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/07012026/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_4/data_log/job/2026/January/07012026/coverage/htmlcov new path : /data_4/data_log/job/2026/January/07012026/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 : 7047 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 : 0.13613319396972656 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Wed Jan 7 11:20:30 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 mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 7047 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 2026-01-07 11:20:34.953377: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-07 11:20:34.986560: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-07 11:20:34.988451: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2c1c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-07 11:20:34.988480: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-07 11:20:34.993337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-07 11:20:35.248472: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xc4e7700 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-07 11:20:35.248550: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-07 11:20:35.249821: 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 2026-01-07 11:20:35.251944: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:20:35.280441: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:20:35.295480: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:20:35.299353: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:20:35.324375: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:20:35.327935: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:20:35.367048: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:20:35.368906: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:20:35.369449: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:20:35.370330: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-07 11:20:35.370354: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-07 11:20:35.370366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-07 11:20:35.372492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6482 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 2026-01-07 11:20:36.423410: 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 2026-01-07 11:20:36.423494: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:20:36.423512: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:20:36.423528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:20:36.423543: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:20:36.423557: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:20:36.423572: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:20:36.423587: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:20:36.424719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:20:36.426037: 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 2026-01-07 11:20:36.426077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:20:36.426097: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:20:36.426111: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:20:36.426126: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:20:36.426140: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:20:36.426154: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:20:36.426169: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:20:36.427275: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:20:36.427311: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-07 11:20:36.427320: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-07 11:20:36.427327: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-07 11:20:36.428451: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6482 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 : [] 2026-01-07 11:20:46.754828: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:20:47.097343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:20:49.171951: E tensorflow/stream_executor/cuda/cuda_driver.cc:910] failed to synchronize the stop event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2026-01-07 11:20:49.172038: E tensorflow/stream_executor/gpu/gpu_timer.cc:55] Internal: Error destroying CUDA event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2026-01-07 11:20:49.172081: E tensorflow/stream_executor/gpu/gpu_timer.cc:60] Internal: Error destroying CUDA event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2026-01-07 11:20:49.172126: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 8B (8 bytes) from device: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2026-01-07 11:20:49.172141: E tensorflow/stream_executor/stream.cc:5485] Internal: Failed to enqueue async memset operation: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2026-01-07 11:20:49.172163: W tensorflow/core/kernels/gpu_utils.cc:69] Failed to check cudnn convolutions for out-of-bounds reads and writes with an error message: 'Failed to load in-memory CUBIN: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered'; skipping this check. This only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2026-01-07 11:20:49.172176: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 8B (8 bytes) from device: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered 2026-01-07 11:20:49.172188: I tensorflow/stream_executor/stream.cc:4963] [stream=0xcf638d0,impl=0xcf628c0] did not memzero GPU location; source: 0x7f2a6effb020 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 error in detect the image : temp/1767781230_2731196_957285035_a42482e51c93c8025d243dd179aee85b.jpg 2 root error(s) found. (0) Internal: cuDNN launch failure : input shape([1,128,80,80]) filter shape([3,3,128,128]) [[node res3a_branch2b/convolution (defined at usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:3007) ]] [[mrcnn_detection/ExpandDims_3/_86]] (1) Internal: cuDNN launch failure : input shape([1,128,80,80]) filter shape([3,3,128,128]) [[node res3a_branch2b/convolution (defined at usr/local/lib/python3.8/dist-packages/keras/backend/tensorflow_backend.py:3007) ]] 0 successful operations. 0 derived errors ignored. [Op:__inference_keras_scratch_graph_13584] Function call stack: keras_scratch_graph -> keras_scratch_graph Detection mask done ! Trying to reset tf kernel 2731566 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1343 tf kernel not reseted sub process len(results) : 0 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 0 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 : 6248 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'] WARNING : results is empty ! time spent for convertir_results : 0.817476749420166 time spend for datou_step_exec : 22.658467292785645 time spend to save output : 4.649162292480469e-05 total time spend for step 1 : 22.65851378440857 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 ! begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.021445751190185547 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[], 'temp/1767781230_2731196_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 6248 ############################### 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 : 0.2407832145690918 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Wed Jan 7 11:20:54 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 : 6248 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-01-07 11:20:57.909564: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-07 11:20:57.934558: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-07 11:20:57.936800: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2c20000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-07 11:20:57.936879: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-07 11:20:57.941487: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-07 11:20:58.092561: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xc4a4540 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-07 11:20:58.092625: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-07 11:20:58.094084: 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 2026-01-07 11:20:58.094599: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:20:58.098014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:20:58.101069: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:20:58.101540: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:20:58.105232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:20:58.107093: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:20:58.112583: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:20:58.114017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:20:58.114101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:20:58.114820: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-07 11:20:58.114878: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-07 11:20:58.114894: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-07 11:20:58.116201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5731 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. 2026-01-07 11:20:58.242930: 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 2026-01-07 11:20:58.243113: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:20:58.243155: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:20:58.243181: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:20:58.243205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:20:58.243234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:20:58.243256: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:20:58.243280: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:20:58.244777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:20:58.246411: 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 2026-01-07 11:20:58.246470: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:20:58.246507: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:20:58.246542: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:20:58.246577: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:20:58.246607: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:20:58.246627: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:20:58.246649: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:20:58.248126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:20:58.248173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-07 11:20:58.248185: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-07 11:20:58.248195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-07 11:20:58.249740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5731 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 : [] 2026-01-07 11:21:07.431281: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:21:07.621722: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:21:09.064326: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.075301: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.076185: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.077040: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.077906: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.078806: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.079699: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2282521344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.079757: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.080727: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.080760: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.088685: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.088716: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.089226: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.089243: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.095748: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.095776: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.096254: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.096274: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.127558: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.127602: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.128119: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.128137: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.133963: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.133989: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.134474: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.134492: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:09.168724: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.169258: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.171104: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.171629: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.216322: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.216867: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.219029: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.219527: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.227444: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.227961: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.232783: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.233310: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.246027: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.246584: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.248246: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.248756: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.255102: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.255601: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.257467: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.257993: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.264600: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.265094: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.266769: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.267305: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.298972: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.299456: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.299939: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.300440: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.304422: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.304899: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.323393: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.323877: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.324382: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.324886: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.338776: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.339307: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.339825: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.340312: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.344592: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.345067: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.349593: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.350163: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.362289: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.362770: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.366902: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.367416: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.367922: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.368398: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.389333: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.389856: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.390387: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.390891: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.391382: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.391892: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.392407: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.392919: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.401835: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.402331: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.408491: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.408963: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.432432: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.432486: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2026-01-07 11:21:09.433389: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.434382: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.441555: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.442289: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.443036: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.443749: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.451670: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.452188: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.467522: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.468412: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.469325: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.470175: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.474840: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.475496: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.476003: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.476486: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.477376: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.487527: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.487999: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.498088: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.498602: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.499121: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.499627: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.500142: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:09.500684: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 2732119 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 565 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 : 1758 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 : 0.0006854534149169922 nb_pixel_total : 16902 time to create 1 rle with old method : 0.04542112350463867 length of segment : 107 DEBUG bbox = [16, 52, 668, 1128] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.1055598258972168 nb_pixel_total : 480745 time to create 1 rle with new method : 0.03316688537597656 length of segment : 632 DEBUG bbox = [0, 0, 116, 440] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.0004711151123046875 nb_pixel_total : 36642 time to create 1 rle with old method : 0.08363080024719238 length of segment : 133 DEBUG bbox = [0, 390, 54, 550] DEBUG masks shape = (720, 1280) time for calcul the mask position with numpy : 0.00010228157043457031 nb_pixel_total : 4793 time to create 1 rle with old method : 0.011566638946533203 length of segment : 51 time spent for convertir_results : 0.5418531894683838 time spend for datou_step_exec : 18.780463933944702 time spend to save output : 3.504753112792969e-05 total time spend for step 1 : 18.78049898147583 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 : 0.015942096710205078 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, 0.9988366, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.99774724, [(710, 22, 23), (925, 22, 47), (608, 23, 146), (893, 23, 104), (598, 24, 234), (849, 24, 159), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (489, 47, 589), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (477, 56, 607), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (413, 103, 677), (410, 104, 680), (405, 105, 685), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (369, 113, 721), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (349, 120, 741), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (287, 134, 802), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (234, 148, 855), (230, 149, 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(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/1767781254_2731196_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 : 0.16751885414123535 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Wed Jan 7 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 havn't enough memory gpu , need / 3000 l 3632 free memory gpu now : 1758 wait 20 seconds l 3637 free memory gpu now : 1758 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2026-01-07 11:21:38.829345: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-07 11:21:38.862535: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-07 11:21:38.864828: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2c20000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-07 11:21:38.864862: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-07 11:21:38.870682: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-07 11:21:39.000983: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x9322bc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-07 11:21:39.001049: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-07 11:21:39.002077: 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 2026-01-07 11:21:39.003582: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:21:39.009576: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:21:39.013778: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:21:39.015334: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:21:39.020738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:21:39.022726: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:21:39.030492: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:21:39.031737: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:21:39.031823: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:21:39.032353: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-07 11:21:39.032370: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-07 11:21:39.032380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-07 11:21:39.033284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1162 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. 2026-01-07 11:21:39.235938: 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 2026-01-07 11:21:39.236094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:21:39.236116: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:21:39.236135: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:21:39.236154: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:21:39.236172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:21:39.236190: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:21:39.236209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:21:39.236896: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:21:39.238575: 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 2026-01-07 11:21:39.238732: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:21:39.238774: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:21:39.238884: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:21:39.238934: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:21:39.238988: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:21:39.239022: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:21:39.239054: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:21:39.240299: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:21:39.240347: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-07 11:21:39.240363: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-07 11:21:39.240377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-07 11:21:39.241695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1162 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 : [] 2026-01-07 11:21:50.301308: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:21:50.482448: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:21:51.933736: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.933806: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.940306: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.940335: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.947008: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:51.947035: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.947561: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:51.947579: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.977793: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.977833: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.983603: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:51.983625: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:51.984125: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:51.984141: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2026-01-07 11:21:52.019177: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.019727: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.072858: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.073402: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.078282: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.078802: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.090025: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.090563: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.092070: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.092572: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.099278: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.099790: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.105572: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.106127: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.107682: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.108190: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.135089: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.135619: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.136120: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.136618: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.140335: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.140858: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.156353: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.156881: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.157422: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.157933: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.170367: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.170899: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.179248: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.179783: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.192058: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.192580: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.196726: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.197251: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.218256: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.218781: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.219300: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.219798: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.220292: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.220786: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.258686: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.258734: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2026-01-07 11:21:52.259595: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.260457: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.267409: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.267911: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.275912: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.276413: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.291949: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.292825: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.293697: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.294609: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.298693: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.299192: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.299685: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.300178: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.301094: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.311295: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.311794: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.322137: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.322706: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.323242: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.323888: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.324440: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2026-01-07 11:21:52.324973: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 651.88M (683540480 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (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 2733091 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 346 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 : 1758 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 : 0.6446053981781006 nb_pixel_total : 3693156 time to create 1 rle with new method : 0.2605295181274414 length of segment : 2042 time spent for convertir_results : 2.8583083152770996 time spend for datou_step_exec : 44.372883558273315 time spend to save output : 3.337860107421875e-05 total time spend for step 1 : 44.37291693687439 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 : 0.018988609313964844 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, 0.98501354, [(675, 120, 112), (520, 121, 481), (1051, 121, 380), (502, 122, 948), (486, 123, 982), (470, 124, 1015), (455, 125, 1046), (442, 126, 1092), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1223), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (363, 135, 1267), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (332, 152, 1363), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (323, 159, 1390), (322, 160, 1393), (321, 161, 1397), (319, 162, 1402), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (310, 168, 1429), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (283, 181, 1500), (280, 182, 1508), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (242, 195, 1615), (239, 196, 1624), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 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['1001,2150,936,2144,759,2089,685,2072,610,2037,280,1972,195,1963,128,1971,54,1825,53,1748,39,1677,39,1452,29,1237,27,757,21,695,27,543,39,458,93,308,116,278,210,206,291,179,363,135,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,2021,306,2094,411,2148,535,2168,613,2171,717,2165,833,2128,914,2112,994,2081,1068,2046,1106,1967,1255,1931,1368,1879,1444,1846,1670,1785,1855,1744,1939,1662,2015,1581,2015,1496,2039,1363,2064,1177,2101,1097,2141'])], 'temp/1767781274_2731196_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3690117 proportion of common points : 0.9994101229722977 #&_# 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 : 0.22745108604431152 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:sam Wed Jan 7 11:22:05 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 sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0020346641540527344 nb_pixel_total : 12162 time to create 1 rle with old method : 0.029117822647094727 time for calcul the mask position with numpy : 0.0018575191497802734 nb_pixel_total : 5616 time to create 1 rle with old method : 0.013917684555053711 time for calcul the mask position with numpy : 0.0015115737915039062 nb_pixel_total : 7504 time to create 1 rle with old method : 0.02154707908630371 time for calcul the mask position with numpy : 0.001886606216430664 nb_pixel_total : 83945 time to create 1 rle with old method : 0.19423985481262207 time for calcul the mask position with numpy : 0.0016570091247558594 nb_pixel_total : 16318 time to create 1 rle with old method : 0.039150238037109375 time for calcul the mask position with numpy : 0.0017533302307128906 nb_pixel_total : 38540 time to create 1 rle with old method : 0.0959939956665039 time for calcul the mask position with numpy : 0.0015387535095214844 nb_pixel_total : 9873 time to create 1 rle with old method : 0.022841930389404297 time for calcul the mask position with numpy : 0.0014519691467285156 nb_pixel_total : 14318 time to create 1 rle with old method : 0.03294491767883301 time for calcul the mask position with numpy : 0.0014979839324951172 nb_pixel_total : 29397 time to create 1 rle with old method : 0.09336256980895996 time for calcul the mask position with numpy : 0.002361774444580078 nb_pixel_total : 3775 time to create 1 rle with old method : 0.010773658752441406 time for calcul the mask position with numpy : 0.0019538402557373047 nb_pixel_total : 13916 time to create 1 rle with old method : 0.04106736183166504 time for calcul the mask position with numpy : 0.002354860305786133 nb_pixel_total : 2940 time to create 1 rle with old method : 0.010239839553833008 time for calcul the mask position with numpy : 0.002155303955078125 nb_pixel_total : 4272 time to create 1 rle with old method : 0.014318227767944336 time for calcul the mask position with numpy : 0.0022161006927490234 nb_pixel_total : 1226 time to create 1 rle with old method : 0.0040471553802490234 time for calcul the mask position with numpy : 0.0017747879028320312 nb_pixel_total : 885 time to create 1 rle with old method : 0.003478527069091797 time for calcul the mask position with numpy : 0.0023779869079589844 nb_pixel_total : 10801 time to create 1 rle with old method : 0.0283510684967041 time for calcul the mask position with numpy : 0.0014753341674804688 nb_pixel_total : 3954 time to create 1 rle with old method : 0.009006738662719727 time for calcul the mask position with numpy : 0.001565694808959961 nb_pixel_total : 2370 time to create 1 rle with old method : 0.0063250064849853516 time for calcul the mask position with numpy : 0.0015773773193359375 nb_pixel_total : 6634 time to create 1 rle with old method : 0.016428709030151367 time for calcul the mask position with numpy : 0.0015969276428222656 nb_pixel_total : 2082 time to create 1 rle with old method : 0.0050506591796875 time for calcul the mask position with numpy : 0.0015070438385009766 nb_pixel_total : 16375 time to create 1 rle with old method : 0.03935980796813965 time for calcul the mask position with numpy : 0.0014929771423339844 nb_pixel_total : 3509 time to create 1 rle with old method : 0.00837564468383789 time for calcul the mask position with numpy : 0.0015292167663574219 nb_pixel_total : 13105 time to create 1 rle with old method : 0.03146529197692871 time for calcul the mask position with numpy : 0.0013720989227294922 nb_pixel_total : 1602 time to create 1 rle with old method : 0.0038101673126220703 time for calcul the mask position with numpy : 0.0014166831970214844 nb_pixel_total : 3899 time to create 1 rle with old method : 0.009052038192749023 time for calcul the mask position with numpy : 0.0013549327850341797 nb_pixel_total : 4273 time to create 1 rle with old method : 0.010021209716796875 time for calcul the mask position with numpy : 0.001344442367553711 nb_pixel_total : 3340 time to create 1 rle with old method : 0.007528781890869141 time for calcul the mask position with numpy : 0.0013551712036132812 nb_pixel_total : 2457 time to create 1 rle with old method : 0.005666017532348633 time for calcul the mask position with numpy : 0.0014595985412597656 nb_pixel_total : 5489 time to create 1 rle with old method : 0.012432336807250977 time for calcul the mask position with numpy : 0.0013861656188964844 nb_pixel_total : 8641 time to create 1 rle with old method : 0.01994156837463379 time for calcul the mask position with numpy : 0.0014312267303466797 nb_pixel_total : 1421 time to create 1 rle with old method : 0.0034821033477783203 time for calcul the mask position with numpy : 0.0014042854309082031 nb_pixel_total : 2407 time to create 1 rle with old method : 0.0058727264404296875 time for calcul the mask position with numpy : 0.0014431476593017578 nb_pixel_total : 2750 time to create 1 rle with old method : 0.006457090377807617 time for calcul the mask position with numpy : 0.0013499259948730469 nb_pixel_total : 2781 time to create 1 rle with old method : 0.00628972053527832 time for calcul the mask position with numpy : 0.0014958381652832031 nb_pixel_total : 12983 time to create 1 rle with old method : 0.030220985412597656 time for calcul the mask position with numpy : 0.0013875961303710938 nb_pixel_total : 10626 time to create 1 rle with old method : 0.024016618728637695 time for calcul the mask position with numpy : 0.0013966560363769531 nb_pixel_total : 5409 time to create 1 rle with old method : 0.012476444244384766 time for calcul the mask position with numpy : 0.0014946460723876953 nb_pixel_total : 27200 time to create 1 rle with old method : 0.06173849105834961 time for calcul the mask position with numpy : 0.0015153884887695312 nb_pixel_total : 1029 time to create 1 rle with old method : 0.002490997314453125 time for calcul the mask position with numpy : 0.0013239383697509766 nb_pixel_total : 1249 time to create 1 rle with old method : 0.002864837646484375 time for calcul the mask position with numpy : 0.00140380859375 nb_pixel_total : 4163 time to create 1 rle with old method : 0.0093841552734375 time for calcul the mask position with numpy : 0.0013210773468017578 nb_pixel_total : 1649 time to create 1 rle with old method : 0.0038208961486816406 time for calcul the mask position with numpy : 0.0013070106506347656 nb_pixel_total : 343 time to create 1 rle with old method : 0.0008385181427001953 time for calcul the mask position with numpy : 0.0014462471008300781 nb_pixel_total : 4176 time to create 1 rle with old method : 0.009474515914916992 time for calcul the mask position with numpy : 0.0014426708221435547 nb_pixel_total : 858 time to create 1 rle with old method : 0.0022182464599609375 time for calcul the mask position with numpy : 0.0013859272003173828 nb_pixel_total : 875 time to create 1 rle with old method : 0.0021810531616210938 time for calcul the mask position with numpy : 0.0013699531555175781 nb_pixel_total : 597 time to create 1 rle with old method : 0.001458883285522461 time for calcul the mask position with numpy : 0.0014295578002929688 nb_pixel_total : 2387 time to create 1 rle with old method : 0.02306962013244629 time for calcul the mask position with numpy : 0.0014109611511230469 nb_pixel_total : 2324 time to create 1 rle with old method : 0.005439281463623047 time for calcul the mask position with numpy : 0.0014486312866210938 nb_pixel_total : 1637 time to create 1 rle with old method : 0.003951072692871094 time for calcul the mask position with numpy : 0.0013608932495117188 nb_pixel_total : 576 time to create 1 rle with old method : 0.0013315677642822266 time for calcul the mask position with numpy : 0.001329183578491211 nb_pixel_total : 691 time to create 1 rle with old method : 0.0017108917236328125 time for calcul the mask position with numpy : 0.005536317825317383 nb_pixel_total : 337 time to create 1 rle with old method : 0.0009140968322753906 time for calcul the mask position with numpy : 0.0013606548309326172 nb_pixel_total : 1708 time to create 1 rle with old method : 0.004055976867675781 time for calcul the mask position with numpy : 0.0014998912811279297 nb_pixel_total : 13039 time to create 1 rle with old method : 0.03023672103881836 time for calcul the mask position with numpy : 0.0014302730560302734 nb_pixel_total : 1206 time to create 1 rle with old method : 0.0029680728912353516 time for calcul the mask position with numpy : 0.0014758110046386719 nb_pixel_total : 2770 time to create 1 rle with old method : 0.00657200813293457 time for calcul the mask position with numpy : 0.0014455318450927734 nb_pixel_total : 585 time to create 1 rle with old method : 0.0014731884002685547 time for calcul the mask position with numpy : 0.0014510154724121094 nb_pixel_total : 1055 time to create 1 rle with old method : 0.002596139907836914 time for calcul the mask position with numpy : 0.0014350414276123047 nb_pixel_total : 1076 time to create 1 rle with old method : 0.002574920654296875 time for calcul the mask position with numpy : 0.0013666152954101562 nb_pixel_total : 3092 time to create 1 rle with old method : 0.007481575012207031 time for calcul the mask position with numpy : 0.0014712810516357422 nb_pixel_total : 8601 time to create 1 rle with old method : 0.02028036117553711 time for calcul the mask position with numpy : 0.0013878345489501953 nb_pixel_total : 2276 time to create 1 rle with old method : 0.005512237548828125 time for calcul the mask position with numpy : 0.0014286041259765625 nb_pixel_total : 877 time to create 1 rle with old method : 0.0021293163299560547 time for calcul the mask position with numpy : 0.0015177726745605469 nb_pixel_total : 18534 time to create 1 rle with old method : 0.04224586486816406 time for calcul the mask position with numpy : 0.0015213489532470703 nb_pixel_total : 16672 time to create 1 rle with old method : 0.03931689262390137 time for calcul the mask position with numpy : 0.0014085769653320312 nb_pixel_total : 1742 time to create 1 rle with old method : 0.003916025161743164 time for calcul the mask position with numpy : 0.0014183521270751953 nb_pixel_total : 4205 