, 4944-4983, 4989-4999, 5025-5064, 5070-5079, 5110-5150, 5156-5167, 5196-5235, 5241-5250, 5273-5312, 5318-5327, 5344-5370, 5376-5386, 5414-5445, 5451-5460, 5479-5518, 5524-5534, 5563-5577, 5580, 5583, 5590-5593, 5602-5619, 5643-5674, 5680-5693, 5715-5754, 5760-5770, 5794-5825, 5831-5844, 5868-5899, 5905-5918, 5940-5979, 5985-5995, 6015-6041, 6047-6057, 6082-6100, 6106-6116, 6133-6159, 6165-6175, 6188-6214, 6220-6229, 6246-6285, 6291-6300, 6324-6363, 6369-6379, 6397-6431, 6437-6451, 6476-6515, 6521-6531, 6554-6580, 6586-6596, 6620-6651, 6657-6670, 6720-6753, 6759-6772, 6803-6837, 6843-6860, 6905-6955, 6961-6988, 7019-7053, 7059-7076, 7106-7132, 7138-7148, 7174-7204, 7210-7222, 7243-7282, 7288-7298, 7316-7355, 7361-7370, 7387-7413, 7419-7429, 7458-7486, 7492-7503, 7531-7565, 7571-7584, 7623-7657, 7663-7677, 7709-7746, 7752-7768, 7794-7833, 7839-7848, 7864-7890, 7896-7905, 7920-7946, 7952-7961, 7978-8004, 8010-8020, 8062-8102, 8108-8118, 8161-8201, 8207-8217, 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124 103 17% 49-85, 91-104, 127-154, 160-170, 193-227, 233-249 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py 5462 5063 7% 49-95, 101-125, 153-199, 205-229, 259-306, 312-337, 366-413, 419-444, 479-517, 523-537, 580-619, 625-639, 669-681, 690-693, 702-715, 741-772, 778-790, 811-837, 843-853, 917-983, 989-1027, 1072-1142, 1148-1187, 1232-1302, 1308-1347, 1387-1436, 1442-1469, 1495-1541, 1547-1571, 1613-1687, 1693-1722, 1748-1794, 1800-1824, 1863-1916, 1922-1950, 1971-2008, 2014-2029, 2049-2086, 2092-2107, 2160-2228, 2234-2261, 2306-2388, 2394-2423, 2467-2549, 2555-2584, 2632-2674, 2680-2702, 2728-2772, 2778-2801, 2827-2870, 2876-2899, 2917-2956, 2962-2971, 2987-3013, 3019-3029, 3088-3148, 3154-3185, 3224-3262, 3268-3283, 3366-3426, 3432-3463, 3498-3537, 3543-3558, 3603-3655, 3661-3685, 3735-3782, 3788-3808, 3858-3905, 3911-3934, 3988-4036, 4042-4065, 4110-4162, 4168-4193, 4239-4291, 4297-4322, 4359-4398, 4404-4423, 4465-4512, 4518-4542, 4574-4601, 4607-4618, 4651-4677, 4683-4694, 4714-4753, 4759-4768, 4803-4861, 4867-4889, 4913-4958, 4964-4987, 5001-5030, 5036-5048, 5066-5096, 5102-5115, 5133-5159, 5165-5174, 5201-5247, 5253-5277, 5305-5351, 5357-5381, 5413-5460, 5466-5492, 5524-5572, 5578-5603, 5634-5681, 5687-5712, 5743-5791, 5797-5822, 5853-5889, 5895-5911, 5939-5991, 5997-6025, 6056-6091, 6097-6113, 6141-6193, 6199-6226, 6253-6288, 6294-6309, 6350-6406, 6412-6439, 6467-6497, 6503-6516, 6550-6594, 6600-6623, 6688-6742, 6748-6773, 6807-6840, 6846-6861, 6909-6964, 6970-7001, 7032-7098, 7104-7144, 7177-7251, 7257-7299, 7332-7403, 7409-7451, 7489-7545, 7551-7583, 7615-7682, 7688-7729, 7761-7831, 7837-7878, 7912-7989, 7995-8038, 8072-8148, 8154-8197, 8234-8317, 8323-8370, 8403-8474, 8480-8522, 8559-8642, 8648-8695, 8733-8789, 8795-8827, 8866-8925, 8931-8964, 9004-9076, 9082-9123, 9168-9245, 9251-9294, 9343-9396, 9402-9432, 9457-9513, 9519-9549, 9599-9653, 9659-9689, 9743-9804, 9810-9842, 9875-9934, 9940-9972, 10006-10050, 10056-10079, 10105-10140, 10146-10160, 10186-10221, 10227-10241, 10268-10304, 10310-10325, 10345-10384, 10390-10399, 10412-10438, 10444-10453, 10470-10496, 10502-10512, 10529-10555, 10561-10571, 10594-10633, 10639-10648, 10664-10690, 10696-10706, 10724-10750, 10756-10765, 10792-10821, 10827-10838, 10853-10892, 10898-10907, 10923-10949, 10955-10965, 10980-11019, 11025-11034, 11050-11076, 11082-11092, 11123-11153, 11159-11170, 11210-11242, 11248-11261, 11299-11330, 11336-11349 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_parsing_ops.py 1066 994 7% 52-103, 109-137, 164-209, 215-227, 252-291, 297-306, 326-361, 367-381, 400-434, 440-453, 520-582, 588-628, 708-782, 788-834, 925-1095, 1101-1211, 1314-1499, 1505-1632, 1689-1755, 1761-1800, 1886-2018, 2024-2118, 2138-2179, 2185-2195, 2211-2250, 2256-2265, 2289-2319, 2325-2337 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_array_ops.py 58 42 28% 76-119, 125-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_conversion_ops.py 189 163 14% 61-106, 112-130, 157-192, 198-214, 279-325, 331-355, 386-425, 431-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_math_ops.py 53 37 30% 63-95, 101-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_random_ops.py 511 460 10% 43-83, 89-109, 141-180, 186-204, 232-267, 273-289, 303-329, 335-345, 361-396, 402-418, 453-493, 499-519, 548-581, 587-602, 626-662, 668-684, 717-745, 751-767, 799-835, 841-858, 884-920, 926-942 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_resource_variable_ops.py 631 526 17% 38-57, 62-69, 87-106, 111-118, 144-155, 160-167, 189-208, 213-219, 247-264, 269-278, 330-356, 362-371, 389-422, 428-442, 467-479, 486-488, 497-507, 538-579, 585-603, 618-647, 653-664, 701-721, 726-734, 771-791, 796-804, 841-861, 866-874, 911-931, 936-944, 981-1001, 1006-1014, 1051-1071, 1076-1084, 1111-1131, 1136-1144, 1173-1203, 1209-1226, 1247-1265, 1271-1280, 1303-1332, 1338-1350 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_rnn_ops.py 350 306 13% 84-130, 136-157, 220-257, 263-275, 338-375, 381-393, 460-501, 507-525, 597-625, 631-641, 753-782, 788-798, 866-912, 918-938, 987-1022, 1028-1039 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_script_ops.py 136 104 24% 39-80, 86-104, 128-140, 144, 152, 154-157, 165-179, 194-226, 232-247 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sdca_ops.py 322 287 11% 37-76, 82-91, 170-329, 335-421, 497-617, 623-709, 730-751, 756 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sendrecv_ops.py 97 79 19% 42-91, 97-116, 138-173, 178-193 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_set_ops.py 187 160 14% 56-94, 100-116, 167-208, 214-232, 258-292, 298-312, 378-423, 429-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sparse_ops.py 1272 1150 10% 68-108, 114-132, 169-209, 215-232, 298-327, 333-343, 409-439, 445-455, 484-519, 525-539, 559-594, 600-614, 663-695, 701-716, 750-781, 787-799, 870-924, 930-965, 1040-1103, 1109-1143, 1173-1200, 1206-1218, 1242-1269, 1275-1287, 1315-1342, 1348-1360, 1427-1456, 1462-1474, 1505-1533, 1539-1550, 1586-1621, 1627-1642, 1686-1723, 1729-1744, 1780-1815, 1821-1836, 1880-1917, 1923-1938, 1973-2002, 2008-2019, 2061-2089, 2095-2106, 2153-2181, 2187-2200, 2225-2256, 2262-2274, 2309-2336, 2342-2353, 2387-2418, 2424-2438, 2472-2503, 2509-2523, 2572-2607, 2613-2630, 2652-2680, 2686-2698, 2734-2773, 2779-2799, 2845-2882, 2888-2904, 2985-3025, 3031-3049 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_special_math_ops.py 172 145 16% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_spectral_ops.py 714 630 12% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290, 303-329, 335-344, 364-403, 409-418, 438-477, 483-492, 512-551, 557-566, 586-625, 631-640, 660-699, 705-714, 734-773, 779-788, 819-849, 855-868, 900-930, 936-949, 981-1011, 1017-1030, 1058-1089, 1095-1108, 1137-1168, 1174-1187, 1216-1247, 1253-1266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_state_ops.py 513 432 16% 46-69, 75, 96-114, 120, 141-159, 165, 181-197, 203, 226-242, 248, 263-277, 283, 300-329, 335-346, 403-427, 432-444, 501-525, 530-542, 602-627, 632-645, 689-709, 715, 756-776, 782, 825-845, 851, 894-914, 920, 961-981, 987, 1044-1064, 1070, 1129-1149, 1155, 1213-1233, 1239, 1282-1302, 1308, 1355-1375, 1381, 1413-1435, 1441, 1456-1482, 1488, 1512-1538, 1544 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateful_random_ops.py 353 314 11% 37-67, 73-85, 106-126, 131-139, 157-191, 197-214, 233-264, 270-284, 304-335, 341-356, 378-409, 415-430, 451-482, 488-502, 522-553, 559-574, 601-630, 636-649 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateless_random_ops.py 354 315 11% 40-75, 81-95, 123-155, 161-177, 200-228, 234-246, 268-299, 305-318, 343-373, 379-393, 416-447, 453-467, 489-521, 527-541, 565-593, 599-612, 636-667, 673-687 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_string_ops.py 1301 1175 10% 64-128, 134-159, 178-217, 223-232, 257-299, 305-317, 363-399, 405-421, 451-477, 483-493, 518-550, 556-570, 592-619, 625-635, 655-691, 697-712, 734-775, 781-800, 826-861, 867-884, 912-941, 947-959, 980-1022, 1028-1040, 1083-1137, 1143-1166, 1212-1243, 1249-1262, 1313-1343, 1349-1362, 1378-1417, 1423-1432, 1453-1481, 1487-1497, 1526-1569, 1575-1586, 1625-1675, 1681-1697, 1718-1760, 1766-1778, 1884-1913, 1919-1933, 1989-2043, 2049-2073, 2134-2190, 2196-2221, 2271-2313, 2319-2337, 2363-2402, 2408-2417, 2495-2570, 2576-2598, 2647-2699, 