time to create 1 rle with old method : 0.009996891021728516 time for calcul the mask position with numpy : 0.001447439193725586 nb_pixel_total : 1513 time to create 1 rle with old method : 0.003521442413330078 time for calcul the mask position with numpy : 0.001375436782836914 nb_pixel_total : 8433 time to create 1 rle with old method : 0.01951432228088379 time for calcul the mask position with numpy : 0.0014224052429199219 nb_pixel_total : 1337 time to create 1 rle with old method : 0.0032432079315185547 time for calcul the mask position with numpy : 0.0013806819915771484 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006775856018066406 time for calcul the mask position with numpy : 0.0014934539794921875 nb_pixel_total : 9081 time to create 1 rle with old method : 0.02131056785583496 time for calcul the mask position with numpy : 0.0013551712036132812 nb_pixel_total : 3167 time to create 1 rle with old method : 0.00738215446472168 time for calcul the mask position with numpy : 0.0014846324920654297 nb_pixel_total : 1110 time to create 1 rle with old method : 0.002562284469604492 time for calcul the mask position with numpy : 0.001455545425415039 nb_pixel_total : 710 time to create 1 rle with old method : 0.0018301010131835938 time for calcul the mask position with numpy : 0.0013768672943115234 nb_pixel_total : 968 time to create 1 rle with old method : 0.002353191375732422 time for calcul the mask position with numpy : 0.001394033432006836 nb_pixel_total : 615 time to create 1 rle with old method : 0.0018107891082763672 time for calcul the mask position with numpy : 0.0014235973358154297 nb_pixel_total : 221 time to create 1 rle with old method : 0.0005598068237304688 time for calcul the mask position with numpy : 0.0013797283172607422 nb_pixel_total : 248 time to create 1 rle with old method : 0.0006730556488037109 time for calcul the mask position with numpy : 0.0013751983642578125 nb_pixel_total : 1122 time to create 1 rle with old method : 0.002732992172241211 time for calcul the mask position with numpy : 0.0014033317565917969 nb_pixel_total : 738 time to create 1 rle with old method : 0.0019545555114746094 time for calcul the mask position with numpy : 0.0013327598571777344 nb_pixel_total : 974 time to create 1 rle with old method : 0.002353668212890625 time for calcul the mask position with numpy : 0.0014064311981201172 nb_pixel_total : 9659 time to create 1 rle with old method : 0.022243738174438477 time for calcul the mask position with numpy : 0.001439809799194336 nb_pixel_total : 7502 time to create 1 rle with old method : 0.016837358474731445 time for calcul the mask position with numpy : 0.0014426708221435547 nb_pixel_total : 1502 time to create 1 rle with old method : 0.003468036651611328 time for calcul the mask position with numpy : 0.0013058185577392578 nb_pixel_total : 291 time to create 1 rle with old method : 0.0007572174072265625 time for calcul the mask position with numpy : 0.001325368881225586 nb_pixel_total : 1624 time to create 1 rle with old method : 0.003693103790283203 time for calcul the mask position with numpy : 0.001367807388305664 nb_pixel_total : 1629 time to create 1 rle with old method : 0.003904581069946289 time for calcul the mask position with numpy : 0.0013353824615478516 nb_pixel_total : 1671 time to create 1 rle with old method : 0.004124641418457031 time for calcul the mask position with numpy : 0.0013742446899414062 nb_pixel_total : 924 time to create 1 rle with old method : 0.002290964126586914 time for calcul the mask position with numpy : 0.0013632774353027344 nb_pixel_total : 11152 time to create 1 rle with old method : 0.02469325065612793 time for calcul the mask position with numpy : 0.0013821125030517578 nb_pixel_total : 594 time to create 1 rle with old method : 0.0014328956604003906 time for calcul the mask position with numpy : 0.0013241767883300781 nb_pixel_total : 889 time to create 1 rle with old method : 0.0020706653594970703 time for calcul the mask position with numpy : 0.0013914108276367188 nb_pixel_total : 1171 time to create 1 rle with old method : 0.0027947425842285156 time for calcul the mask position with numpy : 0.001390695571899414 nb_pixel_total : 1319 time to create 1 rle with old method : 0.0031516551971435547 time for calcul the mask position with numpy : 0.0013306140899658203 nb_pixel_total : 884 time to create 1 rle with old method : 0.0020990371704101562 time for calcul the mask position with numpy : 0.0014133453369140625 nb_pixel_total : 947 time to create 1 rle with old method : 0.0022275447845458984 time for calcul the mask position with numpy : 0.0013284683227539062 nb_pixel_total : 476 time to create 1 rle with old method : 0.0011372566223144531 time for calcul the mask position with numpy : 0.0013401508331298828 nb_pixel_total : 1207 time to create 1 rle with old method : 0.0029053688049316406 time for calcul the mask position with numpy : 0.0028409957885742188 nb_pixel_total : 339 time to create 1 rle with old method : 0.00098419189453125 time for calcul the mask position with numpy : 0.0014581680297851562 nb_pixel_total : 2257 time to create 1 rle with old method : 0.0057032108306884766 batch 1 Loaded 102 chid ids of type : 4677 Number RLEs to save : 9422 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 : 0.019821643829345703 save_final save missing photos in datou_result : time spend for datou_step_exec : 15.223519086837769 time spend to save output : 0.02016425132751465 total time spend for step 1 : 15.243683338165283 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1767781325_2731196_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 102 ############################### 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 : 0.16534781455993652 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:frcnn Wed Jan 7 11:22:20 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/1767781340_2731196_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 0.081s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 2.571014881134033 time spend to save output : 0.00020623207092285156 total time spend for step 1 : 2.571221113204956 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 : 0.018111228942871094 [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 : 0.014480113983154297 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, 0.06384293, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05225528, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.0122725405, None)], 'temp/1767781340_2731196_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 : 0.13379311561584473 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Wed Jan 7 11:22:23 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 : 0.010793685913085938 time to convert the images to numpy array : 0.0017557144165039062 total time to convert the images to numpy array : 0.012968063354492188 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 : 2950 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 : 2950 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.013437271118164062 time used to do the prediction : 0.06063270568847656 save descriptor for thcl : 355 time to traite the descriptors : 0.08556032180786133 storage_type for insertDescriptorsMulti : 1 To insert : 916235064 Catched exception ! Connect or reconnect ! time to insert the descriptors : 1.7620561122894287 Inside saveOutput : final : False verbose : False time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 9.5367431640625e-06 save missing photos in datou_result : time spend for datou_step_exec : 7.421115159988403 time spend to save output : 2.2083537578582764 total time spend for step 1 : 9.62946891784668 step2:argmax Wed Jan 7 11:22:33 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', 0.01770907, 332, '355'), 'temp/1767781343_2731196_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 : 0.017443418502807617 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 : 0.024817943572998047 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 : 0.0185544490814209 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 : 4.76837158203125e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0004146099090576172 time spend to save output : 0.061281442642211914 total time spend for step 2 : 0.06169605255126953 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', 0.01770907, 332, '355'), 'temp/1767781343_2731196_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 : 0.19026494026184082 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Wed Jan 7 11:22:33 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 inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory 2026-01-07 11:22:43.409416: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2026-01-07 11:22:43.410107: 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 2026-01-07 11:22:43.410347: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:22:43.410407: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:22:43.414893: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:22:43.414982: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:22:43.421644: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:22:43.423014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:22:43.437511: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:22:43.438579: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:22:43.439195: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2026-01-07 11:22:43.474513: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2026-01-07 11:22:43.476335: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2984000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2026-01-07 11:22:43.476355: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2026-01-07 11:22:43.479245: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xd8daf30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2026-01-07 11:22:43.479266: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2026-01-07 11:22:43.480115: 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 2026-01-07 11:22:43.480277: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:22:43.480297: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2026-01-07 11:22:43.480406: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2026-01-07 11:22:43.480431: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2026-01-07 11:22:43.480461: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2026-01-07 11:22:43.480496: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2026-01-07 11:22:43.480528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:22:43.481292: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2026-01-07 11:22:43.481346: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2026-01-07 11:22:43.481401: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2026-01-07 11:22:43.481413: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2026-01-07 11:22:43.481422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2026-01-07 11:22:43.482378: 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 : 2950 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] 2026-01-07 11:22:53.355746: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.02G (3246391296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 12.835034370422363 time used to load_weights : 0.18013834953308105 0it [00:00, ?it/s] 3it [00:00, 609.90it/s]2026-01-07 11:22:59.591632: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2026-01-07 11:22:59.686106: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 2026-01-07 11:22:59.687309: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR temp/1767781353_2731196_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1767781353_2731196_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1767781353_2731196_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : ERROR in datou_step_exec, will save and exit ! Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620] Function call stack: predict_function File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2339, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2533, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3147, in datou_step_tfhub2 classes, outputs, features = this_model.predict_image_paths(list_paths, keep_aspect_ratio=keep_aspect_ratio, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 288, in predict_image_paths Y_pred, F_pred = self.model.predict(valid_generator, validation_steps) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 88, in _method_wrapper return method(self, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 1268, in predict tmp_batch_outputs = predict_function(iterator) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 580, in __call__ result = self._call(*args, **kwds) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 650, in _call return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1661, in _filtered_call return self._call_flat( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1745, in _call_flat return self._build_call_outputs(self._inference_function.call( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 593, in call outputs = execute.execute( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, [1171252487, 1171252764, 1171252784] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.01872706413269043 save_final ERROR in last step tfhub_classification2, Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_34620] Function call stack: predict_function time spend for datou_step_exec : 26.149381637573242 time spend to save output : 0.023036718368530273 total time spend for step 0 : 26.172418355941772 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171252784': [(1171252784, 'jrm', 0.9677492, 4674, '3609'), 'temp/1687511175_1882837_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.9853587, 4674, '3609'), 'temp/1687511175_1882837_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.9262757, 4674, '3609'), 'temp/1687511175_1882837_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg']} got : None ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.20746564865112305 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Wed Jan 7 11:22:59 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 TFHub with tf2 ! we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds inside check gpu memory havn't enough memory gpu , need / 3096 l 3632 free memory gpu now : 6 wait 20 seconds l 3637 free memory gpu now : 6 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] 2026-01-07 11:25:23.109368: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 2026-01-07 11:25:23.111264: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model_1" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_2 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_3 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_2[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_3[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 10.381882905960083 time used to load_weights : 0.1561591625213623 found 3 data found 0 labels begin to do the prediction : ERROR in datou_step_exec, will save and exit ! Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model_2/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_69245] Function call stack: predict_function File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2339, in datou_exec output = datou_step_exec(sNext, args, cache, context, map_info, verbose, mtr_user_id) File "/home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py", line 2533, in datou_step_exec return lib_process.datou_step_tfhub2(param, json_param, args, cache, context, map_info, verbose) File "/home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py", line 3147, in datou_step_tfhub2 classes, outputs, features = this_model.predict_image_paths(list_paths, keep_aspect_ratio=keep_aspect_ratio, File "/home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py", line 288, in predict_image_paths Y_pred, F_pred = self.model.predict(valid_generator, validation_steps) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 88, in _method_wrapper return method(self, *args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py", line 1268, in predict tmp_batch_outputs = predict_function(iterator) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 580, in __call__ result = self._call(*args, **kwds) File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py", line 650, in _call return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1661, in _filtered_call return self._call_flat( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 1745, in _call_flat return self._build_call_outputs(self._inference_function.call( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py", line 593, in call outputs = execute.execute( File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, [1171275372, 1171291875, 1171275314] begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.02054119110107422 save_final ERROR in last step tfhub_classification2, Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[{{node model_2/module/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/stem_conv2d/StatefulPartitionedCall/Conv2D}}]] [Op:__inference_predict_function_69245] Function call stack: predict_function time spend for datou_step_exec : 143.1653344631195 time spend to save output : 0.02136397361755371 total time spend for step 0 : 143.18669843673706 need to delete datou_research and reload, so keep current state 1 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : None probably due to empty image bug ERROR expected : {'1171291875': [(1171291875, 'tapis_vide', 0.97062814, 4723, '3655'), 'temp/1691745841_1143057_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9674145, 4723, '3655'), 'temp/1691745841_1143057_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96509415, 4723, '3655'), 'temp/1691745841_1143057_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg']} got : None ERROR tfhub2 FAILED ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 51 thcl : 358 photo_hashtag_type : 337 ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2917673587799072 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rotate Wed Jan 7 11:25:27 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/1767781527_2731196 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(1767781528), 0.0, 0.0, 14, '', 0, 0, '1767781527_2731196_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', 0, 2448, 3264, 0, 1767781528,'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(1767781528), 0.0, 0.0, 14, '', 0, 0, '1767781527_2731196_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', 0, 3264, 2448, 0, 1767781528,'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(1767781528), 0.0, 0.0, 14, '', 0, 0, '1767781527_2731196_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', 0, 2448, 3264, 0, 1767781528,'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 : 1.1166632175445557 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 1.3956446647644043 time spend to save output : 3.6716461181640625e-05 total time spend for step 1 : 1.395681381225586 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 /1402780256Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780257Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780258Didn'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 : 0.01761317253112793 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1402780256: ['917849322', 'temp/1767781527_2731196_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1402780257: ['917849322', 'temp/1767781527_2731196_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1402780258: ['917849322', 'temp/1767781527_2731196_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 : 0.24351286888122559 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:thcl Wed Jan 7 11:25:29 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 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 : 0.0002567768096923828 time to convert the images to numpy array : 1.0570929050445557 total time to convert the images to numpy array : 1.057750940322876 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 6 wait 20 seconds l 3637 free memory gpu now : 6 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : orientation_carte_grise_all_2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3785 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 1.537024736404419 time used to do the prediction : 0.21011877059936523 save descriptor for thcl : 500 time to traite the descriptors : 0.0609893798828125 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.5429403781890869 time spend for datou_step_exec : 28.724650382995605 time spend to save output : 7.009506225585938e-05 total time spend for step 1 : 28.72472047805786 step2:argmax Wed Jan 7 11:25:57 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 : 0.00025582313537597656 time spend to save output : 1.0967254638671875e-05 total time spend for step 2 : 0.00026679039001464844 step3:rotate Wed Jan 7 11:25:57 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/1767781558_2731196 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.7091131210327148 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 : 0.8077914714813232 time spend to save output : 2.8133392333984375e-05 total time spend for step 3 : 0.8078196048736572 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 : 0.018954992294311523 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/1767781528_2731196_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 : 0.14582061767578125 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:crop Wed Jan 7 11:25:58 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 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 : 30013715 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1767781562_2731196 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(1767781563), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0.jpg', 0, 57, 51, 0, 1767781563,'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(1767781563), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0.jpg', 0, 50, 45, 0, 1767781563,'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(1767781563), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0.jpg', 0, 51, 54, 0, 1767781563,'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(1767781563), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0.jpg', 0, 43, 52, 0, 1767781563,'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 30013715 time of upload the photos Elapsed time : 4.440358638763428 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/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos upload in portfolio : 30013717 Result OK ! uploaded one batch 0 Elapsed time : 24.47861933708191 time spend for datou_step_exec : 32.44274950027466 time spend to save output : 1.3828277587890625e-05 total time spend for step 1 : 32.442763328552246 step2:tile Wed Jan 7 11:26:31 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 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/1767781558_2731196_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 : 30013736 with name tile_taggage_varroa feed_id_new_photos : 30013736 filename : temp/1767781558_2731196_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/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.025095701217651367 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/1767781600_2731196 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(1767781600), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0.jpg', 0, 480, 480, 0, 1767781600,'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 30013736 Importing ! upload mediasElapsed time : 0.64937424659729 , 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 : 0.7372326850891113 time spend for datou_step_exec : 9.030219793319702 time spend to save output : 3.552436828613281e-05 total time spend for step 2 : 9.030255317687988 step3:rotate Wed Jan 7 11:26:40 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 : 30013739 Needs to change image size ! time for calcul the mask position with numpy : 0.0007219314575195312 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0055713653564453125 .time for calcul the mask position with numpy : 0.0003998279571533203 nb_pixel_total : 1157 time to create 1 rle with old method : 0.003456592559814453 . 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 : 0.0004086494445800781 nb_pixel_total : 694 time to create 1 rle with old method : 0.0022857189178466797 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00045037269592285156 nb_pixel_total : 1162 time to create 1 rle with old method : 0.003763914108276367 . 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 : 0.00038552284240722656 nb_pixel_total : 221 time to create 1 rle with old method : 0.0007252693176269531 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003638267517089844 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0034444332122802734 . 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 : 0.0003902912139892578 nb_pixel_total : 143 time to create 1 rle with old method : 0.00048804283142089844 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003571510314941406 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0034515857696533203 . 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 : 0.0003840923309326172 nb_pixel_total : 414 time to create 1 rle with old method : 0.0013284683227539062 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003571510314941406 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0034248828887939453 . 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 : 0.00038623809814453125 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0028731822967529297 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035643577575683594 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002725362777709961 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00036454200744628906 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007641315460205078 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003910064697265625 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0033261775970458984 .time for calcul the mask position with numpy : 0.00035643577575683594 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002649545669555664 . 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 : 0.0003864765167236328 nb_pixel_total : 694 time to create 1 rle with old method : 0.0022194385528564453 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003554821014404297 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0028662681579589844 . 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 : 0.00040268898010253906 nb_pixel_total : 221 time to create 1 rle with old method : 0.0007274150848388672 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003483295440673828 nb_pixel_total : 1155 time to create 1 rle with old method : 0.003002643585205078 . 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 : 0.0003802776336669922 nb_pixel_total : 143 time to create 1 rle with old method : 0.000461578369140625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003459453582763672 nb_pixel_total : 1160 time to create 1 rle with old method : 0.0027763843536376953 . 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 : 0.0003535747528076172 nb_pixel_total : 414 time to create 1 rle with old method : 0.0010793209075927734 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003476142883300781 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002889871597290039 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003421306610107422 nb_pixel_total : 1 time to create 1 rle with old method : 2.6941299438476562e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.00040721893310546875 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0034437179565429688 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003540515899658203 nb_pixel_total : 1158 time to create 1 rle with old method : 0.009674310684204102 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00035190582275390625 nb_pixel_total : 264 time to create 1 rle with old method : 0.000720977783203125 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003895759582519531 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0032601356506347656 .time for calcul the mask position with numpy : 0.0003502368927001953 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027709007263183594 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00039076805114746094 nb_pixel_total : 727 time to create 1 rle with old method : 0.0018551349639892578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003540515899658203 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0028281211853027344 . 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 : 0.00037360191345214844 nb_pixel_total : 250 time to create 1 rle with old method : 0.0007429122924804688 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003514289855957031 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0027379989624023438 . 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 : 0.0003829002380371094 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005555152893066406 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003559589385986328 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0028488636016845703 . 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 : 0.0003514289855957031 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011997222900390625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003635883331298828 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0027856826782226562 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003440380096435547 nb_pixel_total : 1 time to create 1 rle with old method : 0.00011324882507324219 Needs to change image size ! time for calcul the mask position with numpy : 0.00036978721618652344 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0036754608154296875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004413127899169922 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0035436153411865234 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0004000663757324219 nb_pixel_total : 234 time to create 1 rle with old method : 0.0007982254028320312 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004096031188964844 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003916263580322266 .time for calcul the mask position with numpy : 0.0003643035888671875 nb_pixel_total : 1157 time to create 1 rle with old method : 0.003648042678833008 . 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 : 0.0003833770751953125 nb_pixel_total : 727 time to create 1 rle with old method : 0.002271890640258789 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036835670471191406 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0038383007049560547 . 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 : 0.0003323554992675781 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006227493286132812 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00034880638122558594 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0028836727142333984 . 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 : 0.0003521442413330078 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005638599395751953 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035119056701660156 nb_pixel_total : 1161 time to create 1 rle with old method : 0.002816915512084961 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003685951232910156 nb_pixel_total : 450 time to create 1 rle with old method : 0.0014297962188720703 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003533363342285156 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002753019332885742 . 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 : 0.0003612041473388672 nb_pixel_total : 1237 time to create 1 rle with old method : 0.003347635269165039 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003521442413330078 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002737760543823242 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003407001495361328 nb_pixel_total : 234 time to create 1 rle with old method : 0.0007131099700927734 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 30013739 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1767781604_2731196 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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', 0, 320, 320, 0, 1767781607,'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(1767781607), 0.0, 0.0, 14, '', 0, 0, '1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', 0, 320, 320, 0, 1767781607,'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 30013739 time of upload the photos Elapsed time : 4.995082378387451 Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 batch 1 Loaded 28 chid ids of type : 529 Number RLEs to save : 1197 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 10.065687417984009 time spend to save output : 6.127357482910156e-05 total time spend for step 3 : 10.065748691558838 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, '1402780569'] Looping around the photos to save general results len do output : 24 /1402780571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780594Didn'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, '1402780569', 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 : 0.027952194213867188 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1402780571: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1402780572: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1402780573: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1402780574: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1402780575: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1402780576: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1402780577: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1402780578: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1402780579: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1402780580: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1402780581: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1402780582: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1402780583: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1402780584: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1402780585: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1402780586: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1402780587: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1402780588: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1402780589: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1402780590: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1402780591: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1402780592: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1402780593: ['937852786', 'temp/1767781558_2731196_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1402780594: ['937852786', 'temp/1767781558_2731196_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 : 0.1274874210357666 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:flip Wed Jan 7 11:26:52 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/1767781612_2731196 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(1767781612), 0.0, 0.0, 14, '', 0, 0, '1767781612_2731196_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', 0, 640, 640, 0, 1767781612,'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(1767781612), 0.0, 0.0, 14, '', 0, 0, '1767781612_2731196_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', 0, 640, 640, 0, 1767781612,'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 : 0.7039287090301514 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 : 0.8355157375335693 time spend to save output : 6.175041198730469e-05 total time spend for step 1 : 0.8355774879455566 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 /1402780595 /1402780596 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 : 0.01686382293701172 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1402780595': ['911785586', 'temp/1767781612_2731196_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1402780596': ['911785586', 'temp/1767781612_2731196_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 : 0.15751361846923828 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:crop Wed Jan 7 11:26:53 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 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 : 30013765 Result OK ! uploaded one batch 0 Elapsed time : 24.275789737701416 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 24.36016058921814 time spend to save output : 4.1484832763671875e-05 total time spend for step 1 : 24.360202074050903 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] Looping around the photos to save general results len do output : 8 /1402780597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1402780605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('686', None, None, None, None, None, None, None, None) ('686', None, '950103132', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 time used for this insertion : 0.020189762115478516 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1402780597': ['950103132', 'temp/1767781613_2731196_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1402780598': ['950103132', 'temp/1767781613_2731196_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1402780599': ['950103132', 'temp/1767781613_2731196_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1402780600': ['950103132', 'temp/1767781613_2731196_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1402780602': ['950103132', 'temp/1767781613_2731196_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1402780603': ['950103132', 'temp/1767781613_2731196_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1402780604': ['950103132', 'temp/1767781613_2731196_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1402780605': ['950103132', 'temp/1767781613_2731196_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : angular_coeff list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.18627619743347168 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:angular_coeff Wed Jan 7 11:27:17 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 : 0.12229180335998535 time spend to save output : 3.361701965332031e-05 total time spend for step 1 : 0.12232542037963867 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 : 0.13559389114379883 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_crop Wed Jan 7 11:27:18 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 : 0.15493369102478027 time spend to save output : 4.673004150390625e-05 total time spend for step 1 : 0.15498042106628418 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 : 0.00612640380859375 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Wed Jan 7 11:27:18 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 : 0.34134817123413086 time spend to save output : 4.2438507080078125e-05 total time spend for step 1 : 0.34139060974121094 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 : 0.16485905647277832 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:blur_detection Wed Jan 7 11:27:19 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/1767781638_2731196_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.36656880378723145 time spend to save output : 5.316734313964844e-05 total time spend for step 1 : 0.3666219711303711 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 BFBFBFBFmaybe url didn't existe for photo_id 987515195 , please check it BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFmaybe url didn't existe for photo_id 987515248 , please check it BFBFBFBFBFBFBFBFBFBFFFFFwe have missing 4 photos in the step downloads : photo missing : [987515212, 987515195, 987515248, 987515175] 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 : 138.57781171798706 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Wed Jan 7 11:29:37 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 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 : 0.03699636459350586 time to convert the images to numpy array : 0.018959999084472656 time to import caffe and check if the image exist : 0.05073380470275879 time to convert the images to numpy array : 0.019708633422851562 time to import caffe and check if the image exist : 0.052782535552978516 time to convert the images to numpy array : 0.021633148193359375 time to import caffe and check if the image exist : 0.059201955795288086 time to convert the images to numpy array : 0.018976211547851562 time to import caffe and check if the image exist : 0.06191301345825195 time to convert the images to numpy array : 0.019478321075439453 time to import caffe and check if the image exist : 0.08228802680969238 time to convert the images to numpy array : 0.03623151779174805 time to import caffe and check if the image exist : 0.08346033096313477 time to convert the images to numpy array : 0.039443016052246094 time to import caffe and check if the image exist : 0.09372925758361816 time to convert the images to numpy array : 0.03145647048950195 time to import caffe and check if the image exist : 0.08317422866821289 time to convert the images to numpy array : 0.04586529731750488 time to import caffe and check if the image exist : 0.0753486156463623 time to convert the images to numpy array : 0.11903190612792969 total time to convert the images to numpy array : 0.19521546363830566 list photo_ids error: [] list photo_ids correct : [987515182, 987515183, 987515184, 987515185, 987515186, 987515187, 987515188, 987515189, 987515190, 987515192, 987515193, 987515196, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515226, 987515227, 987515228, 987515230, 987515231, 987515232, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515233, 987515234, 987515235, 987515236, 987515237, 987515238, 987515245, 987515246, 987515247, 987515249, 987515250, 987515224, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515207, 987515208, 987515209, 987515211, 987515213, 987515215] 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 havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 6 wait 20 seconds l 3637 free memory gpu now : 6 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory havn't enough memory gpu , need / 2500 l 3632 free memory gpu now : 6 wait 20 seconds WARNING: Logging before InitGoogleLogging() is written to STDERR F0107 11:30:22.350823 2731196 syncedmem.cpp:78] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) /home/admin/workarea/git/Velours/python/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/.local/lib/python3.8/site-packages/Crypto/Hash/SHA256.py 46 29 37% 72-80, 89-93, 104-112, 122, 135-140, 145, 158, 171-185 /home/admin/.local/lib/python3.8/site-packages/Crypto/Hash/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/Numbers.py 11 7 36% 36-42 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/Primality.py 154 141 8% 65-116, 134-213, 244-277, 314-335, 354-369 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/_IntegerBase.py 226 121 46% 43, 47, 51, 55, 60, 65, 69, 73, 77, 81, 85, 89, 94, 99, 103, 107, 111, 115, 119, 123, 127, 131, 135, 139, 143, 147, 152, 156, 160, 164, 168, 172, 176, 181, 185, 189, 193, 197, 201, 205, 209, 213, 217, 221, 225, 230, 249-295, 322-343, 364-391 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/_IntegerGMP.py 407 329 19% 98, 104, 110-118, 134-137, 155-191, 196-212, 215, 218, 222, 226, 248-276, 292-308, 312-314, 317-319, 322-324, 327, 330, 333, 336, 339, 343, 347-356, 359-368, 371-380, 383-392, 395-407, 411-446, 449-450, 453-455, 461-473, 476-491, 494-509, 512-528, 531-542, 546-552, 555-561, 564-575, 578-588, 591-597, 600-605, 611-617, 622, 625, 630-632, 636, 639, 644-653, 658-675, 680-684, 693-708, 711-713, 719-728, 734-738, 744-750, 755-762 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/PublicKey/RSA.py 325 267 18% 96-105, 109, 113, 117-119, 123-125, 129-131, 135-137, 141, 145, 148-150, 153-174, 179, 182, 185, 193, 196-202, 205, 209-210, 213-218, 221-225, 304-368, 376, 379, 382, 385, 388, 391, 394, 426-475, 518-618, 635-638, 646-647, 652-655, 660-661, 665-670, 676-688, 693-712, 756-791 /home/admin/.local/lib/python3.8/site-packages/Crypto/PublicKey/__init__.py 33 29 12% 46-60, 65-73, 79-94 /home/admin/.local/lib/python3.8/site-packages/Crypto/Random/__init__.py 16 6 62% 31, 35, 39, 43, 48, 52 /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/PKCS1_v1_5.py 14 9 36% 42-46, 49-52 /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/pkcs1_15.py 49 39 20% 49, 53, 73-84, 106-138, 191-207, 221 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/_file_system.py 8 1 88% 46 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/_raw_api.py 164 105 36% 42-46, 61, 66, 77, 99, 113, 118-126, 130, 134, 137-143, 149, 152, 155, 158, 162-261, 268-269, 272, 275-276, 279-284, 304-305, 307-309, 314, 318 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/asn1.py 316 251 21% 34-39, 47-49, 52, 55-56, 59, 62-68, 71, 107-141, 147-153, 160-163, 171-180, 187-197, 210-220, 225-246, 289-291, 297-306, 319, 325-341, 400-404, 409, 412, 415, 418, 421, 424, 427, 430-431, 434-435, 446-447, 460, 470-478, 501-507, 512-543, 589, 598, 643-644, 650-664, 680, 686-698, 743-749, 756-757, 772, 778-787, 827-836, 839, 842, 845, 856-869, 887, 892-919, 928-939 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/number.py 223 199 11% 40-47, 53-59, 72-81, 93-98, 111-114, 120-123, 128-136, 147-157, 173-206, 246-339, 359-374, 407-447, 464-483, 489-490, 492-493 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/py3compat.py 82 59 28% 66-110, 114, 116, 118-121, 123, 125-134, 136, 138, 147, 150, 153, 166-171 /home/admin/.local/lib/python3.8/site-packages/Crypto/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/MySQLdb/__init__.py 38 9 76% 20, 63-65, 98, 102, 106, 110, 114 /home/admin/.local/lib/python3.8/site-packages/MySQLdb/_exceptions.py 22 0 100% /home/admin/.local/lib/python3.8/site-packages/MySQLdb/connections.py 135 22 84% 48, 170, 177, 218, 245, 257, 260, 286, 303, 320, 327-328, 333-336, 343-348 /home/admin/.local/lib/python3.8/site-packages/MySQLdb/constants/CLIENT.