2705-2720 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_summary_ops.py 386 335 13% 33-52, 57-63, 80-104, 109-119, 136-160, 165-176, 189-208, 213-219, 233-252, 257-264, 278-312, 318-332, 350-376, 381-394, 409-429, 434-442, 458-478, 483-492, 510-536, 541-554, 569-589, 594-602, 618-638, 643-652, 669-689, 694-704 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_tpu_ops.py 3018 2798 7% 59-98, 104-118, 141-169, 175-185, 206-267, 273-300, 315-335, 340-346, 370-397, 403-413, 436-468, 473-489, 537-598, 603-647, 701-779, 784-841, 855-884, 890-900, 916-954, 960-978, 1002-1037, 1042-1062, 1079-1104, 1109-1119, 1144-1181, 1186-1208, 1237-1278, 1283-1304, 1335-1379, 1384-1406, 1435-1477, 1482-1503, 1534-1578, 1583-1605, 1632-1673, 1678-1698, 1727-1770, 1775-1796, 1827-1868, 1873-1895, 1924-1965, 1970-1991, 2022-2066, 2071-2093, 2124-2167, 2172-2194, 2221-2261, 2266-2286, 2315-2358, 2363-2384, 2411-2452, 2457-2477, 2506-2549, 2554-2575, 2604-2643, 2648-2669, 2700-2743, 2748-2770, 2795-2836, 2841-2860, 2880-2916, 2922-2935, 2958-3003, 3009-3031, 3044-3063, 3068-3074, 3089-3108, 3113-3119, 3138-3175, 3181-3200, 3221-3261, 3267-3288, 3311-3342, 3348-3358, 3388-3436, 3442-3463, 3494-3543, 3549-3570, 3600-3648, 3654-3675, 3706-3755, 3761-3782, 3811-3859, 3865-3886, 3916-3965, 3971-3992, 4023-4072, 4078-4099, 4129-4177, 4183-4204, 4235-4284, 4290-4311, 4342-4391, 4397-4418, 4447-4495, 4501-4522, 4552-4601, 4607-4628, 4657-4706, 4712-4733, 4763-4812, 4818-4839, 4869-4917, 4923-4944, 4975-5024, 5030-5051, 5073-5121, 5127-5148, 5175-5207, 5212-5231, 5245-5264, 5269-5275, 5291-5317, 5323-5331, 5359-5393, 5399-5412, 5428-5454, 5460-5468, 5486-5525, 5531-5550, 5580-5663, 5668-5722, 5749-5793, 5799-5820, 5844-5872, 5878-5888, 5905-5931, 5937-5946 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_user_ops.py 43 28 35% 32-57, 63-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker.py 145 117 19% 39-45, 49-54, 83-132, 160-193, 202-208, 221-242, 254-268, 321-335, 339-345, 393-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker_v2.py 140 116 17% 37-43, 59-64, 78-93, 110-127, 150-197, 221-261, 266-281, 287-293, 332-335, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_impl.py 59 25 58% 168-169, 298-299, 342-357, 392-424, 433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_util.py 446 380 15% 61-69, 97-133, 137, 162-227, 231-234, 250-254, 279-289, 295-299, 303, 308-318, 323-355, 362-374, 383, 388-392, 405-411, 428, 444-457, 471-476, 490-716, 721-728, 734-766, 771-781, 786-805, 810-813, 817-821, 826-838, 846-867, 932-987 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/histogram_ops.py 28 13 54% 77-100, 146-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_grad.py 91 69 24% 39-50, 64-69, 84-91, 105-113, 131-156, 167, 188-381 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops.py 51 32 37% 195-203, 228-239, 245-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops_impl.py 1010 802 21% 75-81, 93, 108-113, 133-150, 171, 193, 212-236, 258, 277-301, 315-320, 360, 401, 422-447, 481, 515, 537-546, 581-594, 610-625, 641-657, 706-715, 772-847, 907-956, 990-1036, 1070-1155, 1183-1255, 1315-1333, 1470-1511, 1523-1590, 1628-1631, 1668-1671, 1698-1717, 1748-1752, 1784-1792, 1830-1843, 1885-1899, 1945-1963, 2012-2062, 2086-2097, 2122-2132, 2168-2175, 2230-2241, 2277-2289, 2327-2339, 2377-2385, 2426-2437, 2455-2457, 2471-2473, 2540, 2586-2656, 2694-2728, 2823-2824, 2930-2931, 2988-2992, 3064-3079, 3129-3133, 3175-3179, 3211-3215, 3238-3242, 3278-3282, 3305-3309, 3329-3354, 3393-3408, 3444-3466, 3471-3483, 3519-3565, 3627-3640, 3690-3771, 3826-3844, 3861-3885, 3893, 3906, 3919, 4036, 4053-4055, 4137, 4222, 4295-4300, 4354-4356, 4408, 4427 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops.py 509 330 35% 68, 76, 97, 113, 117, 132-134, 137, 215-222, 225-229, 237, 260-263, 266-268, 272, 300-303, 306-308, 312, 346-349, 352-354, 358, 407-409, 412-427, 431, 480, 482, 487, 495-518, 522, 563-565, 568-595, 598, 626-628, 631-663, 666, 688-690, 693, 696, 708-715, 726-733, 758-771, 785, 803-813, 828-845, 860-877, 903-913, 926, 939-944, 959-973, 988-1002, 1028-1041, 1056, 1075-1093, 1109-1129, 1144-1165, 1185-1186, 1189-1201, 1204, 1236, 1265, 1269, 1318, 1345, 1370, 1394, 1410-1425, 1443 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops_v2.py 241 105 56% 59, 67, 88-89, 168-171, 239-243, 257-259, 263, 300-303, 316-319, 323, 364-367, 380-381, 385, 429-432, 445-446, 450, 509, 511, 515, 518, 541, 544, 546, 551-552, 554-555, 561, 612-614, 628-650, 653, 684, 698-708, 711, 757, 796, 803, 864, 907, 947, 987, 1004, 1006, 1013-1017, 1037, 1048, 1054-1058, 1064, 1072-1076 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/initializers_ns.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/inplace_ops.py 41 16 61% 53-64, 90, 116, 142, 160-161, 191, 221, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/io_ops.py 120 54 55% 68-71, 93-94, 131-137, 142, 161-171, 193-206, 224-228, 240-244, 259-262, 278-282, 287, 298-301, 337-338, 369-371, 410-417, 446-451, 479-481, 511-512 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/adjoint_registrations.py 50 18 64% 37, 48, 54, 60-64, 76-80, 92, 106, 118-123, 134 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/cholesky_registrations.py 31 6 81% 36, 46, 57, 70, 83, 96 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/inverse_registrations.py 68 31 54% 40, 51, 57, 68, 74, 87, 132-185, 200, 213, 225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg_impl.py 400 323 19% 95-97, 126-128, 135-145, 150-161, 166-178, 183-203, 208-229, 262-339, 439-490, 496-538, 591-618, 623-635, 657-673, 746-802, 848-896, 949-959, 1000-1036, 1041-1068, 1073-1094 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator.py 387 259 33% 188-213, 218-222, 227, 232, 238, 242, 246, 250, 257-264, 269, 282, 288, 303-308, 322, 338-339, 344-349, 365-366, 381-382, 387-391, 404-407, 424-425, 430-435, 448-451, 468-469, 474-479, 483-493, 500-508, 527-528, 532-544, 560-561, 564-568, 586-587, 591-592, 598, 629-653, 656, 659-661, 690-696, 699-704, 718-723, 726-733, 747-752, 756-763, 768-771, 814-846, 850-852, 893-900, 914-917, 937-945, 966-970, 974-986, 990-991, 995, 1022-1023, 1026, 1039-1040, 1044, 1056-1059, 1062, 1078-1081, 1084-1093, 1105-1106, 1109, 1122-1127, 1137, 1142, 1155, 1161, 1166-1169, 1174-1176, 1190-1202, 1212-1215, 1220 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_addition.py 160 105 34% 99-138, 144-149, 168-187, 195-211, 217-223, 233-235, 253, 258, 264, 280-290, 301-302, 307-317, 330-331, 334, 346-347, 350-355, 367, 371-375, 412-424 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_adjoint.py 72 39 46% 116-155, 160, 163, 166, 169, 173-174, 178-179, 183, 187, 190-192, 195, 198-200, 203, 207, 210-212, 215, 218-221, 224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_algebra.py 112 49 56% 37-48, 53, 58, 63, 68, 73, 90-96, 113-119, 138-144, 163-169, 186-192, 228, 231, 270, 273, 315, 318, 361, 364, 404, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_diag.py 153 119 22% 156-215, 219, 223-238, 242-260, 263-273, 276-279, 282-285, 288-299, 302-308, 311-314, 317-341, 344, 348, 352, 356-362, 378-386 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_lower_triangular.py 191 152 20% 224-241, 250-252, 259-283, 290-301, 304-310, 314-318, 324, 328-344, 348-367, 370-417, 420-425, 428-431, 460-510, 513-520, 523-526, 529-550, 553, 557-563, 579-587 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_circulant.py 204 144 29% 93-126, 130-138, 171, 177, 180-186, 190, 194, 210-224, 239-260, 272-273, 285-286, 300-302, 305-321, 324-331, 345-350, 356, 366-368, 373, 381-402, 405-425, 428-430, 433-436, 439-450, 455-478, 490-512, 747, 758, 927, 1077, 1089-1095 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_composition.py 86 62 28% 147-187, 191, 195-211, 215-233, 239-248, 251-254, 257-260, 270-279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_diag.py 83 49 41% 143-168, 172-173, 178-179, 182-184, 188, 191, 196-205, 210, 217-220, 223-224, 227, 230-234, 237-240, 243, 246, 249-251, 254, 257-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_full_matrix.py 38 17 55% 137-151, 155-172, 177, 180, 183, 187, 190 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_householder.py 86 52 40% 126-156, 160-162, 168-169, 172-174, 177, 180, 185, 200-207, 211-212, 219, 223, 228, 231-236, 240-243, 247-254, 258, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_identity.py 234 171 27% 48-74, 79-83, 87, 91-98, 255-292, 295-301, 304-308, 311, 314, 317, 322-350, 354-358, 361, 364, 367, 371-380, 384, 396-400, 403, 406, 411-437, 442-470, 599-630, 634-638, 641-644, 647, 651, 656-657, 664-668, 671-675, 678, 681, 685-689, 693-702, 706, 718-731, 734, 739, 747 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_inversion.py 52 27 48% 117-168, 173, 176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 206 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176-178, 181-187, 190, 210-224, 229, 234-235, 239-263, 267, 271, 275-278 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_tridiag.py 122 87 29% 175-190, 199-213, 216-230, 236-266, 270-287, 290-294, 299-331, 334-341, 344-359, 369, 373 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_util.py 150 122 19% 105-116, 122-125, 130-135, 151, 160-161, 182-187, 201-209, 228-236, 241-243, 251-255, 311-354, 359-373, 383-467, 491-502 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_zeros.py 157 118 25% 179-232, 235-241, 244-248, 251, 256, 261, 266-294, 297-320, 323-326, 330-333, 336, 348, 353-399, 404-432, 437-441, 445, 449-456, 459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/matmul_registrations.py 65 28 57% 37-50, 65-66, 73-74, 82, 102-105, 112-115, 125, 142, 159, 175, 191, 209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/registrations_util.py 29 21 28% 27-44, 49-62, 71-78, 86-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/solve_registrations.py 55 22 60% 37-50, 67, 75-76, 83-84, 92, 112, 129, 146, 162, 181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/conjugate_gradient.py 52 37 29% 76-136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/gen_sparse_csr_matrix_ops.py 624 562 10% 48-77, 83-95, 109-137, 143-154, 177-207, 213-224, 239-266, 272-283, 303-329, 335-347, 399-459, 465-495, 518-544, 550-560, 573-599, 605-614, 677-703, 709-719, 739-766, 772-782, 797-825, 831-843, 937-965, 971-983, 1083-1133, 1139-1164, 1183-1215, 1221-1234, 1248-1275, 1281-1291, 1307-1335, 1341-1353 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_grad.py 127 101 20% 30-34, 40-41, 65-66, 73, 80-81, 95-169, 175-222, 231-233 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_ops.py 157 96 39% 56, 61-69, 74, 80-87, 105-116, 124-144, 181-241, 253, 257, 261, 265, 269, 273, 277, 281, 286, 289, 294, 301, 307, 310, 330-352, 357, 360-370, 373, 376-377 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_grad.py 389 345 11% 53-54, 61-366, 372-378, 392-443, 449-454, 462-480, 486-510, 516-524, 537-601, 609-633, 639-675, 690-811, 816-819, 824-827, 833-848, 854-867, 881-898, 917-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops.py 166 121 27% 65-79, 139-140, 181-186, 227, 293-367, 393-398, 419-424, 446-447, 469-470, 535-540, 607, 682-761 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops_impl.py 35 24 31% 42-80 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3632-3638, 3642-3662, 3668-3715, 3728, 3738-3741 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_config.py 305 240 21% 207-228, 303, 323, 358, 412-434, 454-463, 467, 471, 475, 491-516, 520-531, 535-537, 541-548, 553-568, 572-589, 593-603, 607-619, 623-627, 632-665, 704-758, 778-803, 827-885, 893-901 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_ops.py 165 110 33% 62-76, 304-313, 318, 339-370, 406, 442-447, 545-567, 597-689, 779-801, 830-833, 867-875, 907-918, 965, 1013-1020, 1033-1047 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/partitioned_variables.py 68 48 29% 104-154, 181-218, 233-237, 288-311 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/proto_ops.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_array_ops.py 187 158 16% 92-201, 230-243, 273-305, 332-373, 431-448, 474-477, 502-506, 520-528, 574-634, 660-689 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_ops.py 46 34 26% 64-124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py 61 44 28% 69-141, 147-183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_concat_ops.py 110 87 21% 67-70, 115-118, 136-202, 219-238, 253-289, 295-297, 302-310, 315-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_config.py 6 1 83% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_conversion_ops.py 58 39 33% 36-39, 49-52, 57, 64-106, 113-137, 141, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_dispatch.py 243 116 52% 82, 90-102, 114, 119-160, 175, 182-231, 246, 252-253, 258-261, 264-284, 417, 427, 435-437, 441, 445-447, 452-456, 515, 548, 550, 559-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_factory_ops.py 121 99 18% 78-84, 133-143, 168-240, 260-271, 277-310, 336-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_functional_ops.py 39 25 36% 70-91, 112-128 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_gather_ops.py 98 80 18% 88-118, 167-261, 270-295 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_getitem.py 161 136 16% 97-103, 124-187, 200-226, 244-340, 360-361, 381-389, 394-406, 437-456, 462-471 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_map_ops.py 132 103 22% 169-331, 360-364, 372-386, 395-403, 409-418, 423-427, 434-459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_math_ops.py 172 110 36% 88-105, 112-114, 193-256, 262, 271, 280, 289, 298-308, 313-324, 469-542, 551, 561, 572, 583, 594-608, 612, 618-619, 626-627 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_operators.py 44 1 98% 74 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_squeeze_op.py 53 40 25% 52-122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_string_ops.py 192 153 20% 59-78, 121-175, 219-220, 280-281, 322-325, 385-394, 400-452, 493-512, 563-575, 626-643, 648, 721-803 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_tensor.py 823 674 18% 268-311, 363-428, 472-497, 531-553, 587-608, 637-659, 718-795, 837-858, 888-897, 927-936, 959-983, 992, 1011-1020, 1031-1032, 1056, 1079, 1105, 1131-1134, 1161-1166, 1192-1196, 1227-1233, 1255-1266, 1290-1291, 1315-1316, 1345-1360, 1377-1383, 1410-1435, 1456-1466, 1491-1494, 1509-1533, 1570-1583, 1652-1793, 1828-1839, 1878-1905, 1927-1930, 1985-2001, 2030-2031, 2038-2041, 2075-2086, 2096-2099, 2105-2108, 2112-2117, 2140-2143, 2151, 2158, 2162, 2167, 2182-2212, 2226, 2241-2257, 2260, 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170-179, 206-212, 232-244, 247-260, 269, 274, 279, 282-309, 328-340, 344, 385, 420-436, 440, 444, 448-462, 466-471, 474-480, 526-544, 548, 552, 556-582, 586-603, 606-614, 633-637, 697-719, 723, 728, 732-746, 762-794, 797-805, 896-941, 945, 949, 953-993, 1019-1071, 1074-1089, 1100-1104, 1123, 1147, 1151-1158, 1162-1168, 1179, 1190, 1200, 1234-1255, 1261-1264, 1268, 1271-1277, 1281-1287, 1291-1301, 1305-1327, 1345-1346, 1350-1354 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell_wrapper_impl.py 224 164 27% 109-173, 179-183, 187, 191, 195, 198-199, 202-203, 209-215, 225-250, 271-289, 293-308, 312-319, 337-338, 342, 346, 349-350, 369-381, 385-396, 400-407, 424-425, 429, 433, 436-438, 442-443, 446-448, 453-465, 471-494, 498-504, 508-515 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_grad.py 14 5 64% 26-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/script_ops.py 174 76 56% 59-67, 83-85, 102-119, 124-150, 171-172, 183, 203-212, 230-253, 257, 282, 295, 319, 336, 347, 357-364, 452-457, 517-524, 536, 555 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sdca_ops.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/session_ops.py 128 85 34% 38, 56-60, 63-64, 67, 71-76, 85, 90, 94-99, 103-108, 117-118, 123-124, 129-130, 135, 173-178, 214-219, 239-243, 247, 251, 256-267, 272-288, 293-302 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets_impl.py 55 31 44% 51-57, 81-90, 118-133, 200-201, 277-280, 356-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/dct_ops.py 77 60 22% 33-47, 97-179, 223-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/fft_ops.py 217 146 33% 35-42, 48-60, 65-108, 116-140, 150-170, 198, 203-204, 209-212, 217-218, 223-226, 231-232, 237-240, 250-320, 336-360, 393-403, 434-444 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mel_ops.py 57 39 32% 46-48, 64-66, 73-89, 161-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mfcc_ops.py 20 9 55% 89-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/reconstruction_ops.py 60 48 20% 54-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/shape_ops.py 82 68 17% 33-54, 107-214 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/signal.