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/MySQLdb/constants/FIELD_TYPE.py 29 0 100% /home/admin/.local/lib/python3.8/site-packages/MySQLdb/constants/FLAG.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/MySQLdb/constants/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/MySQLdb/converters.py 34 8 76% 55-56, 60, 65, 76, 92, 96, 100 /home/admin/.local/lib/python3.8/site-packages/MySQLdb/cursors.py 248 93 62% 89-90, 94, 96, 100-106, 109, 112-113, 117, 124-133, 148, 159, 191-196, 201-202, 218, 233, 270-271, 307-323, 342, 372-377, 382-386, 392, 405-414, 417-419, 431, 435-440, 445-448, 452-455, 458, 461-464 /home/admin/.local/lib/python3.8/site-packages/MySQLdb/release.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/MySQLdb/times.py 76 49 36% 22, 27, 32, 39-42, 49-63, 69, 76-80, 91-92, 96-115, 119-129, 133-140, 145, 150 /home/admin/.local/lib/python3.8/site-packages/PIL/BmpImagePlugin.py 218 179 18% 76-264, 276-284, 291-355, 366, 384, 388-449 /home/admin/.local/lib/python3.8/site-packages/PIL/ExifTags.py 340 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/GifImagePlugin.py 585 526 10% 71-74, 77-80, 84-108, 112-120, 124-139, 142-155, 158-410, 413-430, 433-456, 459, 480-491, 506-543, 547-564, 568-574, 578-649, 653, 658-670, 674-680, 684-746, 756-793, 812-844, 849-854, 865-872, 882, 886-905, 914-967, 971-983, 1002-1015, 1036-1048 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpGradientFile.py 68 53 22% 32-43, 47, 51, 55, 59, 70-98, 105-137 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpPaletteFile.py 26 20 23% 28-53, 56 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100% /home/admin/.local/lib/python3.8/site-packages/PIL/PaletteFile.py 24 19 21% 25-48, 51 /home/admin/.local/lib/python3.8/site-packages/PIL/PngImagePlugin.py 871 574 34% 137-140, 146-151, 172-173, 181-183, 188, 191, 197, 219-223, 234-248, 267-270, 294, 308-323, 335, 341-342, 348, 378-382, 385, 392-394, 398-421, 427-430, 434-435, 437, 439-440, 446, 449, 457, 461-464, 468-484, 488-490, 496-499, 508-515, 521-524, 530-531, 539-542, 555-585, 589-625, 628-630, 634-651, 654-680, 683-695, 716-717, 737-741, 762-763, 766, 771-778, 784-794, 799-811, 814-826, 829-919, 922, 928, 944-945, 948-952, 958, 969, 975-976, 982-984, 989, 992, 994-998, 1004-1015, 1018-1022, 1025-1028, 1037, 1092-1094, 1097-1098, 1102-1223, 1227, 1234-1250, 1257-1274, 1287-1289, 1317-1323, 1331-1332, 1335-1339, 1342-1346, 1351-1371, 1374-1376, 1393-1394, 1398-1402, 1405, 1414-1416, 1431-1453 /home/admin/.local/lib/python3.8/site-packages/PIL/PpmImagePlugin.py 219 188 14% 46, 58-65, 68-91, 94-141, 152, 155-157, 160-191, 198-216, 219-264, 267-276, 283-302, 310-329 /home/admin/.local/lib/python3.8/site-packages/PIL/TiffImagePlugin.py 1123 782 30% 277-279, 283-293, 324-327, 330-331, 337, 341, 358-362, 365, 368, 371-376, 379, 382-386, 390, 430, 506-507, 514-515, 532-533, 544, 552, 558, 568, 584-610, 613, 641, 644-655, 663, 669, 724, 728-732, 736-738, 743-747, 760, 766, 770-772, 776-781, 785, 793-797, 823-825, 838-844, 847-848, 863-865, 869-942, 945-952, 985-986, 1007-1011, 1024-1028, 1031, 1034, 1037, 1040-1041, 1044-1053, 1070-1076, 1082-1103, 1107-1113, 1117-1124, 1127-1162, 1166, 1175, 1185-1196, 1199-1201, 1204-1232, 1238-1325, 1330-1551, 1587-1877, 1906-1917, 1921-1942, 1945-1967, 1971-1972, 1975, 1978-1980, 1983, 1986-1990, 1993-2000, 2003-2006, 2010-2017, 2020, 2023-2024, 2027-2028, 2031-2035, 2038-2042, 2045-2049, 2052-2055, 2058-2061, 2064-2065, 2068-2100, 2103-2125, 2129-2152 /home/admin/.local/lib/python3.8/site-packages/PIL/TiffTags.py 46 1 98% 50 /home/admin/.local/lib/python3.8/site-packages/PIL/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/_binary.py 28 9 68% 22, 26, 37, 47, 57, 77, 90, 94, 98 /home/admin/.local/lib/python3.8/site-packages/PIL/_deprecate.py 25 12 52% 44, 46-47, 50-54, 57-58, 62-65 /home/admin/.local/lib/python3.8/site-packages/PIL/_util.py 11 2 82% 11, 19 /home/admin/.local/lib/python3.8/site-packages/PIL/_version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/apiclient/__init__.py 10 2 80% 7-10 /home/admin/.local/lib/python3.8/site-packages/av/__init__.py 21 1 95% 12 /home/admin/.local/lib/python3.8/site-packages/av/about.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/av/audio/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/codec/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/container/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/av/deprecation.py 36 17 53% 33-34, 37-42, 45-55, 58-68, 77-81 /home/admin/.local/lib/python3.8/site-packages/av/filter/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/av/sidedata/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/av/video/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/bcrypt/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/boto3/__init__.py 31 17 45% 35, 61-70, 80-84, 93, 102, 109 /home/admin/.local/lib/python3.8/site-packages/boto3/compat.py 32 17 47% 29, 34, 38-48, 58, 67-86 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/__init__.py 11 8 27% 30-39 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/action.py 44 30 32% 28-64, 85-97, 129-141 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/attr.py 20 13 35% 24-30, 39-44, 48-52 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/base.py 15 10 33% 18-26, 31 /home/admin/.local/lib/python3.8/site-packages/boto3/docs/client.py 10 7 30% 18-27 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/home/admin/.local/lib/python3.8/site-packages/botocore/docs/__init__.py 11 8 27% 28-38 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/docstringparser.py 119 83 30% 24-26, 29-30, 34-36, 39-42, 45, 48, 51, 61-64, 67-79, 82-87, 90, 93, 98, 101, 106-107, 110-111, 114, 117-118, 126-128, 131-133, 136-138, 141-143, 148, 151-152, 160-170, 178-181, 184, 187-200 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/restdoc.py 112 74 34% 25-33, 36-37, 43, 49, 56, 62, 68, 74-78, 81, 84-85, 88-97, 101-103, 120-128, 133, 141, 145, 149, 153, 156-157, 175-183, 187, 191, 201-209, 212, 215, 218 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/style.py 288 206 28% 22-25, 29, 33, 36, 39, 42-43, 46, 49, 52, 55, 58, 61, 64, 70-73, 76, 79, 82, 91-101, 104, 107, 110-111, 114-115, 118-121, 124-126, 129-132, 135, 138, 141, 144, 147, 150-153, 156-157, 160-161, 164-165, 168-169, 172-175, 178-181, 184-185, 188-191, 194-195, 198-201, 204-205, 208-219, 222, 225-230, 233-254, 257-258, 261-262, 265-267, 270-271, 274-277, 280-283, 286-289, 293-296, 299-302, 305, 308, 311, 314, 317-319, 322-323, 332-334, 337-342, 345-351, 354-357, 360-361, 364-368, 371-374, 377-378, 381-387, 390-391, 394-397, 400-401, 404-406, 409-412, 415-418 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/client.py 170 130 24% 28-32, 39-43, 46, 49-67, 71, 75-81, 84-86, 90-94, 97, 100, 103-110, 113-132, 167-168, 171-174, 177, 180-192, 195-196, 199-210, 213-214, 217-223, 226-229, 232-246, 249-254, 257-264, 267-276, 281-290 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/docstring.py 38 19 50% 33-36, 40, 43, 58-60, 63, 70-72, 75-81, 86, 91, 96 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/example.py 127 107 16% 36-41, 46, 50-56, 60-66, 70-81, 85-110, 114-125, 128-132, 135-139, 142-146, 152-158, 166-169, 177-208 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/method.py 97 86 11% 33-39, 60-78, 99-105, 117-123, 169-281 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/paginator.py 49 39 20% 22-24, 31-43, 46-65, 94-167 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/params.py 132 108 18% 33-34, 39, 43, 47-56, 60-77, 82-95, 98, 101-105, 108-117, 120-121, 124-125, 134-145, 149, 159-177, 182-212, 215-216, 219-220 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/service.py 53 36 32% 24-32, 46-55, 58-59, 66, 69-75, 78, 81-88, 91-96, 99-102 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/shape.py 39 31 21% 27-32, 61-89, 92-98, 101-107, 110-117 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/sharedexample.py 143 123 14% 33-38, 41-55, 58-72, 88-97, 101-125, 128-147, 150-164, 169-170, 173, 176-180, 183-187, 190-192, 195-198, 213-218 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/utils.py 75 33 56% 27, 50, 69-77, 117-128, 160-166, 176-180, 197 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/waiter.py 35 25 29% 22-24, 31-38, 41-56, 83-119 /home/admin/.local/lib/python3.8/site-packages/botocore/endpoint.py 153 117 24% 54-70, 85-94, 97, 100-102, 105-118, 122-124, 127-128, 131-158, 166-183, 186-230, 236-245, 249-266, 269, 274, 285-303, 314, 323-327 /home/admin/.local/lib/python3.8/site-packages/botocore/errorfactory.py 32 21 34% 28, 44, 47-51, 58, 70-74, 77-88 /home/admin/.local/lib/python3.8/site-packages/botocore/eventstream.py 246 149 39% 33-34, 40-43, 49-52, 58-61, 71-72, 110, 123, 135-136, 148-149, 161-162, 175-176, 189-190, 203-204, 225-229, 251-253, 265, 279, 285-287, 293-295, 306, 318, 328, 334-337, 340-343, 384, 396-397, 400-406, 409-411, 414-416, 419-421, 424-428, 431, 442-444, 452, 455-459, 462-468, 471-472, 475-477, 480-483, 487-488, 491-494, 497-502, 506-507, 515-524, 527, 530, 577-581, 584-587, 590-594, 597-602, 605-612, 616 /home/admin/.local/lib/python3.8/site-packages/botocore/exceptions.py 200 32 84% 24-28, 40-42, 45, 82-84, 87, 369-370, 410-420, 423-430, 436, 620-622 /home/admin/.local/lib/python3.8/site-packages/botocore/handlers.py 434 313 28% 80, 90-106, 125-134, 138-152, 163-185, 189-198, 202-205, 210-214, 218-224, 228-236, 246, 256, 260-272, 276, 285, 289-297, 305-312, 316-330, 365-374, 378-392, 396-401, 414-420, 427-429, 434-443, 450-465, 475-477, 483-496, 505-514, 518-522, 544-562, 573-584, 588-593, 597-598, 603-605, 620-637, 658-668, 672, 676-690, 696-699, 703-707, 719, 732, 745, 763-773, 777-781, 822-835, 838-855, 858-862, 875, 885, 894-900, 903-905, 911-923, 927-929, 934-935 /home/admin/.local/lib/python3.8/site-packages/botocore/history.py 27 10 63% 22, 31, 34, 37, 40-47 /home/admin/.local/lib/python3.8/site-packages/botocore/hooks.py 249 196 21% 31-35, 60-63, 82, 99, 112, 124, 130-132, 143, 146-147, 158-163, 171-175, 195-215, 228, 243-247, 251, 256, 261, 266-302, 306-336, 339-344, 349-352, 355-356, 359-360, 364-365, 371-372, 378-379, 385-386, 391-420, 423-427, 430, 464, 472-483, 493-497, 500-528, 538-540, 543-564, 572-575, 581-589 /home/admin/.local/lib/python3.8/site-packages/botocore/httpsession.py 210 158 25% 39-41, 45-51, 61-88, 99-105, 109-113, 117-122, 126, 129-134, 137-139, 142-146, 169-195, 199-206, 209-219, 222, 225-234, 237-243, 246-251, 254-274, 277-281, 284-301, 304, 307-359 /home/admin/.local/lib/python3.8/site-packages/botocore/loaders.py 139 98 29% 127-134, 154, 166-175, 192-199, 222-238, 242, 246, 267-287, 310, 327-340, 374-389, 393-400, 419-424, 430-441, 455-456, 460-461 /home/admin/.local/lib/python3.8/site-packages/botocore/model.py 398 242 39% 42, 81-93, 118-126, 145-150, 161, 164, 167, 172, 178-188, 192-195, 199-206, 212, 218, 222, 228, 259-266, 269, 273, 277-281, 284, 288, 292-297, 301-305, 309, 313, 327-330, 334-337, 348-351, 355, 359, 363, 367-370, 374-379, 382-385, 394-397, 401, 404, 437-444, 448-451, 465, 469, 473, 477, 483, 487, 491-495, 500-505, 510-514, 520, 524-525, 529, 533, 537, 541, 544, 547, 551-556, 560, 564, 567, 570, 574-581, 584, 599-600, 603-616, 624-636, 643, 647, 688-691, 702-703, 712-719, 724-734, 737-745, 748-752, 755-762, 765-773, 776, 779-782, 793, 821-823 /home/admin/.local/lib/python3.8/site-packages/botocore/monitoring.py 221 149 33% 42-43, 47-48, 56-61, 73, 89, 92, 97, 104-110, 114-124, 127-129, 136-138, 141, 145-147, 150, 153, 171-173, 176, 179-181, 214-220, 229-235, 287-296, 342-343, 346-347, 362-370, 374, 380, 383, 386, 389-391, 394-408, 412-415, 419, 422, 426-433, 438, 442-444, 448-451, 456-460, 464-467, 470-472, 475-481, 484, 490, 493, 496-500, 503-508, 531-533, 542-550 /home/admin/.local/lib/python3.8/site-packages/botocore/paginate.py 369 304 18% 52-68, 72-79, 83-90, 94-101, 105, 127-136, 140-144, 158-161, 172-173, 178, 181-186, 193-207, 211, 216, 220-232, 236, 239-303, 321-329, 332, 335, 338-344, 349-357, 360-364, 370-397, 401-424, 427-440, 443-444, 448-490, 493-506, 513-534, 541-550, 557-566, 570, 573-576, 579-585, 588-591, 594-596, 599-604, 607, 617-618, 629-646, 668-669, 672-677 /home/admin/.local/lib/python3.8/site-packages/botocore/parsers.py 528 407 23% 135, 147, 150-151, 155, 165-174, 199-207, 214, 233-262, 265, 279-284, 289-291, 299, 302, 306, 310-312, 317-321, 324, 327-329, 334-336, 339-357, 360, 368-370, 373-399, 402-406, 413-421, 427-444, 447-458, 461-467, 471-474, 478, 482, 486, 490, 494, 504-517, 520, 523, 526-538, 541-542, 545-551, 557-560, 575-580, 583-588, 594-609, 612-619, 622, 625, 628-652, 655-656, 660-669, 675-685, 688-706, 709-726, 730-740, 747, 753, 759-761, 770-778, 781, 784-793, 799-800, 806-810, 813-818, 821-823, 826-836, 839-860, 864-879, 885-893, 900, 903-907, 915, 918-920, 925-933, 942-944, 960-970, 973, 986-1006, 1010-1011 /home/admin/.local/lib/python3.8/site-packages/botocore/regions.py 90 69 23% 58, 65, 85, 94-96, 99-102, 106-116, 119-136, 141-165, 169-173, 176-190, 193-195, 199 /home/admin/.local/lib/python3.8/site-packages/botocore/response.py 67 45 33% 44-46, 60-69, 76-87, 92, 97-100, 110-117, 124-127, 133-135, 141, 145-162 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/retries/adaptive.py 70 50 29% 14-35, 44-50, 53-54, 58-77, 90-96, 99-113, 117 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/base.py 6 2 67% 9, 27 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/bucket.py 70 48 31% 10, 13, 16, 24-32, 36, 40-56, 60, 64, 76-77, 80-100, 103, 106-114 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/quota.py 24 16 33% 12-16, 28-32, 46-53, 57 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/special.py 28 17 39% 23-27, 36-48 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/standard.py 200 132 34% 40-61, 71-73, 77-93, 108-129, 136-138, 172-189, 199-204, 215, 218, 223-224, 227, 230, 239-241, 257, 265, 268-272, 290-298, 301-310, 334-336, 341, 348, 351-354, 376-391, 396-398, 402-406, 421-422, 433, 439, 442, 458-460, 463-475, 478, 490-498 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/throttling.py 25 15 40% 13-17, 20-21, 24-28, 35-38, 50 /home/admin/.local/lib/python3.8/site-packages/botocore/retryhandler.py 158 118 25% 52-58, 68, 73-77, 85-87, 93-118, 124-128, 132-144, 148-155, 173-174, 183-187, 219-225, 228, 231, 245-247, 250-263, 266-277, 282, 285-291, 296-297, 300-307, 312, 315-320, 326, 329-340, 359 /home/admin/.local/lib/python3.8/site-packages/botocore/serialize.py 357 275 23% 65-69, 117, 122-130, 135-139, 142, 145-147, 150-157, 162, 168-170, 174-186, 190-192, 200-222, 232-234, 237-243, 246-262, 265-278, 282, 285, 289-292, 295, 298, 314-322, 325-328, 335-357, 360-362, 365-379, 382-385, 388-397, 400, 403, 407, 428-476, 485-493, 502-519, 523-525, 533-569, 572-574, 577, 580-591, 597-599, 606-610, 613-615, 618-641, 644-652, 663-671, 677-682, 685-686, 689-690, 694-695 /home/admin/.local/lib/python3.8/site-packages/botocore/session.py 384 276 28% 102-131, 134-141, 144, 147, 151, 156, 161-165, 169, 173, 177-186, 189-192, 196, 200-215, 218-226, 230, 236-238, 242-245, 248-251, 261-282, 308-313, 316, 340-353, 365-388, 398, 408, 427, 440-443, 468-480, 489, 505-506, 509-512, 515-518, 524-534, 540, 548, 573-588, 602-616, 652, 685, 690, 693-694, 697-707, 713, 719, 722, 725, 800-855, 860-877, 880-884, 892-893, 915-925, 931-932, 935-945, 948-952, 955-959, 964-965, 968, 971-972, 975, 978, 981, 992-995, 1017-1018, 1032-1038, 1051 /home/admin/.local/lib/python3.8/site-packages/botocore/signers.py 216 174 19% 64-71, 75, 79, 83, 90, 120-162, 174-199, 221-242, 271-276, 315-316, 333-351, 354-355, 385-396, 401, 406, 427-457, 462, 506-533, 537, 561-598, 604, 671-719, 725-734 /home/admin/.local/lib/python3.8/site-packages/botocore/translate.py 21 16 24% 21-38, 42-55, 70-76 /home/admin/.local/lib/python3.8/site-packages/botocore/utils.py 1126 888 21% 181-184, 196, 207-214, 218-220, 226-229, 236-256, 262-267, 276-300, 310, 322-331, 337, 340-364, 367-395, 413-432, 435-436, 439-441, 444, 450-453, 456-459, 462-472, 482-513, 516, 523-528, 531-536, 539, 545, 552-558, 569-587, 592-595, 599-604, 611-619, 642-655, 671-676, 681-695, 710-716, 747-762, 775-783, 804-810, 827-843, 855-860, 875-880, 910, 922-923, 926-949, 952-958, 963-966, 971-974, 980-981, 993-1004, 1007, 1011-1016, 1030-1039, 1053-1060, 1077-1124, 1128, 1150-1159, 1170-1179, 1184-1187, 1191-1193, 1197-1212, 1222-1230, 1238, 1243-1250, 1253-1256, 1266-1343, 1358-1376, 1379-1381, 1388-1395, 1398, 1407-1413, 1435-1437, 1440, 1443-1451, 1454-1462, 1465-1470, 1480-1481, 1490-1499, 1515-1523, 1526-1527, 1533-1553, 1556-1567, 1570, 1573-1600, 1608-1615, 1618-1619, 1622-1633, 1636-1662, 1669-1671, 1674-1679, 1682-1688, 1691-1697, 1705-1732, 1738-1744, 1750-1766, 1777-1785, 1788, 1791-1802, 1805, 1808, 1811-1835, 1838-1839, 1847-1853, 1856-1858, 1861-1862, 1865-1876, 1879-1883, 1886, 1889-1891, 1894-1906, 1909-1918, 1921, 1924-1925, 1928-1933, 1936-1942, 1945-1951, 1954-1957, 1960, 1967-1969, 1972, 1978-1985, 1988-1997, 2000, 2003-2013, 2016-2018, 2022-2031, 2037-2043, 2046-2051, 2054-2063, 2069-2075, 2078-2083, 2095-2100, 2111-2112, 2115-2119, 2125-2127, 2138-2139, 2142-2156, 2159-2180, 2183, 2187-2190, 2205-2211, 2221-2232, 2236-2240, 2244-2245, 2249-2254, 2259-2263, 2268-2269, 2272-2273, 2278-2280, 2283, 2286-2296 /home/admin/.local/lib/python3.8/site-packages/botocore/validate.py 164 114 30% 46-49, 55-56, 59-64, 71-84, 89, 92-94, 97-100, 103-128, 132-137, 140, 160-162, 165-166, 169-173, 179-182, 187-202, 215, 219-222, 226-231, 235, 238-244, 250, 254, 260, 266-269, 273-279, 284-285, 288-294 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/exceptions.py 26 6 77% 21-27 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/exceptions.py 67 15 78% 17-18, 22, 28-29, 33, 73-78, 85-87, 135-138 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/six.py 444 211 52% 49-72, 98-99, 112, 118-121, 131-133, 145, 154-157, 192-193, 203, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /home/admin/.local/lib/python3.8/site-packages/botocore/waiter.py 175 140 20% 44-73, 79-82, 87, 90-93, 113-121, 124-125, 132-136, 147-154, 158-162, 167-171, 175-186, 197-208, 212-219, 222-239, 242-259, 262-271, 274-284, 303-307, 310-367 /home/admin/.local/lib/python3.8/site-packages/cached_property.py 93 61 34% 14-15, 30-37, 40-47, 57-59, 62-74, 85-91, 94-95, 98-115, 118, 121, 124-128, 143-144, 147-148 /home/admin/.local/lib/python3.8/site-packages/cffi/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/cffi/api.py 544 341 37% 8-11, 52-59, 82, 97-98, 115-117, 121-123, 129-130, 133-135, 147, 160, 166, 169, 174, 190, 200, 203-211, 217-221, 227-229, 238-240, 284-291, 299, 318, 335, 361-365, 382, 392-403, 411-419, 431, 454-473, 476, 478, 483, 486-487, 495-508, 511-515, 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, 797-800, 812, 818, 821, 832-833, 837, 842, 844, 854-855, 859-860, 864-868, 879, 882-940, 944-947, 950-971, 974-981, 984-999, 1003-1005 /home/admin/.local/lib/python3.8/site-packages/cffi/error.py 19 8 58% 8-15 /home/admin/.local/lib/python3.8/site-packages/cffi/lock.py 10 6 40% 4-7, 11-12 /home/admin/.local/lib/python3.8/site-packages/cffi/model.py 389 160 59% 16, 21, 30-45, 51, 54, 66, 79, 99, 166, 168, 170, 172, 182-183, 186, 189, 196-197, 200, 215, 219, 232, 249-253, 258, 269, 288-290, 304, 314, 318, 359-362, 365-376, 382-394, 400, 405-408, 414, 422-425, 430-462, 467, 471, 495-499, 502-505, 508-509, 512-514, 520-557, 562, 569-572, 584-587, 598-599, 610, 613, 616-617 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/api.py 195 181 7% 62-497, 515, 543-544 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/assets/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/cd.py 189 164 13% 24-50, 63-71, 80-91, 100-112, 120-129, 138-164, 175-244, 253-283, 291-311, 319-338, 350-388 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/constant.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/legacy.py 19 14 26% 22-50 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/models.py 174 110 37% 20-34, 37-43, 49-62, 66, 70-72, 75, 78-86, 90, 97-103, 107, 111, 119, 127-147, 151, 155-157, 161, 165, 172, 176, 180, 184-192, 201, 208-212, 219, 229, 232, 239-246, 249, 252, 259-272, 278-280, 286, 308-318, 322, 337 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/utils.py 214 158 26% 24-28, 40-46, 54-60, 65-69, 74-78, 83-93, 98-108, 113-118, 123-128, 133, 137-139, 144-149, 154-159, 164-169, 174-179, 184-189, 194, 199, 212-237, 245, 266-276, 280, 284-296, 300-310, 314-334, 342, 353-358, 372-414 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/colorama/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/colorama/ansi.py 74 8 89% 16, 19, 22, 38, 40, 42, 44, 46 /home/admin/.local/lib/python3.8/site-packages/colorama/ansitowin32.py 131 100 24% 13, 25-26, 29, 35, 38, 41, 44-53, 57-61, 75-104, 114, 117-158, 161-167, 171-174, 183-190, 194-196, 200-202, 206-220, 224-242, 246-257 /home/admin/.local/lib/python3.8/site-packages/colorama/initialise.py 48 32 33% 19-20, 25-48, 52-55, 60-64, 68-71, 75-80 /home/admin/.local/lib/python3.8/site-packages/colorama/win32.py 78 68 13% 11, 17-152 /home/admin/.local/lib/python3.8/site-packages/colorama/winterm.py 119 90 24% 25-34, 37, 40-42, 45-47, 50-58, 61-69, 72-75, 78-83, 86-91, 94-101, 104-109, 115-141, 147-166, 169 /home/admin/.local/lib/python3.8/site-packages/cryptography/__about__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/__init__.py 7 1 86% 18 /home/admin/.local/lib/python3.8/site-packages/cryptography/exceptions.py 37 5 86% 11, 33-34, 61-62 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/_oid.py 122 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/aead.py 143 127 11% 10-19, 28-46, 50-62, 70-85, 97-137, 141-145, 149-158, 162-166, 170-175, 179-187, 199-245, 257-310 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/backend.py 1215 945 22% 189, 200, 219, 229-231, 234-244, 250-264, 269-276, 279-285, 294, 299, 304, 308, 323-327, 334-336, 339-342, 346-349, 357-368, 372, 458, 468-482, 506-514, 519-535, 542, 553-582, 592-602, 605-608, 611-614, 633-638, 644-649, 671-721, 729-780, 783-786, 798-814, 817-820, 823-844, 849-857, 862-863, 868-871, 876-892, 897-911, 916-927, 930-933, 936, 939-941, 944, 949, 957, 965-1004, 1007-1016, 1037, 1049, 1055-1056, 1059-1079, 1082-1097, 1100-1105, 1108-1111, 1114-1115, 1120-1165, 1170-1207, 1216-1229, 1237-1240, 1249-1259, 1267-1314, 1319-1326, 1331-1348, 1353-1390, 1393-1394, 1397-1399, 1404-1409, 1414-1417, 1424-1434, 1438-1445, 1459-1478, 1490-1600, 1605-1611, 1622-1625, 1635-1680, 1683, 1688-1709, 1712-1715, 1720-1729, 1734, 1741-1784, 1789-1813, 1818-1833, 1838-1857, 1860, 1868, 1871-1881, 1888, 1892-1900, 1903-1912, 1915-1916, 1919-1921, 1927-1929, 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142-147, 155, 169-170, 177-185, 189, 192-204, 214-218, 225, 235-236 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ec.py 145 111 23% 20, 26-27, 34-60, 70, 76-81, 87-90, 99-108, 117-122, 127-134, 138, 142, 147-162, 165-179, 182-184, 195, 209-214, 219-226, 230, 234, 237-255, 258-279, 286-302, 312-317 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ed25519.py 70 54 23% 17, 22-23, 30-44, 49-56, 59-77, 82-83, 86-94, 97-117, 125-143, 148-155 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/ed448.py 72 54 25% 15, 23-24, 31-45, 50-57, 60-78, 83-84, 87-95, 98-118, 126-144, 149-156 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/hashes.py 45 12 73% 11, 29, 47-53, 65, 79-86 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/hmac.py 46 34 26% 15, 26-48, 52, 55-62, 67-69, 72-79, 82-84 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/poly1305.py 34 23 32% 15, 20-47, 50-54, 57-62, 65-67 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/rsa.py 257 136 47% 37, 46-59, 68-95, 107-162, 172, 180-201, 226-227, 233-234, 242-243, 250-261, 288-289, 306-322, 332-358, 383-384, 395-396, 436, 439-444, 447-451, 494, 520-532, 536, 539, 542-549, 559, 570-571, 581-586 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/utils.py 34 22 35% 11, 15-41, 53, 56 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/x448.py 53 37 30% 15, 22-23, 30-44, 49-56, 61-62, 65-73, 76-79, 87-105, 110-117 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/_conditional.py 74 34 54% 9, 25, 32, 38, 46, 52, 58, 65, 72, 79, 92, 98, 106, 118, 126, 132, 146, 155, 175, 181, 189, 199, 205, 211, 218, 227, 239, 247, 253, 257, 261, 265, 271, 275 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/bindings/openssl/binding.py 84 16 81% 25-28, 42, 88-99, 110-111, 161, 185, 200 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_asymmetric.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_cipheralgorithm.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/_serialization.py 79 35 56% 37-42, 64-67, 83-87, 90-99, 109-116, 126-133, 141-144, 163-168 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/dh.py 112 47 58% 17-19, 24-39, 42-45, 50-54, 58, 62, 66, 71-80, 83-86, 92-96, 100, 104, 109-118, 121-124, 130-134, 138, 142 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/dsa.py 127 57 55% 128-139, 143, 147, 151, 154-158, 161-164, 167, 175-184, 188, 192, 195-199, 202-205, 211, 219-227, 231, 235, 238-242, 245-248, 256-258, 264-266, 270-278, 282-288 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ec.py 216 56 74% 171-184, 315, 321, 327-329, 337-348, 353-361, 364-368, 372, 376, 380, 383-386, 394, 397, 407-417, 422-426, 430, 434, 437-440, 446, 477-480 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ed25519.py 39 14 64% 18-26, 43, 57-65, 69-77, 101 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/ed448.py 37 14 62% 15-23, 40, 54-61, 65-73, 103 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/padding.py 46 20 57% 44-57, 69-74, 85-88, 95-101 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/asymmetric/rsa.py 181 91 50% 129-132, 136-143, 156-190, 194-201, 208-214, 221, 229, 237, 254-288, 310, 316, 331, 335, 339, 343, 347, 351, 363-367, 372-375, 386, 402, 416-420, 423, 426-429, 432 /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, 81-85, 92-106, 113-119, 127-138, 148-152, 159-161, 166-170, 177-181, 185-191, 199-209, 217-221, 229-233, 240-247, 254-260, 268-278, 286-290, 298-302, 309-316, 323-329, 336-349, 356-361, 368-377 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/algorithms.py 128 30 77% 19, 48, 58, 67, 71, 80-84, 88, 97, 101, 120, 124, 142, 146, 155, 159, 178, 182, 200-206, 210, 214, 223, 227 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/base.py 137 61 55% 18, 85, 100, 106, 110-111, 125, 131, 147-150, 174, 178-180, 183-187, 195-199, 202-207, 214-217, 220-223, 226-231, 234-247, 252-257, 263-268 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/ciphers/modes.py 139 50 64% 72, 80-81, 92, 97, 103-109, 116-117, 121, 130-135, 139, 142-149, 165-166, 170, 179-180, 184, 193-194, 198, 232-250, 254, 258, 261-269 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/constant_time.py 5 1 80% 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 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/pkcs12.py 81 49 40% 44-49, 53, 57, 60-63, 69, 72, 84-110, 114, 118, 122, 125-128, 135, 138-141, 153-155, 163-165, 181-225 /home/admin/.local/lib/python3.8/site-packages/cryptography/hazmat/primitives/serialization/ssh.py 723 580 20% 39-49, 105-120, 125-130, 138, 143-144, 149-150, 160-165, 170-172, 177-179, 184-187, 192-195, 200-205, 216-218, 222, 226, 230, 234-239, 243, 247, 251-255, 259-261, 275-277, 283-286, 292-308, 314-316, 322-331, 347-351, 357-362, 368-378, 384-391, 397-398, 401-403, 426-432, 438-442, 448-454, 460-464, 470-474, 491-492, 498-502, 508-516, 522-525, 531-541, 556-560, 577-653, 662-730, 773-792, 796, 801, 805, 809, 813, 817, 821, 825, 829, 833, 836-839, 842, 849-873, 882-888, 895-983, 989, 993-1005, 1011-1025, 1030-1045, 1074-1083, 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82-94, 98, 102-104, 107, 110-113, 116, 121-133, 137, 143-145, 148, 151-154, 157, 162-165, 169, 172, 175-178, 181, 186-189, 193, 196, 199-202, 205, 210-225, 229, 232-237, 242, 245-248, 251, 256-262, 266, 270, 273, 278-281, 284 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/name.py 232 142 39% 64-87, 91-106, 118-164, 168, 172, 180, 191-197, 200-203, 206, 209, 214-225, 230, 241, 247-250, 253, 256, 259, 262, 268, 274, 282-293, 304, 319, 327, 331, 334, 337-340, 345, 348-350, 353, 356-357, 388-391, 394, 397-399, 402-404, 407-412, 423-429, 432-437, 440-460 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/oid.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/__init__.py 16 2 88% 18-19 /home/admin/.local/lib/python3.8/site-packages/cv2/data/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/__init__.py 13 4 69% 6-7, 17, 24 /home/admin/.local/lib/python3.8/site-packages/dateutil/_common.py 25 15 40% 14-17, 20-25, 28, 34, 37-41 /home/admin/.local/lib/python3.8/site-packages/dateutil/_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/easter.py 27 20 26% 52-89 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/__init__.py 33 4 88% 31-32, 47-48 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/_parser.py 812 517 36% 64, 68-69, 92, 108, 118, 122-123, 129-137, 144-145, 149-171, 175-179, 182, 197, 226-231, 238, 323-327, 330-334, 343-346, 349, 352, 355-358, 367-378, 383, 387-388, 390, 404, 408, 412, 415-426, 431-439, 444-446, 448-450, 452-454, 461-472, 485, 490, 493-505, 511, 514, 517, 522-563, 643, 646, 650-651, 657, 708, 735-736, 743-852, 863-864, 867, 870-871, 880-881, 893-897, 901-913, 917-927, 931-936, 940-949, 961-966, 970-976, 981-1002, 1013, 1019, 1026, 1033, 1042-1054, 1057, 1070-1090, 1093-1097, 1103-1109, 1116-1127, 1135-1139, 1147-1150, 1160-1175, 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1493, 1497-1504, 1507, 1513-1533, 1542-1561, 1566-1613, 1625-1725, 1732 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_common.py 161 124 23% 20-28, 55-129, 139-144, 169-177, 196-202, 205, 222-242, 258-264, 290, 293-300, 303-310, 314-317, 321-350, 366-372, 375-393, 396-405, 409, 414, 417 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_factories.py 49 21 57% 22, 34-52, 64-79 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/tz.py 803 557 31% 75, 78, 82, 98, 106, 109-112, 118, 121, 144-152, 155, 158, 162, 166, 180, 183-186, 191, 194, 206-216, 219-225, 228-234, 238, 254-255, 259-260, 287-300, 303-315, 320, 323, 337-342, 345-348, 359, 362-365, 368-370, 466-471, 475, 501, 538, 551, 585, 640-658, 674-704, 714-725, 729-736, 739-741, 763-777, 793-806, 809-819, 822-828, 831-844, 848-850, 853-855, 862, 865, 868, 871, 954-994, 1010-1018, 1021-1024, 1033, 1081-1109, 1112-1150, 1153, 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222-225, 230-235, 240-244, 249-253, 258-281 /home/admin/.local/lib/python3.8/site-packages/dill/logger.py 124 79 36% 123, 126, 129-135, 137-168, 182-184, 187-188, 192-209, 257, 263-266, 268-278, 280-285 /home/admin/.local/lib/python3.8/site-packages/dill/objtypes.py 9 1 89% 18 /home/admin/.local/lib/python3.8/site-packages/dill/pointers.py 60 50 17% 29-34, 44-51, 67-74, 84-115 /home/admin/.local/lib/python3.8/site-packages/dill/session.py 266 226 15% 40-55, 66-75, 78-117, 120-128, 221-262, 266-267, 273, 275, 277, 279, 281, 283-292, 296-304, 308-327, 431-507, 511-512, 571-603 /home/admin/.local/lib/python3.8/site-packages/dill/settings.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dill/source.py 613 574 6% 37-40, 45-49, 54-102, 115-258, 271-329, 345-346, 368-439, 444, 448-463, 468-473, 478, 483-508, 513-521, 525-529, 537-546, 552-560, 571-599, 604-624, 640-666, 679-713, 727-766, 791-827, 833-881, 891-921, 941-1001, 1006, 1009, 1011 /home/admin/.local/lib/python3.8/site-packages/dill/temp.py 94 79 16% 33-40, 44-45, 60-73, 107-118, 130-133, 159-165, 176-180, 190-195, 209-222, 237-246 /home/admin/.local/lib/python3.8/site-packages/easydict/__init__.py 30 12 60% 116, 118, 122, 129, 136-139, 142-143, 147-148 /home/admin/.local/lib/python3.8/site-packages/filetype/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/filetype/filetype.py 21 12 43% 28, 45-46, 63-64, 79-82, 95-98 /home/admin/.local/lib/python3.8/site-packages/filetype/helpers.py 23 12 48% 23-26, 41-44, 76, 92, 108, 124 /home/admin/.local/lib/python3.8/site-packages/filetype/match.py 27 6 78% 35, 69, 86, 103, 120, 137 /home/admin/.local/lib/python3.8/site-packages/filetype/types/__init__.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/filetype/types/application.py 9 1 89% 21 /home/admin/.local/lib/python3.8/site-packages/filetype/types/archive.py 198 28 86% 22, 55, 77, 99, 124, 144, 164, 187, 208, 227, 248, 270, 291, 312, 337, 365, 384, 407, 428, 466, 490, 511, 536, 557, 578, 598, 614, 630 /home/admin/.local/lib/python3.8/site-packages/filetype/types/audio.py 65 9 86% 22, 43, 69, 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% 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/home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/builder.py 324 255 21% 57-79, 83-85, 89-91, 95-97, 101-103, 107-109, 113, 155-184, 218-257, 261-264, 268-278, 288-290, 300, 334-401, 413-418, 424-435, 440-447, 456-458, 462-480, 483, 486-508, 511-512, 523-538, 545-596, 599, 602-607, 613-616, 620-623, 632-659 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/errors.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/geometry.py 62 46 26% 8, 12, 16-19, 38-51, 56-57, 60, 63, 66-67, 73, 76, 91-143 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/table_builder.py 118 99 16% 49, 53, 57-72, 84-86, 89-119, 122-181, 186-188, 191-223 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/unbuilder.py 41 32 22% 6-10, 17-21, 26-34, 37-38, 41-58, 62-81 /home/admin/.local/lib/python3.8/site-packages/fontTools/config/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/designspaceLib/__init__.py 1422 1195 16% 49-56, 64, 68-70, 77-78, 81, 86-95, 105-109, 118-120, 185-296, 311, 315, 322, 329, 339-345, 379-390, 402, 411-421, 431-446, 525-650, 661, 665, 669, 672, 675, 678, 681, 684, 687, 690, 720-732, 744-752, 774-785, 794, 799-812, 830-869, 925-945, 953, 968-972, 976-982, 1031-1052, 1064, 1074-1076, 1116-1137, 1152-1156, 1161, 1187-1208, 1215, 1223, 1249-1271, 1286-1299, 1315-1320, 1338, 1342, 1346, 1350, 1353-1356, 1359-1409, 1421-1439, 1443-1460, 1463-1465, 1469-1497, 1500-1527, 1532-1548, 1551-1555, 1560-1568, 1577-1596, 1599-1702, 1705-1768, 1773-1807, 1810-1814, 1817-1850, 1866-1876, 1880-1883, 1886-1892, 1896-1933, 1936-1959, 1963-2006, 2009-2049, 2061-2099, 2102-2141, 2144-2196, 2201-2262, 2270-2274, 2282-2327, 2330-2332, 2342-2406, 2410, 2414, 2434-2482, 2486-2487, 2524-2608, 2615-2617, 2621-2626, 2630-2641, 2647-2654, 2658-2664, 2667-2668, 2712-2716, 2720, 2726-2728, 2732, 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/home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/ast.py 1046 766 27% 81-84, 146-153, 161-163, 166, 172, 175, 190-192, 195, 202, 207, 210, 217-219, 223, 226, 233-237, 241, 244-250, 254, 258, 265-269, 275-279, 284-288, 296-298, 302, 305, 313-315, 319, 322, 329-331, 334-337, 344-345, 351-352, 355-356, 368-369, 372, 379-380, 386-395, 398-404, 412-414, 417-419, 422-425, 432-433, 437-439, 442-448, 455-456, 459-462, 469-471, 475, 478, 489-492, 496-500, 503, 527-529, 533-545, 549, 552-553, 580-583, 587, 590, 602-604, 608-614, 617-629, 649-652, 655-666, 673-674, 677-681, 688-690, 694-695, 698, 718-726, 730-733, 738-758, 776-784, 788-791, 796-816, 824-826, 830, 835-837, 844-845, 849, 852, 863-864, 869-873, 876-888, 899-900, 905-909, 912-921, 928-929, 933, 940, 947-951, 955, 963-969, 976-977, 981, 984, 992-993, 996, 999, 1007-1008, 1012-1013, 1016, 1026-1027, 1031-1032, 1035, 1051-1053, 1056-1059, 1064-1076, 1088-1091, 1095-1101, 1104-1117, 1126-1127, 1131, 1134, 1143-1144, 1148, 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/home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/lookupDebugInfo.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/arrayTools.py 92 66 28% 20-24, 42, 57-59, 73-75, 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 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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, 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/home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/es256.py 67 34 49% 48, 53-73, 90-101, 117-118, 123, 127-132, 156-160, 164-170, 174-175 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/rsa.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/environment_vars.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/exceptions.py 31 6 81% 22-24, 28, 58, 70 /home/admin/.local/lib/python3.8/site-packages/google/auth/iam.py 42 21 50% 84-86, 90-116, 126, 130-131 /home/admin/.local/lib/python3.8/site-packages/google/auth/jwt.py 237 160 32% 89-115, 120-127, 143-168, 184-185, 201-228, 257-316, 395-406, 424-426, 443-444, 458-461, 491-493, 514-517, 528, 543-560, 570, 574, 579, 584, 589, 637-648, 666-668, 685-686, 700-703, 729-731, 748-751, 763, 779, 790-806, 821-827, 841, 858-864, 868, 873, 878 /home/admin/.local/lib/python3.8/site-packages/google/auth/metrics.py 48 20 58% 46, 54, 62, 70, 80, 89, 98, 106, 114, 120, 126, 132-135, 149-154 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/__init__.py 20 4 80% 55, 60, 65, 103 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/_http_client.py 42 24 43% 36-38, 42, 46, 50, 80-113 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/_mtls_helper.py 134 107 20% 61-65, 81-88, 109-114, 134-144, 148-186, 190-193, 197-207, 211-222, 241-276, 310-337, 360-365, 401-407 /home/admin/.local/lib/python3.8/site-packages/google/auth/transport/mtls.py 35 29 17% 27-39, 53-67, 89-112 /home/admin/.local/lib/python3.8/site-packages/google/auth/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/oauth2/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/google/oauth2/_client.py 126 100 21% 57-69, 85-110, 123-133, 174-217, 259-271, 297-319, 344-367, 396-419, 439-450, 494-508 /home/admin/.local/lib/python3.8/site-packages/google/oauth2/service_account.py 249 165 34% 174-197, 215, 243-246, 260-263, 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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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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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3627-3630, 3635-3643, 3651, 3657-3660, 3663-3678, 3683-3706, 3710, 3720-3725, 3747, 3750-3760, 3764-3767, 3813-3823, 3826-3827, 3830-3835, 3838-3843, 3933-3967, 3970, 3973-3987, 3990-3992, 3996-4000, 4009-4028, 4032, 4036-4052, 4057-4064, 4069-4088, 4096-4100, 4105-4138, 4144-4148, 4154-4166, 4170-4182, 4187, 4197-4202, 4232-4244, 4247-4255, 4258-4275 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/axes_divider.py 276 217 21% 46-55, 58, 61, 72, 76, 92-96, 100, 109, 113, 122, 126, 134, 138, 141, 144, 147-150, 156-157, 163, 180-212, 228, 234-245, 261-264, 286-299, 303-306, 314, 339-343, 347, 351, 355-356, 372-382, 387-390, 401-423, 433-455, 480-489, 492-499, 502-506, 509-512, 515, 523-551, 576, 580-590, 611, 615-625, 629-633, 646-651 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/axes_grid1/axes_grid.py 274 240 12% 14-17, 22-23, 26-31, 34-35, 38-40, 115-171, 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, 495-500, 506, 527-531, 541-564, 577-602, 607-616, 628-678, 682-685, 696-706, 714, 718, 722, 767, 818-835, 853-865, 869, 875-936, 951-957, 961, 967, 973, 983-992, 996-1001, 1004-1009, 1012-1017, 1021, 1026-1030, 1038-1043, 1052-1083, 1093-1144, 1150-1180, 1189-1202, 1212-1250, 1273-1288, 1306-1319, 1325-1327, 1333-1334, 1340, 1350-1351, 1364-1367, 1386-1395, 1403-1404, 1412-1416, 1425-1434, 1446-1448, 1471-1492, 1566-1689, 1729-1807, 1862-1904, 1911-1951, 1955-1962, 1965, 1974-1990, 2021-2028, 2064-2083, 2088-2093, 2118-2125, 2157-2176, 2191-2209, 2261-2283, 2313-2340, 2404-2501, 2505-2508, 2553-2649, 2724-2860, 2969-3184, 3188-3200, 3268-3317, 3324-3335 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axis3d.py 301 250 17% 22, 30-31, 37, 43-50, 71, 74, 77-154, 161-179, 183, 186-191, 194-199, 204, 207-210, 223-228, 235-236, 239-242, 245-283, 290-302, 315-322, 332-346, 350-528, 534-576 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/proj3d.py 101 81 20% 20-29, 39-48, 58-68, 95-107, 125-130, 148-150, 154-162, 167-173, 177-181, 185-192, 199-206, 210, 217-218, 230-231, 235, 239-240, 244-249 /home/admin/.local/lib/python3.8/site-packages/nacl/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_aead.py 132 104 21% 105-163, 184-242, 263-322, 343-401, 422-481, 502-559 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_box.py 107 85 21% 40-46, 69-80, 97-112, 128-147, 160-171, 184-196, 211-227, 244-265, 282-324 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_core.py 73 52 29% 48-61, 80-102, 121-143, 163-182, 199-217, 235-253, 273-294, 314-335, 355-376, 393-412 /home/admin/.local/lib/python3.8/site-packages/nacl/bindings/crypto_generichash.py 68 42 38% 45-87, 125-147, 161-164, 171, 176-180, 212-228, 241-256, 269-281 /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 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_ufunc_config.py 87 23 74% 192-203, 217, 302-310, 356, 432, 437 /home/admin/.local/lib/python3.8/site-packages/numpy/core/arrayprint.py 550 326 41% 29-30, 69, 73, 77, 80, 85-88, 93-96, 252-265, 295, 325-330, 355-359, 362, 365, 392, 395-412, 424, 427, 430-452, 472, 492, 520, 680-684, 687, 696, 709-710, 723-740, 767, 787, 793-796, 799-804, 814, 833, 861-865, 872-894, 898-978, 981-1001, 1081-1087, 1169-1178, 1190, 1201, 1204, 1212-1224, 1230-1237, 1242-1253, 1257, 1260-1263, 1270-1284, 1287-1289, 1292, 1300, 1305, 1308-1310, 1322, 1330-1336, 1339-1346, 1355, 1360, 1362, 1390-1398, 1411-1423, 1430-1470, 1475, 1523, 1530, 1540, 1556, 1595, 1658-1664 /home/admin/.local/lib/python3.8/site-packages/numpy/core/defchararray.py 438 243 45% 57-61, 68, 79-84, 92-94, 98, 124, 150, 177, 203, 229, 255, 259, 283, 307-311, 315, 339-344, 349, 376, 415-416, 420, 451-456, 461, 507, 511, 556, 592, 597, 640, 645, 680, 716, 745, 772, 798, 824, 851, 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747-758, 768, 904, 927-944, 950, 954, 990, 992, 994-995, 1005-1006, 1016, 1028, 1039-1054, 1065, 1072-1075, 1081, 1085-1086, 1091, 1100-1104, 1113, 1122-1138, 1152-1156, 1162, 1167, 1176-1179, 1182-1186, 1199, 1326-1447, 1510-1541, 1557, 1751-2284, 2311-2317, 2339-2345, 2369-2377, 2403-2415 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/polynomial.py 438 319 27% 41, 138-165, 231, 238, 248, 257, 265, 342-366, 370, 432-446, 450, 620-689, 693, 764-772, 776, 830-844, 884-898, 956-961, 965, 1020-1038, 1042-1065, 1180, 1185-1186, 1191, 1197, 1202, 1208, 1220-1231, 1233, 1236, 1239, 1246-1249, 1252-1254, 1257, 1260-1314, 1317, 1320, 1323, 1326-1330, 1333-1337, 1340-1341, 1344-1345, 1348-1353, 1356-1357, 1360-1361, 1364-1368, 1373-1377, 1382-1386, 1389-1391, 1395-1400, 1403-1411, 1414, 1427, 1440 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/scimath.py 70 38 46% 106-110, 135-138, 162-165, 189-192, 196, 239-240, 287-288, 337-338, 342, 376-378, 425-426, 430, 473-475, 519-520, 565-566, 616-617 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/shape_base.py 263 148 44% 31-49, 53, 161-170, 174, 251-260, 264, 359-414, 418, 487-505, 509, 588-602, 648, 718, 722, 727-732, 778, 868-872, 878, 937-942, 989-991, 1034-1036, 1043-1048, 1055-1060, 1064, 1136-1164, 1252 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py 91 46 49% 21-22, 29-34, 97-114, 119, 301-335, 343, 345, 363, 411, 426-427, 470-471 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/twodim_base.py 177 100 44% 34-40, 95-98, 153, 158, 211, 216, 220, 295, 300-303, 346-363, 367, 412-424, 433, 469-472, 498-501, 505, 582-597, 602-615, 741-752, 821-823, 904-906, 911, 938-940, 1023-1025, 1057-1059 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/type_check.py 138 78 43% 70-77, 81, 112-114, 159-160, 202-203, 240-244, 339-340, 390, 395-397, 401, 498-521, 526, 574-583, 588-590, 618, 696, 713, 753-769 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/ufunclike.py 57 30 47% 24-36, 50-53, 65, 70, 117-124, 188-196, 260-268 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/utils.py 454 363 20% 38-46, 83-84, 96-97, 116, 123-124, 221, 260-277, 329-379, 391-407, 415-431, 452-482, 537-628, 672-677, 737-812, 835-947, 950-956, 1028-1043, 1056-1070 /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py 678 507 25% 88, 91, 94, 97, 100, 129, 133, 142, 146-149, 153-154, 165-174, 177-185, 188-190, 196, 203, 208, 212, 230, 235, 286-306, 310, 378-395, 399, 456-467, 550, 618-666, 756-764, 770, 890-982, 1075, 1081, 1159-1176, 1179-1181, 1314-1333, 1453-1473, 1624-1639, 1652-1657, 1666-1674, 1678, 1761-1797, 1801, 1898-1906, 1912, 1995-2011, 2092-2101, 2153-2160, 2166, 2266-2328, 2354-2356, 2360, 2514-2611, 2617-2618, 2707-2739, 2750-2760, 2780-2801, 2806-2812 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ma/core.py 2405 1662 31% 102-113, 122, 124, 204-212, 217-222, 270-277, 282-291, 342, 393, 414-425, 441, 443-451, 456-457, 463-468, 532-534, 543-547, 575-579, 628, 633-636, 646-663, 713, 768-776, 804, 812-813, 831-832, 848-853, 868-869, 884-885, 895, 928-969, 1011-1050, 1057-1080, 1087-1105, 1112-1116, 1155-1192, 1284-1291, 1295-1297, 1305, 1534-1537, 1547, 1621-1636, 1683, 1726-1753, 1788-1809, 1814-1817, 1924-1943, 1969, 1995, 2021, 2047, 2073, 2101-2103, 2139-2143, 2179-2183, 2244-2251, 2323-2331, 2400, 2407, 2414, 2421, 2424, 2439-2447, 2489-2495, 2525-2550, 2581-2590, 2647-2653, 2656, 2659-2670, 2674-2676, 2696-2703, 2834, 2840, 2845-2847, 2857-2861, 2869-2871, 2874-2875, 2882, 2887, 2889, 2895-2896, 2900-2909, 2915-2932, 2938, 2956, 3012, 3033, 3037-3040, 3047, 3054-3058, 3062, 3065, 3074-3121, 3180-3183, 3190-3194, 3204-3210, 3240-3251, 3267, 3276, 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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, 564-568, 571-575, 578-582, 586, 591, 594-597, 600-603, 606-614, 617-622, 625, 640, 653, 678, 700-701, 723-730, 761-767, 796, 823-829, 849-851, 865-866, 894-898, 971-986, 1014-1027, 1054-1060, 1091-1099, 1137-1141 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/chebyshev.py 357 294 18% 152-155, 177-180, 207, 243-274, 302-306, 333-340, 389-394, 441-455, 508-511, 566, 608, 652, 686-698, 742-747, 797-814, 855-872, 935-964, 1052-1091, 1153-1175, 1224, 1277, 1328, 1384, 1422-1437, 1490, 1544, 1670, 1700-1715, 1766-1776, 1827-1843, 1881-1888, 1915-1916, 1946-1953, 1979-1986, 2065-2069 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite.py 267 214 20% 134-139, 180-197, 251-254, 310, 350, 390, 432-443, 485-509, 557, 594, 652-677, 763-799, 871-895, 944, 997, 1048, 1104, 1151-1165, 1218, 1272, 1403, 1433-1448, 1502-1512, 1544-1555, 1594-1622, 1649-1650 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite_e.py 264 211 20% 135-140, 181-197, 250-253, 309, 349, 389, 427-438, 480-504, 550, 587, 645-670, 756-792, 864-887, 936, 989, 1040, 1096, 1143-1156, 1209, 1263, 1395, 1426-1441, 1495-1505, 1537-1548, 1587-1615, 1641-1642 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/laguerre.py 252 200 21% 134-138, 179-193, 245-248, 304, 345, 385, 427-439, 481-505, 551, 588, 646-674, 761-798, 870-893, 942, 995, 1046, 1102, 1149-1162, 1215, 1269, 1400, 1429-1444, 1498-1508, 1547-1572, 1598-1599 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/legendre.py 261 209 20% 140-145, 193-207, 261-264, 319, 361, 405, 447-461, 505-529, 578, 609, 672-701, 789-829, 891-914, 963, 1016, 1067, 1123, 1161-1176, 1229, 1283, 1411, 1441-1455, 1506-1516, 1555-1584, 1611-1612 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polynomial.py 221 166 25% 145-148, 212, 248, 285, 317-325, 361-363, 400-421, 460, 515-542, 623-661, 745-757, 835-845, 895, 948, 999, 1055, 1096-1109, 1157, 1211, 1361, 1390-1401, 1454-1464, 1514, 1518, 1522-1529 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polyutils.py 229 204 11% 71-77, 130-153, 200-208, 248-254, 297-301, 366-368, 372-374, 422-443, 452-453, 469-483, 497-513, 527-529, 547-565, 571-578, 584-592, 606-680, 697-713, 732-750 /home/admin/.local/lib/python3.8/site-packages/numpy/random/__init__.py 17 1 94% 210 /home/admin/.local/lib/python3.8/site-packages/numpy/random/_pickle.py 22 12 45% 31-37, 54-60, 77-83 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/decorators.py 74 61 18% 61-65, 100-105, 143-186, 226-251, 282-304, 323-329 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/nosetester.py 174 157 10% 36-58, 96-109, 164-193, 212-230, 233-250, 259-260, 276-324, 397-463, 523-536, 540-544 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/utils.py 873 732 16% 59-75, 89-95, 109-113, 127-132, 146-151, 156-186, 196-208, 220-244, 249-272, 325-432, 463-473, 545-599, 660-698, 716, 741-745, 749, 753, 758-763, 778-781, 784-787, 790, 795-796, 806-852, 933-934, 1015-1046, 1127-1128, 1135, 1165-1206, 1231-1252, 1289-1290, 1300, 1329-1330, 1353-1354, 1380-1401, 1437-1447, 1455-1473, 1583-1594, 1639-1646, 1681-1709, 1717-1724, 1730-1738, 1743-1749, 1797-1803, 1808-1814, 1844-1850, 1878-1918, 1935-1939, 1956-1961, 2009-2011, 2014-2019, 2022-2027, 2105-2112, 2115-2124, 2127-2149, 2171, 2201, 2205-2234, 2237-2242, 2246-2293, 2300-2305, 2310-2342, 2386-2392, 2403-2409, 2414-2431, 2439-2460, 2465-2476, 2481-2500, 2509-2520 /home/admin/.local/lib/python3.8/site-packages/numpy/version.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/oauth2client/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/oauth2client/_helpers.py 98 60 39% 119-133, 139-140, 156-159, 174-179, 194-202, 222-227, 243-246, 250-255, 272-274, 278, 302-307, 323-328, 333-334, 339-341 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_openssl_crypt.py 40 38 5% 18-135 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pkce.py 14 8 43% 41-49, 66-67 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pure_python_crypt.py 63 39 38% 55-62, 73, 88-92, 113-125, 136, 147-148, 166-184 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pycrypto_crypt.py 38 22 42% 34, 48-49, 64-75, 87, 98-99, 116-124 /home/admin/.local/lib/python3.8/site-packages/oauth2client/client.py 708 520 27% 147-148, 184-187, 213, 222, 231, 239, 255-274, 283, 298-314, 328, 351, 358-359, 367-368, 378, 388, 395, 405-409, 419-423, 434-438, 489-506, 535-536, 545, 554, 562, 580-581, 595-596, 610-633, 641-652, 660-664, 677, 689-697, 701, 705-707, 711-712, 716-722, 726-733, 748-763, 774-819, 827, 841-863, 871, 885-902, 944, 956-960, 971, 980, 996-1005, 1014-1030, 1039-1045, 1101, 1111, 1118, 1124-1147, 1152, 1171-1174, 1187-1190, 1207-1230, 1249-1261, 1271, 1287-1298, 1310-1315, 1331-1340, 1344-1351, 1362-1379, 1385-1415, 1420, 1427, 1433-1435, 1439-1441, 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, 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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, 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/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, 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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, 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/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% 187-193, 259-267, 279-297, 366-372, 379-386, 393-400, 407-414, 433, 454, 477-563, 570-581, 588-625, 650-663, 671-681, 685, 701-706, 725-738, 756-758, 764-767, 774-776, 800-807, 814, 818, 825-855, 865, 879, 885, 888-919, 925-929, 936, 951, 956-981, 1007-1038, 1079-1099, 1107-1122, 1165-1170, 1202-1208, 1212, 1222, 1232-1243, 1252, 1259, 1266-1275, 1287-1300, 1307-1312, 1317-1319, 1330-1341, 1344-1362, 1376-1396, 1411-1431, 1452, 1509-1516, 1569-1577, 1587, 1591-1598, 1608-1631, 1651-1666, 1669, 1687-1696, 1704, 1708, 1714, 1784-1821, 1872, 1882, 1888, 1899-1910, 1915-1916, 1937-1951, 1989-1990, 2039-2044, 2052-2097, 2110-2119, 2143, 2167, 2185, 2203, 2238, 2275, 2313-2319, 2357-2363, 2409-2415, 2461-2467, 2510-2516, 2562-2569, 2605-2611, 2618, 2628-2634, 2641, 2649-2658, 2666, 2672-2673, 2681-2686, 2693-2699, 2712-2715, 2771, 2818, 2846-2858, 2874-2880, 2906-2913, 2961-2964, 3020-3023, 3030, 3034, 3050-3053, 3057-3058, 3070-3080, 3165-3202, 3224-3280, 3284-3290, 3321-3374, 3380-3404, 3408, 3422-3440, 3475-3502, 3507-3521, 3525, 3563-3606, 3610-3612, 3615-3620, 3651-3661, 3725-3802, 3811-3831, 3838-3847, 3859-3890, 3897-3911, 3917-3949, 3959-3983, 3994-4015, 4024-4026, 4036-4044, 4055-4057, 4071-4134, 4146-4152, 4171-4172, 4236-4280, 4283-4284, 4287-4290, 4313-4362, 4377, 4389, 4401, 4431-4526, 4537-4561, 4565-4643, 4649-4662, 4677-4811, 4818-4863, 4872-4885, 4892-4900, 4925, 4930-4935, 4961, 4968-4985, 4992-4999, 5006-5010, 5014-5018, 5053-5058, 5066, 5081-5091, 5099-5101, 5107, 5144-5148, 5157, 5170-5203, 5209-5210, 5222-5228, 5242-5258, 5264-5268, 5283-5312, 5373-5399, 5412, 5479-5496, 5531-5541, 5606-5624, 5631, 5689, 5731, 5734-5737, 5744, 5803-5837, 5858-5861, 5867-5894, 5917-5941, 5947, 5953, 5959, 5988-5999, 6009, 6020-6057, 6065-6081, 6089-6099, 6105-6111, 6130-6138, 6159-6187, 6198-6208, 6291-6293, 6298, 6344-6352, 6359, 6365, 6373-6376, 6401-6408, 6411-6422, 6442-6484, 6517-6572, 6603-6612, 6629-6664, 6690-6701, 6712-6735, 6744-6785, 6789-6795, 6799-6804, 6807-6817, 6821-6822, 6825, 6828, 6831, 6835, 6876-6882, 6923-6929, 6936-6944, 6948-6956, 6960-6968, 6972-6993, 6997-7018, 7029, 7062-7069, 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, 1319-1321, 1325, 1331-1336, 1346-1367, 1376, 1429-1432, 1446, 1458, 1470, 1581-1621, 1631-1632, 1648, 1664, 1713-1742, 1815, 1848, 1862, 1894-1901, 1929-1941, 1957-1960, 2056-2064, 2099, 2120-2127, 2194, 2204, 2210, 2216, 2303-2313, 2391-2393, 2460-2463, 2531-2534, 2568-2574, 2580, 2588, 2596, 2644-2660, 2722-2731, 2778-2781, 2870-2871, 2918, 2970-2994, 3002, 3008, 3018, 3026-3036, 3059-3073, 3091-3111, 3185, 3265-3291, 3337-3343, 3413-3420, 3437, 3451, 3608-3651, 3667, 3683, 3699, 3836, 3880-3893, 3993, 4090, 4173-4176, 4196-4199, 4256-4267, 4311-4313, 4397-4398, 4414, 4456-4464, 4479, 4491, 4503, 4514-4518, 4630, 4649-4670, 4680-4692, 4699, 4720, 4744, 4757, 4770, 4847-4864, 4898, 4918, 4938, 4958, 4972, 4986, 5085, 5106, 5119, 5132, 5145, 5179, 5192, 5205, 5223, 5241, 5254-5268, 5275, 5328-5331, 5406-5407, 5484-5502, 5516-5535, 5542, 5550, 5555, 5563, 5574, 5585, 5661-5680, 5695, 5719, 5785-5791, 5825-5831, 5842, 5853, 5864, 5874, 5885, 5896, 5907, 5917, 5928, 5942, 5963, 5975, 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, 2732, 2771-2776, 2788-2793, 2812-2813, 2855-2860, 2873-2878, 2926-2927, 3026-3027, 3032-3033, 3038-3039, 3044-3045, 3050-3051, 3056-3057, 3261-3274, 3294-3301, 3308-3313, 3317-3320, 3337-3338, 3342-3376 /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/base.py 160 53 67% 16, 36-39, 43, 52, 58, 62, 66, 78, 82, 88, 98, 102, 106, 110, 114, 118, 122, 126, 130, 134, 138, 142, 146, 150, 154, 158, 162, 166, 170, 174, 178, 182, 186, 190, 194, 198, 202, 206, 210, 214, 218, 222, 226, 230, 234, 238, 242, 246, 252, 256, 260 /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/object_array.py 304 234 23% 30, 42, 64-104, 107-109, 117-125, 130-143, 146-147, 150-151, 162-177, 180-205, 210-216, 225-231, 234-235, 238, 241, 244-255, 258-259, 262-269, 272-276, 279-283, 286, 289-290, 293, 296, 299-300, 303-319, 328-353, 356-359, 362, 365-367, 370-393, 396, 399, 402, 405, 408, 411, 414, 417, 420, 423, 426, 429, 432, 435, 438, 441-442, 445, 448, 451, 458-463, 466-473, 476-498 /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/datetimes.py 364 295 19% 87-90, 134-152, 184-212, 239-259, 285-288, 311-314, 338-349, 390-470, 484-488, 495-560, 581-633, 650, 667, 684, 1009-1091, 1140-1211, 1226-1265 /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/numeric.py 108 95 12% 160-310 /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/timedeltas.py 70 50 29% 33-39, 52, 61, 70, 174-211, 220-231, 238-265 /home/admin/.local/lib/python3.8/site-packages/pandas/core/tools/times.py 77 67 13% 53-126, 144-154 /home/admin/.local/lib/python3.8/site-packages/pandas/core/util/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/util/hashing.py 116 99 15% 37, 65-83, 113-175, 196-221, 242-261, 289-310, 322-366 /home/admin/.local/lib/python3.8/site-packages/pandas/core/util/numba_.py 37 26 30% 20, 25-27, 52-63, 88-112 /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 /home/admin/.local/lib/python3.8/site-packages/pandas/core/window/rolling.py 711 562 21% 102-107, 132-160, 163-203, 208-214, 224, 241-246, 261-262, 269-279, 294-310, 313-318, 323, 329-335, 338-353, 357-377, 382-402, 407-410, 414-420, 426-434, 442-454, 466-507, 518-533, 546-556, 584-617, 625-658, 661-664, 686-699, 709-760, 774-844, 853-858, 864-867, 1134-1153, 1159-1162, 1193-1218, 1252-1257, 1275-1279, 1300-1304, 1325-1327, 1343, 1350-1351, 1362-1390, 1403-1419, 1427-1441, 1449-1463, 1471-1485, 1493-1507, 1515-1528, 1537-1548, 1561-1568, 1575-1576, 1584-1585, 1590-1591, 1603-1614, 1623-1630, 1639-1675, 1686-1732, 1751-1790, 1797-1800, 1803-1809, 1849, 1890, 1913, 1991, 2020, 2062, 2111, 2153, 2211, 2270, 2293, 2330-2331, 2373, 2428, 2502, 2545, 2675, 2702-2726, 2733-2741 /home/admin/.local/lib/python3.8/site-packages/pandas/errors/__init__.py 65 16 75% 191-197, 200-204, 397-402, 444-445 /home/admin/.local/lib/python3.8/site-packages/pandas/io/__init__.py 4 2 50% 5-12 /home/admin/.local/lib/python3.8/site-packages/pandas/io/_util.py 6 2 67% 9-10 /home/admin/.local/lib/python3.8/site-packages/pandas/io/api.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/clipboards.py 11 1 91% 20 /home/admin/.local/lib/python3.8/site-packages/pandas/io/common.py 444 337 24% 124-132, 135, 138, 154-156, 161, 166, 183-185, 189-214, 219, 226, 252-260, 268-270, 278, 319-458, 480-482, 522-531, 560-586, 598-600, 615, 630, 645, 702-913, 936, 939-951, 963-969, 977-984, 991-999, 1003-1006, 1017-1025, 1032-1037, 1041-1042, 1053, 1056, 1059-1061, 1064-1066, 1069-1071, 1078-1084, 1087, 1090-1100, 1107-1134, 1139-1148, 1154-1167, 1175-1187, 1209-1212, 1233-1253 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_base.py 413 301 27% 395, 434, 473-515, 523-545, 552, 556, 559-569, 574, 578, 582, 586, 589-591, 596-597, 620-626, 655-690, 716-897, 1103-1121, 1129, 1134, 1192-1233, 1240, 1247, 1254, 1257, 1260-1264, 1280-1298, 1306-1310, 1314, 1322, 1326-1327, 1368-1401, 1470-1513, 1516, 1551, 1575, 1579, 1583, 1586, 1594 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py 141 113 20% 25, 44-45, 49-51, 54-56, 61, 66-69, 72-76, 79-89, 97-158, 166-168, 171-173, 179-183, 186-222, 229-249 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_odswriter.py 138 115 17% 27, 46-62, 71, 76-82, 88-90, 103-149, 164-171, 186-219, 241-278, 293-335 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_openpyxl.py 242 189 22% 35-36, 56-88, 97, 102-103, 109-112, 138-148, 172-177, 207-225, 243, 270-306, 325-339, 366-379, 400-402, 420, 437-439, 450-529, 548-549, 553-555, 558-560, 566, 569-570, 573-574, 577-592, 597-626 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_pyxlsb.py 56 38 32% 34-37, 41-43, 46-52, 56, 59-60, 63-66, 71-80, 87-112 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_util.py 102 71 30% 24-27, 43, 65-86, 90-93, 115-125, 149-158, 163, 168, 173, 178, 197-209, 214, 219, 223-236, 260-271, 295-305, 327-332 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlrd.py 62 43 31% 33-35, 39-41, 44-50, 54, 57-58, 61-62, 67-126 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlsxwriter.py 83 63 24% 101-172, 192-210, 219, 223-224, 230, 241-275 /home/admin/.local/lib/python3.8/site-packages/pandas/io/feather_format.py 43 28 35% 54-96, 139-162 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/__init__.py 4 2 50% 5-8 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/console.py 33 28 15% 15-47, 63-76, 87-94 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/format.py 908 752 17% 112, 209-214, 217-231, 234, 243-261, 279-295, 298-322, 325-363, 366-374, 377, 386-421, 426, 429, 432, 435, 442-451, 457-460, 468-476, 480-484, 504-510, 537-549, 585-609, 615-621, 625, 631, 635, 639, 643, 647, 651, 655, 659, 663, 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820, 830-835, 838, 859, 863-864, 871-872, 880-883, 894-900, 903-911, 914-921, 925-926, 930-931, 945-948, 952-960, 965-967, 970, 974, 982, 991-992, 996-997, 1011, 1014-1016, 1021, 1025, 1039-1043, 1057-1060, 1064-1072, 1075, 1080-1082, 1086, 1090, 1101 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/printing.py 203 183 10% 43-59, 66-71, 107-130, 140-161, 195-234, 240-241, 245-275, 279, 320-456, 481-497, 504 /home/admin/.local/lib/python3.8/site-packages/pandas/io/gbq.py 21 14 33% 12, 18-23, 173-186, 214-215 /home/admin/.local/lib/python3.8/site-packages/pandas/io/html.py 367 291 21% 54, 67-80, 105, 127-134, 150-161, 242-247, 257-258, 278, 294, 310, 326, 341, 360, 375, 401, 420, 431, 458-476, 501-558, 576-579, 603-606, 609-630, 633-634, 637, 640, 643, 646, 649-652, 655, 658-661, 664-679, 698-702, 728-729, 732, 737, 740-765, 768, 786-824, 827-842, 845-848, 851, 855-862, 866-886, 918-935, 939-940, 944-968, 972-1021, 1192-1212 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_json.py 501 386 23% 84, 107, 127, 146-204, 222-237, 240, 243-244, 266-269, 272-273, 281-286, 292-301, 327-379, 383, 408, 433, 458, 483, 743-784, 817-862, 872-878, 894-919, 925, 931, 935, 939, 946-981, 987-1008, 1017-1018, 1021, 1025, 1029, 1033, 1036-1061, 1064, 1072, 1099-1122, 1128-1131, 1134-1141, 1144, 1150-1160, 1163, 1176-1241, 1251-1280, 1283, 1291-1298, 1301-1307, 1315-1346, 1354-1374, 1377-1382, 1387-1415 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_normalize.py 142 126 11% 35-39, 86-120, 146-166, 184-191, 237-244, 388-536 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_table_schema.py 134 114 15% 42-43, 79-96, 101-120, 124-151, 195-224, 283-316, 355-382 /home/admin/.local/lib/python3.8/site-packages/pandas/io/orc.py 52 38 27% 80-97, 162-205 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parquet.py 168 131 22% 46-75, 88-115, 121-122, 125, 128, 133-141, 153-194, 205-237, 244-247, 259-287, 299-342, 405-425, 493-509 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/arrow_parser_wrapper.py 67 53 21% 21-25, 31-42, 48-79, 101-136, 149-164 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/base_parser.py 578 491 15% 96, 109-176, 202-234, 240, 245, 253-267, 294-340, 349-352, 359-383, 387-406, 410-434, 438-451, 455-503, 515-593, 617-662, 683-766, 784-837, 845, 853, 862-874, 891-898, 911, 917, 931-933, 957-964, 991-1013, 1016-1050, 1055-1096, 1105-1165, 1221-1302, 1308-1327, 1353-1362, 1371-1384, 1388 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/c_parser_wrapper.py 177 149 16% 50, 61-192, 196-199, 208-220, 232-337, 341-346, 349-357, 360-365, 375-402, 412-423 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/python_parser.py 630 576 9% 50, 67-176, 179-243, 250-300, 307-323, 333-336, 343-364, 376-573, 587-621, 627-630, 642-683, 698, 701-754, 773-777, 792-825, 828-846, 863-872, 875-878, 885-899, 902-905, 910, 928-989, 992-1075, 1078-1156, 1159-1163, 1180-1204, 1234-1245, 1251-1267, 1272-1281, 1292-1294, 1297, 1313, 1321, 1345-1351 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/readers.py 486 399 18% 473, 478, 483, 502-513, 531-537, 546-583, 640, 697, 754, 811, 884-912, 969, 1026, 1083, 1140, 1213-1242, 1312-1352, 1368-1407, 1410-1412, 1415-1456, 1460-1465, 1472-1620, 1623-1627, 1634-1683, 1686, 1689-1724, 1727-1733, 1736, 1744, 1802-1803, 1808-1840, 1845-1853, 1858-1878, 1936-2008, 2019-2028, 2050-2052, 2074-2106, 2123-2127 /home/admin/.local/lib/python3.8/site-packages/pandas/io/pickle.py 28 15 46% 92-103, 178-204 /home/admin/.local/lib/python3.8/site-packages/pandas/io/pytables.py 2255 1832 19% 118-124, 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5124-5150, 5157-5172, 5194-5230, 5234-5253, 5259-5265, 5271-5289 /home/admin/.local/lib/python3.8/site-packages/pandas/io/sas/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/sas/sasreader.py 51 29 43% 29, 40, 44, 47, 55, 69, 83, 134-180 /home/admin/.local/lib/python3.8/site-packages/pandas/io/spss.py 22 12 45% 18-20, 54-67 /home/admin/.local/lib/python3.8/site-packages/pandas/io/sql.py 746 610 18% 71-72, 85-90, 96-118, 126-139, 148-165, 178-188, 208-219, 238, 253, 332-353, 368, 383, 464-469, 494, 510, 629-663, 758-769, 804-805, 821-841, 868-886, 889, 892-894, 898-900, 903-914, 928-930, 941-946, 949-986, 992-1030, 1043-1066, 1077-1113, 1117-1145, 1148-1159, 1162-1189, 1207-1244, 1247-1314, 1317-1343, 1352, 1355, 1368, 1382, 1399, 1403, 1407, 1418, 1437, 1442, 1458-1470, 1475-1502, 1526-1544, 1547-1548, 1552-1556, 1560-1563, 1627-1631, 1653-1671, 1738-1765, 1782-1815, 1826-1840, 1908-1933, 1937, 1940-1943, 1946-1956, 1959-1964, 1974-1983, 2001-2005, 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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, 252-257, 261-267, 276-277, 287-289, 292-293, 300, 305, 314, 339, 360, 370, 381-406, 417-419, 426, 430, 440, 447, 458, 476, 478-480, 490-492, 496-500, 505-515, 523-528 /home/admin/.local/lib/python3.8/site-packages/paramiko/hostkeys.py 190 132 31% 57, 69-71, 89-104, 119-123, 138-140, 143-144, 147, 150-155, 158-161, 164-175, 178, 186-187, 190, 198-206, 218-224, 230, 233-234, 237, 243, 246-253, 257-268, 271-276, 279-282, 295-304, 309-311, 338-367, 375-381, 384 /home/admin/.local/lib/python3.8/site-packages/paramiko/kex_curve25519.py 82 29 65% 31-32, 39, 47-48, 62, 65, 70-102 /home/admin/.local/lib/python3.8/site-packages/paramiko/kex_ecdh_nist.py 87 59 32% 26-30, 33-47, 50-54, 59-63, 66-108, 111-139 /home/admin/.local/lib/python3.8/site-packages/paramiko/kex_gex.py 180 153 15% 60-67, 70-90, 93-104, 110-124, 127-162, 167-189, 192-209, 212-252, 255-283 /home/admin/.local/lib/python3.8/site-packages/paramiko/kex_group1.py 84 62 26% 50-53, 56-68, 71-76, 88-95, 99-120, 124-155 /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 /home/admin/.local/lib/python3.8/site-packages/paramiko/pipe.py 84 60 29% 34-38, 43-46, 49-52, 55, 58-61, 64-67, 70-71, 81-93, 96-99, 102, 105-108, 111-114, 117-118, 123-125, 128-130, 133-135, 144-148 /home/admin/.local/lib/python3.8/site-packages/paramiko/pkey.py 363 228 37% 70-75, 84-85, 88, 145-199, 223-229, 240, 264, 273-277, 286, 289, 292, 295, 299, 309, 321-329, 340, 347, 369-373, 383, 400, 412, 456-457, 471, 485, 515, 522-523, 527, 539-543, 550-551, 562-563, 567-568, 573-603, 612-689, 704-738, 758-772, 775-780, 807, 820-833, 854-864, 895-897, 904-906, 913-933, 943-944, 947-950, 954, 957 /home/admin/.local/lib/python3.8/site-packages/paramiko/primes.py 69 58 16% 32-49, 60-61, 64-107, 113-122, 125-148 /home/admin/.local/lib/python3.8/site-packages/paramiko/proxy.py 57 36 37% 32-33, 59-69, 78-86, 96-119, 122, 126, 131, 134 /home/admin/.local/lib/python3.8/site-packages/paramiko/rsakey.py 106 44 58% 61-62, 66-78, 84, 88, 95, 107, 111, 117, 120, 124, 139-160, 163, 171, 188-191, 200-201, 210-225 /home/admin/.local/lib/python3.8/site-packages/paramiko/server.py 97 60 38% 87, 104, 123, 148, 180, 205, 236, 264-266, 296-298, 309-310, 331, 342, 372, 397, 413, 432, 456-462, 482, 508, 524, 564, 582, 596, 616-623, 635, 667-671, 678, 681-697, 723, 732 /home/admin/.local/lib/python3.8/site-packages/paramiko/sftp.py 92 59 36% 135-137, 142-151, 156-166, 169, 172-179, 182-202, 205-209, 212-224 /home/admin/.local/lib/python3.8/site-packages/paramiko/sftp_attr.py 150 128 15% 53-60, 72-81, 84, 89-95, 98-112, 115-143, 146-158, 162-169, 173-229, 239 /home/admin/.local/lib/python3.8/site-packages/paramiko/sftp_client.py 351 291 17% 78-84, 113-133, 164-170, 173-182, 194-195, 204, 218, 237-260, 276-324, 358-382, 396-398, 420-423, 440-443, 456-460, 468-470, 491-496, 509-514, 523-526, 537-541, 554-558, 574-580, 591-595, 606-616, 630-638, 656-662, 673, 676-685, 714-727, 757-759, 795-799, 839-849, 856-857, 861-882, 886-919, 922-924, 930-942, 949-957 /home/admin/.local/lib/python3.8/site-packages/paramiko/sftp_file.py 230 189 18% 62-73, 76, 82, 92-110, 113-128, 139-147, 157-177, 180-190, 194-212, 225, 234, 246, 256, 264-272, 283-286, 296-301, 313-319, 334-341, 351-356, 404-416, 436, 471-482, 504-527, 532-535, 538-546, 551-565, 568-587, 591-594 /home/admin/.local/lib/python3.8/site-packages/paramiko/sftp_handle.py 67 51 24% 49-53, 67-72, 92-106, 126-144, 157, 168, 178, 185-187, 190, 193 /home/admin/.local/lib/python3.8/site-packages/paramiko/sftp_server.py 322 290 10% 118-127, 130-134, 137-162, 165-174, 186-193, 210-220, 225-241, 244-254, 257-264, 267-275, 278-289, 296-356, 360-374, 377-534 /home/admin/.local/lib/python3.8/site-packages/paramiko/sftp_si.py 42 21 50% 51, 59, 68, 108, 140, 156, 174, 184, 208, 222, 239, 251, 266, 282-289, 302, 316 /home/admin/.local/lib/python3.8/site-packages/paramiko/ssh_exception.py 67 30 55% 65-67, 70, 83-84, 87, 107-109, 112, 127-130, 133-134, 165-167, 170, 203-213, 216 /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, 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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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132, 142, 153, 157-180, 184, 188, 192-195, 199 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/remaining_credit_model.py 54 33 39% 46-51, 61, 71-74, 84, 94-97, 101-124, 128, 132, 136-139, 143 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/remaining_credit_model_child.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/remaining_credit_model_reseller.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/remove_contact_from_list.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/remove_credits.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/request_contact_export.py 62 37 40% 48-57, 68, 80, 90, 100-103, 114, 126, 130-153, 157, 161, 165-168, 172 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/request_contact_export_custom_contact_filter.py 97 60 38% 54-73, 84, 95-102, 113, 124-131, 142, 153-160, 171, 183, 194, 206, 217, 229, 233-256, 260, 264, 268-271, 275 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/request_contact_import.py 124 71 43% 62-93, 104, 116, 127, 139, 150, 162, 173, 185, 196, 208, 218, 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/request_contact_import_new_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/request_sms_recipient_export.py 56 35 38% 46-52, 63, 75, 86, 97-106, 110-133, 137, 141, 145-148, 152 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/schedule_smtp_email.py 61 36 41% 48-58, 69, 81, 91, 102, 113, 125, 129-152, 156, 160, 164-167, 171 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_report.py 56 35 38% 46-52, 63, 74-81, 91, 101-104, 108-131, 135, 139, 143-146, 150 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_report_email.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/send_sms.py 81 48 41% 52-66, 76, 86-89, 99, 109-112, 123, 135, 146, 158, 169, 181, 185-208, 212, 216, 220-223, 227 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_smtp_email.py 178 44 75% 97, 99, 103, 107, 109, 111, 113, 115, 117, 119, 121, 123, 190, 213, 259, 303, 326, 349, 372, 395, 418, 441, 464, 487, 491-514, 518, 522, 526-529, 533 /home/admin/.local/lib/python3.8/site-packages/sib_api_v3_sdk/models/send_smtp_email_attachment.py 63 38 40% 48-58, 69, 81, 92, 103-106, 117, 129, 133-156, 160, 164, 168-171, 175 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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 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1098-1105, 1109 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_bicluster.py 178 145 19% 35-47, 58-69, 74-83, 93-100, 103-105, 119-126, 133-164, 167-179, 291, 301-315, 444-454, 457-481, 486-520, 532-540, 544-546 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_birch.py 231 194 16% 27-37, 50-89, 140-152, 155-164, 171-175, 179-242, 281-290, 293-297, 303-315, 320-321, 437-441, 460-461, 464-518, 529-534, 555-561, 564-568, 588-595, 614-617, 623-659 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_dbscan.py 52 37 29% 141-145, 276-283, 310-361, 389-390 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_feature_agglomeration.py 19 12 37% 38-51, 70-73 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py 526 454 14% 85-144, 152-158, 289-298, 354-428, 484-541, 579-601, 771-781, 785-844, 848-853, 858-861, 869-887, 924-951, 979-1052, 1077, 1105, 1124-1127, 1131, 1154-1160, 1183-1189, 1193, 1264-1337, 1346-1403, 1571-1579, 1585, 1591, 1597, 1600-1633, 1660-1781, 1805-1813, 1835-1891, 1914-1917, 1920, 1988-2019 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_mean_shift.py 116 94 19% 68-86, 92-109, 186-191, 223-240, 358-364, 377-450, 465-468 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_optics.py 218 193 11% 210-222, 246-287, 291-298, 327-338, 448-503, 509-538, 571-578, 631-646, 690-709, 716-722, 736-744, 790-898, 920-927 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_spectral.py 120 96 20% 76-158, 260-284, 462-476, 499-544, 568, 571, 580 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/__init__.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_base.py 47 28 40% 37-44, 57-77, 131, 154-158 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_dict_learning.py 359 309 14% 27-28, 113-191, 297-354, 394-435, 547-632, 766-891, 899-905, 910-926, 945-946, 1066-1071, 1089, 1096, 1115, 1118, 1122, 1126, 1304-1318, 1336-1358, 1546-1560, 1578-1599, 1623-1650 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_factor_analysis.py 139 119 14% 155-167, 183-264, 282-294, 306-310, 320-339, 354-362, 379, 384-390, 396-414 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py 181 154 15% 49-50, 57-60, 69-95, 104-122, 128-136, 140-143, 147, 272-301, 399-411, 431-540, 557, 574-575, 593-600, 617-624 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_incremental_pca.py 88 76 14% 170-173, 191-216, 237-319, 350-358 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_kernel_pca.py 106 80 25% 151-168, 176, 179-185, 192-256, 259-267, 283-295, 310-318, 331-344, 361-369, 372 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_lda.py 238 208 13% 75-132, 303-318, 322-335, 341-362, 396-426, 456-476, 479, 491-494, 510-537, 556-609, 624-640, 658-664, 692-740, 757-763, 787-813, 837 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_nmf.py 422 383 9% 38, 51, 55-61, 91-167, 172-187, 192-203, 207-233, 238-248, 311-401, 413-432, 508-538, 544-633, 638-715, 789-850, 1022-1093, 1268-1279, 1282, 1307-1326, 1342-1343, 1358-1374, 1391-1392 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_pca.py 164 92 44% 59-97, 105-109, 376-386, 394, 402-405, 414, 416, 424-427, 435-436, 439, 445, 468, 476-477, 484, 500-564, 583-592, 613, 616 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_sparse_pca.py 68 52 24% 116-126, 144-175, 198-206, 309-316, 334-362 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_truncated_svd.py 58 39 33% 125-129, 146-147, 164-208, 223-225, 242-243, 246 /home/admin/.local/lib/python3.8/site-packages/sklearn/exceptions.