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/spectral_ops.py 131 103 21% 70-94, 120-155, 224-276, 281-285, 331-365, 426-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/util_ops.py 33 20 39% 47-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/window_ops.py 77 52 32% 47-51, 70-90, 110-117, 135-141, 165, 192, 217-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sort_ops.py 53 33 38% 65-66, 107-109, 128-140, 155-204, 209-211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_grad.py 130 88 32% 50-60, 83-96, 101-103, 109-115, 136-145, 166-201, 206, 212-241, 247, 253, 273-286, 291, 297, 306-312, 317-322 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_ops.py 516 356 31% 66-70, 87-91, 95-101, 118-124, 147-179, 202-211, 324-331, 336-366, 430-434, 490-517, 550, 595, 614-655, 678-680, 720-731, 784-830, 840, 889-914, 955, 995-1008, 1062, 1129-1144, 1211-1216, 1259-1269, 1319-1333, 1387-1392, 1435-1445, 1490-1494, 1552-1564, 1663, 1672-1718, 1751-1765, 1829-1871, 1925-1935, 1958, 1977-1979, 2013, 2041-2043, 2107-2114, 2179-2187, 2401-2419, 2476-2480, 2508-2519, 2546-2557, 2595-2619, 2643-2645, 2683-2685, 2756-2777, 2795-2806 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/special_math_ops.py 357 299 16% 87-101, 129-130, 157-158, 186-187, 214-215, 242-243, 266-267, 290-291, 296-303, 319-324, 404, 409-468, 490-551, 580-679, 684-687, 693-700, 707-714, 723-726, 731-802, 807-853, 861-869, 881-973 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/standard_ops.py 83 1 99% 115 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_grad.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_ops.py 131 81 38% 44-52, 74, 101-114, 130-133, 161-164, 192-195, 224-228, 249-251, 301-304, 363-366, 415-418, 478-481, 532-535, 596-599, 648-651, 700-703, 755-758, 810-813, 872-914 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/stateful_random_ops.py 279 177 37% 90, 95-97, 110-136, 140-145, 149-151, 155, 168-182, 206-207, 212-216, 220-223, 230-231, 235, 239, 243-250, 254, 257, 270-278, 369-387, 410, 439-444, 467-473, 500-507, 517-519, 529-530, 541-548, 552, 557, 562, 565, 585-589, 600, 621-626, 629, 657-664, 667, 705-721, 737-741, 789-794, 810, 832-840, 880-890, 913-916, 939 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/traceme.py 27 16 41% 35-41, 44-45, 48-49, 53-60 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_mlir.py 14 4 71% 27, 34, 41, 47 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow.py 30 6 80% 38, 51, 61, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py 65 39 40% 19-21, 31, 35-36, 40-55, 59, 63-71, 74, 78-82, 87-90 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tfe.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder_impl.py 231 176 24% 93-112, 122-131, 145-153, 168-182, 201-214, 220-228, 267-300, 345-393, 412-428, 437, 442-446, 456-465, 478-492, 507-511, 525-555, 570-615, 638-667, 688-706, 710-716, 731-740, 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100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer.py 128 95 26% 69-82, 90-96, 100, 110-112, 120-121, 132-137, 145-153, 160-163, 166-168, 193-204, 207-232, 244-254, 264-269, 285-294, 298-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer_v2.py 44 27 39% 74-96, 100, 110-112, 120-122, 131, 138-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer.py 133 90 32% 79-99, 119-142, 155-156, 159-161, 179-214, 217-227, 244-249, 263-273, 276-279, 360-372, 376, 380, 384, 387-388, 393-394, 402-403, 413-414, 421-422, 432-433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer_cache.py 24 8 67% 43-48, 60-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tf2.py 14 1 93% 40 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/module_util.py 31 12 61% 24, 47, 50-56, 60-61, 63 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/api.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/bfloat16.py 26 13 50% 49-68, 78-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/client.py 162 118 27% 34-35, 46, 50-54, 59-68, 75-81, 85-87, 106-141, 157-176, 181, 186-191, 196-200, 203, 206, 213-216, 220, 224, 228, 232, 236, 240, 244, 248-258, 270-281, 286-315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/device_assignment.py 180 148 18% 36-56, 81-102, 108, 113, 118, 129, 133, 148-151, 157-158, 162-163, 167-168, 175, 198-213, 257-413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/feature_column.py 220 158 28% 106-155, 219-285, 295-304, 310, 314, 318, 322, 330, 334, 338, 341, 344, 347, 351, 373, 397-402, 405, 409, 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303-350, 353-359, 363-399, 402-414, 420-463, 466-471, 474-481, 484, 488-508, 512-587, 591-604, 607-609, 617, 622-624, 627, 638-639, 642-645, 650-653, 658, 726-780, 886, 898-903, 926-1028, 1093-1354, 1373-1415, 1430-1461, 1532-1611, 1683, 1743, 1797, 1819-1831, 1843-1845, 1848, 1852-1858, 1861-1864, 1867, 1871, 1895-1896, 1936-1963, 1984-2001 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_embedding.py 574 454 21% 106-126, 158-162, 197, 202, 223-229, 263-268, 316-322, 381-399, 458-482, 522, 691-781, 790, 801, 810, 822, 833, 837, 841, 845, 849, 853-908, 945-1012, 1033-1034, 1045-1112, 1116-1118, 1135-1163, 1175-1197, 1215-1237, 1246-1248, 1255-1267, 1273-1274, 1280-1281, 1291, 1294, 1297, 1301, 1308-1309, 1312, 1315, 1319-1377, 1384-1386, 1389-1397, 1401, 1406-1477, 1484-1486, 1489-1497, 1503, 1508-1580, 1587, 1591, 1595-1643, 1648-1657, 1662, 1667-1671, 1676-1697, 1703-1721, 1732-1758 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_embedding_gradient.py 53 37 30% 45-50, 71-97, 118-126, 142-179 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_feed.py 285 232 19% 52-83, 98-104, 120-121, 165-198, 206-209, 214, 219, 237-251, 258, 276-295, 300, 312, 331-337, 347, 360-362, 378-382, 404-432, 445-458, 482-496, 529-546, 596-604, 618, 621, 679-722, 757-765, 782-794, 834-882, 897-920, 934-935 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_function.py 28 11 61% 35, 38, 48-54, 58, 66-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_optimizer.py 64 43 33% 56-70, 86-107, 144-161, 184-192, 206, 220, 224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_sharding.py 91 68 25% 36-38, 41-44, 48-51, 60-62, 67, 82-91, 98, 114-120, 132-135, 161-188, 204-216, 239-252 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_strategy_util.py 95 73 23% 53-127, 146-206 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu_system_metadata.py 101 75 26% 56-149, 154-166, 174-176, 196-212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/training_loop.py 82 70 15% 56-177, 201-222 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adadelta.py 46 28 39% 59-67, 70-72, 75-81, 84-86, 97-99, 110-112, 124-126 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad.py 48 26 46% 63-70, 73-80, 84-90, 93-94, 98-99, 107-108, 116-117, 126-127 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad_da.py 59 39 34% 75-88, 91-100, 104-109, 112-116, 128-132, 144-148, 161-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adam.py 93 67 28% 101-111, 114-119, 127-136, 139-147, 150-153, 167-170, 184-207, 210, 221-223, 226, 231-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_loops.py 21 13 38% 51-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_session_run_hooks.py 491 361 26% 61, 65, 69, 83, 87, 99-108, 111-112, 125-139, 142-152, 155, 162-163, 166-167, 170, 212-231, 234-237, 243-247, 250-263, 266-270, 273-275, 306-315, 352-360, 363-366, 369-371, 374-377, 382-386, 412-417, 420-422, 425-427, 430, 433-442, 500, 503, 506, 509, 546-557, 560, 563-569, 572-588, 591, 594-602, 605-609, 613-634, 637-656, 669-678, 681, 684-690, 693, 696-702, 705-736, 743, 760-761, 764, 767-775, 809-817, 822-827, 831-839, 842-861, 864-865, 873-884, 903, 906-909, 913-933, 949-951, 955, 958-977, 991, 994, 1033-1037, 1041-1044, 1047-1055, 1058-1072, 1075-1078, 1085-1104 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_management.py 266 201 24% 45-47, 61-63, 96-128, 167, 213-247, 270-305, 322-324, 351-364, 381-388, 410, 438-456, 476-484, 489-490, 506-508, 614-664, 668, 672, 686, 698, 702-717, 721-722, 743, 748, 771-824, 844-852 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_ops.py 76 60 21% 123-203, 332-416, 467-473 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_state_pb2.