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/isotonic.py 109 84 23% 56-76, 117-131, 222-225, 228-231, 237-247, 252-295, 325-342, 361-385, 400, 404-407, 414-416, 419 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py 207 160 23% 81-101, 124-179, 197-207, 218-221, 238, 245-249, 252, 282-293, 309-314, 323-330, 353-357, 385-388, 485-489, 514-575, 588-642 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_bayes.py 203 180 11% 162-174, 197-300, 324-332, 343-356, 361-386, 515-526, 546-633, 641-650, 656-661, 685-694 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py 492 409 17% 58-75, 124-168, 311, 440-551, 706-717, 751-874, 879, 893-898, 1031, 1085-1146, 1157-1171, 1200-1356, 1518, 1526, 1529, 1532, 1725-1740, 1743, 1746, 1749, 1880-1889, 1913-1958, 1961, 2078-2087, 2263-2276, 2279, 2282, 2285, 2444, 2452, 2455, 2458 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/glm.py 158 122 23% 32-35, 40-48, 133-141, 161-298, 313-317, 333-335, 371-376, 380-388, 458, 465, 469-470, 540, 547, 551-552, 654, 664-666, 670-673 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/link.py 40 13 68% 68, 71, 74, 77, 84, 87, 90, 93, 100, 103, 106, 109-110 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_huber.py 88 74 16% 52-122, 229-234, 255-307 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_least_angle.py 435 384 12% 166-171, 301, 442-798, 917-926, 930-936, 940-994, 1017-1035, 1177-1188, 1195-1197, 1283-1310, 1438-1442, 1449, 1467-1529, 1685-1695, 1823-1832, 1835, 1858-1904 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py 546 500 8% 75-82, 114-133, 162-169, 202-246, 286-302, 343-355, 396-428, 432-458, 462-475, 632-819, 957-1009, 1261-1275, 1306-1435, 1463-1478, 1499, 1751-1767, 1789-2062, 2085-2088, 2091 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_omp.py 273 245 10% 72-138, 194-264, 349-408, 490-544, 632-636, 655-687, 735-764, 870-876, 894-919 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_passive_aggressive.py 34 20 41% 173-191, 216-228, 254-256, 401-418, 435-437, 464-466 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_perceptron.py 6 1 83% 164 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ransac.py 153 133 13% 47-54, 215-226, 256-464, 480-482, 502-504, 507 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py 614 520 15% 41-114, 118-132, 137-156, 161-217, 221-228, 232-235, 366, 385-518, 527-534, 539-600, 737, 762, 895-899, 924-946, 950, 954-966, 982-984, 995-999, 1002-1003, 1008, 1014, 1025-1029, 1032-1040, 1043-1050, 1057, 1060, 1070, 1073, 1122-1130, 1135, 1140-1143, 1176-1192, 1223-1236, 1261-1276, 1281-1290, 1297-1313, 1319-1338, 1347-1355, 1370-1385, 1394-1397, 1402-1412, 1421-1433, 1454-1581, 1590-1597, 1627-1665, 1917-1920, 1943-1959, 1963, 1966 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_sag.py 75 63 16% 67-85, 234-344 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py 446 345 23% 56-62, 65-68, 80-102, 117-119, 127-156, 160-168, 171-174, 178-182, 187-241, 258-281, 285-288, 298, 307, 315, 323, 331-355, 413-450, 478-488, 494-535, 539-578, 583-605, 616-649, 684-695, 729, 975, 986-987, 1027-1028, 1031-1069, 1096-1097, 1100, 1103, 1129, 1141-1166, 1192-1193, 1201-1225, 1252, 1270-1276, 1290, 1294-1363, 1582, 1593 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_theil_sen.py 113 89 21% 57-74, 112-128, 147, 178-193, 298-306, 309-343, 359-400 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_isomap.py 54 38 30% 131-141, 144-169, 190-193, 211-212, 229-230, 254-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_locally_linear.py 225 201 11% 49-73, 108-114, 159-189, 292-521, 644-655, 658-665, 688-689, 705-706, 725-734 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_mds.py 111 89 20% 71-132, 234-276, 379-387, 390, 398, 418-419, 439-461 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_spectral_embedding.py 179 148 17% 52-72, 89-95, 120-141, 221-360, 463-469, 472, 481, 503-536, 560-579, 601-602 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py 258 224 13% 58-64, 94-121, 163-193, 255-272, 345-401, 453-477, 664-679, 684-841, 854-910, 932-934, 950-951 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/__init__.py 78 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_base.py 78 71 9% 67-131, 175-202, 234-251 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py 511 452 12% 48-52, 83-128, 132-137, 202-210, 296-355, 456-557, 618-639, 758-785, 852-875, 935-946, 1068, 1192-1200, 1214-1247, 1251-1261, 1269-1299, 1458-1540, 1653-1660, 1771-1778, 1846-1858, 1966-2060, 2135-2152, 2225-2281, 2365-2403, 2477-2506 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/base.py 37 33 11% 26-45, 81-114 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/confusion_matrix.py 61 47 23% 71-72, 107-162, 255-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/det_curve.py 44 36 18% 65-68, 88-129, 210-229 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/precision_recall_curve.py 47 35 26% 77-81, 107-140, 203-225 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/roc_curve.py 47 35 26% 73-77, 100-132, 210-230 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_ranking.py 336 292 13% 83-106, 199-224, 294-317, 326-349, 522-547, 595-648, 688-730, 811-823, 913-956, 1005-1046, 1090-1106, 1149-1191, 1238-1250, 1290-1299, 1303-1307, 1407-1411, 1458-1466, 1564-1569, 1646-1717 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_regression.py 168 137 18% 88-122, 182-194, 257-271, 335-351, 408-416, 477-492, 552-584, 676-723, 753-756, 808-821, 857, 896 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py 226 132 42% 52-60, 77, 81-92, 107-122, 133-134, 155-166, 169-171, 199, 204, 236-242, 276-288, 291, 326-362, 365, 383-392, 397, 426-459, 485-530, 614 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/__init__.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_bicluster.py 32 22 31% 12-17, 22-28, 38-45, 80-86 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_supervised.py 170 139 18% 43-69, 74-83, 127-149, 214-229, 289-299, 383-389, 453-473, 542, 611, 710, 768-798, 889-919, 998-1020, 1091-1100, 1115-1123 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_unsupervised.py 93 76 18% 33-34, 109-117, 135-149, 214-248, 281-298, 339-363 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/pairwise.py 412 339 18% 45-61, 135-164, 194-198, 272-323, 399-439, 451-508, 512-514, 587-601, 670-673, 722-723, 782-804, 833-841, 861-862, 880-886, 910-911, 967-978, 1004-1005, 1033-1041, 1067-1075, 1101-1108, 1136-1142, 1180-1191, 1241-1251, 1296-1298, 1342, 1347, 1354-1373, 1379-1405, 1422-1434, 1443-1470, 1594-1635, 1747-1790, 1845, 1937-1954 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/__init__.py 32 1 97% 37 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py 342 251 27% 96-116, 127-136, 141-142, 160-184, 244-265, 268, 274-306, 310-314, 380-386, 390-406, 421-429, 433, 437, 450, 473-489, 510-511, 514-523, 539-540, 556-557, 573-574, 590-591, 607-608, 624-625, 631-639, 643-644, 704, 708-721, 747-892, 896-968, 1278-1284, 1288, 1608-1611, 1619 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_split.py 467 353 24% 78-83, 92-95, 99, 106, 155-161, 183-185, 235, 238-245, 262-264, 273-298, 324-333, 354, 428, 432-444, 499, 502-536, 562, 636, 640-690, 693-695, 731-732, 831-834, 859-887, 934-944, 968-971, 997, 1057, 1060-1075, 1099-1102, 1128, 1156-1169, 1195-1202, 1226-1229, 1232, 1284, 1340, 1350-1354, 1386-1388, 1413, 1416, 1484-1489, 1492-1503, 1575-1580, 1583-1594, 1626, 1691-1696, 1699-1757, 1793-1794, 1803-1864, 1906-1909, 1933-1937, 1941-1945, 1966, 1972, 1993, 2017-2018, 2058-2073, 2168-2199, 2211-2241, 2250-2252 /home/admin/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py 380 335 12% 231-279, 288-304, 309-313, 438-446, 543-657, 666-709, 838-892, 937-964, 980-1021, 1039-1045, 1165-1182, 1189-1197, 1202-1209, 1353-1417, 1444-1476, 1483-1518, 1625-1645, 1671 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/__init__.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_base.py 443 394 11% 61-66, 86-111, 135-139, 160-197, 223-248, 274-296, 307-315, 318-358, 361-525, 529, 538, 547, 581-594, 649-765, 816-846, 855, 889-901, 978-1083, 1139-1170 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_classification.py 158 136 14% 153-159, 179, 195-221, 239-275, 414-421, 441-487, 504-529, 548-616 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_graph.py 60 35 42% 16-21, 29-36, 106-113, 187-194, 307-311, 327, 345-347, 371, 374, 490-494, 510, 528-529, 553, 556 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_kde.py 86 66 23% 101-119, 124-139, 164-179, 197-211, 233, 256-283, 286 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_lof.py 80 54 32% 184-190, 218-223, 246, 265-299, 320-327, 346-356, 384-393, 421, 450-458, 486-498, 521-526 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nca.py 157 129 18% 169-176, 197-244, 265-268, 304-377, 400-443, 453-456, 483-524, 527 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nearest_centroid.py 70 54 23% 91-92, 107-181, 202-205 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_regression.py 62 40 35% 152-157, 161, 170, 190, 206-229, 352-358, 378, 395-426 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_unsupervised.py 10 2 80% 118, 142 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/__init__.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_data.py 810 681 16% 71-80, 161-217, 323-325, 335-341, 362-363, 386-417, 432-441, 456-463, 466, 545-561, 683-685, 695-699, 726-727, 762-860, 877-897, 914-937, 940, 1007, 1017-1020, 1040-1041, 1065-1085, 1100-1110, 1125-1134, 1137, 1198-1215, 1319-1323, 1344-1387, 1402-1416, 1431-1444, 1447, 1536-1555, 1632-1635, 1639-1641, 1646-1651, 1668-1681, 1701-1708, 1739-1837, 1891-1937, 2001-2002, 2023-2024, 2043-2045, 2048, 2083-2098, 2157-2158, 2179-2180, 2200-2205, 2208, 2253, 2272-2282, 2299-2310, 2313, 2321, 2350-2378, 2484-2489, 2499-2519, 2531-2568, 2589-2625, 2630-2694, 2699-2717, 2737-2750, 2768-2771, 2789-2793, 2796, 2920-2931, 3020-3022, 3043-3044, 3047, 3050-3077, 3092-3106, 3139-3152, 3158-3163, 3169-3185, 3192-3207, 3217-3219, 3228-3243, 3267-3290, 3293, 3394-3395 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_discretization.py 117 102 13% 131-134, 153-237, 242-271, 288-318, 337-353 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py 272 241 11% 42-67, 70-74, 77-110, 113-156, 159, 318-322, 325-333, 339-397, 416-420, 442-443, 459-505, 524-600, 617-635, 721-724, 743-771, 787-793, 809-844 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_function_transformer.py 41 26 37% 11, 91-97, 100-102, 106-109, 128-132, 147, 162, 166-171, 174 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_label.py 274 229 16% 100-102, 116-118, 132-138, 152-163, 166, 262-274, 289-298, 321, 343-350, 387-403, 406, 471-569, 577-613, 619-657, 725-726, 742-754, 773-798, 816-824, 827-831, 847-864, 881-898, 902 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/__init__.py 3 0 100% /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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268-273, 278-283, 302-335, 380-426, 436-437, 442-445, 462, 476, 485, 488-490, 506-510, 514, 518-521 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/saved_model_lib.py 205 160 22% 53, 60, 66-75, 79, 86-91, 107-119, 123-126, 133-138, 148, 155-161, 180-192, 197-198, 203-208, 223-240, 263-321, 359, 363-372, 375, 379-382, 386, 390, 394, 405-411, 415-423, 426-429, 432-437, 440-443, 451-458, 463-467 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/saved_model_module.py 16 9 44% 46-49, 86-93 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tensor_info.py 134 107 20% 35-41, 45, 50-56, 61-67, 75, 80, 85, 89, 93-97, 102-115, 132, 139-144, 161-185, 202-206, 223-243, 262-281, 298-307 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tf_utils.py 89 60 33% 32-34, 68-70, 98-115, 132-133, 150-165, 182-195, 227, 232-236, 241-249, 253-258 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tools/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/uncompressed_module_resolver.py 37 24 35% 32-34, 37, 41-47, 65-77, 80-87 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/_VF.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/__config__.py 7 3 57% 9, 16, 20 /home/admin/.local/lib/python3.8/site-packages/torch/__future__.py 5 1 80% 16 /home/admin/.local/lib/python3.8/site-packages/torch/__init__.py 362 162 55% 20, 27, 59-142, 148, 175-188, 207, 214-231, 262-278, 298, 307, 329-331, 377, 502, 508, 515, 533-555, 565-571, 577, 622, 634, 640, 664, 669, 674, 679, 684, 689, 694, 699, 704, 709, 714, 719, 724, 729, 734, 739, 744, 768, 772, 789, 829-833, 896, 949-954 /home/admin/.local/lib/python3.8/site-packages/torch/_classes.py 25 11 56% 6-7, 10-13, 22-24, 28, 48 /home/admin/.local/lib/python3.8/site-packages/torch/_jit_internal.py 591 404 32% 35, 41-43, 59-111, 141-161, 168-174, 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/home/admin/.local/lib/python3.8/site-packages/torch/_ops.py 117 23 80% 42, 45, 48, 51, 54, 58, 62, 66, 83, 86, 92, 96, 101, 112-118, 133-134, 147, 191, 199-202, 248 /home/admin/.local/lib/python3.8/site-packages/torch/_prims/__init__.py 610 380 38% 177-180, 184-189, 192-194, 219-220, 253-295, 329, 408, 591, 639-641, 645, 665, 678, 705, 718, 737-738, 745-750, 764-769, 781, 851-863, 869, 890-925, 929-938, 947, 974-1026, 1030-1036, 1041-1052, 1086-1098, 1111-1185, 1194-1200, 1231-1274, 1284-1292, 1309-1333, 1337-1340, 1361-1376, 1380-1385, 1404-1420, 1424, 1445, 1449, 1474-1479, 1484-1508, 1518, 1537-1549, 1553, 1570-1571, 1594-1598, 1604, 1630-1631, 1635, 1653-1658, 1663-1670, 1674-1675, 1695-1698, 1702, 1719-1720, 1724, 1745-1762, 1766, 1786, 1790, 1814-1818, 1827, 1907-1908, 1914, 1934-1939, 1945, 1971-1972, 1984, 2011-2015, 2027 /home/admin/.local/lib/python3.8/site-packages/torch/_prims/utils.py 450 367 18% 26, 33, 67-111, 121-137, 147, 151, 155, 173-201, 211-216, 226-239, 255-322, 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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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/home/admin/.local/lib/python3.8/site-packages/torch/autograd/grad_mode.py 83 36 57% 22, 32-74, 77, 80, 178-179, 182, 232, 235, 238, 284-289, 292, 295, 298 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/gradcheck.py 759 681 10% 24, 33-37, 46-51, 56-63, 83-115, 141-153, 173-186, 192-204, 214-228, 235-239, 246-262, 268-273, 286-295, 317-380, 386-395, 400-404, 409-411, 416-422, 426-429, 433-440, 445-465, 471-474, 481-499, 526-554, 558-575, 580-605, 611-613, 621-633, 637-641, 645-678, 682-688, 695-700, 705-712, 756, 769-810, 818-852, 857-886, 889-942, 945-992, 996-1001, 1005, 1010-1017, 1023, 1029-1035, 1042-1052, 1058-1103, 1107-1130, 1134-1135, 1139-1142, 1146-1151, 1157-1171, 1175-1176, 1188-1191, 1213-1237, 1241-1244, 1249-1264, 1269-1280, 1286-1310, 1400-1414, 1420-1448, 1522-1570 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/graph.py 22 14 36% 65-66, 69, 72, 116-132 /home/admin/.local/lib/python3.8/site-packages/torch/autograd/profiler.py 252 205 19% 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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582-589, 597-598, 605-616 /home/admin/.local/lib/python3.8/site-packages/torch/fx/operator_schemas.py 221 176 20% 14, 30, 34, 43-45, 61, 64-84, 88-120, 138-154, 158-182, 186-230, 259-328, 353-367, 392-408 /home/admin/.local/lib/python3.8/site-packages/torch/fx/proxy.py 223 134 40% 40-43, 47, 63-84, 94-105, 115-151, 160, 169, 184, 191-192, 196, 234-238, 241, 246, 249, 252-260, 263-284, 288, 291, 297-319, 327-330, 336-338, 341, 352-355, 358, 362, 365, 368, 372, 375, 378, 384-386, 396-397 /home/admin/.local/lib/python3.8/site-packages/torch/fx/subgraph_rewriter.py 152 133 12% 20-31, 41-42, 49-86, 90-132, 250-428 /home/admin/.local/lib/python3.8/site-packages/torch/hub.py 304 250 18% 20-55, 83-90, 94-98, 103, 108-110, 122-140, 144-145, 150-169, 176-241, 244-290, 294-295, 299-304, 308-322, 336-341, 352, 394-407, 446-458, 530-541, 563-573, 591-633, 640-643, 647-660, 699-731 /home/admin/.local/lib/python3.8/site-packages/torch/jit/__init__.py 52 16 69% 75, 130, 194, 215-217, 220, 223, 230-235, 242, 248, 253 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_async.py 10 2 80% 84, 96 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_builtins.py 42 1 98% 163 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_check.py 87 75 14% 63-82, 85-112, 125-132, 140-181, 195-240 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_decomposition_utils.py 6 3 50% 5-8 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_freeze.py 29 23 21% 101-121, 167-173, 207-220 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_fuser.py 73 61 16% 12-17, 30-63, 69-74, 77, 80-107, 140 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_ir_utils.py 14 7 50% 6-8, 11-12, 15, 18 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_monkeytype_config.py 102 75 26% 15-18, 24-32, 38-53, 61-66, 69, 73-149, 155, 163, 179-183 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_recursive.py 501 443 12% 39-47, 50-52, 55-66, 70-79, 82-93, 102-107, 118, 127-330, 347-362, 368-375, 378-384, 399-414, 423-435, 453-458, 469-595, 602-608, 611-612, 616-638, 643-651, 654-657, 660-669, 672-685, 693-740, 747-776, 784-797, 809-824, 827-845, 851, 857-867, 870-877, 886-910 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_script.py 595 404 32% 62, 71-72, 151, 155, 161-162, 182, 185, 188, 191, 194, 197, 200-205, 208, 211-213, 218-225, 228-229, 232, 244-248, 254, 272, 279-280, 292-318, 326, 334-350, 355, 358, 366-379, 427-434, 437-443, 446-452, 457-461, 464, 467-470, 475, 496, 501-503, 506-523, 526-545, 548, 559-561, 592-598, 614-620, 624-633, 642-665, 673, 682, 691, 705-706, 714, 728, 731, 734, 737, 740, 743, 747-749, 760-761, 764-785, 788-814, 817, 820, 827-832, 835, 838, 841, 844, 849-854, 860-865, 870-874, 943, 959-969, 972-1002, 1006-1007, 1022, 1036, 1239-1351, 1359-1361, 1370-1389, 1394-1416, 1420-1422, 1430-1457, 1461-1466, 1473-1475, 1480-1483, 1486, 1489-1503, 1508-1509, 1512-1531, 1536, 1539, 1542, 1545-1570, 1573, 1577-1578 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_serialization.py 70 57 19% 78-84, 150-169, 173-184, 188-194, 198-209, 251-261, 285-291 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_state.py 63 35 44% 27-37, 47, 51, 66-67, 71-74, 78, 82-83, 98-102, 105, 108-114, 118-119 /home/admin/.local/lib/python3.8/site-packages/torch/jit/_trace.py 453 383 15% 34-54, 61-72, 84-92, 95-140, 144-160, 173-185, 222-277, 281-283, 287, 292-305, 321-535, 554-559, 563-577, 581-584, 734-830, 922-1005, 1014, 1023-1087, 1090, 1093-1095, 1098-1100, 1103, 1106, 1119, 1129-1130, 1171-1176 /home/admin/.local/lib/python3.8/site-packages/torch/jit/annotations.py 254 213 16% 34-37, 54-56, 59-63, 66-91, 97, 101-109, 113-127, 132-144, 154-170, 175-232, 245-250, 255-270, 276-287, 290-301, 306-398, 402-405 /home/admin/.local/lib/python3.8/site-packages/torch/jit/frontend.py 612 469 23% 30-31, 42, 100-105, 108, 118-125, 133-134, 138-139, 153-166, 170-183, 189-216, 234-265, 270-281, 285-288, 292-294, 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310-322, 326, 329, 333, 336, 340, 343-365 /home/admin/.local/lib/python3.8/site-packages/torch/multiprocessing/spawn.py 98 71 28% 16-19, 22, 36, 50-52, 55, 66-76, 81-83, 89, 105-160, 165-166, 179-199, 235-240 /home/admin/.local/lib/python3.8/site-packages/torch/nn/__init__.py 20 13 35% 32-47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/_reduction.py 31 26 16% 8-20, 28-43, 47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/common_types.py 25 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/functional.py 1164 929 20% 13, 461-482, 492-503, 562-587, 597-608, 657-671, 682-696, 743-757, 768-782, 829-843, 854-868, 885-912, 926-948, 962-980, 994-1012, 1027-1037, 1052-1061, 1076-1080, 1084-1088, 1116-1121, 1125-1129, 1157-1162, 1166-1170, 1211-1214, 1228-1231, 1248-1252, 1260-1264, 1282-1302, 1320-1346, 1364-1385, 1408-1414, 1422-1428, 1453, 1455, 1489-1493, 1503-1509, 1529-1535, 1544-1550, 1573-1579, 1600-1606, 1628-1634, 1668-1676, 1733-1735, 1745-1747, 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/home/admin/.local/lib/python3.8/site-packages/torch/nn/init.py 193 102 47% 24-54, 102, 104, 106, 109, 114, 116-119, 136, 154, 176, 190-192, 233-238, 256-281, 287, 323-327, 350-353, 360, 396, 405-406, 444-451, 469-495, 514-526, 535-537 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/modules/__init__.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/modules/fused.py 57 26 54% 13-16, 22-25, 31-34, 40-43, 49-52, 58-61, 67-70, 76-79, 85-88, 94-97, 104-107, 113-116, 123-125 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/conv_fused.py 250 143 43% 44-66, 69, 72-79, 82, 85-87, 90-92, 95-114, 118, 121, 129-133, 158-185, 197-218, 221-258, 296-300, 342, 349, 353, 376-382, 385, 390, 428-432, 474, 481, 485, 508-514, 517, 522, 568-572, 637, 654, 658, 690-704, 707, 713, 716-719, 722-725 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/linear_fused.py 84 65 23% 37-57, 60, 63-65, 68, 71-73, 76-78, 81-118, 126-130, 139-155, 158-167 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/qat/modules/linear_relu.py 20 9 55% 32, 35, 39, 42-47 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/modules/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/dynamic/modules/linear_relu.py 22 10 55% 27, 30-39, 42, 46, 50 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/bn_relu.py 36 14 61% 21, 26-28, 33, 38, 42, 57, 62-64, 69, 74, 78 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/conv_relu.py 74 40 46% 28, 36-43, 47, 51-55, 59-61, 78, 86-92, 96, 100-104, 108-110, 127-128, 136-142, 146, 150-160, 164-166 /home/admin/.local/lib/python3.8/site-packages/torch/nn/intrinsic/quantized/modules/linear_relu.py 17 5 71% 25, 28, 32, 36, 40 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/__init__.py 23 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/_functions.py 137 119 13% 10-103, 107-172, 178-230, 234-265, 270-271, 275 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/activation.py 397 222 44% 47-50, 54, 57-58, 101-102, 157-160, 163, 166-167, 216-227, 230, 233-234, 262, 265-266, 290, 325-326, 329, 354, 387-388, 391, 394-395, 423-424, 427, 430-431, 468-469, 472, 509-511, 514, 517-518, 553-555, 558, 561-562, 603-604, 607, 610-611, 637-638, 641, 644, 684, 719-720, 723, 726, 767-769, 772, 775-776, 799, 833-835, 838, 841, 874-875, 878, 881, 945-983, 986-999, 1003-1006, 1060-1164, 1220-1223, 1226, 1229, 1252, 1275, 1311-1312, 1315-1317, 1320, 1323, 1367-1368, 1371-1373, 1376, 1379, 1405-1406, 1438-1439, 1442-1444, 1447, 1450 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/adaptive.py 114 92 19% 122-161, 164-167, 170-241, 247-259, 278-279, 296-312 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/batchnorm.py 197 143 27% 37-63, 66-71, 74-77, 80, 83, 98-107, 129-130, 135-168, 190-209, 213-214, 217-227, 297-298, 332-333, 406-407, 439-440, 512-513, 545-546, 661-665, 668-669, 674-675, 681-748, 795-819 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/channelshuffle.py 13 4 69% 45-46, 49, 52 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/container.py 345 202 41% 19-24, 76, 80, 85-86, 93-98, 102-105, 108-109, 112-117, 121, 125-127, 148-149, 187, 189, 195, 200-201, 204-211, 225-228, 232-234, 243-245, 263, 320-322, 326, 329, 332, 336, 340, 344, 349, 357-359, 365, 371, 377, 391-412, 446-449, 453-458, 462, 466, 469-477, 485-488, 491, 494, 497, 500-502, 510-513, 522-527, 530-543, 546, 587-590, 593-599, 602-603, 611-615, 618-620, 623, 626, 629, 636, 639, 651-653, 658-659, 667-669, 675-680, 690, 699, 704, 709, 714, 729-751, 754-766, 769, 772-774, 777-779, 782-783 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/conv.py 331 168 49% 51, 84, 86, 89-94, 98, 115-122, 152-166, 169-171, 287-294, 299-303, 307, 450, 580-585, 590-602, 607, 616, 632-665, 774-780, 785-795, 940, 1080-1086, 1091-1102, 1124-1127, 1146-1150, 1155-1169, 1173-1180, 1185, 1233-1251, 1254, 1302-1320, 1323, 1371-1389, 1392, 1438-1457, 1460, 1506-1525, 1528, 1574-1593, 1596 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/distance.py 25 9 64% 37-40, 43, 72-74, 77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/dropout.py 33 12 64% 13-18, 21, 58, 100, 149, 191, 233, 282 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/flatten.py 48 27 44% 40-42, 45, 48, 106-116, 119-125, 129-135, 138, 141 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/fold.py 38 15 61% 136-141, 144, 148, 287-291, 294, 298 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/instancenorm.py 72 40 44% 18-19, 23, 26, 29, 32, 38-62, 67-72, 143, 146-147, 182, 185-186, 259, 262-263, 298, 301-302, 375, 378-379, 414, 417-418 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/lazy.py 79 49 38% 15, 18, 23, 26, 29, 33, 37, 41, 45, 49, 175-178, 185-194, 208-217, 224, 231-236, 248-256, 260 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/linear.py 91 36 60% 42, 45, 100, 117, 130, 180-191, 194-197, 200, 203, 243-250, 253-254, 257-263 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py 201 83 59% 16-20, 25-27, 93, 96, 207-208, 211, 217-220, 286-289, 292, 367-369, 372, 461-462, 465, 527, 530, 610, 613, 707-711, 714, 773-774, 777, 838, 841, 918-919, 922, 978-979, 982, 1020, 1023, 1159-1161, 1164, 1210, 1213, 1262-1263, 1266, 1319-1320, 1323, 1392-1397, 1400, 1477-1481, 1484, 1591-1595, 1598, 1739-1741, 1744 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py 660 395 40% 22-24, 30-38, 79-81, 111-113, 131-139, 176-184, 201, 304, 307, 310, 313, 315, 317, 319, 343, 347, 350, 352, 354, 359, 363, 382, 385, 388, 390, 392, 397, 447-465, 489-503, 527-540, 557, 572, 593, 608-611, 614-621, 667-670, 689, 708, 727, 738, 752, 763, 774, 785, 796, 808, 813, 817, 821, 911-915, 923, 943-951, 986-994, 1001-1013, 1016-1053, 1077-1079, 1100-1102, 1105-1122, 1132-1174, 1179-1192, 1217, 1222, 1227-1231, 1236, 1241-1245, 1249-1253, 1258-1266, 1275-1277, 1292-1300, 1308, 1312, 1357-1386, 1403-1407, 1435-1437, 1474, 1485-1489, 1497, 1501-1504, 1508-1515, 1519-1522, 1524, 1532, 1559, 1570, 1583-1584, 1595, 1599, 1604, 1610-1619, 1642-1643, 1665-1669, 1690-1691, 1713-1717, 1772-1773, 1807-1818, 1836, 1880-1882, 1892-1908, 1912, 1915, 1924, 1928-1949, 1952-1962, 1965-1975 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/normalization.py 97 36 63% 48-52, 55, 59, 69-73, 76, 80, 178-179, 193, 248-264, 267-269, 272, 276 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/padding.py 83 28 66% 19-20, 23, 26, 75-76, 125-126, 165-166, 174, 177, 216-217, 267-268, 319-320, 328, 331, 370-371, 421-422, 461-462, 512, 515 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/pixelshuffle.py 23 8 65% 49-50, 53, 56, 99-100, 103, 106 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/pooling.py 241 104 57% 21-27, 30, 88, 162, 240, 248, 312-315, 318, 395-398, 401, 461-464, 467, 475, 534-539, 542, 613-619, 622, 699-705, 708, 712-715, 765-778, 782, 834-847, 851, 865-869, 872, 914, 969, 978-980, 983, 1016, 1058, 1101, 1108-1109, 1112, 1140, 1179, 1218 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/rnn.py 473 382 19% 21, 43-124, 127-131, 140-175, 182-191, 194-196, 199-205, 210-221, 225-226, 229-232, 235-237, 241-254, 257-292, 296, 299-304, 411-420, 425, 430, 433-499, 673, 676-683, 692-695, 703-705, 712, 719, 722-784, 898-900, 905, 910, 913-967, 983-997, 1000-1005, 1008-1010, 1072-1074, 1077-1108, 1173-1174, 1177-1197, 1264-1265, 1268-1288 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/sparse.py 130 71 45% 129-133, 142-144, 154-155, 158, 163-174, 205-218, 318-341, 344-345, 348-350, 383, 390-400, 432-447 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/transformer.py 187 144 23% 57-83, 136-149, 156, 161-163, 186-190, 203-246, 267-270, 288-299, 361-388, 391-393, 410-466, 471-475, 479-480, 523-546, 549-551, 570-580, 585-589, 594-598, 602-603, 607, 611-616 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/upsampling.py 37 17 54% 141-150, 153, 157-162, 207, 253 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/utils.py 35 17 51% 32-38, 57-75 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/__init__.py 10 3 70% 11-14 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_functions.py 88 60 32% 14-30, 34, 41-45, 49, 56-75, 79-82, 89-104, 108, 118-124 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_replicated_tensor_ddp_utils.py 13 7 46% 18-23, 27, 31 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/comm.py 81 70 14% 29-38, 56-58, 76-104, 126-149, 186-199, 228-241 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py 94 76 19% 17-37, 122-145, 148-169, 172, 175, 178, 181, 199-232 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py 440 339 23% 46-53, 57-60, 67-79, 83-137, 159-168, 172-178, 186-193, 200-230, 237, 538-664, 667-672, 675-677, 691-766, 769-776, 780-794, 798-839, 850-860, 865-898, 905-916, 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/home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/modules/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/modules/linear.py 7 3 57% 20-22 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/conv.py 84 50 40% 29-35, 38, 48-65, 71-95, 126-130, 148, 181-185, 202, 206, 239-243, 260, 264 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/embedding_ops.py 56 38 32% 27-36, 39, 50-64, 67-72, 95-104, 107, 120-134, 137-142 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/linear.py 38 26 32% 30-34, 37, 45-69, 72-77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/activation.py 242 222 8% 64-83, 86, 90-150, 160-219, 224-247, 305, 321-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/rnn.py 230 196 15% 34-48, 51-73, 76-80, 83, 93-104, 108-115, 126-128, 131-136, 140-143, 151-157, 160-209, 218-241, 277-312, 315-361, 364, 368-379, 383 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/conv.py 108 58 46% 19-35, 51-54, 67-71, 74, 78, 87-90, 103-107, 110, 114, 123-126, 139-143, 146, 150, 160-177, 195-198, 211-221, 224, 228, 239-242, 254-266, 269, 273, 283-286, 299-309, 312, 316 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/linear.py 23 11 52% 25-26, 29, 42-44, 48-55 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/rnn.py 262 210 20% 11, 14-22, 25-29, 32-36, 41-54, 57-79, 83, 86, 89, 92, 95, 105-107, 110, 115-146, 150-162, 172-173, 176, 179-199, 203-214, 224-225, 228, 231-251, 255-266, 274-289, 292-312, 322, 329-331, 334-341, 350-353, 365-375, 378-388, 391-453, 456, 460-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/sparse.py 29 12 59% 19-21, 24, 27-28, 34, 60-63, 66, 69-70, 78 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/utils.py 73 61 16% 6-41, 52-55, 64-67, 76-77, 83-87, 98-112, 124-133, 136-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/conv.py 111 64 42% 56-67, 72, 77-84, 120-131, 136, 141-147, 184-195, 200, 205-211, 253-259, 264, 269-271, 313-319, 324, 329-331, 373-379, 384, 389-391 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/linear.py 58 43 26% 34-39, 43-55, 58, 61-66, 70-72, 83-112, 122-126 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/rnn.py 506 403 20% 12, 16-33, 37-38, 41-42, 46-47, 59-123, 126, 129-140, 147-170, 173-179, 184-191, 197-198, 202-204, 208-210, 214-216, 221-254, 258-341, 346-364, 367, 370, 391, 394, 401-426, 432-440, 446-455, 461-463, 469-474, 479-482, 486, 490-504, 616, 619, 622-625, 633-670, 677-685, 691-699, 704-706, 710-713, 717, 725-763, 766, 769-774, 777-778, 783-789, 795-841, 845-873, 877-886, 889, 892, 897-901, 907-909, 913-915, 937-938, 941, 944-962, 966, 989, 992, 995-1001, 1008, 1029, 1032, 1035-1039, 1047 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/functional.py 143 114 20% 40-42, 70-72, 89-91, 106-109, 154-168, 213-227, 273-287, 325-327, 358-363, 374-378, 390-394, 409-411, 431-441, 446-450, 460-462, 475-481, 492-494, 499-503, 516-518, 570-571, 592-593, 614-615 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/__init__.py 38 13 66% 44-50, 53, 58-60, 63, 82, 85, 89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/activation.py 94 49 48% 28-29, 32, 35, 39, 49-51, 54, 58, 62-63, 67, 78-80, 83, 87, 91-92, 96, 108-111, 114, 118, 122-123, 127, 138-140, 143, 147-148, 159-162, 165-172, 176, 180-181, 185 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/batchnorm.py 64 38 41% 8-11, 15-26, 30-43, 52-53, 56, 61-62, 67, 73, 82-83, 86, 91-92, 97, 103 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/conv.py 381 265 30% 25-30, 37, 46-80, 83, 86, 89, 92-104, 119-124, 128-129, 152-160, 166-179, 182-186, 189, 195-212, 216-238, 249-265, 316-324, 329, 332-336, 341-342, 345, 348, 353-360, 370, 416-423, 428, 431-435, 439, 442, 445, 450-456, 467, 513-521, 526, 529-533, 537, 540, 543, 548-554, 565, 577-582, 588-592, 602-623, 634-651, 699-706, 711, 714, 719-720, 723-724, 727-728, 733-735, 740, 787-794, 799, 802, 807-808, 811-812, 815-816, 821-823, 828, 876-883, 888, 891, 896-897, 900-901, 904-905, 910-912, 917 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/dropout.py 13 4 69% 15, 18, 22, 26 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/embedding_ops.py 134 95 29% 12-22, 26-29, 34-37, 40, 48-50, 54-61, 65, 93-110, 113-116, 119, 122, 125-129, 132, 135, 145-175, 179-190, 220-225, 229-234, 239, 249-276, 280-292 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/functional_modules.py 105 69 34% 34-35, 38, 43-45, 49-52, 56-58, 62-65, 69-71, 75-78, 93, 98-99, 103-104, 108-109, 113-114, 118-119, 123-125, 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/home/admin/.local/lib/python3.8/site-packages/torch/package/_mangling.py 25 14 44% 16-22, 25-26, 34-38, 41, 53-58, 62 /home/admin/.local/lib/python3.8/site-packages/torch/package/_package_pickler.py 57 48 16% 18-28, 34-98, 102-107 /home/admin/.local/lib/python3.8/site-packages/torch/package/_package_unpickler.py 15 9 40% 15-16, 20-26 /home/admin/.local/lib/python3.8/site-packages/torch/package/_stdlib.py 19 12 37% 14-15, 19-29 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/find_first_use_of_broken_modules.py 10 7 30% 21-29 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/is_from_package.py 7 3 57% 13-16 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/trace_dependencies.py 24 21 12% 17-60 /home/admin/.local/lib/python3.8/site-packages/torch/package/file_structure_representation.py 60 50 17% 13-15, 27-32, 41-43, 53-61, 64-66, 72-101, 126-132 /home/admin/.local/lib/python3.8/site-packages/torch/package/find_file_dependencies.py 70 55 21% 15-18, 21-23, 26-28, 31-32, 35-43, 46-49, 52-55, 59-99 /home/admin/.local/lib/python3.8/site-packages/torch/package/glob_group.py 31 19 39% 42-45, 48, 51, 54-55, 61-64, 70-82 /home/admin/.local/lib/python3.8/site-packages/torch/package/importer.py 101 75 26% 52, 73-133, 143-162, 169, 172, 185, 197-203, 206-224, 227-232 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_exporter.py 441 342 22% 74, 107-109, 128-152, 200-241, 256-290, 299-301, 320-350, 368-381, 393-396, 399-408, 411-415, 418-428, 434-485, 498-503, 513-559, 586-686, 696, 706-707, 724-726, 743-745, 762-764, 788, 829, 860, 874, 879-930, 933, 939-944, 947-961, 965-972, 977-979, 985-1035, 1039, 1048-1052, 1056-1058, 1061-1063, 1066-1067, 1078, 1083-1089, 1098, 1107, 1116, 1125, 1133-1136, 1146, 1161-1163 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_importer.py 346 274 21% 70-111, 132-134, 147-148, 168-169, 183-264, 273, 290, 303-304, 311, 320-356, 359-371, 374-376, 382-383, 388-394, 397-402, 406-425, 429-446, 457-461, 472-502, 505-527, 535-545, 548-555, 560-574, 582-595, 598-602, 616-617, 624, 641-642, 657-658, 661-663, 668-676, 679-680, 683-704 /home/admin/.local/lib/python3.8/site-packages/torch/profiler/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/profiler/profiler.py 171 127 26% 27, 70-77, 80-81, 84, 87-98, 101-107, 110-111, 117-128, 144-145, 155-156, 163-164, 171-172, 179, 182-186, 211-232, 240, 249-266, 393-424, 464-465, 468, 471-474, 477-479, 485-497, 500-501, 504-507 /home/admin/.local/lib/python3.8/site-packages/torch/quantization/__init__.py 14 2 86% 18-19 /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fake_quantize.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuse_modules.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuser_method_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/observer.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/qconfig.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quant_type.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantization_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize_jit.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/stubs.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quasirandom.py 66 54 18% 48-68, 85-104, 120-129, 135-137, 148-153, 156-171, 174-179 /home/admin/.local/lib/python3.8/site-packages/torch/random.py 46 33 28% 18, 23, 36-42, 49-55, 62, 85-129 /home/admin/.local/lib/python3.8/site-packages/torch/return_types.py 18 2 89% 11, 14 /home/admin/.local/lib/python3.8/site-packages/torch/serialization.py 527 356 32% 38-40, 93-114, 118-119, 123-124, 133-147, 151-157, 165-169, 178, 184, 188-190, 211, 214, 225, 230, 233, 237, 247, 250, 255-256, 259-260, 265-269, 273-277, 286-293, 299-305, 310-312, 323-325, 374-381, 385-525, 529-604, 707-711, 713, 721-726, 734-946, 957, 964-979, 987, 1013, 1039-1040 /home/admin/.local/lib/python3.8/site-packages/torch/sparse/__init__.py 24 9 62% 11-12, 209-218 /home/admin/.local/lib/python3.8/site-packages/torch/special/__init__.py 37 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/storage.py 475 260 45% 13-14, 31, 70-71, 76, 79, 82, 87, 93-95, 98, 106, 110-113, 116-121, 125, 129, 133, 137, 141, 145, 149, 153, 157, 161, 165, 169, 173-177, 188-195, 200-207, 210, 218, 259-278, 281-284, 292-293, 297, 303-357, 373, 379, 384, 389, 396, 403-424, 431, 442, 445, 448-470, 473-490, 497-516, 519-520, 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84-164 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_deprecated.py 59 30 49% 32-36, 55-57, 72-80, 116-141 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_legacy.py 80 38 52% 45-47, 57, 61, 65, 68, 72, 75, 79, 83, 86, 90, 93, 97, 100, 104, 107, 111, 123-130, 133, 137, 141, 145-150, 154, 158 /home/admin/.local/lib/python3.8/site-packages/torch/torch_version.py 36 20 44% 21-27, 30, 33, 58-71, 74-81 /home/admin/.local/lib/python3.8/site-packages/torch/types.py 44 11 75% 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75 /home/admin/.local/lib/python3.8/site-packages/torch/utils/__init__.py 11 2 82% 10, 15 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_crash_handler.py 17 8 53% 9, 12-17, 21, 25 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_mode_utils.py 60 38 37% 27-33, 48, 52, 59, 66-102, 110-132 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_pytree.py 118 79 33% 43, 46, 49, 52, 55, 58, 61, 64, 74-81, 84-86, 90, 100-103, 106, 109-113, 116, 120-121, 124, 130-145, 152-175, 178-179, 190-216 /home/admin/.local/lib/python3.8/site-packages/torch/utils/backcompat/__init__.py 15 2 87% 13, 16 /home/admin/.local/lib/python3.8/site-packages/torch/utils/checkpoint.py 155 140 10% 7-19, 24-25, 38-46, 50-52, 59-97, 101-150, 230-237, 286-307, 321-392 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/__init__.py 15 3 80% 41-42, 47 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/collate.py 72 64 11% 49-76, 131-183 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py 36 27 25% 9-12, 15, 20-22, 25-40, 45, 48-52 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/pin_memory.py 50 42 16% 20-45, 49-72 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/_utils/serialization.py 6 2 67% 10-11 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42-45, 49-51, 55-57, 61-63, 70, 75, 79-80, 84-85, 89-90, 94-95, 99-100, 104-105, 109-110 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/dataframes.py 198 106 46% 33-34, 37-38, 51, 54, 57-62, 65-67, 70, 73, 77-81, 84-88, 91-95, 100-103, 108, 111, 116, 119, 129-133, 136, 139, 143-146, 154-156, 159, 162, 171-174, 177, 180, 189-191, 194, 197, 205-207, 210, 213, 221-223, 226, 229, 237-239, 242, 245-246, 250-255, 261-263, 272, 276, 279, 282-285, 294-297, 300, 303, 306-308, 316-317 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/datapipes.py 100 70 30% 21, 24-26, 32, 35-37, 43-44, 47-54, 60, 63-78, 84-85, 88-106, 112-114, 117-120, 123-130 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/dataframe/structures.py 12 7 42% 13-15, 18-21 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/datapipe.py 179 100 44% 21-22, 116-123, 127, 132, 135-141, 152-154, 157-162, 166-168, 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33-47, 50 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/callable.py 21 7 67% 15, 49-52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combinatorics.py 24 11 54% 44-49, 52-54, 59-60, 63 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combining.py 54 35 35% 35-42, 45-51, 54-56, 80-87, 90-96, 99-101 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/grouping.py 39 25 36% 40-46, 49-59, 62-70 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/utils.py 17 9 47% 32-42, 45, 48 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/common.py 116 91 22% 21-22, 30-39, 50-76, 80-90, 94-101, 138-173, 183, 186-187, 190-192, 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75-109, 114-133, 139-168, 179-196, 199, 210-219, 222, 225-241 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kinetics.py 100 72 28% 20, 116-156, 160-167, 176-197, 211-229, 236, 239, 242-248, 312-323 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kitti.py 58 39 33% 61-84, 105-109, 112-128, 131, 135, 139-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lfw.py 125 96 23% 42-58, 61-63, 66-72, 75-82, 85, 88, 91, 123-126, 129-144, 147-151, 161-170, 173, 205-207, 210-235, 245-255 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lsun.py 94 75 20% 18-31, 34-50, 53, 79-93, 96-136, 146-161, 164, 167 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py 243 152 37% 65-66, 70-71, 75-76, 80-81, 91-104, 107-111, 118-119, 122-128, 138-150, 153, 157, 161, 165, 168, 176-195, 198-199, 294-298, 302, 306, 310, 314, 318, 321, 324, 329-339, 421-428, 432-433, 437-438, 441, 444-461, 467-474, 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 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585-673, 794-819, 862-891 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/roi_heads.py 481 441 8% 28-49, 69-79, 91-94, 109-126, 131-162, 168-213, 220-231, 244-295, 300-326, 331-342, 347-360, 368-383, 389, 394-400, 405-425, 429-461, 466-471, 476-489, 522-548, 551-557, 560-566, 570-601, 605-610, 614-616, 620-628, 636-666, 676-725, 742-876 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/rpn.py 175 149 15% 29-41, 53-62, 73-79, 83-86, 90-111, 161-181, 184-186, 189-191, 197-229, 232-240, 250-297, 314-337, 363-391 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssd.py 273 223 18% 49-53, 58-60, 63, 71-73, 80-87, 90-103, 108-112, 117-121, 201-242, 248-251, 260-319, 327-410, 415-461, 466-535, 539-548, 552-568, 643-679 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssdlite.py 119 79 34% 33, 49-51, 74-78, 85-87, 90, 100-104, 109-113, 125-148, 152-161, 169-184, 262-328 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/transform.py 185 159 14% 14-16, 22, 32-71, 96-105, 110-146, 149-157, 165-166, 173-194, 200-218, 221-225, 228-245, 253-267, 270-275, 279-293, 297-309 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/efficientnet.py 265 159 40% 59, 76-81, 85, 100-102, 113-162, 165-169, 179-223, 226-230, 255-342, 345-352, 355, 366-374, 382-429, 754-757, 782-785, 810-813, 838-841, 866-869, 894-897, 930-933, 966-969, 1003-1006, 1040-1043, 1077-1080 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/googlenet.py 185 143 23% 42-100, 103-108, 112-163, 167-170, 173-181, 196-212, 218-224, 227-228, 239-246, 250-263, 268-270, 273-275, 319-342 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/inception.py 299 243 19% 38-93, 96-101, 105-155, 159-162, 165-173, 180-192, 195-208, 211-212, 217-224, 227-236, 239-240, 247-263, 266-282, 285-286, 291-300, 303-313, 316-317, 322-336, 339-360, 363-364, 371-378, 382-395, 400-402, 405-407, 457-475 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mnasnet.py 119 73 39% 38-45, 60-63, 70-77, 84-87, 93-94, 113-156, 159-162, 174-209, 306-314, 339-341, 366-368, 393-395, 420-422 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenet.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv2.py 105 72 31% 23-32, 43-78, 81-84, 112-181, 186-191, 194, 265-275 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv3.py 138 97 30% 29-33, 54-61, 65, 76-124, 127-130, 155-223, 226-233, 236, 242-285, 295-303, 398-401, 428-431 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/_utils.py 25 18 28% 10-17, 21-23, 33-45 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/raft.py 288 219 24% 31-59, 62-68, 75-92, 103-110, 120-143, 146-148, 151-159, 169-190, 193-202, 209-212, 215-220, 225, 236-255, 258-260, 270-273, 276, 286-291, 294-299, 310-320, 323-325, 339-349, 359-370, 374-400, 403-409, 449-460, 464-511, 747-798, 824-826, 878-880 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/googlenet.py 95 59 38% 27-28, 31-34, 37, 42-43, 46-47, 53-54, 58-71, 77-81, 84-94, 104-106, 178-207 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/inception.py 130 81 38% 28-29, 32-35, 38, 44-45, 48-49, 55-56, 59-60, 66-67, 70-71, 77-78, 81-82, 88-91, 94-112, 115-116, 122, 132-147, 150-160, 170-172, 250-276 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenet.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv2.py 62 33 47% 26-27, 30-33, 36-38, 49-51, 54-57, 60-64, 136-152 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv3.py 88 53 40% 34-36, 39, 42, 54-72, 86-87, 90-93, 104-106, 109-112, 115-122, 133-158, 231-234 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/resnet.py 120 70 42% 39-40, 43-57, 60-62, 67-70, 73-88, 91-95, 100-103, 106-112, 121-124, 135-149, 315-317, 364-366, 413-417, 456-460 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/shufflenetv2.py 86 43 50% 37-38, 41-49, 55-57, 60-63, 75-82, 99-113, 253-254, 306-307, 351-352, 396-397 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/utils.py 30 24 20% 8-18, 22-42, 48-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/regnet.py 273 142 48% 62, 81-105, 122-141, 144-148, 168-182, 195-200, 234-266, 276, 287-293, 307-374, 377-384, 393-402, 1123-1126, 1148-1151, 1173-1178, 1200-1205, 1227-1232, 1254-1259, 1281-1286, 1308-1313, 1335-1338, 1360-1363, 1389-1392, 1418-1421, 1447-1450, 1476-1479, 1505-1508 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/resnet.py 236 150 36% 42, 56, 73-87, 90-105, 128-141, 144-163, 178-223, 233-264, 268-282, 285, 295-303, 668-670, 693-695, 724-726, 755-757, 786-788, 813-817, 842-846, 870-874, 904-907, 937-940 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/_utils.py 27 18 33% 14-18, 21-37 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/deeplabv3.py 127 81 36% 50, 61-66, 71, 79-82, 87-101, 109-113, 121-128, 203-218, 257-273, 312-328, 365-381 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/fcn.py 64 36 44% 38-47, 103-110, 152-168, 210-226 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/lraspp.py 70 44 37% 38-41, 44-51, 56-68, 71-79, 83-93, 157-175 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/home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ciou_loss.py 25 20 20% 45-71 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/deform_conv.py 65 50 23% 63-92, 126-151, 154-159, 170, 182-195 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/diou_loss.py 32 26 19% 45-57, 66-87 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/drop_block.py 68 52 24% 28-52, 74-102, 114-119, 129, 132-133, 145, 155 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/feature_pyramid_network.py 100 80 20% 33, 84-110, 122-134, 149-156, 163-170, 184-204, 218-220, 229-235, 243-249 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/focal_loss.py 18 14 22% 35-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/giou_loss.py 24 20 17% 43-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/misc.py 93 67 28% 27-33, 45-49, 56-62, 65, 85-114, 153, 202, 237-243, 246-250, 253-254, 282-298, 309-310, 313 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/poolers.py 123 92 25% 20-34, 45, 68-72, 80-85, 89-96, 101-107, 114-135, 140-144, 169-228, 278-288, 291-292, 295-296, 303-304, 325-331, 341 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_align.py 29 18 38% 46-57, 71-75, 78, 81-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_pool.py 28 17 39% 40-49, 58-61, 64, 67-68 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_align.py 31 18 42% 53-61, 78-83, 86, 89-97 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_pool.py 30 17 43% 42-51, 60-63, 66, 69-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/stochastic_depth.py 34 24 29% 26-44, 56-59, 62, 65-66 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/_pil_constants.py 18 8 56% 17-25 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/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 89 69% 24-39, 66, 80, 83-92, 124, 130, 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 376 91 76% 35-37, 56, 58, 63, 88, 101-110, 117, 119, 133, 139-141, 149-150, 163, 172, 185-187, 204-205, 229-231, 257-259, 285-287, 302, 325-326, 343, 366-372, 376, 389-391, 417-426, 436-437, 528-529, 540, 556, 563-568, 576, 633-636, 657-659, 673-693 /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 481 396 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, 605-623, 628-636, 639-648, 651-660, 663-669, 673-696 /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 836 45% 44, 58, 71, 83, 101, 109-111, 116, 135-141, 146, 163-167, 180-184, 200-309, 319-322, 325-330, 333-340, 343-362, 365-373, 378, 383, 405, 408-411, 420-423, 434-475, 495, 498-501, 517-537, 542, 548-555, 565-586, 591, 598, 601, 604, 611, 644, 660, 677, 758, 765, 778, 815, 845, 882, 887-888, 908-911, 915-920, 923-928, 932-935, 974, 978, 982-983, 991, 993, 1003-1004, 1008, 1020, 1025, 1030-1031, 1038-1058, 1097, 1104, 1149, 1158, 1172, 1178, 1193, 1205-1209, 1220, 1231, 1242-1246, 1264-1324, 1356-1363, 1380-1388, 1398-1410, 1414-1421, 1424-1430, 1433-1441, 1446-1453, 1457-1470, 1474-1487, 1490-1497, 1500-1507, 1519-1520, 1530-1531, 1538-1548, 1555, 1562-1568, 1570, 1582-1587, 1599-1604, 1616-1621, 1627-1631, 1638-1644, 1649-1663, 1669-1677, 1685, 1692-1694, 1699, 1705-1724, 1728-1743, 1747-1767, 1771-1789, 1795-1817, 1822-1832, 1835-1843, 1849-1866, 1872-1882, 1885-1904, 1911, 1915, 1925-1942, 1946-1954, 1959, 1979, 2000, 2013, 2024, 2056, 2081, 2109-2117, 2121-2129, 2134-2143, 2146-2155, 2158-2163, 2171, 2191, 2209, 2234, 2238-2245, 2291-2315, 2327-2329, 2333-2342, 2347-2365, 2368-2376, 2386-2411, 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 517 265 49% 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, 278-283, 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 70 8% 2-9, 13-39, 42-68, 71-78, 81-88, 91-98 /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 977 45% 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, 529-580, 587-631, 659-660, 667-670, 681-684, 707-709, 728, 736-737, 745-746, 780-783, 817, 825, 828, 830-832, 840-860, 870-877, 893-949, 975-1168, 1172, 1178-1181, 1185-1187, 1192-1193, 1198-1199, 1203-1205, 1208-1288, 1336-1344, 1358, 1361-1363, 1375-1386, 1389-1407, 1411-1460, 1464-1510, 1515-1555, 1560-1566, 1574, 1577, 1580-1581, 1584-1587, 1590, 1593, 1596-1598, 1606, 1615, 1623, 1631, 1645, 1667, 1672-1682, 1685-1688, 1692-1745, 1749-1752, 1757-1795, 1801, 1805-1814, 1821, 1830, 1838-1875, 1888-1904, 1913-1929, 1934-1951, 1955-1967, 1976-1979, 1985-1995, 2003-2006, 2012-2016, 2020, 2026, 2031-2034, 2042-2044, 2047-2051, 2055-2078, 2118, 2129, 2136-2139, 2158-2159, 2164-2213, 2216-2251, 2268-2274, 2277-2287, 2290-2292, 2323, 2326-2338, 2341-2342, 2353-2355, 2365, 2375-2376, 2381, 2386, 2432, 2438, 2442, 2450, 2452, 2458, 2461, 2463, 2465, 2471, 2473, 2475, 2477, 2479, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2515, 2521, 2525, 2529, 2531, 2535, 2538, 2544, 2546, 2548, 2550, 2552, 2554, 2558, 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 925 28% 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, 638-744, 747-761, 764-779, 783-809, 813-864, 873-902, 907-936, 957, 962-965, 971-976, 979, 981, 1006-1008, 1012-1014, 1017-1018, 1047-1078, 1086-1087, 1095, 1098, 1102, 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, 1685, 1687, 1689-1690, 1692, 1694, 1696, 1698, 1700, 1703, 1705, 1708, 1710, 1712, 1716, 1719, 1721, 1730-1739, 1743-1745, 1749-1769, 1775-1783, 1786-1818, 1821-1836, 1840-1875 /home/admin/workarea/git/Velours/python/mtr/datou/datou_local_cache_db.py 157 117 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 73 33% 12-15, 18-20, 24-34, 48, 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 793 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, 1231-1242, 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 2039 1346 34% 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, 1115, 1118-1137, 1161, 1163, 1166-1187, 1196-1272, 1279-1466, 1470-1499, 1503-1579, 1586-1674, 1678-1855, 1867, 1914, 1921-1924, 1931-1933, 1936, 1967-1968, 1972, 1976-1998, 2016-2017, 2031-2078, 2133, 2166-2169, 2214-2216, 2223-2237, 2240-2249, 2254-2255, 2258-2267, 2270-2291, 2293-2294, 2358-2370, 2374-2419, 2437, 2441-2442, 2453-2477, 2480, 2496, 2507, 2511, 2516-2620, 2627-2815, 3023-3034, 3039, 3042-3043, 3048-3050, 3053, 3077-3079, 3088, 3099-3111, 3122-3123, 3136, 3166, 3182, 3187-3194, 3453-3556, 3560-3599, 3603-3676 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_send_or_copy.py 554 434 22% 19-195, 200-268, 273-332, 336-379, 383-488, 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 298 137 54% 16-17, 21, 26, 40, 47-48, 70, 97-98, 106, 125-126, 143-155, 179-183, 194-196, 198-201, 213-219, 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/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 321 215 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, 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 26 42% 19, 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 1594 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, 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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, 173-179, 183-186, 189-190, 193-194 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/swift_upload_manager.py 151 73 52% 44, 47-48, 54, 63, 72-73, 76-79, 105, 107, 125-127, 130, 133-142, 145-156, 163, 180-184, 187-193, 197-210, 213, 216-217, 224-240 /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, 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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 90 58 36% 13-15, 43-62, 86-100, 104-126, 130-163, 167-170, 174-179 /home/admin/workarea/git/Velours/python/prod/memo/lib_sla.py 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 833 432 48% 44, 52, 68-100, 125-183, 190-197, 202-212, 230-231, 236-238, 245-247, 249-252, 255-257, 260, 285-286, 304, 307, 310-311, 316-318, 321-329, 336-337, 342-343, 349-350, 358-361, 373-419, 427, 433, 441, 443, 455-535, 591-593, 599-626, 669-671, 679-714, 758-783, 841-876, 924-925, 930, 957-963, 986, 1022-1027, 1041, 1060, 1073, 1079, 1087-1091, 1094, 1099, 1104, 1109, 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/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/rpn/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/rpn/generate_anchors.py 38 6 84% 100-105 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/rpn/proposal_layer.py 79 10 87% 36-38, 47, 79-80, 86, 162-163, 167 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/utils/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/utils/blob.py 24 10 58% 33-45 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/utils/timer.py 18 1 94% 32 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/tools/_init_paths.py 11 0 100% /home/admin/workarea/install/segment-anything/segment_anything/__init__.py 3 0 100% /home/admin/workarea/install/segment-anything/segment_anything/automatic_mask_generator.py 128 35 73% 107-110, 118, 121, 167, 175, 179, 212-220, 314, 335-372 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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, 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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 /usr/lib/python3/dist-packages/urllib3/util/retry.py 150 91 39% 186-187, 202-218, 229-232, 240-249, 253-265, 270-275, 278-283, 286-289, 300-305, 311, 317, 339, 350-355, 376-442, 445 /usr/lib/python3/dist-packages/urllib3/util/ssl_.py 148 77 48% 31-34, 43-44, 50-56, 61-63, 104-149, 162-174, 193, 198, 211-217, 332, 337-348, 354, 357-360, 372-383, 395, 401-407 /usr/lib/python3/dist-packages/urllib3/util/timeout.py 63 25 60% 102, 121, 126-153, 192, 204-208, 223-226, 252-254, 256 /usr/lib/python3/dist-packages/urllib3/util/url.py 205 79 61% 102, 112, 120, 127-129, 150-169, 172, 193-207, 239, 252, 258-259, 264, 269, 277, 282-294, 299, 304-314, 354, 358, 372, 374-376, 379-381, 391-394, 401-404, 411, 431-432 /usr/lib/python3/dist-packages/urllib3/util/wait.py 76 37 51% 8-9, 48-68, 72-87, 92, 97, 111, 121-122, 135-138, 153 /usr/lib/python3/dist-packages/yaml/__init__.py 184 118 36% 15-16, 31-37, 45, 62-67, 73-78, 85-89, 96-101, 123-132, 142, 152, 162, 172, 182, 192, 201-213, 224-243, 250, 262-283, 290, 298, 306, 316-322, 331-337, 345-350, 359-364, 373, 382, 391-397, 418, 425 /usr/lib/python3/dist-packages/yaml/composer.py 92 31 66% 18-22, 26-27, 40-41, 65-70, 74-75, 82, 96, 100-115, 126 /usr/lib/python3/dist-packages/yaml/constructor.py 479 295 38% 32, 38-39, 44-45, 52, 61, 69, 71-72, 74, 82-98, 102, 107-108, 114, 119, 125-129, 134, 141, 148-157, 175-177, 186-204, 208-209, 213, 221-222, 234-235, 242, 244, 246, 248, 250, 252, 254-261, 271-292, 295-307, 323-350, 356-373, 377-394, 397-400, 406-408, 417-424, 427, 487, 490-492, 495, 498, 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/usr/lib/python3/dist-packages/yaml/events.py 61 6 90% 9-13, 17-19 /usr/lib/python3/dist-packages/yaml/loader.py 47 24 49% 14-19, 34-39, 44-49, 58-63 /usr/lib/python3/dist-packages/yaml/nodes.py 29 7 76% 4-7, 9-23 /usr/lib/python3/dist-packages/yaml/parser.py 352 210 40% 101, 110-111, 157, 163, 167-180, 197-199, 209-215, 218-246, 268, 275-277, 283-291, 293-300, 302-310, 320-323, 333, 338-341, 343-346, 348-351, 357-369, 377-379, 382-398, 403-415, 434-435, 437-438, 453-458, 472-474, 477-500, 503-510, 513-524, 527-529, 538-540, 543-567, 570-581, 584-585, 588 /usr/lib/python3/dist-packages/yaml/reader.py 122 69 43% 27-31, 34-40, 76-85, 90-92, 96, 108-109, 119, 123-135, 141-143, 149-175, 178-185 /usr/lib/python3/dist-packages/yaml/representer.py 248 176 29% 19-24, 27-31, 34-63, 78-83, 86-101, 104-129, 132, 137-142, 145, 148, 151-155, 158-162, 165, 172-189, 199, 207, 210-213, 216-217, 220-221, 224-228, 231, 275-283, 286, 289-290, 293, 313-356, 360-364 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/usr/lib/python3/dist-packages/yaml/tokens.py 76 17 78% 7-12, 20-23, 78-80, 85-87, 92-94 /usr/local/lib/python3.8/dist-packages/Cython/Shadow.py 292 147 50% 21-26, 29-35, 44-74, 92, 95, 101, 103, 133-135, 142-143, 149-152, 155-158, 164-169, 172, 175, 179, 182-188, 194-198, 201, 203, 215, 230, 232, 236, 239-241, 244-246, 249-254, 257, 262, 268-278, 281-284, 290-305, 308-313, 321-324, 327-331, 334-338, 347-348, 351, 360-377, 382, 458, 461-464, 467 /usr/local/lib/python3.8/dist-packages/Cython/__init__.py 6 2 67% 11-12 /usr/local/lib/python3.8/dist-packages/IPython/__init__.py 32 12 62% 31, 90-98, 125-126, 151-152 /usr/local/lib/python3.8/dist-packages/IPython/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/alias.py 107 66 38% 71-88, 101-108, 130-134, 138-160, 163, 166-186, 199-202, 206-209, 213, 218-221, 229-230, 235-236, 240, 243-246, 249-250, 254-258 /usr/local/lib/python3.8/dist-packages/IPython/core/application.py 251 170 32% 35-39, 58-63, 91-100, 122, 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3611-3660, 3680-3699, 3702, 3707 /usr/local/lib/python3.8/dist-packages/IPython/core/latex_symbols.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/logger.py 108 92 15% 34-51, 55-57, 60, 70-127, 132-152, 156-165, 182-185, 191-201, 210-215 /usr/local/lib/python3.8/dist-packages/IPython/core/macro.py 28 19 32% 25-36, 39, 42, 46, 49-53 /usr/local/lib/python3.8/dist-packages/IPython/core/magic.py 256 154 40% 55, 64-74, 142, 203, 220-250, 324, 334-339, 343, 351, 363-375, 399-411, 442-445, 469-475, 510-541, 545-546, 552-573, 610-654, 659-661, 675-683, 687-703 /usr/local/lib/python3.8/dist-packages/IPython/core/magic_arguments.py 102 16 84% 130, 135-136, 153, 164, 172, 190, 203-204, 206, 232, 262, 269, 272-274 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/magics/auto.py 38 27 29% 29-31, 51-60, 107-128 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/basic.py 249 188 24% 22-23, 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42-53, 56-60, 70 /usr/local/lib/python3.8/dist-packages/IPython/utils/data.py 6 3 50% 22-23, 28 /usr/local/lib/python3.8/dist-packages/IPython/utils/decorators.py 14 10 29% 38-49 /usr/local/lib/python3.8/dist-packages/IPython/utils/dir2.py 34 28 18% 16-20, 38-51, 64-84 /usr/local/lib/python3.8/dist-packages/IPython/utils/encoding.py 23 7 70% 30, 53-58, 64-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/frame.py 17 11 35% 50-53, 66-67, 81-82, 91-94 /usr/local/lib/python3.8/dist-packages/IPython/utils/generics.py 9 2 78% 12, 30 /usr/local/lib/python3.8/dist-packages/IPython/utils/importstring.py 12 10 17% 27-39 /usr/local/lib/python3.8/dist-packages/IPython/utils/io.py 130 76 42% 28-31, 41-42, 47-49, 52-64, 68-73, 84, 122-132, 136-139, 143-145, 149-150, 153-154, 170-188, 207-211, 216-218, 223-227, 232-236, 246-248 /usr/local/lib/python3.8/dist-packages/IPython/utils/ipstruct.py 76 56 26% 85-88, 111-123, 146-151, 165-166, 180-182, 196-198, 212-215, 223-229, 232, 245, 263, 271, 360-390 /usr/local/lib/python3.8/dist-packages/IPython/utils/module_paths.py 11 8 27% 61-70 /usr/local/lib/python3.8/dist-packages/IPython/utils/openpy.py 45 35 22% 24-40, 46-58, 75-79, 100-103 /usr/local/lib/python3.8/dist-packages/IPython/utils/path.py 191 148 23% 27, 30-53, 57, 67, 76-81, 87-90, 99-109, 146-162, 190-211, 220-230, 239-249, 254-256, 260-262, 266-268, 272-274, 278-280, 297-302, 307-311, 321-327, 341-351, 362-363, 375-382, 395-419, 429-436 /usr/local/lib/python3.8/dist-packages/IPython/utils/process.py 29 17 41% 15, 17, 47-50, 55-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/py3compat.py 108 72 33% 18-19, 22-23, 27-29, 32-34, 38-40, 45-59, 66-76, 93-140, 147, 156-158, 165-168, 179, 189 /usr/local/lib/python3.8/dist-packages/IPython/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/IPython/utils/strdispatch.py 33 23 30% 25-26, 31-33, 38-40, 44-52, 55, 58-61, 65-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/sysinfo.py 40 24 40% 54-65, 81-82, 97-99, 119, 123, 128-129, 134, 152-165 /usr/local/lib/python3.8/dist-packages/IPython/utils/syspathcontext.py 32 22 31% 24, 27-31, 34-40, 46, 49-53, 56-62 /usr/local/lib/python3.8/dist-packages/IPython/utils/tempdir.py 23 12 48% 24-26, 29-30, 35, 38, 51-53, 56-57 /usr/local/lib/python3.8/dist-packages/IPython/utils/terminal.py 59 37 37% 27-33, 53, 58, 62, 68-69, 73, 81-105, 110-112, 117-119, 123-125, 129 /usr/local/lib/python3.8/dist-packages/IPython/utils/text.py 255 192 25% 22, 43-47, 52-56, 61, 66-70, 101, 106-110, 115-119, 124-128, 148-165, 186-201, 215-230, 274-285, 306-309, 329-334, 342-346, 354-356, 397-409, 417-425, 460-473, 484, 509-510, 542-569, 593-610, 618-624, 629-635, 644-647, 702-707, 732-739, 763-770 /usr/local/lib/python3.8/dist-packages/IPython/utils/timing.py 35 24 31% 33, 42, 51-52, 58-67, 83-96, 106, 115 /usr/local/lib/python3.8/dist-packages/IPython/utils/wildcard.py 51 31 39% 44-52, 56, 61-73, 78-85, 92-111 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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, 753, 759-766, 769, 775-776, 793, 797-810, 814-820, 824-835, 857, 866-875, 880-895, 898-903, 906 /usr/local/lib/python3.8/dist-packages/backcall/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/backcall/backcall.py 66 48 27% 12-13, 18-25, 40, 44, 51-106 /usr/local/lib/python3.8/dist-packages/boto/__init__.py 281 191 32% 75, 88-97, 102-111, 125-126, 140-141, 155-156, 170-171, 185-186, 206-207, 223-224, 239-240, 254-255, 270-271, 286-287, 302-303, 317-318, 332-333, 351-352, 366-367, 381-382, 397-398, 414-415, 437-453, 470-471, 494-508, 530-545, 562-563, 577-578, 599-607, 626-627, 643-644, 660-661, 683-684, 702-703, 720-721, 737-738, 747-748, 767-768, 788-789, 811-812, 834-835, 856-857, 878-879, 901-902, 924-925, 947-948, 970-971, 993-994, 1016-1017, 1039-1040, 1062-1063, 1078-1079, 1094-1095, 1143-1197, 1209-1214 /usr/local/lib/python3.8/dist-packages/boto/auth.py 584 467 20% 49-51, 102-105, 108-115, 118-121, 124-128, 132-134, 137-140, 143-144, 155, 158, 167-169, 172-173, 176-193, 203-205, 208-209, 212-221, 232-233, 236-247, 258-259, 266-271, 280-282, 290-298, 309-322, 334-340, 343-350, 357-367, 370-374, 377-383, 388-395, 404-414, 417-419, 422-430, 433-441, 444-451, 454-459, 462, 465-479, 482-487, 490-504, 512-516, 519-525, 536-582, 592-595, 598-601, 606-611, 617-622, 625-629, 636-645, 658-690, 696, 703-738, 741-744, 747-753, 764-806, 825, 828-834, 837-845, 856-874, 885-896, 908-910, 913-924, 935-955, 967-982, 1007-1033, 1037-1054, 1059-1098 /usr/local/lib/python3.8/dist-packages/boto/auth_handler.py 10 2 80% 52, 60 /usr/local/lib/python3.8/dist-packages/boto/cacerts/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/compat.py 47 26 45% 28-29, 35-36, 45-47, 68-102 /usr/local/lib/python3.8/dist-packages/boto/connection.py 605 493 19% 79-80, 84-85, 123, 131, 138, 146-158, 173-181, 189-190, 197-199, 234-238, 243-246, 249, 255, 264-269, 276-280, 290-300, 341-358, 361, 367-384, 390-391, 402-413, 474-572, 575, 578, 581, 587, 591, 594, 598, 602, 608, 614, 622-642, 645-662, 665-698, 701-705, 708-721, 724-778, 781, 784-851, 854-855, 858-859, 863-874, 877-881, 884, 897-1033, 1037-1059, 1066-1070, 1077-1078, 1091, 1103, 1106, 1109-1116, 1119-1122, 1158-1162, 1168-1186, 1190-1208, 1211-1227 /usr/local/lib/python3.8/dist-packages/boto/endpoints.py 79 57 28% 44-50, 55-56, 61-78, 81-82, 87-92, 100-103, 118-121, 126, 130, 149, 163-166, 177, 186, 197, 210-222, 227-232, 237-239 /usr/local/lib/python3.8/dist-packages/boto/exception.py 287 166 42% 42-43, 46, 49, 79-135, 138-142, 145-148, 151, 155, 159, 162-170, 173-176, 181-185, 188, 191-196, 204-205, 208-211, 254-256, 259, 262-267, 270-272, 280-281, 284, 287, 295-296, 299, 303-306, 310-312, 334-340, 343-347, 350-353, 356-359, 376-383, 403-405, 408, 411-416, 458-459, 466-467, 474-475, 482-483, 490-491, 532-534, 537, 549-551, 554, 565-566, 574-575, 578, 592-593 /usr/local/lib/python3.8/dist-packages/boto/gs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/gs/acl.py 187 133 29% 58-59, 63, 67-75, 80-82, 87-88, 91-93, 96-97, 100-107, 110-115, 118-126, 132-135, 138-141, 144-149, 152-155, 158-164, 172-175, 178, 181-205, 208-216, 219-223, 243-250, 254-264, 267-271, 274-284, 287-308 /usr/local/lib/python3.8/dist-packages/boto/gs/user.py 26 20 23% 25-29, 32, 35, 38-43, 46-54 /usr/local/lib/python3.8/dist-packages/boto/handler.py 29 19 34% 30-32, 35-38, 41-46, 49, 54-57, 60 /usr/local/lib/python3.8/dist-packages/boto/https_connection.py 49 34 31% 41-44, 47, 59-62, 75-83, 105-114, 118-135 /usr/local/lib/python3.8/dist-packages/boto/jsonresponse.py 108 89 18% 30-32, 35-41, 44-47, 50, 53-55, 64-74, 77-86, 89-91, 94-109, 112-119, 127-132, 135-137, 140-155, 158-168 /usr/local/lib/python3.8/dist-packages/boto/plugin.py 38 22 42% 53-56, 60-66, 70-78, 86, 91-93 /usr/local/lib/python3.8/dist-packages/boto/provider.py 247 178 28% 183-214, 217-219, 222, 227-229, 232, 237-239, 242, 247-263, 267-378, 383-402, 413-434, 437-441, 444-465, 468-473, 476, 479, 484 /usr/local/lib/python3.8/dist-packages/boto/pyami/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/pyami/config.py 158 108 32% 42, 47-49, 59, 61, 65-69, 78, 88-93, 96-103, 111-121, 124, 127, 130-134, 137-141, 144-148, 151, 166-169, 172-180, 183-186, 189-191, 194-202, 205-217, 220-235 /usr/local/lib/python3.8/dist-packages/boto/regioninfo.py 89 66 26% 44, 56-57, 77-82, 100-115, 121-134, 161-182, 208-220, 226-229, 235-239, 247-249, 259-262, 265, 268, 271-276, 289-290 /usr/local/lib/python3.8/dist-packages/boto/resultset.py 111 98 12% 47-62, 65-76, 79-82, 85-134, 140-142, 145-148, 151, 154, 157-160, 163-176 /usr/local/lib/python3.8/dist-packages/boto/s3/__init__.py 17 11 35% 43-44, 54-55, 63-74 /usr/local/lib/python3.8/dist-packages/boto/s3/acl.py 111 89 20% 34-36, 39-51, 54-64, 67-72, 75-82, 88-89, 92, 95-97, 100-101, 104-108, 111-114, 117-121, 130-135, 138-140, 143-156, 159-171 /usr/local/lib/python3.8/dist-packages/boto/s3/bucket.py 700 576 18% 71, 95-97, 100, 103, 106, 109, 112-117, 131, 143, 175-194, 197-231, 282, 328, 363, 369-390, 394-411, 424-426, 469-472, 521-522, 535, 606-609, 623-625, 630, 662-730, 757-759, 766-788, 847-889, 894-908, 912-922, 926-936, 940-944, 948-963, 989-1002, 1028-1040, 1043-1046, 1072-1080, 1110-1119, 1123-1124, 1134-1146, 1162-1171, 1193-1197, 1206-1207, 1216-1227, 1235-1240, 1243-1249, 1253-1260, 1288-1308, 1323-1339, 1350-1366, 1377-1389, 1396-1404, 1438-1441, 1448, 1454-1461, 1483, 1489-1493, 1522-1526, 1530-1538, 1544-1551, 1560-1563, 1570-1576, 1586-1594, 1598-1606, 1618-1632, 1644, 1651-1658, 1669-1673, 1679-1687, 1736-1767, 1775-1806, 1815-1822, 1826, 1829-1835, 1838-1845, 1849-1864, 1867, 1870-1878 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlistresultset.py 67 54 19% 29-41, 54-59, 62, 73-86, 99-105, 108, 121-134, 147-151, 154 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlogging.py 50 41 18% 28-33, 36-47, 50, 53-57, 60-65, 69-83 /usr/local/lib/python3.8/dist-packages/boto/s3/connection.py 282 212 25% 58-62, 67-69, 76, 79-82, 85-88, 91-95, 98-99, 106, 113, 119, 122-126, 132-135, 176-199, 205-208, 211-212, 215, 226, 232-237, 298-355, 361-380, 386-438, 443-453, 468-469, 508-511, 528-555, 577-581, 606-627, 644-647, 653-667 /usr/local/lib/python3.8/dist-packages/boto/s3/cors.py 80 69 14% 65-78, 81, 84, 87-100, 103-117, 126-130, 133, 140-144, 194-210 /usr/local/lib/python3.8/dist-packages/boto/s3/deletemarker.py 28 23 18% 26-31, 34-38, 41-55 /usr/local/lib/python3.8/dist-packages/boto/s3/key.py 690 590 14% 106-135, 138-147, 150, 154-157, 160, 163, 168-169, 172-175, 180-184, 187-192, 197-207, 210, 219-223, 226-231, 234-242, 246-259, 262-273, 280, 304-334, 348, 352-361, 381-385, 397-402, 408-415, 441-451, 501-507, 515-519, 522-542, 551, 557, 561, 566-573, 576, 580-581, 584-585, 588-589, 592-593, 596, 605-610, 624-635, 639, 695-713, 760, 767-966, 969-1021, 1036-1045, 1108-1132, 1214-1311, 1374-1375, 1437-1444, 1493, 1503-1575, 1602, 1658-1664, 1722-1739, 1795-1804, 1829-1831, 1853-1856, 1859-1867, 1875-1889, 1893-1912, 1929-1935 /usr/local/lib/python3.8/dist-packages/boto/s3/keyfile.py 74 53 28% 35-44, 47-49, 52-85, 88-89, 92-94, 97, 102, 107, 110, 113, 116, 119, 122, 125, 128, 131, 134 /usr/local/lib/python3.8/dist-packages/boto/s3/lifecycle.py 148 113 24% 48-65, 68, 71-76, 79-86, 89-99, 111-112, 115, 118-121, 124-128, 131-137, 152-154, 157-161, 164-171, 178-182, 185, 188-203, 210-213, 230-231, 234-236, 242, 246, 250, 259-263, 266, 273-278, 310-311 /usr/local/lib/python3.8/dist-packages/boto/s3/multidelete.py 64 48 25% 41-44, 47-50, 53, 56-66, 82-85, 88-92, 95, 98-107, 121-123, 126-134, 137 /usr/local/lib/python3.8/dist-packages/boto/s3/multipart.py 160 133 17% 46-52, 55, 59, 62-71, 86-90, 93-96, 99, 102-111, 118-125, 134-146, 149, 152, 155-162, 165-175, 178-200, 211-226, 253-261, 290-301, 317-318, 330 /usr/local/lib/python3.8/dist-packages/boto/s3/prefix.py 16 10 38% 24-25, 28, 31-34, 38-41 /usr/local/lib/python3.8/dist-packages/boto/s3/tagging.py 52 34 35% 7-8, 11, 14-17, 20, 24, 29-33, 36, 39-40, 43-47, 54-58, 61, 64-68, 71 /usr/local/lib/python3.8/dist-packages/boto/s3/user.py 23 18 22% 24-28, 31, 34-39, 42-49 /usr/local/lib/python3.8/dist-packages/boto/s3/website.py 122 85 30% 24-26, 57-63, 66-72, 75, 78-89, 94-98, 101, 104-106, 109-114, 131-133, 136, 152-153, 156-159, 162, 165, 168-171, 191-192, 195-198, 201, 204-209, 213, 218-223, 245-247, 250, 283-288, 291 /usr/local/lib/python3.8/dist-packages/boto/storage_uri.py 488 377 23% 57, 62, 66, 69-70, 77-78, 82-83, 87-89, 103-149, 152, 158-160, 165-172, 176-177, 180-184, 187-191, 194-196, 199-203, 209-218, 224-227, 231-234, 237-240, 288-299, 302-318, 321, 328-332, 335-344, 348-355, 365-366, 379-390, 402-413, 417-421, 425-429, 433-438, 441-443, 446-453, 457-464, 468-470, 475-491, 496-504, 508-515, 518-520, 524, 528, 536, 540, 544, 548, 552, 556, 560, 564, 568-576, 579-581, 584-585, 588-591, 596-605, 610-619, 624-625, 630-631, 636-640, 646-649, 653-655, 661-675, 680-696, 700-706, 713-724, 733-735, 738-740, 743-745, 749-754, 757-759, 762-764, 767-769, 773, 779-787, 791-795, 800-802, 805-811, 816-822, 826-833, 838-840, 845-849, 875-880, 889, 893, 897, 901, 905, 909-911, 915, 919, 923, 927, 932, 937, 944 /usr/local/lib/python3.8/dist-packages/boto/utils.py 575 467 19% 108-111, 119-169, 173-183, 187-202, 212-237, 241, 246-266, 269-270, 273-337, 340-343, 346-347, 350-351, 354-355, 358-359, 383, 399-405, 413-427, 432-441, 454-460, 464-466, 470-481, 485-498, 505-508, 518-543, 549-554, 557-576, 579, 582, 588, 616-619, 629-646, 689-691, 694, 697-700, 703, 706-709, 712, 715-717, 720-728, 731, 734-741, 744-750, 753-765, 780-782, 785-787, 790, 793-797, 800-803, 808-859, 863-872, 876-881, 897-899, 920-944, 959-972, 1000, 1004-1029, 1038, 1048-1049, 1060, 1070-1083, 1093-1098 /usr/local/lib/python3.8/dist-packages/boto/vendored/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/exceptions.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/regions.py 81 60 26% 58, 65, 85, 94-96, 99-102, 106-116, 120-124, 128-152, 156-160, 163-177, 180-182, 186 /usr/local/lib/python3.8/dist-packages/boto/vendored/six.py 444 208 53% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /usr/local/lib/python3.8/dist-packages/cachetools/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/cachetools/abc.py 25 14 44% 21-24, 29-36, 39-43 /usr/local/lib/python3.8/dist-packages/cachetools/cache.py 60 38 37% 6, 9, 12, 21-27, 30, 38-41, 44-57, 60-62, 65, 68, 71, 74, 79, 84, 89 /usr/local/lib/python3.8/dist-packages/cachetools/decorators.py 73 69 5% 11-44, 52-88 /usr/local/lib/python3.8/dist-packages/cachetools/keys.py 24 14 42% 17-20, 23, 26, 29, 40-43, 49-52 /usr/local/lib/python3.8/dist-packages/cachetools/lfu.py 22 14 36% 10-11, 14-16, 19-20, 23-24, 28-33 /usr/local/lib/python3.8/dist-packages/cachetools/lru.py 27 18 33% 10-11, 14-16, 19-20, 23-24, 28-33, 36-39 /usr/local/lib/python3.8/dist-packages/cachetools/rr.py 19 11 42% 8, 15-20, 25, 29-34 /usr/local/lib/python3.8/dist-packages/cachetools/ttl.py 156 118 24% 12-13, 16, 19-22, 28-29, 32-35, 38-43, 46, 49, 52, 59-64, 67-72, 75-84, 87-99, 102-106, 109-116, 119-126, 129-136, 139-141, 145-147, 152, 157, 161-172, 175-177, 180-181, 184-185, 188-189, 196-203, 206-208 /usr/local/lib/python3.8/dist-packages/cycler.py 177 107 40% 73, 110, 118, 122, 127, 131, 157-178, 185-189, 218-223, 229, 240-243, 255-262, 265, 268-273, 284-292, 303-311, 317-322, 325-333, 337-347, 396-397, 425, 454-465, 509, 513-516, 519-526, 548-556 /usr/local/lib/python3.8/dist-packages/cython.py 10 6 40% 9-17 /usr/local/lib/python3.8/dist-packages/decorator.py 270 120 56% 49-60, 64-67, 70-73, 100, 114-115, 117, 119-120, 133, 139, 143, 155-156, 168, 174, 186-189, 217, 233-235, 241, 246, 271, 279, 282-283, 291, 299-300, 306, 314-316, 318, 321, 329, 339-348, 357-454 /usr/local/lib/python3.8/dist-packages/defusedxml/ElementTree.py 64 25 61% 21-23, 52, 79-105, 108, 113, 120 /usr/local/lib/python3.8/dist-packages/defusedxml/__init__.py 21 15 29% 25-51 /usr/local/lib/python3.8/dist-packages/defusedxml/common.py 65 42 35% 23, 31-34, 37-38, 46-52, 55-56, 64-68, 71-72, 81-90, 98-105, 115-122, 125-132 /usr/local/lib/python3.8/dist-packages/gast/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/gast/ast3.py 192 145 24% 10-160, 174, 189-194, 200-207, 214-232, 239, 260-264, 270-306, 310-376, 391 /usr/local/lib/python3.8/dist-packages/gast/astn.py 24 1 96% 21 /usr/local/lib/python3.8/dist-packages/gast/gast.py 78 46 41% 9-11, 292, 302-304, 308-316, 327-331, 342-366, 375-380 /usr/local/lib/python3.8/dist-packages/h5py/__init__.py 57 23 60% 27-32, 37, 89-90, 97-113 /usr/local/lib/python3.8/dist-packages/h5py/_hl/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/h5py/_hl/attrs.py 133 92 31% 63, 76-78, 90, 100, 105, 122-221, 232-247, 253, 257-275, 284-286 /usr/local/lib/python3.8/dist-packages/h5py/_hl/base.py 216 108 50% 23-24, 40-45, 53-63, 88, 112, 126-128, 131, 134, 139-141, 144, 155-162, 185, 188-193, 197-199, 204-207, 219-221, 227, 237-239, 251, 265, 281, 285-287, 291, 294-295, 308, 312, 326, 340-344, 347-349, 359-363, 366-368, 381-404, 409, 413, 417, 421, 447, 450-452, 455 /usr/local/lib/python3.8/dist-packages/h5py/_hl/compat.py 70 44 37% 12-41, 51, 56-57, 67-72, 80-85, 94-95, 99-100, 113-115, 127-134 /usr/local/lib/python3.8/dist-packages/h5py/_hl/dataset.py 474 340 28% 47-54, 68-172, 183-205, 216-217, 221, 225, 229-244, 260, 263-267, 272-274, 280, 291, 297-299, 311-313, 319-322, 328-331, 337, 343, 349, 358-361, 369-376, 383-385, 391-393, 400, 423-438, 447-450, 458-462, 470-474, 488-582, 592-708, 720, 724, 730, 743-758, 770, 777-790, 800, 811, 817, 821-824, 841 /usr/local/lib/python3.8/dist-packages/h5py/_hl/datatype.py 24 11 54% 37, 43-45, 49-56 /usr/local/lib/python3.8/dist-packages/h5py/_hl/files.py 238 113 53% 46-48, 52, 78, 89, 95, 103-107, 115, 117, 119, 125-127, 130-137, 143-146, 158-167, 172, 174-206, 210-215, 239, 245-253, 259, 266-268, 274-275, 281-282, 287-298, 304, 310-314, 379, 382-383, 386-393, 400-402, 411, 418, 438-439, 451-452, 465-478 /usr/local/lib/python3.8/dist-packages/h5py/_hl/filters.py 175 141 19% 78-84, 88-101, 113-244, 263-283, 302-342 /usr/local/lib/python3.8/dist-packages/h5py/_hl/group.py 234 168 28% 41, 62-69, 132-140, 158-174, 189-206, 223-238, 247-253, 260-262, 271-274, 301-342, 368-393, 399, 404, 409-410, 415, 455-494, 504-507, 530-534, 560-565, 569-579, 603, 606, 609, 622, 627, 630-631, 634 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections.py 295 221 25% 58-95, 111, 117-118, 150-151, 171, 176, 180-182, 185, 197-207, 211-218, 222, 226, 230, 252-269, 279-283, 293, 299, 304, 315-320, 337, 340-341, 345-414, 417-419, 424-442, 453-475, 482-488, 495-508, 519-598 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections2.py 43 33 23% 26-45, 57-74, 83-91, 94-95, 102-105 /usr/local/lib/python3.8/dist-packages/h5py/_hl/vds.py 52 33 37% 56-90, 94, 97-99, 120-123, 126-127 /usr/local/lib/python3.8/dist-packages/h5py/h5py_warnings.py 19 6 68% 16-17, 34-36, 39 /usr/local/lib/python3.8/dist-packages/h5py/version.py 21 3 86% 32, 34, 36 /usr/local/lib/python3.8/dist-packages/imagecodecs/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/imagecodecs/imagecodecs.py 300 226 25% 330, 350-351, 356, 371, 375-383, 387, 390, 395, 401-402, 408-479, 484, 489-510, 515-603, 608-641, 646-666, 675, 684, 689, 698, 703-706, 711-722, 727-734, 745-788, 796-802, 814 /usr/local/lib/python3.8/dist-packages/imageio/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/fetching.py 105 88 16% 62-114, 150-184, 210-225, 230-232, 237-247 /usr/local/lib/python3.8/dist-packages/imageio/core/findlib.py 74 65 12% 23-29, 37-81, 99-160 /usr/local/lib/python3.8/dist-packages/imageio/core/format.py 276 178 36% 94, 103, 108, 111, 115, 118, 126, 142, 149, 155, 164-170, 179-185, 192, 199, 216-221, 228, 235, 238-239, 242-244, 247-248, 256-261, 267, 272-275, 289, 300, 331, 343-349, 359, 367-370, 386-392, 401-414, 419, 422-425, 437, 447, 458, 484-502, 517-521, 527, 531, 551, 557, 560-565, 569-609, 633, 635, 655, 657, 659-665, 678-697, 705-725, 735 /usr/local/lib/python3.8/dist-packages/imageio/core/functions.py 157 124 21% 100-121, 139-142, 172-186, 213-231, 259-267, 291-308, 354-374, 397-427, 453-455, 479-496, 542-561, 586-615 /usr/local/lib/python3.8/dist-packages/imageio/core/request.py 319 274 14% 19-20, 89-128, 133-262, 271, 279, 289, 295, 310-350, 359-372, 381-421, 427-428, 435-437, 440-467, 476-482, 492-496, 501-530, 533, 539-561, 564-565, 568, 571 /usr/local/lib/python3.8/dist-packages/imageio/core/util.py 263 210 20% 30-34, 38-42, 55-108, 122-134, 139-143, 149, 155-158, 164-169, 180-185, 205-211, 214-225, 228-230, 252-257, 265-273, 281, 290-309, 316, 324-328, 335-339, 346-350, 355, 358, 361, 364, 376-383, 387-395, 398-400, 404-408, 423-466, 479-491, 506-520, 531-542, 548-555 /usr/local/lib/python3.8/dist-packages/imageio/plugins/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/imageio/plugins/_freeimage.py 603 405 33% 63-67, 74-87, 422, 429-432, 439-447, 450-454, 461-480, 485-514, 518-521, 526-527, 530-531, 537, 544-549, 555-557, 564, 573-603, 609, 615, 620-625, 628, 631-640, 645-652, 660-723, 728-797, 807-839, 842-856, 876-895, 941-975, 980-1031, 1041-1083, 1089-1110, 1113-1163, 1170-1187, 1220-1245, 1271-1295, 1298-1299, 1305-1321, 1326-1328 /usr/local/lib/python3.8/dist-packages/imageio/plugins/bsdf.py 140 113 19% 14-52, 62-63, 66-73, 76, 126-130, 133-135, 143-180, 185, 188-194, 198-231, 240-249, 256-257, 261-289 /usr/local/lib/python3.8/dist-packages/imageio/plugins/dicom.py 147 122 17% 31-33, 41-56, 89-100, 105, 111-155, 159-161, 165-168, 171-199, 202-231, 239-266 /usr/local/lib/python3.8/dist-packages/imageio/plugins/example.py 43 23 47% 54-56, 67-69, 82-84, 89, 93, 97-106, 111, 124, 129, 133, 138 /usr/local/lib/python3.8/dist-packages/imageio/plugins/feisem.py 41 32 22% 27, 38-41, 55-84 /usr/local/lib/python3.8/dist-packages/imageio/plugins/ffmpeg.py 304 258 15% 27, 30-31, 57-66, 178-189, 192-194, 204-239, 257-327, 339-344, 357-358, 361, 370-386, 389, 394-468, 472-474, 478-502, 523-527, 530-532, 537-572, 575, 582-617, 629-636, 639-641, 646-649, 653-661, 667-698 /usr/local/lib/python3.8/dist-packages/imageio/plugins/fits.py 40 24 40% 15-23, 80, 84, 90-102, 105, 108, 112-116, 120 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimage.py 177 122 31% 48, 52-59, 63-70, 76, 79-80, 83, 86-88, 91-93, 99-102, 106-109, 113-132, 135, 168-174, 177-178, 215-219, 225-240, 243-259, 303-309, 312-314, 323-340, 349-362, 365-368, 393-397, 485-510 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimagemulti.py 144 104 28% 27-32, 35, 38, 41-45, 48-55, 62-67, 71, 75-86, 90-94, 97, 137-140, 197-200, 203-205, 225-254, 263-299, 308-322 /usr/local/lib/python3.8/dist-packages/imageio/plugins/gdal.py 35 19 46% 15-23, 40-43, 46, 52-54, 57, 60, 63-65, 68 /usr/local/lib/python3.8/dist-packages/imageio/plugins/grab.py 63 37 41% 25, 28-40, 44, 47, 50, 66-70, 73-79, 95-99, 102-111 /usr/local/lib/python3.8/dist-packages/imageio/plugins/lytro.py 304 225 26% 63, 69, 74, 78, 83, 101-103, 109-136, 142-143, 148, 152, 157-171, 177-195, 212-214, 220-280, 285, 289, 295-302, 308-312, 319-325, 348-371, 375-383, 388-391, 410-412, 418-435, 441-442, 447, 451, 456-470, 476-494, 511-513, 519-559, 564, 568, 574-582, 588-592, 599-607, 630-653, 657-665, 670-673 /usr/local/lib/python3.8/dist-packages/imageio/plugins/npz.py 37 17 54% 40, 44, 51-55, 58, 61, 65-69, 73, 81, 85, 88, 91 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillow.py 365 285 22% 76, 79-99, 102-108, 111-115, 119-146, 149-150, 153-155, 159, 162-165, 168-181, 184-186, 190-197, 200, 203-218, 221, 298, 301-315, 323-357, 360-364, 429, 433-440, 443-455, 461-478, 486-496, 499-503, 564, 568-575, 578-590, 596-613, 621-639, 642-648, 653-655, 667-680, 684-695, 705-792, 797-835 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillow_info.py 5 1 80% 100 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillowmulti.py 164 134 18% 60, 74-101, 106, 109-118, 138-151, 156-177, 181-186, 189, 193-223, 228-232, 245-262, 268-279, 297-305, 317-337, 347-364 /usr/local/lib/python3.8/dist-packages/imageio/plugins/simpleitk.py 62 38 39% 15-37, 95-98, 101-104, 110-112, 115, 118, 122-127, 130-131, 136-137, 140, 143-144, 147-148 /usr/local/lib/python3.8/dist-packages/imageio/plugins/spe.py 126 96 24% 255, 260, 264-295, 298-303, 307, 310-370, 373-399, 402-405, 408-411, 414-438, 456-465 /usr/local/lib/python3.8/dist-packages/imageio/plugins/swf.py 179 147 18% 25-27, 68-71, 74-76, 82-138, 141, 144, 147-150, 154-179, 188-215, 218, 224-239, 242-267, 270-288, 292-298, 302-320, 323 /usr/local/lib/python3.8/dist-packages/imageio/plugins/tifffile.py 91 65 29% 19-23, 208, 212, 218-229, 232-234, 237-240, 243-256, 259-276, 281-297, 300, 303-311, 314-322 /usr/local/lib/python3.8/dist-packages/ipykernel/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipykernel/_version.py 11 5 55% 7-11, 13 /usr/local/lib/python3.8/dist-packages/ipykernel/comm/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/ipykernel/comm/comm.py 86 52 40% 21-22, 28, 40, 51-59, 63-66, 76, 82-99, 103-118, 122, 135, 144, 150-152, 156-163 /usr/local/lib/python3.8/dist-packages/ipykernel/comm/manager.py 72 52 28% 37-40, 44, 48-51, 56, 66-72, 77-99, 104-113, 117-129 /usr/local/lib/python3.8/dist-packages/ipykernel/connect.py 58 41 29% 31-37, 58-81, 91-103, 128-137, 163-178 /usr/local/lib/python3.8/dist-packages/ipykernel/jsonutil.py 81 59 27% 73-106, 133-197 /usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py 441 316 28% 20-22, 61-63, 80, 163-171, 176-209, 216-223, 228-282, 287, 291, 295-331, 343-349, 358-365, 377-383, 393, 397-408, 412-441, 450, 459, 466, 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49-60, 74-87, 101-113, 125, 134-135, 140-146, 155-158, 215-217, 238-243 /usr/local/lib/python3.8/dist-packages/ipywidgets/__init__.py 21 7 67% 31-33, 38-40, 50 /usr/local/lib/python3.8/dist-packages/ipywidgets/_version.