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_utils.py 161 125 22% 63-67, 82-85, 98-104, 125-135, 181-201, 286-291, 297-378, 384-386, 406-427, 447-459, 463, 469-476 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/coordinator.py 141 104 26% 142-159, 178-185, 201-244, 251-255, 263, 296-299, 311, 319-320, 353-395, 401, 405-407, 444-457, 478-481, 484-496, 500, 504, 508-509 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/device_setter.py 64 45 30% 53-54, 66-68, 93-98, 111-133, 202-231 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/evaluation.py 106 74 30% 47-61, 74-78, 92-94, 97, 100, 105-109, 112, 116-130, 145-149, 153, 156, 160-169, 225-277 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scale.py 158 90 43% 83, 88, 125, 141-162, 167-176, 180-188, 193, 198, 202, 228-239, 242, 245-246, 249, 252, 257-260, 275-277, 282, 324-335, 340, 344, 348, 351, 355-400, 403-409, 414, 424, 426, 428, 432 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scale_optimizer.py 79 52 34% 61-74, 78, 114-126, 129-136, 139-141, 147-150, 174-184, 211-221, 226-229, 233, 237, 241, 245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scaling_gradient_tape.py 73 53 27% 43, 115-125, 163-181, 190, 200, 238-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/mixed_precision.py 51 30 41% 33-74, 219, 332, 339-362, 382-386, 413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/mixed_precision_global_state.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/ftrl.py 72 53 26% 95-130, 134-138, 141-150, 154-170, 186-202, 218-235, 252-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/gen_training_ops.py 1619 1436 11% 57-77, 83, 115-135, 141, 167-191, 197, 226-248, 254, 282-306, 312, 353-378, 384, 414-434, 440, 490-510, 516, 552-572, 578, 617-637, 643, 662-681, 687, 720-744, 750, 780-800, 806, 835-854, 860, 886-906, 912, 952-972, 978, 1011-1038, 1043-1056, 1086-1113, 1118-1131, 1156-1185, 1190-1206, 1236-1266, 1271-1286, 1313-1343, 1348-1365, 1404-1437, 1442-1459, 1498-1527, 1532-1547, 1575-1601, 1606-1618, 1665-1692, 1697-1712, 1747-1773, 1778-1791, 1829-1856, 1861-1874, 1893-1916, 1921-1933, 1965-1995, 2000-2017, 2049-2078, 2083-2099, 2127-2153, 2158-2171, 2199-2225, 2230-2243, 2269-2296, 2301-2313, 2351-2377, 2382-2395, 2423-2451, 2456-2472, 2500-2530, 2535-2553, 2585-2616, 2621-2638, 2668-2699, 2704-2722, 2769-2800, 2805-2822, 2860-2887, 2892-2908, 2949-2977, 2982-2998, 3034-3067, 3072-3090, 3126-3156, 3161-3179, 3211-3239, 3244-3259, 3288-3316, 3321-3335, 3375-3402, 3407-3423, 3452-3474, 3480, 3509-3534, 3540, 3571-3595, 3601, 3632-3658, 3664, 3714-3737, 3743, 3782-3804, 3810, 3852-3874, 3880, 3917-3943, 3949, 3982-4003, 4009, 4038-4061, 4067, 4109-4131, 4137 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/gradient_descent.py 28 10 64% 51-53, 56, 63, 69, 73-77, 80-81 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/input.py 387 284 27% 74-75, 103-114, 174-202, 256-267, 315-317, 363-376, 383, 399-401, 404-415, 418, 421, 425-430, 434, 438, 442, 446-449, 453-463, 467-475, 509-577, 582-597, 602-627, 631-634, 638-642, 647-656, 660-667, 671-678, 698-709, 714-718, 723-735, 740-752, 756, 765-791, 804-832, 840-876, 885-919, 1009, 1066, 1177, 1234, 1335, 1399, 1498, 1562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/learning_rate_decay.py 87 58 33% 97-103, 150-179, 268-280, 356-368, 444-451, 507-514, 579-591, 664-676, 757-771 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/momentum.py 40 22 45% 80-83, 86-87, 90-98, 101-102, 111-112, 121-122, 131-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/monitored_session.py 481 348 28% 153-188, 192-247, 251, 255, 259, 263, 267, 271, 275, 279, 283, 288-300, 315, 337-427, 512-601, 614, 639-644, 648-657, 660-661, 690-694, 698-707, 710-711, 734-751, 756-758, 774, 819-834, 852-853, 857, 861, 871, 874, 877, 880, 883-887, 893-897, 902-910, 915-929, 939, 950, 1034, 1118-1124, 1132, 1154-1155, 1159, 1163, 1173-1177, 1185, 1188-1197, 1200, 1206-1207, 1230-1231, 1235-1238, 1249-1272, 1276-1295, 1299-1316, 1342-1344, 1349-1351, 1354-1365, 1368-1384, 1412-1414, 1418, 1422-1453, 1458-1481, 1484-1486, 1501-1514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/moving_averages.py 131 103 21% 87-114, 152-178, 220-266, 378-382, 387, 421-473, 485, 509-511, 545-561 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/optimizer.py 403 311 23% 58-61, 76-80, 85-87, 97, 102, 109, 112, 115, 118-132, 139, 142, 146-151, 158, 161, 165-176, 188, 191, 194, 199-213, 327-343, 353, 399-412, 458-519, 523-529, 561-640, 667-735, 755-773, 783, 794-811, 816-843, 848-857, 862-866, 869-875, 883, 894-898, 914, 924, 932, 944, 957, 980-982, 1002, 1032-1038, 1057, 1074, 1090-1094, 1109-1116, 1134-1142, 1156-1163, 1171-1179, 1202-1239, 1245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_adagrad.py 44 25 43% 63-74, 77-82, 85-90, 95-96, 103-104, 111-112, 120-121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_gradient_descent.py 30 13 57% 57-62, 65, 74, 83, 93, 103-107 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/py_checkpoint_reader.py 40 17 58% 31-48, 61, 73-74, 98-99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/quantize_training.py 15 4 73% 45-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner_impl.py 176 128 27% 97-118, 138-165, 175-192, 196, 200, 204, 208, 212, 231, 236, 248-283, 293-298, 327-356, 368-385, 390, 411, 451-480 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/rmsprop.py 70 50 29% 107-118, 121-131, 134-142, 145-161, 173-189, 201-218, 231-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saver.py 480 383 20% 84, 104-124, 144-154, 173-179, 194, 206-207, 254-285, 298-311, 335-361, 379-390, 409-417, 462, 484-557, 574-583, 598-610, 800-843, 846-848, 851, 856-902, 906-914, 926-927, 931-942, 957-973, 981, 992-1008, 1021, 1032, 1045-1049, 1061-1062, 1075-1080, 1139-1217, 1253, 1283-1334, 1346, 1460, 1471-1490, 1496-1514, 1580-1599, 1603-1607, 1626-1634, 1679-1728 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/functional_saver.py 119 48 60% 51, 64-73, 118, 148, 153-154, 165-169, 179-182, 188-191, 202-263, 284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_hook.py 16 4 75% 44, 51, 55, 59 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object.py 34 8 76% 41, 47-51, 55, 78, 101 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object_util.py 161 98 39% 54-56, 63-64, 67-70, 84-85, 90-95, 101, 111, 120, 139, 143, 145-169, 175-183, 190, 196-209, 230-303, 319, 342 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/server_lib.py 200 151 24% 57-95, 146-150, 153-164, 173, 184, 194, 213, 235, 285-313, 318, 324, 327, 330-334, 348-361, 365, 374, 388-392, 407-411, 427-434, 457-464, 473-491, 527-530, 534-538, 542-546, 555-573 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_manager.py 149 120 19% 42-47, 144-155, 189-227, 288-319, 353-383, 411-442, 453-459, 473, 487, 502-512, 529-551, 557-558, 561-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_run_hook.py 41 13 68% 110, 127, 150, 169, 186, 211, 226-228, 240, 245, 255, 263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/slot_creator.py 62 48 23% 55-101, 124-135, 161-173, 190-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/summary_io.py 12 1 92% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/supervisor.py 339 239 29% 308-357, 360-369, 380-390, 405-416, 427-433, 444-455, 464-469, 478-484, 493-500, 509, 518, 530, 539, 548, 557, 561, 570, 579, 588, 597, 606, 615, 624, 628-636, 661-688, 720-745, 772-780, 801-808, 831-847, 859, 869, 879, 883, 898-902, 910-918, 928-931, 999-1023, 1038-1040, 1043-1051, 1066-1073, 1076-1077, 1081-1098, 1112-1114, 1117-1122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/sync_replicas_optimizer.py 150 114 24% 181-205, 223, 248-358, 375-378, 392, 403, 417, 439-458, 462, 476-478, 481-497, 501-513 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/base.py 342 122 64% 71-76, 79-82, 86, 93-95, 98, 112, 126, 143-155, 161, 165, 170-173, 181-183, 187, 211, 233, 241-253, 266, 277-278, 291-307, 320, 323-324, 338-348, 352-354, 364-367, 369, 373, 376, 411, 414, 489-494, 521-526, 544-546, 550, 558, 562, 566, 570, 601, 624, 629, 633, 637-640, 726, 736-737, 754, 780-790, 822, 825, 829-839, 876, 883, 923-925, 933, 1004-1005, 1022-1023 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/data_structures.py 536 236 56% 28-30, 70, 72, 74, 92, 94, 97, 144, 167, 171, 184, 187, 193, 205, 214, 221, 228, 232, 236, 