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/docutils.py 6 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/domwidget.py 27 14 48% 26-28, 36-38, 41-50 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/interaction.py 289 226 22% 11-12, 17-18, 33-34, 51-62, 73-85, 90-93, 99-128, 132-152, 177-232, 245-268, 272, 278-290, 295-307, 312-343, 348-359, 364-380, 388-393, 444, 507-538, 555-556, 570, 576 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/trait_types.py 78 33 58% 45-52, 84-87, 100-103, 125-128, 137-140, 162-165, 168, 195-206, 220 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/util.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/valuewidget.py 12 5 58% 20, 24-27 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget.py 403 283 30% 13-15, 32-39, 42-49, 59, 65-71, 80-116, 129-131, 141-162, 173-174, 183-195, 208-211, 216-223, 243-256, 259-265, 281-283, 302-303, 312, 317-318, 323-339, 349-354, 357-369, 372, 398, 411-415, 419, 427-438, 443-448, 455, 467-471, 481-489, 506-523, 526, 529-533, 540-545, 557, 571, 586, 590-594, 600-606, 609, 624-628, 633-642, 646-662, 668-689, 693, 697, 702, 707, 712-732, 736-737, 740-755, 758-763 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_bool.py 31 3 90% 22-24 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_box.py 37 5 86% 63-65, 68-69 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_button.py 41 12 71% 61-63, 68-73, 86, 94, 104-105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_color.py 14 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_controller.py 29 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_core.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_date.py 13 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_description.py 23 6 74% 28-34 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_float.py 168 62 63% 24-26, 36-39, 44-49, 54-59, 70-73, 78-83, 88-93, 262, 266, 270, 274, 278-281, 290-294, 298-308, 312-315 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_int.py 185 67 64% 42-44, 53-61, 73-75, 85-93, 98-101, 106-111, 116-121, 202, 206, 210, 214, 218-221, 243-247, 251-261, 265-268 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_layout.py 61 7 89% 82-85, 93-96 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_link.py 36 15 58% 24-34, 50-52, 56, 75, 105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_media.py 102 46 55% 44-51, 71-75, 86-88, 92-96, 101-111, 116-133, 159, 163, 166, 194, 197, 223, 226 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_output.py 64 33 48% 76-77, 97-105, 109-113, 117-127, 131-132, 136, 142, 146, 157-159 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selection.py 295 148 50% 11-12, 119-132, 136-139, 175-191, 196-200, 205-218, 222-225, 230-235, 239-243, 247-254, 258-260, 264-271, 274-280, 316-327, 331-335, 340-344, 349-352, 357-363, 368-371, 375-377, 381-383, 387-389, 392-395, 529-531, 535-540, 544-547, 553-555, 559-565, 615-619, 624-629 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selectioncontainer.py 36 14 61% 27-30, 44-46, 57-61, 65-68 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_string.py 68 15 78% 30-32, 79-81, 91-92, 106-108, 120-123 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_style.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_templates.py 198 138 30% 80-85, 89-91, 95-96, 100-106, 157-158, 162-167, 172, 176-242, 247, 281-289, 293-295, 300-312, 315-330, 333-345, 349-355, 397-398, 403-450, 454 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_upload.py 39 11 72% 24-26, 58-64, 68 /usr/local/lib/python3.8/dist-packages/jedi/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jedi/_compatibility.py 339 252 26% 18-19, 34-45, 52-53, 56, 59, 63-88, 92-111, 115-150, 158-162, 169-205, 223-224, 230-233, 246-248, 266, 273-275, 285, 311-312, 319, 324-325, 329-330, 334-335, 339-340, 350-352, 360-370, 376, 387-396, 404-410, 414-426, 432-440, 445-451, 456-466, 483-530, 536-582, 591-624 /usr/local/lib/python3.8/dist-packages/jedi/api/__init__.py 341 241 29% 58-62, 127-203, 209-248, 251, 254, 275, 278-283, 286-291, 310-311, 314-319, 322-342, 345-350, 370-371, 375-408, 422, 425, 429-431, 454, 471-480, 483-488, 502-514, 517-522, 541-558, 569-600, 603-634, 648-649, 657, 661-671, 683, 686-687, 715, 719-727, 765, 769-777, 800-801, 830-845, 849-855, 863-869, 884-886, 898-901 /usr/local/lib/python3.8/dist-packages/jedi/api/classes.py 297 193 35% 37, 47-49, 53, 81-86, 93, 98-104, 115, 175-187, 202, 208-211, 216-219, 224-227, 260-270, 273, 276, 311-329, 355-368, 374-377, 384-387, 402-403, 406-411, 416-427, 447-448, 451-466, 472-489, 497-522, 525, 542-552, 555-561, 570, 582, 594, 604-613, 616-624, 647-649, 664, 670-675, 678-684, 687-693, 696-697, 709-716, 719, 728, 732-738, 747-748, 758-761, 764, 770, 773, 782-783, 793, 803, 812-814, 824, 836, 839, 853, 861, 870, 879-883 /usr/local/lib/python3.8/dist-packages/jedi/api/completion.py 348 296 15% 30, 35-40, 44-68, 72-73, 80-81, 85-89, 96, 102-114, 117-151, 174-282, 285-295, 298-317, 320-323, 326-337, 340-355, 358-360, 363-365, 371-389, 401-421, 424-431, 442-448, 455-499, 503-516, 539-577, 583-619 /usr/local/lib/python3.8/dist-packages/jedi/api/completion_cache.py 19 11 42% 5-9, 14-19 /usr/local/lib/python3.8/dist-packages/jedi/api/environment.py 219 166 24% 36-37, 41-45, 49, 65-67, 70-107, 110-111, 114, 129, 134-136, 145, 148, 157-169, 173-177, 190-198, 202-238, 242-252, 257-264, 284-321, 334-338, 351-363, 374-377, 385-393, 398-420, 424-425, 432-454, 458-473, 477-480 /usr/local/lib/python3.8/dist-packages/jedi/api/errors.py 19 7 63% 8, 16, 21, 26, 31, 36, 39 /usr/local/lib/python3.8/dist-packages/jedi/api/exceptions.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/api/file_name.py 115 103 10% 17-54, 64-81, 85-99, 103-156 /usr/local/lib/python3.8/dist-packages/jedi/api/helpers.py 319 272 15% 26, 30-35, 39-42, 47, 51-61, 66-71, 77, 83-117, 124-156, 163-179, 183-201, 206-209, 213, 217, 220-268, 272-336, 343-357, 361-371, 375-421, 427-439, 449-465, 474-493, 497-500 /usr/local/lib/python3.8/dist-packages/jedi/api/interpreter.py 23 12 48% 12, 19, 24-25, 28, 34-41 /usr/local/lib/python3.8/dist-packages/jedi/api/keywords.py 34 25 26% 8-15, 22, 30-57 /usr/local/lib/python3.8/dist-packages/jedi/api/project.py 214 164 23% 40-51, 56-61, 65, 78, 82, 92-98, 106-118, 139-151, 156-161, 169-205, 208-213, 236, 248, 251, 256-343, 346, 350-353, 358-362, 375-411, 415 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/__init__.py 133 110 17% 19-23, 26-36, 39, 42-48, 51, 56-58, 64-73, 89, 92-98, 104-108, 112-118, 122-138, 142-218, 225 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/extract.py 239 210 12% 20-29, 36-41, 49-93, 100-126, 130, 134-138, 147-149, 153, 160-164, 172-200, 204, 209-292, 296-306, 310-316, 320-337, 341-353, 357-363, 371-379, 383-386 /usr/local/lib/python3.8/dist-packages/jedi/api/strings.py 64 47 27% 27-50, 54-58, 68-77, 81-86, 90-93, 97-98, 102-109 /usr/local/lib/python3.8/dist-packages/jedi/cache.py 65 40 38% 32-43, 60-73, 84-93, 105-113 /usr/local/lib/python3.8/dist-packages/jedi/common/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jedi/common/utils.py 24 18 25% 6-13, 21-26, 31-36 /usr/local/lib/python3.8/dist-packages/jedi/common/value.py 54 31 43% 3-4, 7-11, 48, 55, 59-61, 68-74, 77, 80, 83-84, 87, 90, 93, 96, 99-104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/jedi/debug.py 80 50 38% 22, 36-56, 74-75, 81-82, 89-97, 103-109, 113-120, 124-127, 136-140 /usr/local/lib/python3.8/dist-packages/jedi/file_io.py 54 30 44% 8, 11, 14, 17, 20, 23, 28, 31, 34, 37, 40-57, 62, 68-69, 72-75 /usr/local/lib/python3.8/dist-packages/jedi/inference/__init__.py 107 76 29% 86-108, 111, 117-121, 126-130, 135-136, 139-140, 144, 147-179, 183-194, 197 /usr/local/lib/python3.8/dist-packages/jedi/inference/analysis.py 126 101 20% 32-37, 41, 45, 50-51, 54, 58, 61, 65, 68, 71, 81-91, 98-109, 116-127, 138-217 /usr/local/lib/python3.8/dist-packages/jedi/inference/arguments.py 228 156 32% 19-31, 53-68, 76-108, 135, 138, 148-172, 180-183, 188, 191-231, 234-239, 242-247, 250, 253-279, 284, 287-288, 291, 296, 300, 304, 308, 311, 314, 317, 321-333, 337-349 /usr/local/lib/python3.8/dist-packages/jedi/inference/base_value.py 285 179 37% 27-34, 39, 42, 45-47, 50, 53, 56, 62-70, 73-77, 84-98, 101-104, 107-120, 123-126, 130-132, 136, 152, 155-163, 166, 169-176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 210, 213-218, 221-223, 226-227, 230-231, 234-235, 241, 244-245, 249, 253, 256, 260, 263, 266, 274, 283-289, 294, 297-298, 305-306, 309, 312, 317, 320, 325-326, 329, 334-335, 338, 341, 344, 349-371, 376, 379-382, 387, 390, 393, 396, 399, 402-410, 413, 416, 419-436, 440-448, 456 /usr/local/lib/python3.8/dist-packages/jedi/inference/cache.py 72 44 39% 25-45, 80, 90-121 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/__init__.py 38 26 32% 8-16, 25-26, 29-32, 35-37, 40, 48-53, 57, 63-68 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/access.py 327 243 26% 82-98, 109-112, 117, 121-140, 145, 151, 154, 158-159, 167-175, 180-181, 184, 187, 190, 193, 196-199, 202, 205-219, 222, 225-227, 230-234, 237-250, 253, 256, 259-265, 270-285, 288, 291, 294, 297, 300, 303-313, 316, 319-323, 327-351, 354-396, 399-401, 404, 407-409, 412-421, 424-425, 428-456, 459-461, 467-482, 485, 488-490, 493, 507-516, 519-538, 541, 548-552, 557-564 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/getattr_static.py 97 81 16% 16-21, 25-31, 35-39, 43-54, 58-65, 73-127, 131, 135, 152-184 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/mixed.py 155 108 30% 47-49, 52, 58, 63-66, 69-72, 75-78, 81-83, 86, 96, 108-109, 113-117, 121-129, 134-135, 138, 146, 156-175, 179-249, 256-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/subprocess/__init__.py 236 176 25% 20-21, 37-38, 45-50, 54, 58-70, 75-77, 80-87, 90, 93, 103, 108-110, 113-129, 132-147, 150-151, 160-162, 165-166, 176-206, 211-219, 222, 225-226, 229-273, 283, 288-292, 295-308, 311-327, 330-357, 362-364, 367, 370-374, 377, 380, 383-388, 396-398, 402 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/subprocess/functions.py 66 45 32% 15, 19, 23-24, 28, 35-43, 47, 54, 61-66, 74-78, 82, 86, 91-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/value.py 394 269 32% 36-41, 46-47, 50-69, 73, 77, 84, 90, 93, 96, 99, 102, 105, 108, 111, 114, 118-133, 136-137, 140, 144-148, 152, 155, 159, 162-170, 173-178, 188-198, 201, 205-208, 211-228, 231-236, 239-245, 248-267, 270, 273, 276, 280, 283, 289-291, 298, 301-304, 307, 312-315, 318, 323-326, 329-330, 333-336, 339-343, 346-350, 354-358, 362, 365, 370-371, 375, 378-383, 386, 389-397, 402-404, 407, 410-413, 416, 421-423, 433-434, 437, 442-444, 447-448, 460-478, 482-485, 488-507, 510, 517, 537-587, 591-600, 606, 611-618, 624-629 /usr/local/lib/python3.8/dist-packages/jedi/inference/context.py 294 199 32% 20-21, 25, 28-34, 41-86, 89-106, 109-112, 115, 118, 121, 124, 127, 130, 133, 137, 140, 144, 147, 150, 154-159, 167-168, 172, 176, 179, 182, 185, 188, 191, 194, 197, 200, 204, 207, 210, 213, 216, 221-222, 225-248, 251-287, 290-297, 302, 312, 315-327, 330, 334, 338, 346, 351, 354, 358, 361, 366, 369, 378-380, 383, 386, 389, 392, 397, 404, 408, 411, 418-432, 483-500 /usr/local/lib/python3.8/dist-packages/jedi/inference/docstrings.py 144 116 19% 52-56, 61-75, 82-100, 108-133, 154-161, 179-183, 187-235, 244-245, 256-268, 273-290, 296-307 /usr/local/lib/python3.8/dist-packages/jedi/inference/filters.py 215 115 47% 26-28, 32, 36, 43, 46, 49, 52, 56-68, 75-78, 81, 86, 89, 98, 110-115, 118-120, 123-127, 130-141, 146-151, 154, 158-166, 171-172, 175, 180, 185-189, 193-195, 198, 206, 209-214, 217-223, 226, 229-230, 235, 238, 241, 244, 252-254, 258, 270-278, 281-291, 296-299, 307, 331-334, 340 /usr/local/lib/python3.8/dist-packages/jedi/inference/flow_analysis.py 84 65 23% 15-20, 23-26, 29, 39-42, 46-83, 87-110, 114-123 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/annotation.py 228 193 15% 34-45, 49-59, 63-76, 88-107, 112-132, 139-182, 186-195, 204-230, 247-272, 276-282, 295-304, 308-313, 351-370, 374, 378-381, 385, 389-408, 414-425, 429-433, 437-443 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/base.py 217 149 31% 18-20, 23-32, 35, 38, 53-54, 57-63, 68, 73-81, 86, 89, 93, 96-117, 122-137, 147, 162-163, 166, 169-178, 181, 184-185, 188, 192-193, 196, 199-201, 205-242, 247-248, 252-261, 264-279, 284-293, 296, 310-314, 318, 322-332, 338, 342, 345, 348, 353-355, 359, 362, 365, 370-373, 376, 379 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/conversion.py 138 122 12% 11-47, 51-59, 64-92, 96-98, 109-142, 146-153, 158-167, 175-208 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/generics.py 67 38 43% 15-23, 28-32, 35, 40-41, 45, 48, 53-63, 70-71, 74-78, 81, 86, 89, 92, 95, 98, 101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/stub_value.py 69 43 38% 13-14, 17, 25-34, 37, 43-50, 53, 60, 65-70, 73, 78-81, 88-101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/type_var.py 84 65 23% 9-18, 27-48, 53-71, 74, 77, 80-85, 89, 93, 98-105, 108, 111-114, 117 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typeshed.py 158 130 18% 23-26, 33-53, 57-69, 81-89, 95-124, 130-143, 154-229, 233-249, 258-264, 271, 281-295 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typing.py 236 154 35% 40, 43-96, 105-130, 137, 140, 151, 159, 174, 179, 183, 189-228, 237-240, 244, 247, 250, 253-266, 269, 278-286, 293, 296-303, 306-310, 313-316, 321-323, 327-355, 368-369, 376, 381-386, 397-399, 402, 406-407, 413, 426-429, 433, 436-442, 445-449, 455-457 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/utils.py 17 14 18% 11-30 /usr/local/lib/python3.8/dist-packages/jedi/inference/helpers.py 122 97 20% 17-21, 29-43, 65-109, 113-121, 125-129, 133, 137-139, 143, 147, 151, 160-163, 167-192, 196, 200-207 /usr/local/lib/python3.8/dist-packages/jedi/inference/imports.py 284 237 17% 39, 42-43, 46, 53-70, 75-96, 100-118, 122-125, 134-152, 169-224, 229, 235-238, 247-260, 269-273, 284-326, 331-356, 365-427, 432-440, 450-464, 473-510, 514-519, 523-548, 558-563 /usr/local/lib/python3.8/dist-packages/jedi/inference/lazy_value.py 37 18 51% 7-9, 12, 15, 21, 27, 32, 35, 40-44, 47-48, 52-55, 61 /usr/local/lib/python3.8/dist-packages/jedi/inference/names.py 450 314 30% 16-23, 38, 44, 47-54, 58, 61, 64, 67-69, 73, 76, 80, 87, 99-101, 104, 109-110, 113-126, 129-132, 135-139, 142-211, 214-215, 219, 223, 228, 231-237, 240, 243-245, 248-251, 255, 260-261, 264, 278-279, 287-290, 307-331, 334-346, 351-354, 357-360, 363-368, 371, 378, 381, 392, 396-401, 409-416, 419-424, 427, 432-434, 437, 441, 444-450, 453-456, 460, 463-488, 491-496, 502, 506-525, 530-531, 534-538, 541-543, 548, 551, 554, 562-563, 566-574, 578-584, 588-590, 593, 597, 600, 609, 612, 615, 620-634, 640-644, 651-652, 656 /usr/local/lib/python3.8/dist-packages/jedi/inference/param.py 130 113 13% 14-18, 23-26, 29, 32-47, 50, 73-225, 246, 250-257 /usr/local/lib/python3.8/dist-packages/jedi/inference/parser_cache.py 4 1 75% 6 /usr/local/lib/python3.8/dist-packages/jedi/inference/recursion.py 67 47 30% 55, 64-75, 81-90, 100-105, 108-109, 112-153 /usr/local/lib/python3.8/dist-packages/jedi/inference/references.py 182 157 14% 29-42, 46, 53-69, 73-77, 81-96, 100-113, 117-160, 164-175, 179-193, 197-218, 222-224, 228-245, 257-270, 274-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/signature.py 108 72 33% 9-34, 39-40, 44, 48, 51-54, 57, 60, 63-65, 70-71, 74, 80-82, 86-89, 93-97, 100-117, 122-124, 128, 132-134, 137, 146, 149 /usr/local/lib/python3.8/dist-packages/jedi/inference/syntax_tree.py 545 485 11% 45-63, 69-137, 144-150, 155, 161-237, 241-261, 270-352, 357-366, 385-444, 448-469, 477-487, 493-502, 506-518, 526-538, 542, 546, 550, 554-567, 571-642, 647-730, 741-782, 789-809, 814, 821-846 /usr/local/lib/python3.8/dist-packages/jedi/inference/sys_path.py 148 125 16% 18-29, 43-72, 79-97, 105-135, 139-147, 151-170, 174-177, 189-207, 211-215, 230-271 /usr/local/lib/python3.8/dist-packages/jedi/inference/utils.py 55 23 58% 13, 20, 26, 32, 74-78, 84-86, 89, 92, 96, 99-103, 112-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/value/decorator.py 7 3 57% 11-12, 15 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/dynamic_arrays.py 114 87 24% 36-40, 52-123, 128-130, 144-145, 148-149, 152-165, 168, 173-175, 178, 181-187, 192-194, 197, 202-204 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/function.py 305 223 27% 32-33, 37, 40, 45-54, 61-64, 67-71, 74, 79-81, 84, 87, 90-114, 117-118, 121-123, 126, 132-161, 164-165, 168, 171, 176-177, 180, 185-186, 189, 194-197, 201, 206, 211-248, 251-263, 268-312, 315, 321, 327-360, 365-366, 369, 377-378, 381, 391, 394, 401, 406-407, 410-421, 424, 427, 431-470 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/instance.py 353 227 36% 28-30, 33, 36, 41-42, 45-58, 63-64, 67, 74-81, 86-87, 94-97, 100, 103, 106, 109, 113, 117, 120-121, 125, 128, 134, 137, 144-146, 149-155, 159, 162, 168-172, 176, 179-199, 203-215, 223-239, 242-250, 253-275, 278-283, 291-297, 303-307, 314-322, 328-349, 352, 355-370, 373-393, 396, 406-414, 418-422, 427-428, 431, 434, 437-438, 446-448, 451, 455, 461-462, 465-466, 469-473, 476-480, 483, 489, 492, 497, 500, 508-510, 514, 517, 522-523, 527-529, 532, 535, 545-546, 549, 552, 555, 561, 569-575, 578-580, 583-592, 595-597, 605, 608, 613-614, 617-619 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/iterable.py 404 270 33% 27, 37-40, 49-52, 55, 58, 62, 68, 71, 75, 81-82, 85, 88, 91, 95-122, 133, 136-163, 168-170, 173-174, 177, 182, 190, 193, 196-203, 206, 210, 213-215, 220-224, 231-237, 251, 255, 262-267, 270-271, 274-280, 283, 286, 290-291, 295-306, 311, 321-329, 332-334, 338-343, 350-358, 362, 365-407, 414-417, 420, 427-429, 433-439, 447-453, 457-458, 462-470, 473, 479, 490-491, 494-499, 502, 505, 508, 523-524, 527-528, 531-548, 552, 558, 561, 564, 567, 572-574, 577-579, 582, 589-620, 625-630, 633-635, 642-658 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/klass.py 204 151 26% 59-61, 66-75, 81-87, 90, 100-105, 112-126, 130-131, 136, 139-144, 147, 151, 154, 158-187, 190-221, 227-229, 232, 235-237, 243-267, 275-289, 292-296, 300-309, 314-317, 329-330, 336-356, 360-361, 365-379 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/module.py 129 85 34% 22-24, 27-35, 45-56, 63-73, 76-77, 80, 83, 88, 92-98, 101-104, 111-128, 136, 144-156, 159-164, 167-169, 175-178, 181, 184-186, 194-219, 222, 225 /usr/local/lib/python3.8/dist-packages/jedi/parser_utils.py 194 159 18% 26-56, 60-69, 79, 83-94, 99-109, 113-124, 128-142, 158-175, 182-188, 196-219, 223-228, 235-244, 252-269, 280, 287-292, 299-311, 322-326 /usr/local/lib/python3.8/dist-packages/jedi/plugins/__init__.py 31 1 97% 21 /usr/local/lib/python3.8/dist-packages/jedi/plugins/flask.py 11 8 27% 7-20 /usr/local/lib/python3.8/dist-packages/jedi/plugins/pytest.py 99 71 28% 21-25, 31-41, 44-61, 67-77, 83-90, 95-98, 108-112, 117, 123-142, 147-153, 156-164 /usr/local/lib/python3.8/dist-packages/jedi/plugins/registry.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/plugins/stdlib.py 440 292 34% 106-132, 138-143, 157-173, 181-188, 194, 200-208, 213-217, 223-224, 227, 230-235, 238-241, 246-253, 258-259, 263, 268, 278-289, 294-328, 336, 341, 346-347, 350, 358-360, 363, 366, 371-372, 375-377, 382, 391-392, 395-397, 403, 408, 423-470, 475-478, 481-485, 488-502, 505-509, 514, 519, 524-526, 529-530, 535-537, 540-549, 553, 560, 568, 573, 581-586, 591-612, 617-618, 621, 626-628, 631, 634-637, 642-643, 647-661, 666, 675, 680-681, 685, 688, 693, 702-711, 717-734, 801-808, 814-817, 821, 824-825, 828-833, 838-842 /usr/local/lib/python3.8/dist-packages/jedi/settings.py 19 2 89% 72, 75 /usr/local/lib/python3.8/dist-packages/joblib/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/joblib/_compat.py 15 3 80% 11, 24-25 /usr/local/lib/python3.8/dist-packages/joblib/_memmapping_reducer.py 180 132 27% 28, 38-39, 70, 73-78, 81-95, 98, 107-119, 152-178, 183, 189-202, 213-236, 243-252, 281-286, 293-298, 301-361, 374-434 /usr/local/lib/python3.8/dist-packages/joblib/_memory_helpers.py 65 63 3% 5-105 /usr/local/lib/python3.8/dist-packages/joblib/_multiprocessing_helpers.py 34 11 68% 20-21, 34-37, 51-53, 61-64 /usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py 271 174 36% 38-39, 78-79, 92, 99, 123, 132-136, 153, 163-184, 188, 203-205, 209-212, 216-220, 230-238, 242-245, 249, 253, 258-260, 282-284, 288-344, 348-360, 367-368, 392-399, 407-409, 432-462, 467-489, 493-497, 509-519, 523-547, 551-555, 561-564, 567-574, 579-583, 590, 593, 604, 607-624, 631 /usr/local/lib/python3.8/dist-packages/joblib/_store_backends.py 196 137 30% 26-31, 152-174, 179-193, 198-200, 205-208, 212, 217-223, 227-238, 242-243, 247-249, 253-260, 264-270, 274, 278, 282-294, 298-322, 326-328, 332, 345-348, 352, 356-388, 395-415 /usr/local/lib/python3.8/dist-packages/joblib/backports.py 48 37 23% 22-30, 37-76, 80-81 /usr/local/lib/python3.8/dist-packages/joblib/compressor.py 315 209 34% 12-13, 17-18, 22-23, 27-28, 61, 65, 73, 78, 107-110, 115, 127, 130-131, 136-140, 145-152, 164, 168-172, 178-186, 202, 206-209, 220, 225-235, 239-243, 248-249, 289-321, 330-348, 353, 357-358, 362, 366-367, 371-372, 377-383, 386-388, 391-393, 396-401, 406-424, 430-440, 446-470, 478-485, 492-493, 502-511, 515-520, 537-562, 566-568 /usr/local/lib/python3.8/dist-packages/joblib/disk.py 59 42 29% 27-38, 44-52, 59-63, 90-101, 106-124 /usr/local/lib/python3.8/dist-packages/joblib/executor.py 32 21 34% 28-50, 58-59, 63-65, 68-69, 72-73 /usr/local/lib/python3.8/dist-packages/joblib/externals/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle.py 623 480 23% 65-66, 85, 88-95, 109-115, 119-124, 128-139, 151-164, 169-202, 209-227, 257-274, 334-339, 343-384, 388, 406, 410-432, 440-443, 448-465, 473-477, 480-488, 491, 496-499, 505-509, 517-541, 551-556, 579-582, 591-608, 617-692, 706-760, 767-812, 820-847, 850-851, 854, 865-879, 886-892, 900-941, 944, 948, 953-954, 961-967, 974-989, 996-1033, 1036, 1039, 1050, 1055, 1060, 1066, 1073, 1083-1089, 1093-1094, 1109, 1122-1128, 1138-1139, 1143-1145, 1149, 1153, 1157-1161, 1186, 1194-1251, 1260, 1271-1281, 1296-1297, 1305-1315, 1335-1348, 1357-1397 /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle_fast.py 227 169 26% 47, 60-63, 70-78, 83-84, 91, 103-132, 136-158, 162-174, 190-198, 203-208, 212-213, 218-260, 264, 268, 272, 276-279, 283, 287, 291, 295, 306-312, 320-330, 346-370, 374-385, 419-431, 463-475, 481-483, 498-501, 504-534, 537-547 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/_base.py 287 271 6% 34-615, 623-627 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/_posix_reduction.py 41 21 49% 20, 29-31, 36-43, 51-52, 55-58, 67-68, 71-74 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat.py 18 8 56% 14, 19, 23, 29-38 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat_posix.py 4 1 75% 11 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/context.py 135 95 30% 37-85, 91-97, 101, 118-153, 164-165, 170-171, 175-206, 214-215, 219-220, 224-225, 229-230, 234-235, 239-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/process.py 57 42 26% 20-31, 35-39, 42-64, 67-81, 89, 100-108 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/queues.py 131 102 22% 36-62, 66-67, 72-75, 79-111, 121-175, 182-183, 187-189, 195-210, 214-215, 219, 225-229, 234-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/reduction.py 126 59 53% 27-30, 63-66, 79-82, 87, 91, 101, 109, 113, 121, 127-129, 146, 149, 154-165, 176-177, 185-197, 202-210, 218, 223, 232-234, 240, 246-250, 256 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/utils.py 94 75 20% 11-12, 20-21, 25-28, 32-46, 52-60, 67-116, 125-138, 143-145, 149-172 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/cloudpickle_wrapper.py 60 44 27% 7-8, 16-17, 20-24, 29-31, 38, 42-44, 48-49, 55-83, 95-113 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py 507 411 19% 88-89, 94, 132-138, 143, 146-147, 150-155, 158-159, 171-174, 177-179, 182-184, 189-197, 214, 217, 223-229, 232, 236-237, 245-248, 254-256, 262-268, 271-272, 275, 283-286, 289-312, 317-325, 337, 342-347, 372-465, 487-505, 542-757, 766-785, 794-797, 803-810, 882-933, 939-951, 955-994, 998-1014, 1019-1022, 1025-1048, 1073-1081, 1084-1117 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/reusable_executor.py 91 70 23% 34-37, 84-142, 150-155, 158-159, 163-194, 199-207, 212-213 /usr/local/lib/python3.8/dist-packages/joblib/format_stack.py 209 188 10% 34-35, 45-68, 72, 88-94, 104-116, 120-147, 151-176, 181-322, 337-365, 371-401 /usr/local/lib/python3.8/dist-packages/joblib/func_inspect.py 176 154 12% 47-79, 84-93, 110-162, 173-177, 190-193, 198-203, 228-318, 322-325, 330-349, 356-359 /usr/local/lib/python3.8/dist-packages/joblib/hashing.py 117 85 27% 24, 33-42, 49, 58-64, 67-75, 78-94, 101-103, 111-127, 141-150, 155, 174-182, 189-242, 258-267 /usr/local/lib/python3.8/dist-packages/joblib/logger.py 76 55 28% 28-31, 35-36, 40-44, 48-57, 77, 81, 85, 96-124, 136-156 /usr/local/lib/python3.8/dist-packages/joblib/memory.py 374 270 28% 57-63, 92-100, 106, 109, 112, 114-133, 135, 146-148, 153-160, 166-182, 225-244, 248-253, 257-277, 281, 284, 293-295, 306-307, 310-313, 316-317, 320-325, 329, 332-333, 358, 361, 365, 415-453, 483-545, 562-563, 568, 574-576, 583, 588-590, 594-595, 605-625, 636-713, 717-724, 730-745, 764-795, 804, 884, 887-901, 905, 914-921, 952, 956-962, 971-974, 978-979, 990-992, 999, 1008-1010 /usr/local/lib/python3.8/dist-packages/joblib/my_exceptions.py 53 20 62% 24, 27-33, 46-48, 51-59, 75, 80, 84, 89, 94-99, 112-113 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle.py 204 161 21% 13-14, 78-82, 91-104, 112-161, 165-178, 195-209, 234-249, 253-260, 272-295, 320-332, 342-355, 361, 415-515, 526-548, 588-607 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_compat.py 105 75 29% 21-25, 38-61, 71-75, 90-92, 96-120, 140-142, 148-154, 164-173, 176, 185-192, 198, 227-247 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_utils.py 92 65 29% 22-23, 27-28, 36-37, 45-49, 54-56, 73-90, 95-100, 105-112, 144-182, 187-197, 229-245 /usr/local/lib/python3.8/dist-packages/joblib/parallel.py 362 293 19% 40-41, 65-73, 83-124, 181-209, 212, 215, 218-222, 234, 241-249, 254-255, 259, 267-270, 282-291, 297-311, 327-329, 332-340, 360-363, 388-389, 620-696, 699-701, 704-705, 709-725, 728-730, 733-734, 744-759, 769-771, 783-836, 842-849, 855-887, 895-940, 943-1032, 1035 /usr/local/lib/python3.8/dist-packages/joblib/pool.py 116 83 28% 42-43, 75-87, 91-99, 120-127, 130-131, 135-137, 140, 143-177, 199-207, 210-216, 296-313, 316-329 /usr/local/lib/python3.8/dist-packages/jupyter_client/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/adapter.py 256 210 18% 17-25, 38-52, 64, 67, 70-74, 81, 84-96, 103-109, 125-127, 132-146, 149-151, 154-157, 160-170, 173-179, 182-190, 194-195, 200-202, 205-214, 219-220, 231-234, 239-258, 261-266, 269-282, 285-290, 297-307, 310-317, 321-339, 344-346, 349-358, 363-364, 386-398 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/channels.py 45 28 38% 29-32, 35-37, 41-48, 52-58, 62, 65-70, 74, 79, 82 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/client.py 188 138 27% 23-29, 34, 69, 77, 81, 85, 89, 94-101, 112-150, 162-177, 194-212, 216-224, 231-235, 239-253, 311-388 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/channels.py 50 32 36% 11-12, 35-38, 41-43, 47-57, 61-67, 71, 74-79, 83, 88, 91 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/client.py 161 121 25% 25-31, 36, 77-115, 127-142, 159-179, 183-191, 198-202, 262-339 /usr/local/lib/python3.8/dist-packages/jupyter_client/channels.py 119 82 31% 61-80, 87-88, 91-99, 109-132, 136-170, 174, 178, 182-185, 189-192, 195-200, 210 /usr/local/lib/python3.8/dist-packages/jupyter_client/channelsabc.py 25 7 72% 16, 20, 24, 37, 41, 45, 49 /usr/local/lib/python3.8/dist-packages/jupyter_client/client.py 172 120 30% 26-30, 53, 78, 82, 86, 90, 105-118, 125-134, 139, 148-155, 160-167, 172-179, 184-190, 195-202, 206-218, 258-277, 295-300, 322-329, 363-370, 379-381, 390-396, 404-406, 410-412, 420-422, 441-443 /usr/local/lib/python3.8/dist-packages/jupyter_client/clientabc.py 45 14 69% 32, 36, 40, 44, 48, 52, 60, 64, 68, 72, 76, 80, 84, 88 /usr/local/lib/python3.8/dist-packages/jupyter_client/connect.py 247 186 25% 79-167, 191-224, 253-274, 296, 319-325, 329-330, 350, 355-356, 377-396, 402-406, 413-419, 423-430, 438-446, 455-461, 465-481, 492-497, 511-523, 531-538, 542-551, 555-557, 561, 565, 569, 573 /usr/local/lib/python3.8/dist-packages/jupyter_client/jsonutil.py 50 34 32% 38-44, 53-59, 63-72, 76-84, 88-92 /usr/local/lib/python3.8/dist-packages/jupyter_client/kernelspec.py 196 139 29% 44-47, 50-58, 65, 73, 82, 90-103, 108, 111, 130, 134, 146-159, 163-183, 190-200, 204-222, 229-237, 252-270, 277-289, 292-297, 315-347, 351-354, 359, 366, 370, 376 /usr/local/lib/python3.8/dist-packages/jupyter_client/launcher.py 59 51 14% 56-158 /usr/local/lib/python3.8/dist-packages/jupyter_client/localinterfaces.py 170 129 24% 29-30, 34-42, 48-52, 58-59, 68-89, 96-108, 113-121, 127-135, 140-164, 173-196, 201-203, 216-248, 254, 259, 264, 269, 274 /usr/local/lib/python3.8/dist-packages/jupyter_client/manager.py 349 261 25% 42, 48, 52, 61, 64, 75-77, 83-85, 102, 109, 113, 126-127, 134, 137, 141-143, 147-149, 157-166, 174-205, 212, 217-219, 222-225, 239-267, 274-283, 286-287, 301-306, 311-315, 323-338, 342-347, 368-379, 406-418, 423, 430-456, 464-478, 488-498, 502-509, 523-524, 538-543, 551-562, 583-594, 621-634, 641-675, 683-697, 707-717, 721-728, 735-736, 744-755, 760-771, 784-789 /usr/local/lib/python3.8/dist-packages/jupyter_client/managerabc.py 27 8 70% 21, 29, 33, 37, 41, 45, 49, 53 /usr/local/lib/python3.8/dist-packages/jupyter_client/multikernelmanager.py 185 104 44% 31-39, 60-63, 67, 73, 76-90, 94-105, 109, 119, 123, 126, 130-147, 157-160, 174-188, 198, 212, 216-222, 233, 247, 258, 275-276, 286-287, 402, 422-428, 442-456, 461-463, 473-475, 488-490, 500-502, 506-512 /usr/local/lib/python3.8/dist-packages/jupyter_client/session.py 442 311 30% 27, 65-75, 148-153, 157, 172, 177, 184, 187-191, 201-205, 209, 212, 215, 218, 221, 226-228, 232-247, 305-315, 323-333, 338-340, 344, 364, 368, 376-384, 388, 393-396, 411-412, 423-425, 432-434, 488-496, 508-514, 519-521, 525-568, 571, 580-590, 600-605, 630-664, 711-766, 784-794, 810-828, 853-865, 869-876, 883-889, 916-954, 957-961, 965-978 /usr/local/lib/python3.8/dist-packages/jupyter_core/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_core/paths.py 194 155 20% 33-38, 47-51, 59-68, 78-98, 109-114, 118-122, 147-166, 170-174, 186-202, 209-213, 232-247, 266-287, 291, 316-344, 361-377, 394, 415-448, 452-456 /usr/local/lib/python3.8/dist-packages/jupyter_core/version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/__init__.py 18 5 72% 22, 37-40 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/affine_transformations.py 107 77 28% 16-17, 22-24, 28-31, 55-59, 84-90, 114-118, 145-156, 171-180, 194-195, 212-219, 236-242, 246-251, 281, 285-289, 292-298, 301-308, 311-317, 320-335 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py 120 97 19% 93-99, 124-173, 180-223, 227-233, 237-261, 273-284, 288, 292-295, 299 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/directory_iterator.py 65 4 94% 72-73, 106, 162 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/image_data_generator.py 213 111 48% 17-18, 301, 308-310, 316, 327-331, 335-342, 347-349, 354-356, 361-363, 421, 649-666, 708, 712, 714, 717-720, 725-728, 734-739, 761, 764, 771-778, 783-790, 795, 804, 814, 819, 877, 882, 885, 888, 902-903, 933-988 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/iterator.py 146 51 65% 50, 54, 59, 74, 78-95, 101, 104, 112-116, 127, 175, 180-183, 188-193, 201, 205-209, 243-250, 253, 255-257, 263-268, 272, 277, 285, 292 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/numpy_array_iterator.py 87 75 14% 46-52, 68-150, 156-183 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/utils.py 121 54 55% 16-18, 47, 71-76, 107-109, 111, 118-119, 121-122, 125-127, 132, 153-154, 179, 217, 251-286, 305, 312-319 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/sequence.py 155 135 13% 55-110, 143-149, 197-240, 254-259, 334-354, 360, 364-378, 386-402, 427-432, 446-454 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/text.py 198 166 16% 21, 42-63, 88, 128-138, 179-197, 212-251, 263-267, 281, 298-324, 338, 355-370, 382-383, 400-438, 449-455, 482-487, 500-519 /usr/local/lib/python3.8/dist-packages/parso/__init__.py 8 3 62% 56-58 /usr/local/lib/python3.8/dist-packages/parso/_compatibility.py 36 15 58% 27-29, 38-43, 49-51, 60-64, 69 /usr/local/lib/python3.8/dist-packages/parso/cache.py 103 67 35% 66, 68, 89-94, 101-111, 120-144, 148-160, 164-174, 178-179, 183-186, 190-193, 197-202 /usr/local/lib/python3.8/dist-packages/parso/file_io.py 20 11 45% 6, 12-13, 19-23, 26, 31-32, 35 /usr/local/lib/python3.8/dist-packages/parso/grammar.py 127 89 30% 33-40, 77-79, 90-155, 158-161, 169-172, 175, 178-183, 190-191, 194-196, 199-201, 210-216, 219, 223, 234-260 /usr/local/lib/python3.8/dist-packages/parso/normalizer.py 137 74 46% 19-24, 27-33, 36-39, 42-48, 52-53, 56-57, 60-65, 68, 71, 74-77, 101, 117-120, 125-138, 141, 144, 147, 150, 158, 161, 164, 167-171, 174-181, 184-186, 191, 194-197, 200-203 /usr/local/lib/python3.8/dist-packages/parso/parser.py 106 79 25% 37-38, 49-54, 59-71, 76-77, 81, 84, 89-96, 119-121, 124-143, 146-151, 154-160, 163-166, 174-198, 201-211 /usr/local/lib/python3.8/dist-packages/parso/pgen2/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/parso/pgen2/generator.py 178 152 15% 45-47, 56-57, 60, 74-87, 90-93, 96-98, 102-112, 117, 130, 133, 145-157, 170-207, 211-224, 228-232, 244-274, 282-294, 303-344, 352-377 /usr/local/lib/python3.8/dist-packages/parso/pgen2/grammar_parser.py 101 81 20% 18-23, 27-38, 42-58, 62-69, 73-94, 98-111, 115-122, 125-126, 129-135, 141-142, 145, 150-151, 154-156, 159 /usr/local/lib/python3.8/dist-packages/parso/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/parso/python/diff.py 502 437 13% 52, 56-58, 62-64, 68, 72, 81-118, 122-138, 142-146, 154-167, 171-173, 177-184, 192-195, 199-203, 207-220, 224-230, 238-247, 256-258, 261-264, 282-340, 343-344, 347-388, 391-405, 412-431, 439-453, 456-514, 523-527, 530-550, 553, 557-562, 565-586, 589, 594-599, 603, 606-609, 612-624, 627-640, 646-665, 672-685, 693-719, 723-861, 864-886 /usr/local/lib/python3.8/dist-packages/parso/python/errors.py 758 567 25% 26-33, 37-48, 56-57, 65-72, 76, 83-93, 97-121, 125-131, 135-136, 141-148, 153, 157, 160-170, 176-202, 205-247, 251-253, 256, 259, 267-269, 272-282, 285-291, 295-315, 318-349, 352, 355, 360-362, 365-368, 375-376, 384-385, 389, 400-401, 409, 413, 421, 425, 433-437, 446-456, 464, 467, 477-483, 491-522, 530-535, 546-548, 558, 561-570, 579-581, 584-590, 599-600, 608, 616-633, 643-661, 667-693, 703, 706-730, 736-750, 758, 765-768, 771-823, 833-845, 853-860, 871, 874-889, 892, 895-897, 902-983, 991-995, 1004-1009, 1015, 1021-1024, 1030-1031, 1038-1040, 1049-1108 /usr/local/lib/python3.8/dist-packages/parso/python/parser.py 102 85 17% 66-71, 74-80, 90-108, 112-118, 121-188, 191-200, 203-217 /usr/local/lib/python3.8/dist-packages/parso/python/pep8.py 492 426 13% 39-40, 43, 46-51, 56-105, 114-119, 126-144, 148, 153-172, 176-178, 182-280, 283-286, 289-290, 293-341, 344-375, 378-519, 522-537, 545-629, 632-685, 688-700, 727 /usr/local/lib/python3.8/dist-packages/parso/python/prefix.py 56 35 38% 11-16, 20-25, 28-29, 35, 69-94 /usr/local/lib/python3.8/dist-packages/parso/python/token.py 12 1 92% 10 /usr/local/lib/python3.8/dist-packages/parso/python/tokenize.py 458 395 14% 60-61, 70, 75, 80-111, 119-124, 136-265, 274-278, 283, 289-295, 298, 301-304, 307, 310, 313, 317-331, 335-371, 376-377, 384-389, 401-672, 676-705, 709-722 /usr/local/lib/python3.8/dist-packages/parso/python/tree.py 642 403 37% 48-49, 79-97, 111-119, 126, 134-143, 154, 180, 192, 204, 211, 221-249, 267, 270-275, 311-314, 318, 321, 343, 349, 355, 361, 364-372, 378, 381-386, 400-401, 412-417, 426-429, 436-453, 469, 475-485, 496, 503-509, 523-566, 586-588, 591, 597, 601, 607-624, 630-639, 645-654, 660, 667-673, 692-694, 701, 704, 711, 714, 734-736, 744-752, 758-763, 779, 782, 794-798, 810-815, 818-821, 833-842, 845, 848, 861, 865, 869-877, 882-888, 891-905, 914-918, 932, 937, 940, 944-960, 969, 976, 996, 1000, 1003-1008, 1016, 1023, 1040-1061, 1072-1075, 1084-1090, 1096-1105, 1117-1120, 1128-1131, 1139-1144, 1152-1159, 1165-1166, 1173-1176, 1179, 1186-1201, 1207, 1216-1222, 1228-1229, 1241, 1254, 1257, 1260, 1263, 1266, 1270 /usr/local/lib/python3.8/dist-packages/parso/tree.py 195 124 36% 18-20, 38-41, 48-58, 66-75, 82-101, 108-127, 186-197, 203, 207-208, 211-216, 219, 222, 225-228, 232-239, 243-246, 253-254, 266-271, 278, 281, 285, 288-292, 295, 306-328, 331, 334, 338-341, 350-351, 354, 376-377, 380 /usr/local/lib/python3.8/dist-packages/parso/utils.py 86 68 21% 38-68, 80-109, 117-119, 123-140, 146-152, 155-159, 162, 171-176 /usr/local/lib/python3.8/dist-packages/pickleshare.py 194 152 22% 43-45, 50-51, 62, 65, 73-86, 91-107, 111-123, 127-133, 139-154, 158-177, 186-198, 204-211, 215, 220-224, 227, 230, 240-243, 259-275, 279, 282, 297, 300, 302, 304-306, 311-347, 350 /usr/local/lib/python3.8/dist-packages/pooch/__init__.py 20 14 30% 36-50 /usr/local/lib/python3.8/dist-packages/pooch/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/pooch/core.py 120 84 30% 197-230, 410, 413, 424, 545-568, 575-576, 589-590, 610-637, 658-673, 702-711, 725-733 /usr/local/lib/python3.8/dist-packages/pooch/downloaders.py 82 69 16% 14-15, 47-60, 139-143, 161-203, 253-261, 277-310 /usr/local/lib/python3.8/dist-packages/pooch/processors.py 75 50 33% 38, 64-81, 88, 117-133, 162-182, 213, 240-252, 262-279 /usr/local/lib/python3.8/dist-packages/pooch/utils.py 101 54 47% 33, 96, 150, 173-193, 214-216, 246, 248, 255-256, 261-271, 308-315, 346-362, 386-393, 430-436 /usr/local/lib/python3.8/dist-packages/pooch/version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/application.py 442 346 22% 93-94, 220-336, 343-353, 366-374, 385, 395-399, 410-431, 437-477, 482, 492-523, 530-537, 546-559, 569, 579-581, 585-591, 614-778, 801-811, 826-839, 849-851, 862-869, 875-884, 916-929, 938-939, 959-981, 993-1006, 1019, 1030, 1034-1036, 1043-1053, 1064-1065, 1073, 1078, 1087-1132, 1136-1140, 1145, 1148, 1158-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/current.py 64 39 39% 7-8, 11-13, 50, 54-58, 62-66, 75, 97-103, 111-112, 127-134, 148-165 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/dummy.py 17 5 71% 21, 28, 35, 44, 47 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/run_in_terminal.py 44 33 25% 13-14, 50-57, 73-116 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/auto_suggest.py 57 28 51% 23, 44, 47, 82, 93, 98, 107-110, 121, 132-145, 155-156, 161-164, 175, 180-181, 186-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/buffer.py 775 644 17% 82-90, 93, 106-108, 114-128, 136-138, 153-155, 158, 251-316, 319-324, 332-383, 389-405, 409-412, 416, 426-442, 446, 453-464, 468, 472-478, 482-498, 504-516, 524, 536, 557-571, 578, 589-596, 619-628, 636-639, 656-658, 667, 670, 674-680, 684-690, 699-708, 717-726, 733-748, 754-762, 769-774, 784-799, 808-814, 820-822, 831-845, 856-867, 873-875, 883-890, 897-926, 932-943, 950-956, 963-967, 974, 984-1000, 1006-1021, 1035-1074, 1081, 1089, 1103-1108, 1114, 1125-1135, 1141-1144, 1150-1157, 1163-1169, 1185-1221, 1228-1237, 1240-1246, 1256-1277, 1295-1319, 1327-1330, 1342-1418, 1428-1441, 1456-1464, 1476-1483, 1486, 1493-1502, 1506-1531, 1539-1578, 1588-1614, 1628-1630, 1648-1768, 1776-1795, 1803-1807, 1813-1825, 1839-1862, 1873-1883, 1892-1909, 1920-1956 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/cache.py 56 25 55% 37-51, 55-56, 93-101, 117-121 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/base.py 38 13 66% 29-30, 52, 72, 75, 78, 81, 92, 95, 98, 101, 104, 107 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/clipboard/in_memory.py 22 13 41% 23-29, 32-35, 38-41, 44-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/base.py 116 71 39% 48-62, 65-72, 80-82, 90, 95-97, 102-104, 109-111, 120-122, 147-153, 156, 185, 196-197, 212, 217, 225-228, 231, 242, 245, 256, 261-262, 267-272, 275, 284, 290-292, 299-301, 308, 318-333, 338-349 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/filesystem.py 46 36 22% 34-38, 43-98, 107 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/fuzzy_completer.py 70 52 26% 54-60, 65-68, 71-75, 81-116, 131-159, 180-188, 193 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/nested.py 40 28 30% 32-33, 36, 63-75, 81-109 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/word_completer.py 33 26 21% 41-49, 55-83 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/data_structures.