240, 248-252, 257-261, 266, 271, 314, 318, 321, 324, 328, 336, 349-350, 353, 356-366, 369, 372, 375, 381, 387, 390, 444-445, 454-455, 459-462, 465-468, 472, 482, 484-485, 494, 501, 510, 522-523, 532-545, 550, 568-574, 577, 580, 583, 586, 589, 592, 597, 600-601, 604-605, 608, 611-612, 626, 629, 646-648, 654, 657, 660, 666-669, 672-676, 679-683, 689-690, 693, 696, 699, 702, 717, 720, 732, 747, 751-754, 757-760, 770, 777, 785, 806, 808-809, 815, 823-824, 828-832, 845-859, 862-864, 867, 870, 875, 878-879, 882, 892-938, 943, 947-954, 957, 962, 967, 970, 973, 976, 983, 987, 991-998, 1001-1009, 1013, 1048-1055 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/graph_view.py 201 126 37% 78-86, 97-135, 177-179, 192, 211-312, 319-330, 335-356, 379-380, 385-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/layer_utils.py 110 13 88% 33-34, 190, 231, 242, 268, 293-296, 301-304 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/python_state.py 17 1 94% 87 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/tracking.py 131 52 60% 82, 92-94, 98, 102-113, 119-125, 140, 177-178, 185-186, 190-191, 221, 226, 231-234, 237-252, 274-276, 323-324, 330 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py 602 378 37% 69-72, 90-115, 129, 133, 136, 143-145, 147-149, 151, 229, 244, 248-249, 252-254, 272-277, 285, 291-294, 302-308, 324-348, 355-379, 392-418, 433, 465-476, 493, 511-512, 537-607, 616, 621, 626, 631, 636, 640, 651-664, 710-740, 763, 780, 788-806, 810-814, 831-845, 849-850, 864-867, 871, 876, 881, 893, 911-921, 941-945, 959, 963-982, 990, 998, 1002-1014, 1018-1024, 1029, 1036-1037, 1040-1045, 1094-1107, 1125-1140, 1164-1198, 1259, 1263, 1268-1281, 1286-1290, 1333-1335, 1343, 1468-1481, 1485-1490, 1520-1529, 1540-1541, 1568-1601, 1705-1712, 1808-1821, 1825-1830, 1857-1866, 1877-1878, 1902-1933, 2009-2016 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training.py 105 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_ops.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_util.py 95 65 32% 66-68, 88-103, 120-137, 158-162, 172-184, 201-209, 222-239, 243-253 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/warm_starting_util.py 150 123 18% 122-126, 152-156, 173-189, 240-311, 340-372, 396-411, 465-549 /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/user_ops.py 10 1 90% 32 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/all_util.py 36 6 83% 78-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat.py 51 9 82% 60-61, 80, 86, 111, 116, 178-180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat_internal.py 9 3 67% 34-36 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/decorator_utils.py 52 11 79% 26-32, 51, 71, 100, 108, 119, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py 208 81 61% 95, 97, 102-110, 129-131, 188-239, 264, 314-317, 379, 381-382, 390, 439-442, 463-471, 485, 487, 489, 495, 497-500, 553, 565-568, 596-601, 635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py 58 21 64% 69, 81, 100-104, 120-125, 128-131, 156, 170, 181-188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/function_utils.py 59 38 36% 31-32, 36, 51-63, 78-86, 95-101, 106-119, 127-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/is_in_graph_mode.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lazy_loader.py 27 4 85% 50-52, 66-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lock_util.py 43 3 93% 68, 92, 112 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/memory.py 11 4 64% 40-45 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/module_wrapper.py 132 60 55% 37, 44-48, 52, 68-78, 104-105, 108, 133-140, 144-152, 161-162, 170-174, 193-206, 219-227, 230-233, 236, 239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py 303 121 60% 91-93, 100-101, 150, 157-162, 165, 167-171, 178-179, 181, 185, 220-221, 223-224, 231, 256, 332, 335, 379-382, 418-441, 485-486, 489, 496, 508-512, 596, 599, 605, 610, 653-657, 696, 777, 782, 789-804, 809-822, 827-828, 833, 837, 844, 1032-1037, 1189, 1249-1285, 1347-1351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/object_identity.py 114 27 76% 44, 47-48, 51-52, 56, 61, 70, 114, 141, 148, 155, 159, 162-168, 179-181, 190, 199, 202, 205, 232 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/compare.py 87 69 21% 94-118, 139-186, 190, 194-200, 216-255, 274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/serialization.py 29 19 34% 43-76 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_contextlib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_decorator.py 98 16 84% 172-173, 180-181, 188-193, 218, 222, 224, 257, 260, 272, 276 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_export.py 145 47 68% 114, 118, 132, 154, 174, 178, 180, 194-207, 220-228, 241-249, 274, 302, 307, 329-331, 343-344, 388-393 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_inspect.py 138 55 60% 34, 43, 79-90, 95, 118, 126, 131-147, 190-235, 282, 291-293, 298, 327, 342, 347, 362, 377, 382, 397, 402, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_should_use.py 98 67 32% 45-61, 64-68, 72-86, 95, 100-101, 105-107, 113-117, 122-129, 148-161, 179-202, 235 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_stack.py 70 6 91% 37-38, 61, 76, 88, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/all_renames_v2.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/renames_v2.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/doc_controls.py 49 30 39% 258-261, 276-322 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/baseline.py 124 78 37% 72-79, 83-90, 95-112, 128-154, 187-197, 220-227, 264-283, 381-398, 414-427, 497-506, 516-525, 607-621, 636-650 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees.py 715 607 15% 61-81, 95-99, 111-115, 130-147, 168, 201-275, 280-287, 317-388, 393-414, 443-471, 476-489, 497, 505-570, 597-621, 625-639, 643-654, 674-680, 686, 690, 703-706, 709, 717-724, 730-731, 761-776, 824, 830-836, 841-889, 898, 906-911, 916-920, 926, 938-982, 986-1010, 1017-1052, 1056-1089, 1093, 1147-1407, 1414, 1417, 1440-1479, 1489-1506, 1513-1521, 1531-1551, 1556, 1565-1578, 1601-1615, 1641-1686, 1691-1700, 1739-1751, 1770-1781, 1840-1889, 1894-1903, 2038-2063, 2190-2214, 2315-2343, 2357-2360 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees_utils.py 42 27 36% 33-37, 45-57, 61, 66, 73-82, 87-94 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn.py 224 162 28% 46-48, 73-102, 125-153, 157-158, 174-230, 233-254, 260, 272, 287-344, 347-359, 363-364, 371-379, 431-456, 489-504, 552-579, 737-759, 787-807, 946-962, 986-1003, 1151-1172, 1199-1221 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn_linear_combined.py 187 140 25% 43-44, 64-65, 69-71, 76-82, 141-226, 288-383, 554-585, 615-643, 657-682, 820-843, 864-888, 1048-1077, 1106-1136 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/head.py 497 410 18% 58, 78-89, 155, 166, 193, 230-240, 274, 299-354, 382-443, 448-472, 487-494, 510-520, 536-559, 563-567, 571-573, 579-584, 590-598, 615-617, 622-628, 632-637, 646-650, 658-666, 671-679, 738-748, 767-774, 778, 782, 787-808, 812-825, 829-852, 893-994, 1062-1077, 1096-1101, 1105, 1109, 1113-1195, 1199-1223, 1265-1373, 1435-1440, 1460-1467, 1471, 1475, 1479-1505, 1514-1532, 1571-1652, 1661-1664, 1668-1676, 1704-1715 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/kmeans.py 110 69 37% 46-48, 51-52, 55-62, 82-84, 87-99, 119-127, 137-145, 173-224, 404-415, 424-425, 436-438, 454, 470-475, 479 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py 283 217 23% 128-133, 138-174, 181-239, 243-244, 248-249, 262-270, 283-308, 326-375, 394-448, 470-539, 547-548, 555, 559, 583-619, 652-678, 719-748, 759-767, 920-945, 968-991, 1107-1119, 1158-1171, 1178-1186, 1325-1349, 1371-1396 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sdca_ops.py 291 245 16% 95-104, 114, 123, 132, 200-251, 254, 257, 261, 265, 270-271, 280-298, 305-307, 310-312, 316-318, 322-333, 337-348, 356-364, 368-371, 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27% 84-104, 153, 210, 268, 326, 346-356, 365-388, 401-430, 446-450, 454-458, 471-479, 482-484, 487-488, 491-498, 505, 508, 511-512, 515-518, 525-529, 540-549, 552-566, 570-571, 577-592 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/estimator.py 729 611 16% 176-203, 209, 213, 217, 228-231, 246-248, 259-261, 270-271, 321-351, 365-376, 390, 445-462, 478-511, 525-543, 596-639, 646, 717-722, 800, 817-889, 926-1010, 1014-1017, 1020-1021, 1027-1039, 1043, 1048-1054, 1058-1072, 1086, 1097-1100, 1123-1137, 1154-1176, 1179-1182, 1197-1213, 1231-1241, 1250-1354, 1361-1385, 1390-1523, 1527-1563, 1567-1575, 1581-1631, 1636-1658, 1661-1664, 1735-1738, 1756, 1760-1763, 1769-1786, 1793-1805, 1822-1849, 1854-1861, 1871-1941, 1945-1947, 1953-1957, 1962-1968, 1985-1998, 2003-2016, 2021-2026, 2031-2040, 2052, 2065-2110, 2123-2134, 2141-2148, 2338-2341, 2366-2385 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/__init__.py 0 0 100% 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67 27% 103-120, 124-141, 148-176, 179-201, 205-207, 211, 237-244, 247-249, 253, 256-266, 275-277 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/session_run_hook.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/numpy_io.py 70 56 20% 50-53, 70-86, 141-224 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/pandas_io.py 61 46 25% 32-36, 50-52, 91-158 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py 208 177 15% 34-38, 51-60, 78-96, 126-145, 158-169, 172-181, 197-212, 215-230, 243-255, 258-271, 285-297, 300-330, 376-504 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/usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tools/analytics.py 8 2 75% 28, 37 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/_tpu_estimator_embedding.py 227 180 21% 58, 62, 66-68, 73-94, 103-122, 137-174, 270-315, 337-358, 362-363, 368, 372-412, 415-418, 424-429, 434-490, 495-505, 510-519, 524-541, 553-557 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/error_handling.py 65 44 32% 56-58, 73-105, 115-117, 122-125, 136-154 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/iteration_count_estimator.py 64 46 28% 68, 72-81, 84, 87, 90, 107-111, 122-123, 132-150, 169-201 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_config.py 99 60 39% 141-189, 227-265, 272, 276, 280, 284, 288, 291-297, 304-309 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_context.py 367 273 26% 55-58, 90-108, 121, 126, 131, 136-139, 144-147, 163, 173-177, 211-234, 237-240, 248-250, 254, 257-262, 266-284, 288-313, 318-337, 341, 345, 349, 354-355, 359-360, 365-369, 374-395, 399-400, 404, 408-409, 415, 420, 426-427, 434-436, 459-462, 466-478, 482-490, 495-505, 510-517, 531-552, 558-579, 584-596, 601-620, 624-718, 731-747, 756, 762-776, 793-799, 804-811, 814-816, 825-836 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py 1782 1486 17% 139-143, 149-153, 158-163, 204-215, 240-247, 260-262, 269-271, 277-278, 281-283, 346-363, 379-394, 411-416, 419, 422, 426-433, 436-438, 448-449, 452-454, 477-505, 508-522, 525-542, 545-557, 560, 563-570, 573-604, 608-616, 619-628, 641, 652, 694-710, 726-735, 745-749, 762-767, 771-772, 786-799, 812, 815, 818, 825, 828, 834, 837, 840-859, 866-903, 910-992, 999-1114, 1120-1210, 1218-1219, 1224-1267, 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/usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/util.py 36 19 47% 37-41, 65-79, 89, 92-95 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/training.py 337 258 23% 49-50, 55-61, 66-105, 110-114, 155-165, 228-254, 449-472, 479-480, 483, 486-488, 496-499, 502-504, 507-526, 532-535, 538-544, 561-584, 589, 607-639, 644, 649, 653-676, 681, 685-687, 691-718, 722-762, 766-793, 802-827, 835-874, 880-888, 892, 904-943, 947-950, 954-969, 1017-1052, 1066, 1077-1078, 1083-1084 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/util.py 47 24 49% 57-62, 67-72, 79, 82, 85-86, 93, 96, 99-100, 107, 110-113 /usr/local/lib/python3.8/dist-packages/termcolor.py 59 46 22% 102-115, 124, 128-167 /usr/local/lib/python3.8/dist-packages/threadpoolctl.py 323 225 30% 57-58, 124, 168-171, 174, 177, 180-182, 189-218, 223-257, 265-281, 335-343, 347-353, 357, 360, 363, 366, 370-375, 387-409, 416-428, 437-487, 492-516, 523-526, 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12079-12080, 12094-12110, 12121-12184, 12228-12477, 12482-12483, 12488-12495, 12500-12664, 12669-12674, 12679, 12686 /usr/local/lib/python3.8/dist-packages/tornado/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/tornado/concurrent.py 80 50 38% 53, 60-65, 68, 117-135, 154-171, 184-185, 204-207, 229-231, 238, 245, 261-264 /usr/local/lib/python3.8/dist-packages/tornado/escape.py 144 91 37% 54, 61, 75, 83, 88, 102-103, 108, 115, 137-144, 159-165, 173, 178, 183, 192-196, 204, 209, 214, 225-227, 245-256, 309-375, 379-390 /usr/local/lib/python3.8/dist-packages/tornado/gen.py 298 227 24% 97, 127-138, 142-153, 189-241, 254, 279-282, 343-359, 363-367, 375-380, 383-386, 392-397, 400, 403-406, 457, 481-523, 539-544, 586-622, 639-643, 660, 663, 706-714, 720-768, 771-791, 796-802, 808-811, 831-842 /usr/local/lib/python3.8/dist-packages/tornado/ioloop.py 272 176 35% 59-61, 68, 71, 170-179, 201, 215, 231, 236, 241, 264-279, 298, 309-313, 320, 324, 333-341, 368, 374, 380, 399, 408, 417, 425, 438-445, 458, 490-532, 546, 580-587, 602, 620, 629, 644, 654, 663, 680-696, 713-726, 733, 742-763, 767, 787-789, 803-809, 821-825, 835, 838, 871-877, 884-887, 891-894, 901, 904-911, 914-916, 919-946 /usr/local/lib/python3.8/dist-packages/tornado/locks.py 158 107 32% 27, 43-44, 48-51, 115-116, 119-122, 130-142, 146-154, 158, 202-203, 206, 213, 220-225, 232, 240-258, 271, 274, 282, 382-386, 389-395, 399-412, 422-440, 443, 451, 454, 462, 475-476, 480-482, 523, 526, 536, 545-548, 551, 559, 562, 570 /usr/local/lib/python3.8/dist-packages/tornado/log.py 111 87 22% 39-40, 44-45, 56-71, 75-78, 138-161, 164-207, 216-255, 267-336 /usr/local/lib/python3.8/dist-packages/tornado/platform/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py 156 116 26% 36, 45-75, 78-90, 95-104, 107-123, 126-135, 138-139, 142-151, 154, 162, 169, 172-184, 189-194, 202, 205, 224, 229, 253-261, 264-266, 269-275, 278-280, 292, 308, 314, 338-346 /usr/local/lib/python3.8/dist-packages/tornado/queues.py 147 96 35% 40, 62-70, 75, 78, 154-166, 171, 175, 178, 181-184, 199-207, 214-223, 246-252, 260-270, 284-288, 296, 299, 303, 306, 309, 314-316, 320-324, 327, 330, 333-342, 371, 374, 377, 404, 407, 410 /usr/local/lib/python3.8/dist-packages/tornado/util.py 177 116 34% 37-40, 51-63, 81-84, 87, 101, 114, 120, 128, 149-157, 161-165, 177-185, 198-203, 210-213, 228, 270-287, 299, 305, 308, 328-334, 340-350, 355-356, 361-363, 375-380, 383-395, 404-407, 421-430, 436, 448-452, 458, 462-465, 470-472 /usr/local/lib/python3.8/dist-packages/traitlets/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/_version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/config/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/config/application.py 366 257 30% 54, 70, 74, 86-93, 113-117, 151-157, 177-181, 197-199, 209-229, 243-247, 273-282, 287-289, 297, 305-306, 310-332, 336-345, 348-359, 363-378, 385-406, 413, 419-421, 429-434, 438, 443-453, 469-498, 503-541, 550-589, 594, 599-609, 623-642, 646-650, 653-654, 662-664, 708-711 /usr/local/lib/python3.8/dist-packages/traitlets/config/configurable.py 190 142 25% 63-93, 102, 119-129, 134-168, 180-186, 196-200, 211-218, 227-250, 255, 260-286, 294-328, 342-343, 363-367, 373-379, 411-421 /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py 417 299 28% 53-55, 80, 83, 87, 91-93, 99-104, 108, 115-131, 138-147, 172-176, 180, 184-196, 206-216, 220-226, 232, 235, 238, 241-250, 260-266, 270-271, 277, 280-281, 285, 288-289, 292-297, 321-322, 339-344, 347, 356-357, 378-381, 385, 401-408, 411-412, 415-423, 426-427, 436-439, 452-458, 462-473, 477-489, 507-517, 521-527, 587-592, 596-597, 602-610, 636-686, 714-726, 738-748, 751-754, 757-758, 761, 766-768, 772-773, 782-807, 812-833, 846-857 /usr/local/lib/python3.8/dist-packages/traitlets/log.py 10 7 30% 18-27 /usr/local/lib/python3.8/dist-packages/traitlets/traitlets.py 1265 803 37% 52, 99-102, 110-118, 126-146, 153-156, 163-166, 173-178, 203-211, 217, 219, 221, 235-236, 245-252, 271-277, 281-285, 288-291, 294-297, 300-302, 325-332, 336-340, 343-346, 350-351, 406, 439-457, 473-475, 480-484, 499-514, 519-524, 527-543, 556, 559-574, 582-585, 588-594, 597-606, 609-612, 618-625, 632-637, 644-649, 660, 667, 681-686, 690-693, 697-706, 709-712, 729-732, 785, 788, 806-819, 848, 851, 894, 907, 914, 924, 933, 953-959, 965-977, 983-986, 992-1008, 1022-1028, 1031-1045, 1057-1065, 1076-1131, 1134, 1143-1176, 1179-1189, 1192-1198, 1228-1235, 1263-1265, 1284-1286, 1291-1297, 1319-1327, 1331-1334, 1338-1343, 1352, 1377-1387, 1395-1396, 1401, 1405, 1421-1437, 1441-1450, 1458-1459, 1476-1486, 1507, 1510-1524, 1561, 1566, 1574-1586, 1591, 1596, 1600-1601, 1604-1607, 1610-1614, 1655, 1660, 1664, 1666, 1674-1677, 1680-1688, 1691-1692, 1695-1696, 1699-1701, 1705, 1717-1718, 1746, 1752-1755, 1788-1790, 1793-1803, 1806-1809, 1812-1818, 1839-1853, 1869-1871, 1878-1882, 1886-1953, 1973-1977, 1984-1988, 1998-2002, 2009-2012, 2024-2026, 2033-2036, 2046-2054, 2061-2064, 2075-2082, 2087-2091, 2096-2101, 2111-2113, 2120-2123, 2136-2138, 2142-2145, 2155-2163, 2217, 2221, 2226, 2231-2233, 2236-2244, 2247-2257, 2265-2267, 2312-2314, 2317-2321, 2324-2326, 2416, 2421, 2429, 2433-2449, 2458-2461, 2501, 2504-2507, 2511-2515, 2517, 2524-2526, 2529-2533, 2536-2556, 2560, 2562-2563, 2567-2572, 2585-2591, 2602-2605, 2638-2645, 2649-2655, 2659-2663, 2666-2683, 2687-2690 /usr/local/lib/python3.8/dist-packages/traitlets/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/utils/bunch.py 12 8 33% 12-15, 18, 22-24 /usr/local/lib/python3.8/dist-packages/traitlets/utils/getargspec.py 64 58 9% 22-86 /usr/local/lib/python3.8/dist-packages/traitlets/utils/importstring.py 17 13 24% 27-42 /usr/local/lib/python3.8/dist-packages/traitlets/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/wcwidth/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/table_wide.py 1 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/table_zero.py 1 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/wcwidth.py 37 28 24% 116-129, 183-196, 212-220 /usr/local/lib/python3.8/dist-packages/wrapt/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/wrapt/decorators.py 186 91 51% 11-23, 40-41, 55-56, 60, 64, 68, 72, 76, 86, 91, 95, 99-102, 105-106, 112, 117-120, 123, 138, 142, 146, 149-150, 154, 158, 162-163, 165, 205, 208-212, 253-279, 292-294, 322, 343-390, 411, 444-445, 450-451, 454, 464-514 /usr/local/lib/python3.8/dist-packages/wrapt/importer.py 102 75 26% 12, 37-45, 52-98, 103-109, 112-119, 128-135, 145-148, 153, 156-159, 164, 172-221, 227-230 /usr/local/lib/python3.8/dist-packages/wrapt/wrappers.py 472 304 36% 11, 32, 36, 40, 44, 51, 60, 78-87, 91, 95, 99, 103, 107, 111, 114, 117, 121, 124, 130, 134, 138, 141, 144, 147, 150, 153, 156, 159, 162, 165, 168-190, 196-199, 202-216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300, 303-304, 307-308, 311-312, 315-316, 319-320, 323-324, 327-328, 331-332, 335-336, 339-340, 343-344, 347-348, 351-352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 406, 409, 412, 415, 418, 421, 424, 427, 431, 437, 442-453, 456-461, 471-477, 505-533, 542-566, 578-624, 704-719, 727-728, 733-771, 774, 777-780, 791-794, 797-798, 801, 804, 807-811, 819-828, 831, 834-836, 839-858, 870-880, 899-928, 936-947 /usr/local/lib/python3.8/dist-packages/zmq/__init__.py 41 16 61% 23-24, 32-37, 54-60, 64-67 /usr/local/lib/python3.8/dist-packages/zmq/_future.py 323 280 13% 28-112, 129-144, 149, 152-161, 165-168, 176, 187, 196-199, 208-212, 216-241, 249-278, 282-289, 299, 308-315, 319-350, 354-406, 410-445, 448-483, 488-493, 503-509, 513-515, 519-521, 528, 532-533, 540-543 /usr/local/lib/python3.8/dist-packages/zmq/asyncio/__init__.py 48 20 58% 18-19, 28, 34-37, 41-43, 53, 60, 75-76, 83-87, 93 /usr/local/lib/python3.8/dist-packages/zmq/backend/__init__.py 26 15 42% 14-17, 22, 28-40 /usr/local/lib/python3.8/dist-packages/zmq/backend/cython/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/zmq/backend/select.py 15 7 53% 29-35 /usr/local/lib/python3.8/dist-packages/zmq/error.py 79 44 44% 37-50, 58, 61, 90-91, 101-102, 107-108, 120, 123-124, 132-144, 157-162, 165, 168, 183-184 /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:720: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_origin_portfolio_ids = [int(item) for item in options.list_origin_portfolio_ids.split(",")] if options.list_origin_portfolio_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:721: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_photo_ids = [int(item) for item in options.list_photo_ids.split(",")] if options.list_photo_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:722: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval = [int(item) for item in options.interval_rotation.split(",")] if options.interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:723: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval = [float(item) for item in options.interval_resize.split(",")] if options.interval_resize is not "" else None /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:750: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi = [float(item) for item in options.mother_crop_portfolio_multi.split(",")] if options.mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1515: 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:1966: 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:1967: 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:1973: 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:2157: 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:2158: 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:2164: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py:84: SyntaxWarning: "is not" with a literal. Did you mean "!="? if (default_value.shape.ndims is not 0 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py:85: SyntaxWarning: "is not" with a literal. Did you mean "!="? and default_value.shape.ndims is not 1): /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py:285: SyntaxWarning: "is" with a literal. Did you mean "=="? minval_is_zero = minval is 0 # pylint: disable=literal-comparison /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py:286: SyntaxWarning: "is" with a literal. Did you mean "=="? maxval_is_one = maxval is 1 # pylint: disable=literal-comparison /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py:795: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(key) is 1: /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py:804: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(key) is 1: /usr/local/lib/python3.8/dist-packages/zmq/eventloop/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/zmq/eventloop/ioloop.py 64 35 45% 23-26, 42-45, 49-52, 55-60, 64-67, 75, 89-91, 103-106, 114-117, 129-130, 135 /usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py 246 181 26% 44, 49, 53-54, 60-61, 114-138, 142, 146, 150, 154, 182-189, 200-203, 241-243, 255-258, 265, 271-279, 285-287, 295-299, 306-307, 311, 344-393, 397, 401-419, 423, 427, 430, 435-444, 449-469, 473-486, 491-505, 508-509, 513-522, 526-527, 531-532, 536-542, 546 /usr/local/lib/python3.8/dist-packages/zmq/sugar/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/zmq/sugar/attrsettr.py 29 21 28% 15-32, 36, 40-48, 52 /usr/local/lib/python3.8/dist-packages/zmq/sugar/constants.py 53 5 91% 34, 47, 104, 107-108 /usr/local/lib/python3.8/dist-packages/zmq/sugar/context.py 125 78 38% 27, 43-49, 53-54, 57, 60, 64, 77-79, 89-94, 119-132, 139-145, 152-154, 157-163, 180-192, 196, 210-222, 229, 236, 240-243, 247-253, 257-266 /usr/local/lib/python3.8/dist-packages/zmq/sugar/frame.py 29 12 59% 12-14, 51, 61-62, 66-67, 77-78, 82-83 /usr/local/lib/python3.8/dist-packages/zmq/sugar/poll.py 62 48 23% 22-23, 26, 44-57, 61, 71-75, 95-99, 126-152 /usr/local/lib/python3.8/dist-packages/zmq/sugar/socket.py 229 160 30% 32, 39-40, 59-63, 66-67, 75, 78, 86, 99-101, 104-106, 114-117, 124-132, 142-155, 166, 175-177, 186-188, 207-209, 229-231, 261-283, 290-300, 313-328, 390-400, 431-447, 475-481, 499, 519-520, 548-549, 566-568, 592-593, 610-611, 631-632, 646-651, 673-674, 697-704, 725-746, 754-755 /usr/local/lib/python3.8/dist-packages/zmq/sugar/stopwatch.py 15 11 27% 12-21, 25, 29-30 /usr/local/lib/python3.8/dist-packages/zmq/sugar/tracker.py 56 37 34% 54-63, 68-74, 96-118 /usr/local/lib/python3.8/dist-packages/zmq/sugar/version.py 20 7 65% 17-18, 26-29, 36, 41 /usr/local/lib/python3.8/dist-packages/zmq/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/zmq/utils/constant_names.py 16 1 94% 549 /usr/local/lib/python3.8/dist-packages/zmq/utils/jsonapi.py 21 11 48% 26-27, 37-45, 53-56 /usr/local/lib/python3.8/dist-packages/zmq/utils/strtypes.py 23 13 43% 18-20, 24-29, 33-38 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ TOTAL 838645 583296 30% ret : 36608 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 : 0 533.78user 950.62system 865:29:07elapsed 0%CPU (0avgtext+0avgdata 6529144maxresident)k 8219112inputs+63048outputs (24209major+6879575minor)pagefaults 0swaps