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/document.py 525 407 22% 52, 68-71, 98-121, 130, 133-136, 145, 150, 155, 160, 165, 169, 173, 178-179, 184-185, 193-196, 205-224, 231, 237, 243, 248-250, 256-259, 266, 273, 280-281, 291-292, 301-304, 312-315, 324-340, 345, 350, 356, 372-397, 404-405, 420-434, 446-453, 458-461, 475-496, 510-536, 546-547, 556-572, 581-604, 613-625, 634-652, 661-671, 680-690, 696-699, 705-708, 720-725, 742-747, 761-783, 794-816, 828-834, 838, 842, 846-854, 858, 864, 872-876, 890-897, 908-947, 958-997, 1005-1033, 1048-1098, 1104-1111, 1118-1129, 1136-1145, 1154, 1165-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/enums.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/async_generator.py 26 18 31% 32-67 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/inputhook.py 65 42 35% 50-52, 61-63, 78-80, 83, 86, 89, 94-143, 149-154, 157, 166-167, 170 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/utils.py 37 25 32% 9-10, 30-33, 57-83, 90-100 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/app.py 162 97 40% 13, 54-98, 106, 114, 122-123, 131-132, 140, 148, 154, 160, 168, 182, 191-195, 200, 205, 213-225, 234-248, 253-267, 272-286, 291-295, 300-304, 309-313, 319-323, 329, 334-341, 346-347, 354-355, 364-365, 371-375, 383 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/base.py 92 37 60% 20, 26, 38, 49, 67-77, 88, 90, 101-103, 117-123, 126, 129, 142, 147, 150, 159, 162, 165, 174, 177, 186, 189, 210, 213 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/cli.py 27 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/utils.py 14 2 86% 32, 41 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/ansi.py 152 133 12% 30-47, 53-109, 115-187, 193-211, 214, 217, 225-228, 246 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/base.py 66 49 26% 6, 30-37, 68-99, 108-114, 126, 129, 144-145, 148-160, 168-174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/html.py 70 59 16% 30-96, 99, 102, 110-113, 119-123, 129-132 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/pygments.py 14 6 57% 8, 22, 25-30 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/utils.py 24 16 33% 28-29, 40-41, 56-57, 68-85 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/history.py 95 61 36% 33-34, 64-74, 80, 84-85, 101, 120-123, 126-139, 142, 145, 150, 153, 156, 165, 168, 177, 180, 184, 193-194, 197-221, 225-232 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/ansi_escape_sequences.py 12 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/base.py 53 14 74% 48, 52, 60, 65, 95, 104, 107, 110, 114, 117, 120, 123, 126, 131 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/defaults.py 24 17 29% 26-43, 51-58 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/typeahead.py 17 8 53% 54-55, 64-67, 75-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/vt100_parser.py 98 73 26% 48-61, 87-88, 91-92, 98-99, 108-118, 124-168, 176-188, 199-226, 240, 246-247 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/auto_suggest.py 30 22 27% 26-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/basic.py 157 147 6% 27, 31-248 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/completion.py 96 79 18% 21-22, 37-43, 61-79, 90-171, 178-203 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/cpr.py 12 6 50% 14-28 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/emacs.py 270 255 6% 41-335, 339-401, 409-558 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/focus.py 7 2 71% 16, 24 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/mouse.py 63 54 14% 21-146 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/named_commands.py 279 156 44% 56-59, 73-74, 82-83, 91-92, 102-103, 111-112, 118-119, 128-132, 141-145, 153, 162, 176, 184, 192, 200, 208-210, 219-223, 236, 244-246, 254-262, 270, 281-289, 297-302, 310-315, 323-328, 337, 354-364, 373-382, 391-411, 420, 428-436, 444-452, 460, 471-472, 481-482, 491-498, 511, 520, 528, 541, 550, 566-569, 578-586, 599, 609-627, 635, 643, 657, 666-678, 686-687 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/open_in_editor.py 14 7 50% 20, 29-35, 42-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/page_navigation.py 26 19 27% 38, 51-59, 67-79 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/scroll.py 80 69 14% 26-50, 57-80, 87, 94, 101-113, 120-144, 151-160, 169-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/vi.py 949 904 5% 74-76, 80-85, 91-94, 105-126, 133-141, 147-166, 181-289, 304-369, 388-2161, 2165-2210 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/defaults.py 11 2 82% 35-49 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/digraphs.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/emacs_state.py 17 7 59% 18-19, 22, 27, 31, 35-36 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_bindings.py 215 138 36% 57, 101, 104, 126, 137, 149, 158, 189-196, 199-201, 205, 209, 238-281, 297-325, 340-364, 376-389, 397-417, 461-462, 469, 475-476, 480-481, 484-485, 488-489, 514-517, 521-541, 555-556, 563-572, 583, 596-599, 602-607, 617-618, 625-635 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_processor.py 238 184 23% 23-24, 44-47, 53, 56-58, 91-98, 101-117, 124-127, 134-145, 152-204, 213-216, 222-225, 236-292, 298-303, 306-354, 362-375, 382-387, 397-420, 443-451, 454, 462, 466-469, 476, 483, 490-499, 506, 514-525, 530 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/vi_state.py 47 27 43% 7-8, 28-29, 40-76, 81, 86-91, 98-106 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/keys.py 165 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/containers.py 956 776 19% 62-64, 140, 148, 155, 160-166, 174, 215-227, 230, 233, 236, 289-308, 311-318, 321-327, 330-331, 339-364, 381-414, 428-474, 526-545, 548-555, 558-579, 582-583, 591-616, 623-667, 684-730, 769-775, 778-781, 784, 792, 803-841, 867-986, 1000-1011, 1014, 1017, 1020-1022, 1069-1090, 1093-1095, 1098-1100, 1103, 1149-1162, 1166, 1177-1185, 1195-1202, 1221, 1230-1236, 1243-1246, 1252-1255, 1263, 1277, 1284, 1294, 1301, 1309-1312, 1319-1324, 1342-1345, 1349, 1353, 1357, 1361, 1364, 1378-1379, 1490-1524, 1527, 1530-1543, 1551-1558, 1564, 1573-1593, 1604-1620, 1637-1671, 1680-1684, 1688-1695, 1710-1726, 1738-1898, 1924-2140, 2150-2162, 2169-2181, 2188-2191, 2203-2214, 2224-2262, 2277-2281, 2287-2292, 2303-2413, 2424-2507, 2525-2528, 2532-2541, 2545-2558, 2561, 2564, 2578-2579, 2582, 2585, 2588-2591, 2594-2597, 2608-2609, 2614, 2626, 2635-2636, 2639, 2642, 2645, 2648, 2651, 2656, 2663, 2670-2675, 2682-2687, 2695-2699 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/controls.py 311 237 24% 51-58, 88, 91, 100, 106, 126, 154, 179-186, 190-193, 216-266, 323-341, 344, 347, 350, 358, 367-369, 379-380, 384-430, 442-469, 472, 475, 488-491, 496, 545-574, 577, 583-589, 593-596, 606-610, 613, 626, 638-657, 666-670, 681-722, 730-819, 825-883, 886-887, 890-891, 897, 904-908, 929-939 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/dimension.py 91 53 42% 44, 56, 58, 60, 71, 75, 78, 94, 98, 101-111, 118-122, 130-167, 184-193, 201-207 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/dummy.py 17 7 59% 26-37 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/layout.py 188 143 24% 43-70, 73, 79-81, 84-85, 101-166, 175-193, 200, 207-212, 217, 222, 227, 236-241, 248-250, 258-259, 266-269, 276-280, 289-290, 297-300, 306-307, 313-323, 329-339, 345-346, 355-360, 366-375, 382-384, 391-394, 406-417 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/margins.py 119 90 24% 18, 43, 63, 80-81, 84-85, 90-132, 141-142, 145-148, 153-156, 173-175, 178, 183-243, 276-277, 282-283, 288-305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/menus.py 281 228 19% 43-45, 71, 74-81, 91-95, 101-132, 138, 144, 159-167, 173-181, 190-205, 218-232, 245-265, 281-284, 327-340, 343, 346, 353-371, 383-390, 396-507, 513, 519-557, 564-607, 626-664, 680-685, 694, 697-702, 705-720 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/mouse_handlers.py 12 4 67% 18-24, 39-40 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/processors.py 378 291 23% 38, 80, 109-115, 122, 155-157, 168, 187, 193-243, 264-267, 278-312, 323, 326-334, 354-357, 368-398, 404-439, 451-489, 502-503, 509-522, 529, 541, 544-550, 557, 570-571, 575-580, 583, 593, 597-607, 622-629, 632-645, 660-667, 670-684, 707-710, 713-764, 790-798, 803-855, 858-903, 930-931, 937-940, 943, 958, 961-962, 969-975, 985, 988-1029 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/screen.py 100 71 29% 9, 107-120, 123, 128, 131, 152-192, 198, 204, 211-214, 221-227, 234, 242-249, 256-263, 272-293, 300-307, 310 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/utils.py 32 17 47% 23, 26, 29, 35, 39, 48-53, 67-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/base.py 31 15 52% 38, 50, 53-62, 73-74, 77-78, 81-82 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/pygments.py 112 79 29% 29, 69, 86, 94-110, 117-129, 141-143, 193-202, 214-222, 229-335 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/mouse_events.py 14 3 79% 41-42, 45 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/base.py 130 33 75% 164, 174, 177, 180, 183, 186, 189, 192, 195, 198, 201, 204, 207, 210, 213, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/color_depth.py 29 12 59% 52-74 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/defaults.py 27 20 26% 31-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/vt100.py 289 192 34% 123-145, 169-175, 181-190, 237-256, 273, 276-300, 304-312, 321-369, 383-396, 420-432, 449-473, 476, 480, 484, 490, 497, 503-507, 512, 519, 522, 525, 528-534, 540-542, 548, 555, 558, 567-570, 573, 576, 579, 582, 588, 591-596, 599-606, 609-614, 617-622, 625, 628, 634-673, 679-680, 684-685 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/patch_stdout.py 61 43 30% 52-68, 81-93, 100-116, 128-139, 142-144, 147-150, 156-157, 164, 167 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/renderer.py 299 255 15% 27-28, 70-243, 262-263, 266-270, 303-326, 331-369, 377, 386, 395-402, 417-457, 464-480, 488, 494-514, 525-637, 648-657, 664-673, 686-716 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/search.py 86 66 23% 16-17, 56-58, 61, 73-78, 94-119, 126-150, 157-181, 190-217, 226 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1347-1356, 1366-1368, 1450-1480, 1530-1551 /usr/local/lib/python3.8/dist-packages/pywt/_pytesttester.py 44 35 20% 36-42, 113-164 /usr/local/lib/python3.8/dist-packages/pywt/_swt.py 258 241 7% 18-24, 113-150, 187-256, 345-365, 420-502, 581-638, 680-774 /usr/local/lib/python3.8/dist-packages/pywt/_thresholding.py 63 51 19% 18-31, 36-49, 53-55, 59-62, 66-69, 164-170, 230-250 /usr/local/lib/python3.8/dist-packages/pywt/_utils.py 38 28 26% 17, 22-30, 50-66, 86-101 /usr/local/lib/python3.8/dist-packages/pywt/_wavelet_packets.py 328 261 20% 20-24, 56-78, 81-82, 85, 88-93, 96, 99, 102, 105-106, 118-136, 140-149, 153, 168-171, 174, 190-192, 195, 211-216, 236-246, 261-283, 295-303, 307, 311-314, 325-336, 357-363, 382-389, 392, 409, 419-429, 432-450, 470, 479-488, 492-521, 524-530, 553-569, 581-586, 610-631, 653-668, 680-685, 705-733 /usr/local/lib/python3.8/dist-packages/pywt/data/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/pywt/data/_readers.py 27 20 26% 39-41, 74-76, 107-109, 137-139, 173-185 /usr/local/lib/python3.8/dist-packages/pywt/data/_wavelab_signals.py 180 175 3% 69-259 /usr/local/lib/python3.8/dist-packages/pywt/version.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/rsa/__init__.py 10 2 80% 34-36 /usr/local/lib/python3.8/dist-packages/rsa/common.py 57 46 19% 22-25, 50-53, 76-78, 99-102, 112-127, 139-144, 167-179, 183-185 /usr/local/lib/python3.8/dist-packages/rsa/core.py 20 16 20% 23-26, 32-42, 48-53 /usr/local/lib/python3.8/dist-packages/rsa/key.py 242 170 30% 55-56, 110-116, 124-128, 140-146, 163, 177, 207, 210, 214, 218, 221-227, 230, 233, 256-260, 269-277, 291-292, 301-302, 320-321, 332-341, 371-379, 382, 385, 389, 393, 396-402, 412, 415, 418-422, 434-438, 450-453, 477-511, 520-548, 563-564, 573-574, 610-654, 670-684, 697, 721-728, 760-781, 790-803 /usr/local/lib/python3.8/dist-packages/rsa/pem.py 50 42 16% 29-32, 39-78, 97-105, 120-132 /usr/local/lib/python3.8/dist-packages/rsa/pkcs1.py 124 99 20% 40, 95-120, 145-154, 182-189, 243-264, 284-297, 318-319, 337-357, 371-376, 388-397, 411-425, 436-440, 447-458 /usr/local/lib/python3.8/dist-packages/rsa/prime.py 63 52 17% 34-36, 53-62, 82-115, 130-141, 160-167, 182-183, 187-198 /usr/local/lib/python3.8/dist-packages/rsa/randnum.py 30 22 27% 32-43, 50-57, 67-70, 81-96 /usr/local/lib/python3.8/dist-packages/rsa/transform.py 14 9 36% 34, 58-66, 70-72 /usr/local/lib/python3.8/dist-packages/scipy/__config__.py 28 14 50% 12-13, 24-25, 28-37 /usr/local/lib/python3.8/dist-packages/scipy/__init__.py 62 9 85% 65, 126-128, 132-136, 141-142 /usr/local/lib/python3.8/dist-packages/scipy/_distributor_init.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/_lib/_ccallback.py 98 71 28% 15-23, 87-88, 91, 95, 99, 103, 106, 125-131, 135-159, 168-180, 184-202, 207, 216-222, 227 /usr/local/lib/python3.8/dist-packages/scipy/_lib/_numpy_compat.py 275 254 8% 18-50, 58-87, 92-102, 109-197, 203-289, 294-568, 573-781 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259-261, 275-276, 282, 290-292, 294, 297, 310-319, 328-424 /usr/local/lib/python3.8/dist-packages/scipy/_lib/deprecation.py 15 4 73% 11-14, 18-20 /usr/local/lib/python3.8/dist-packages/scipy/_lib/doccer.py 97 21 78% 53, 116-125, 140, 145, 165, 171, 200, 250, 268-274 /usr/local/lib/python3.8/dist-packages/scipy/_lib/six.py 177 112 37% 43-65, 75-76, 87-92, 106-114, 119-121, 127, 134-142, 155, 160, 165, 170, 173, 176-177, 185-192, 202-204, 210-270, 277 /usr/local/lib/python3.8/dist-packages/scipy/_lib/uarray.py 13 5 62% 17-20, 24-25 /usr/local/lib/python3.8/dist-packages/scipy/cluster/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/cluster/hierarchy.py 780 667 14% 155, 162-167, 177-178, 195-200, 278, 360, 442, 527, 629, 731, 1040, 1045-1046, 1049, 1052-1053, 1058, 1061, 1068, 1072, 1075, 1113, 1115, 1118, 1121, 1130, 1133-1136, 1139-1142, 1145-1148, 1164, 1178, 1191, 1204, 1216, 1250-1278, 1299-1310, 1357-1391, 1452-1488, 1529-1551, 1555-1559, 1566, 1675-1700, 1758-1774, 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/usr/local/lib/python3.8/dist-packages/scipy/fft/_basic.py 64 23 64% 9-13, 154, 252, 339, 434, 510, 570, 668, 764, 857, 947, 1040, 1083, 1183, 1226, 1333, 1375, 1463, 1506 /usr/local/lib/python3.8/dist-packages/scipy/fft/_helper.py 10 2 80% 62, 101 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/basic.py 133 87 35% 16-30, 44-58, 72-90, 103, 110, 117, 124, 131, 138, 146-161, 172-186, 198-223, 234-251 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/helper.py 106 84 21% 28-38, 43-77, 86-95, 102-106, 111-133, 137-141, 147-153, 158-170, 190-195, 210 /usr/local/lib/python3.8/dist-packages/scipy/fft/_pocketfft/realtransforms.py 64 42 34% 19-46, 71-99 /usr/local/lib/python3.8/dist-packages/scipy/fft/_realtransforms.py 28 8 71% 63, 121, 179, 237, 387, 451, 568, 618 /usr/local/lib/python3.8/dist-packages/scipy/integrate/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_bvp.py 378 352 7% 29-57, 75-116, 124-142, 152-158, 243-276, 310-317, 322-347, 418-502, 506, 513, 556-577, 590-602, 632, 642-710, 1001-1159 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/base.py 99 80 19% 7-23, 118-151, 155-158, 170-191, 201-209, 212, 215, 231-234, 250-253, 256, 266-267, 270-275 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/bdf.py 245 222 9% 21-26, 31-34, 39-70, 188-242, 245-295, 298-438, 441, 447-451, 454-467 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/common.py 214 189 12% 13-17, 22-24, 39-40, 46-57, 62, 100-120, 153-176, 181-190, 206-238, 292-320, 325-363, 367-432 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/dop853_coefficients.py 153 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/ivp.py 162 143 12% 31-51, 77-78, 109-128, 146-154, 504-662 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/lsoda.py 57 46 19% 108-138, 141-161, 164-172, 177-180, 183-188 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/radau.py 262 230 12% 88-137, 169-177, 286-334, 337-387, 390-529, 532-533, 536, 541-545, 548-562 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/rk.py 190 127 33% 62-72, 89-104, 107, 110, 113-177, 180-181, 480-485, 488-494, 497-503, 506-524, 529-533, 536-549, 554-557, 560-576 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ode.py 507 401 21% 348-353, 357, 361-369, 382-394, 422-437, 441-445, 532-536, 540-541, 545-546, 562-567, 584-587, 620-625, 628-633, 637-656, 660, 673-686, 690-694, 722-723, 739-742, 751-754, 765-769, 786-787, 790-791, 804, 809, 814, 826-831, 862-881, 912-939, 942-987, 990-1014, 1017-1021, 1024-1028, 1044-1100, 1132-1143, 1146-1151, 1154-1168, 1171-1179, 1182-1187, 1210, 1216-1230, 1267-1283, 1287-1333, 1336-1353, 1356-1360, 1363-1367 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_quad_vec.py 262 227 13% 16, 19-24, 28, 36-41, 44-48, 51-56, 64-67, 70-71, 74-79, 83, 87-93, 98-99, 102, 202-400, 404-429, 436-449, 461-504, 512-569, 580-622 /usr/local/lib/python3.8/dist-packages/scipy/integrate/odepack.py 32 22 31% 229-260 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadpack.py 199 172 14% 41, 334-432, 436-465, 469-516, 581-585, 665-674, 799-810, 815, 823, 828, 832, 837-844, 847-883 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadrature.py 312 274 12% 45-49, 117-123, 151-168, 235-252, 256-258, 315-350, 354-381, 454-506, 568-623, 650-660, 668-669, 674-686, 773-806, 934-975 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_bsplines.py 324 291 10% 20-22, 27-30, 38-43, 182-226, 235-239, 245, 303-308, 332-355, 358, 367-370, 391-397, 425-437, 488-571, 581-588, 593, 597-604, 608-617, 735-861, 969-1022 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_cubic.py 259 230 11% 28-72, 142-158, 235-240, 245-255, 268-304, 343-350, 405-435, 438, 445, 450, 619-770, 784-837 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_fitpack_impl.py 413 388 6% 44-48, 215-311, 443-524, 580-607, 654-668, 710-733, 773-791, 890-988, 1039-1057, 1079-1080, 1128-1146, 1196-1229, 1288-1311 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_pade.py 27 22 19% 46-67 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack.py 65 49 25% 156-158, 289-290, 353-368, 417-429, 477-491, 531-534, 586-601, 654-657, 719-722 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack2.py 366 304 17% 171-196, 201-208, 211-232, 235-241, 244-256, 265-276, 303-317, 324-326, 330-332, 342, 380, 405-408, 415-421, 468-471, 524-525, 602-618, 741-770, 793, 801, 805, 841-880, 955-961, 983, 1002-1004, 1048-1069, 1110-1136, 1172-1198, 1273-1281, 1304, 1385-1396, 1486-1504, 1670-1727 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/interpolate.py 920 819 11% 32-34, 84-94, 201-252, 279-313, 318-328, 335, 431-540, 547, 552-581, 585, 591-614, 623-631, 637-646, 649, 652-654, 660-669, 687-700, 708-751, 754-758, 770-777, 784-787, 808-870, 902-923, 986, 1013-1031, 1063-1085, 1108-1167, 1217-1239, 1268, 1284-1296, 1313-1332, 1422, 1443-1477, 1502-1533, 1558-1597, 1600-1603, 1621-1639, 1705-1757, 1814-1843, 1873-1883, 1946-1965, 1978-1984, 1987-1991, 1994-1997, 2028-2062, 2069-2096, 2103-2136, 2163-2169, 2197-2203, 2236-2264, 2287-2312, 2415-2452, 2468-2501, 2505-2516, 2519-2521, 2525-2541, 2601-2676, 2689-2704, 2707, 2710-2712, 2717-2724 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/ndgriddata.py 47 38 19% 59-65, 77-81, 193-228 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/polyint.py 203 169 17% 18, 56-60, 78-80, 86, 90-92, 96-103, 106-111, 114-131, 134-139, 177-188, 216-218, 293-316, 319-326, 329-355, 400-406, 448-461, 502-513, 532-537, 559-577, 599, 602-617, 666 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/rbf.py 105 83 21% 145, 148, 151, 154, 157, 160, 163, 167-216, 223-268, 274-275, 278, 281-290 /usr/local/lib/python3.8/dist-packages/scipy/io/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/_fortran.py 79 61 23% 111-126, 129-136, 161-169, 245-294, 317, 340, 349, 352, 355 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/_fortran_format_parser.py 165 124 25% 34, 62-66, 69-71, 74-80, 84-90, 94, 123-132, 141-144, 147-153, 157-163, 167, 172-174, 177, 180, 185-186, 189-191, 194-205, 234, 237-250, 253-257, 260-261, 264-305, 308-312 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/hb.py 265 215 19% 45, 70-120, 141-205, 217-281, 285-299, 303-308, 313-332, 336-360, 391-400, 403-412, 416, 421, 437-443, 447, 451, 455, 459, 463, 466, 469, 496-504, 534-547 /usr/local/lib/python3.8/dist-packages/scipy/io/idl.py 429 382 11% 81-84, 89-90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 152-153, 163-170, 175-182, 187-224, 233-269, 278-316, 322-425, 431-445, 451-492, 498-546, 552-565, 570-631, 652, 655, 658, 705-873 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/byteordercodes.py 19 12 37% 57-69 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio.py 65 47 28% 19-22, 32-47, 71-80, 215-225, 266-279, 314-317 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio4.py 281 222 21% 91-95, 102-106, 110-127, 135-149, 168-185, 202-207, 221-223, 254-267, 273-301, 313-314, 317-326, 333-334, 351-357, 375, 386-406, 410-422, 442-446, 451-452, 455, 458, 478-490, 504-519, 522-541, 544-560, 567-584, 590-594, 616-618 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5.py 371 312 16% 151-167, 172-175, 179-186, 196-198, 217-233, 252, 261-307, 311-331, 375-400, 422-455, 471-477, 480, 483, 487-496, 500-504, 508-516, 536-559, 562-570, 587-590, 601-630, 633-655, 660-698, 703-718, 721-726, 729-733, 736-738, 742-755, 761-765, 789-798, 802-809, 829-849 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5_params.py 77 11 86% 195-197, 227-231, 235, 242-243, 249-250 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/miobase.py 108 70 35% 20, 179-184, 221-241, 305-318, 324, 328, 332, 363-377, 381-383, 387, 390-393, 398, 403-415 /usr/local/lib/python3.8/dist-packages/scipy/io/mmio.py 444 375 16% 54, 75, 101, 115, 119, 123, 127, 131, 135, 139, 150-151, 164-165, 178-179, 191, 196, 226-269, 294-327, 332-384, 389, 398, 417-425, 451-460, 469-479, 483-485, 490-657, 662-802, 812-828, 833-839 /usr/local/lib/python3.8/dist-packages/scipy/io/netcdf.py 486 393 19% 239-284, 289-293, 297-319, 323, 326, 348-352, 385-397, 408-409, 413-421, 425-428, 431-439, 442, 445-452, 455-479, 482-512, 515-553, 556-558, 561-602, 606-616, 619, 622-631, 634-635, 638-647, 650-733, 736-758, 761-776, 779-782, 785, 789, 793, 796, 799-802, 805-808, 867-876, 881-885, 896, 905, 919, 938-946, 958, 970, 973-988, 991-1021, 1027-1028, 1037-1045, 1058-1065, 1077-1093 /usr/local/lib/python3.8/dist-packages/scipy/linalg/__init__.py 36 4 89% 222-223, 227-228 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_ldl.py 85 74 13% 123-156, 207-241, 268-297, 335-354 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_polar.py 16 11 31% 98-112 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_qz.py 127 110 13% 19-34, 38-43, 47-52, 56-61, 65-71, 76-145, 262-265, 358-405 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_expm_frechet.py 153 138 10% 91-114, 122-127, 166-173, 177-187, 191-203, 207-222, 226-278, 298, 334-350, 393-411 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_matfuncs_sqrtm.py 85 74 13% 52-116, 163-196 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_procrustes.py 18 13 28% 76-91 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_sketches.py 15 8 47% 49-54, 167-168 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_solvers.py 216 194 10% 86-107, 159-199, 214-218, 228-233, 306-324, 446-528, 652-736, 778-844 /usr/local/lib/python3.8/dist-packages/scipy/linalg/basic.py 385 346 10% 27-37, 137-258, 330-359, 433-472, 568-596, 669-702, 706-711, 862-907, 952, 979, 981, 1034-1043, 1157-1246, 1304-1318, 1373-1391, 1451-1470, 1575-1619 /usr/local/lib/python3.8/dist-packages/scipy/linalg/blas.py 86 12 86% 301-310, 341, 352, 381-384 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp.py 364 338 7% 41-47, 51-73, 78-115, 214-267, 374-489, 503-530, 640-695, 767, 858, 951, 1031, 1124-1194, 1199-1203, 1252-1283, 1368-1431 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_cholesky.py 72 61 15% 19-44, 90-92, 154-156, 194-213, 274-286, 334-353 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_lu.py 48 38 21% 71-86, 135-148, 209-223 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_qr.py 130 121 7% 16-25, 121-173, 251-320, 386-424 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_schur.py 104 85 18% 119-178, 191-197, 201-210, 266-295 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_svd.py 88 60 32% 111, 116, 118, 132, 134, 139, 225-232, 273-281, 323-330, 384-391, 459-496 /usr/local/lib/python3.8/dist-packages/scipy/linalg/flinalg.py 30 23 23% 14-19, 23, 32-58 /usr/local/lib/python3.8/dist-packages/scipy/linalg/lapack.py 45 4 91% 808, 814, 826, 830 /usr/local/lib/python3.8/dist-packages/scipy/linalg/linalg_version.py 5 0 100% /usr/local/lib/python3.8/dist-packages/scipy/linalg/matfuncs.py 130 103 21% 52-55, 84-89, 136-138, 195-208, 255-256, 291-295, 330-334, 371-372, 409-410, 447-448, 485-486, 551-590, 626-670 /usr/local/lib/python3.8/dist-packages/scipy/linalg/misc.py 42 30 29% 141-181, 192-194 /usr/local/lib/python3.8/dist-packages/scipy/linalg/special_matrices.py 223 197 12% 62-73, 104-106, 138-140, 193-203, 239-244, 290-301, 344-358, 413-430, 465-471, 535-553, 600-617, 654-662, 698-700, 759-777, 840-863, 938-973, 1033-1043, 1109-1119, 1172-1196 /usr/local/lib/python3.8/dist-packages/scipy/misc/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/scipy/misc/common.py 73 65 11% 35-47, 85-120, 154-159, 195-204, 297-303 /usr/local/lib/python3.8/dist-packages/scipy/misc/doccer.py 29 8 72% 15, 21, 27, 33, 39, 44, 49, 54 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_docstrings.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_support.py 43 13 70% 42, 44, 46, 48, 52, 64-65, 78, 80-81, 83, 89, 91 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/filters.py 399 298 25% 81, 85, 90, 129-133, 141, 156-164, 332-340, 369-377, 404-419, 447-449, 487-495, 525-544, 583-591, 598-623, 648, 754, 783-795, 842-857, 897-909, 954-966, 971-1033, 1069, 1106, 1113-1162, 1201-1202, 1240, 1280, 1348-1364, 1421-1448 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/fourier.py 69 58 16% 42-55, 59-70, 120-129, 179-187, 241-249, 298-306 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/interpolation.py 210 182 13% 93-105, 127-139, 245-263, 329-351, 433-487, 520-539, 589-616, 677-746 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/measurements.py 315 288 9% 178-236, 298-305, 377-458, 463-466, 500-573, 617-618, 669-670, 721-722, 773, 782-884, 946, 1025, 1086, 1155-1164, 1210-1219, 1275-1293, 1355-1364, 1419-1424, 1459-1498 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/morphology.py 403 368 9% 50-53, 106-122, 208, 210, 218-285, 381, 501-513, 629-636, 775-782, 871-892, 1022, 1097-1107, 1213-1216, 1338-1364, 1443-1447, 1526-1530, 1636-1643, 1681-1695, 1739-1752, 1797-1810, 1872-1947, 1986-2063, 2175-2230 /usr/local/lib/python3.8/dist-packages/scipy/optimize/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py 219 182 17% 20, 23-24, 27-31, 34, 61-92, 102-146, 151-173, 177-178, 208-216, 219, 222-232, 238-242, 246-247, 264-265, 268-270, 278-280, 283-286, 304-305, 313-315, 321, 626-701, 705-707, 711-716, 720-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_constraints.py 180 157 13% 94-101, 136-139, 168-170, 173-176, 215-251, 267-274, 284-294, 304-307, 312-317, 324-411, 419-450 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentiable_functions.py 351 317 10% 31-160, 163-165, 168-170, 173-175, 178-181, 184-187, 190-193, 196-200, 223-420, 423-425, 428-429, 432-434, 437-439, 442-444, 447-449, 452-454, 458-461, 472-489, 492-494, 497-501, 504-505, 508-510, 521-528 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentialevolution.py 364 310 15% 294-308, 475-599, 607-637, 644-652, 666-687, 694, 702-704, 711, 730-849, 869-889, 894-908, 928, 948-959, 962, 965, 969-970, 974-975, 1011-1020, 1035-1144, 1150, 1154, 1158-1159, 1163-1195, 1199-1200, 1205-1206, 1211-1216, 1220-1224, 1228-1233, 1237-1242, 1249-1253, 1261-1262, 1265, 1294-1322, 1325, 1341-1346 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_dual_annealing.py 285 250 12% 55-71, 79-111, 115-127, 153-159, 166-195, 199-205, 209-210, 240-256, 259-277, 280-306, 315-355, 362-370, 373-374, 388-405, 409-425, 602-689 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_hessian_update_strategy.py 134 94 30% 52, 70, 87, 100, 136-145, 151-159, 162, 180-201, 217-220, 231-237, 279, 282-288, 311-312, 329-330, 334-375, 407-408, 412-430 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog.py 78 65 17% 78-110, 155-161, 510-581 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_ip.py 247 217 12% 84-119, 189-329, 345-349, 368-375, 399-412, 427-432, 447-453, 471-502, 534-544, 698-822, 1083-1127 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_rs.py 190 171 10% 47-99, 108-134, 161-237, 249-269, 277, 285-288, 295-310, 327-402, 522-558 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_simplex.py 107 98 8% 89-95, 154-166, 212-229, 355-435, 591-659 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_util.py 489 470 4% 57-68, 92-99, 118-121, 183-386, 497-778, 873-881, 971-1085, 1092, 1100-1130, 1138-1145, 1170-1173, 1241-1293, 1350-1396, 1473-1485 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsap.py 20 17 15% 79-105 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/bvls.py 116 109 6% 13-16, 20-177 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/common.py 295 261 12% 36-56, 108-170, 196-221, 235-247, 284-301, 316-324, 350-363, 371, 392-400, 418-439, 448-466, 499-510, 515-541, 548, 555-565, 571, 578-588, 597-600, 605-615, 620-631, 637-648, 660-671, 679-689, 697-707, 712-722, 730-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/dogbox.py 149 138 7% 65-77, 94-106, 125-149, 154-330 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/least_squares.py 255 229 10% 43-92, 98-105, 109-126, 130-149, 153-162, 169-177, 181-186, 190-195, 199-204, 212-237, 748-940 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/lsq_linear.py 82 70 15% 16-24, 218-317 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf.py 290 278 4% 121-126, 133-205, 210-402, 410-564 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf_linear.py 144 132 8% 53-69, 74-90, 95-142, 147-248 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_minimize.py 176 155 12% 479-636, 756-794, 799-806, 811-829 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_numdiff.py 254 237 7% 46-91, 100-103, 107-114, 147-175, 330-398, 404-441, 445-481, 486-561, 625-639 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_remove_redundancy.py 147 136 7% 30-31, 53-54, 83-92, 96-104, 139-230, 266-357, 393-449 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root.py 89 69 22% 153-203, 207-208, 246-257, 266-305, 369, 434, 476, 513, 550, 590, 654 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root_scalar.py 124 104 16% 30-33, 38-43, 47-49, 53-55, 58, 181-287, 306, 325, 343, 366, 392, 423, 442, 461 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo.py 657 595 9% 417-447, 456-655, 673-707, 721-737, 744-757, 762-778, 782-789, 793-795, 799-800, 803-804, 814-835, 838-845, 855-868, 871-879, 888-899, 907-910, 917-955, 974-1020, 1024-1028, 1031-1034, 1044-1052, 1070-1086, 1102-1104, 1124-1182, 1190-1199, 1203-1206, 1225-1240, 1245-1282, 1290-1292, 1302-1357, 1366-1375, 1384-1390, 1395-1403, 1407-1409, 1415-1421, 1431-1458, 1466-1472, 1476-1508, 1514-1531, 1534-1542, 1550, 1555-1568, 1574-1596, 1601-1605, 1610-1617, 1620-1628, 1631-1645, 1651-1671 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/sobol_seq.py 122 114 7% 30-40, 53-58, 97-102, 140-145, 197-372 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/triangulation.py 359 321 11% 8-46, 49, 56-85, 89-115, 119-141, 146-159, 163-174, 182, 195-225, 231-243, 258-298, 315-362, 372-451, 456-464, 467, 470-471, 477-480, 487-490, 497, 503-504, 513-517, 528-530, 536-569, 572, 575-585, 588-592, 596-600, 603-609, 616-626, 629-661 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_spectral.py 106 93 12% 66-164, 207-238, 245-251, 255, 259 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trlib/__init__.py 6 3 50% 7-12 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion.py 135 114 16% 25-35, 38, 43-45, 50-52, 57-59, 62-65, 70-72, 80-95, 98, 133-266 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/canonical_constraint.py 253 234 8% 43-48, 53-69, 78-91, 101-149, 153-181, 185-221, 225-261, 265-327, 337-390 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py 105 96 9% 14-15, 50-218 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py 175 152 13% 29-30, 33-36, 46-48, 51-57, 62-100, 107-112, 314-544 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/projections.py 164 145 12% 10, 41-55, 62-90, 96-172, 179-233, 240-287, 364-406 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/qp_subproblem.py 215 201 7% 45-63, 99-149, 189-234, 286-303, 308, 313, 364-409, 492-638 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/report.py 32 10 69% 11-16, 23-28, 32 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py 148 124 16% 38-57, 60-61, 64, 67, 80-86, 93-96, 100, 108-114, 130-136, 140, 143-163, 179-195, 199-205, 209-221, 226-240, 251-264, 287-347 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_dogleg.py 40 30 25% 31-35, 47-51, 57-62, 98-124 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_exact.py 139 124 11% 35-41, 80-122, 137-143, 173-185, 218-254, 264-285, 290-432 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_krylov.py 11 7 36% 22-59 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_ncg.py 51 42 18% 33-39, 71-128 /usr/local/lib/python3.8/dist-packages/scipy/optimize/cobyla.py 72 60 17% 139-170, 196-258 /usr/local/lib/python3.8/dist-packages/scipy/optimize/lbfgsb.py 136 123 10% 174-208, 265-380, 412-422, 440-456, 468-478 /usr/local/lib/python3.8/dist-packages/scipy/optimize/linesearch.py 320 299 7% 69-103, 150-186, 267-322, 384-468, 481-502, 512-523, 532-603, 644-659, 666-668, 685-726, 773-802, 852-883 /usr/local/lib/python3.8/dist-packages/scipy/optimize/minpack.py 284 255 10% 26-45, 139-165, 206-259, 384-457, 461-478, 482-491, 495-508, 683-813, 821-844, 848, 852, 856-870, 914-916 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nnls.py 21 16 24% 60-82 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nonlin.py 628 491 22% 139, 144-147, 152-154, 158-160, 269-367, 375-415, 434-453, 456-472, 521-530, 533, 536, 539, 542-547, 552-558, 562, 566, 573-658, 667-678, 681, 684-688, 703-708, 712-718, 723-746, 750-752, 756-758, 762-764, 768-770, 773-781, 784-790, 794-797, 803-808, 814-819, 850-884, 951-973, 977-978, 981, 984-988, 991, 994, 997, 1000-1006, 1046-1051, 1115-1121, 1124-1144, 1147-1167, 1170-1192, 1225-1226, 1229-1230, 1233, 1236, 1239, 1242, 1245, 1248, 1275-1276, 1279, 1282, 1285, 1288, 1291, 1294, 1325-1328, 1331-1332, 1335, 1338, 1341, 1344, 1347, 1350-1353, 1438-1476, 1479-1481, 1484-1491, 1494-1498, 1501-1508, 1511-1524 /usr/local/lib/python3.8/dist-packages/scipy/optimize/optimize.py 1239 1166 6% 59-61, 64-67, 70-74, 115-118, 124-129, 132, 140-145, 152, 159-164, 197-200, 229-238, 270-277, 310-317, 321-329, 435-453, 498-682, 690-707, 765, 809-813, 818-820, 841-869, 945-964, 990-1098, 1253-1271, 1297-1406, 1500-1519, 1545-1672, 1742-1750, 1771-1894, 1901-1910, 1914, 1918-1945, 1949-2053, 2056-2059, 2132-2138, 2158-2171, 2234-2239, 2253-2315, 2352-2417, 2426-2430, 2550-2570, 2599-2712, 2716-2728, 2903-2985, 2993-2994, 2998, 3069-3166, 3170-3242, 3246 /usr/local/lib/python3.8/dist-packages/scipy/optimize/slsqp.py 190 178 6% 57-66, 181-212, 237-475, 483-531 /usr/local/lib/python3.8/dist-packages/scipy/optimize/tnc.py 102 76 25% 243-277, 342-413, 420-441 /usr/local/lib/python3.8/dist-packages/scipy/optimize/zeros.py 471 417 11% 54-62, 65-68, 73-81, 86-93, 266-365, 377-469, 546-554, 643-651, 773-781, 878-886, 896-899, 904-908, 919-927, 932-937, 949-972, 980-992, 1002, 1013-1037, 1048-1064, 1067-1077, 1081-1085, 1089, 1092, 1096-1123, 1127-1132, 1139-1213, 1218-1244, 1353-1375 /usr/local/lib/python3.8/dist-packages/scipy/signal/__init__.py 44 2 95% 330, 343 /usr/local/lib/python3.8/dist-packages/scipy/signal/_arraytools.py 49 41 16% 43-46, 54, 92-106, 143-155, 196-209, 237-243 /usr/local/lib/python3.8/dist-packages/scipy/signal/_max_len_seq.py 31 25 19% 104-137 /usr/local/lib/python3.8/dist-packages/scipy/signal/_peak_finding.py 225 201 11% 66-81, 138, 194, 248-250, 264-267, 281-293, 307-319, 459-462, 584-590, 625-640, 673-678, 713-723, 935-1006, 1055-1126, 1164-1190, 1283-1299 /usr/local/lib/python3.8/dist-packages/scipy/signal/_savitzky_golay.py 81 70 14% 98-141, 153-165, 179-209, 219-223, 328-353 /usr/local/lib/python3.8/dist-packages/scipy/signal/_upfirdn.py 40 30 25% 59-63, 67-69, 75-86, 90-100, 207-210 /usr/local/lib/python3.8/dist-packages/scipy/signal/bsplines.py 202 177 12% 19, 28-43, 60-114, 125-129, 148-149, 157-167, 175-185, 189-193, 197, 202-206, 210-237, 241-252, 256-267, 292-295, 319-322, 337-358, 373-394 /usr/local/lib/python3.8/dist-packages/scipy/signal/filter_design.py 1094 1014 7% 47-56, 94-117, 180-193, 255-272, 424-477, 562-583, 662-693, 698-706, 821-828, 881-932, 987-1000, 1064-1071, 1095-1130, 1168, 1193-1200, 1230-1240, 1245-1250, 1421-1515, 1539-1561, 1592-1632, 1691-1704, 1765-1787, 1851-1876, 1938-1965, 2017-2046, 2155-2175, 2294-2386, 2393-2398, 2457-2474, 2520-2534, 2583-2600, 2652-2679, 2731-2759, 2866, 2983, 3094, 3218, 3380, 3385, 3389, 3421-3445, 3525-3615, 3693-3753, 3833-3915, 3993-4054, 4067-4074, 4091-4112, 4129-4154, 4161-4163, 4167-4179, 4201-4263, 4285-4288, 4318-4332, 4340-4357, 4371-4396, 4404-4435, 4449-4463, 4540-4568, 4648, 4728, 4764-4803 /usr/local/lib/python3.8/dist-packages/scipy/signal/fir_filter_design.py 272 250 8% 26-32, 79-85, 127-128, 250-261, 389-482, 593-684, 836-855, 968-1068, 1082-1090, 1215-1265 /usr/local/lib/python3.8/dist-packages/scipy/signal/lti_conversion.py 160 142 11% 76-114, 118-121, 125-126, 130-133, 137-139, 143-148, 176-195, 256-284, 304, 334, 405-504 /usr/local/lib/python3.8/dist-packages/scipy/signal/ltisys.py 919 757 18% 54-58, 66-70, 75, 79-82, 87, 92, 103-106, 117-120, 131-134, 206-221, 229, 236, 243, 250, 274, 284, 295, 383-398, 406-409, 414, 418, 425, 432, 439, 468, 479, 561-578, 583-592, 596, 606, 610-617, 622, 626, 638-639, 651, 663, 676, 697-702, 722-727, 800, 940-958, 963-972, 976, 987, 991-998, 1003, 1007, 1012, 1016, 1028-1030, 1042, 1055, 1067, 1131, 1207-1208, 1304-1317, 1322-1333, 1337, 1347, 1365-1404, 1411-1421, 1428, 1434-1473, 1479-1482, 1485-1488, 1491-1494, 1501-1508, 1513, 1517, 1522, 1526-1527, 1532, 1536-1537, 1542, 1546, 1558-1561, 1578, 1596, 1609, 1677, 1799-1853, 1861-1867, 1926-2032, 2058-2064, 2114-2133, 2200-2222, 2277-2292, 2358-2373, 2432-2437, 2494-2522, 2529, 2540-2580, 2590-2600, 2613-2636, 2655-2710, 2720-2763, 2777-2886, 2896-2912, 3095-3263, 3322-3379, 3431-3465, 3516-3550, 3615-3648, 3712-3722 /usr/local/lib/python3.8/dist-packages/scipy/signal/signaltools.py 1148 1072 7% 27, 47-50, 55-58, 77-93, 197-259, 265-270, 305-331, 365-391, 419-428, 523-544, 572-655, 736-860, 876-881, 889-892, 904-935, 963-977, 984-985, 996-1002, 1019-1037, 1146-1174, 1267-1296, 1353-1359, 1394-1409, 1437-1460, 1537-1549, 1627-1644, 1681-1692, 1817-1885, 1928-1953, 1997-2009, 2098-2120, 2145-2180, 2208-2210, 2265-2297, 2356-2372, 2377-2397, 2401-2424, 2503-2539, 2598-2644, 2648-2675, 2733-2749, 2832-2924, 3054-3134, 3184-3212, 3254-3297, 3391-3433, 3489-3505, 3557-3684, 3844-3885, 3890-3920, 3924-3927, 4000-4039, 4130-4151, 4202-4245 /usr/local/lib/python3.8/dist-packages/scipy/signal/spectral.py 363 339 7% 142-158, 268-289, 452-457, 584-601, 734-771, 870-895, 996-1022, 1172-1178, 1348-1456, 1566-1576, 1669-1870, 1896-1920, 1959-1981, 2001-2002 /usr/local/lib/python3.8/dist-packages/scipy/signal/waveforms.py 120 107 11% 58-88, 139-162, 224-262, 427-430, 440-483, 577-580, 591-593, 669-681 /usr/local/lib/python3.8/dist-packages/scipy/signal/wavelets.py 136 123 10% 29-76, 90-92, 127-198, 253-261, 301-308, 384-388, 462-473 /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/windows.py 289 243 16% 21-23, 28-31, 36-39, 112-121, 168-174, 226-238, 290-302, 349-357, 442, 501, 548, 609-610, 700-708, 790, 795, 856-878, 925-933, 1020, 1098, 1207-1216, 1271-1279, 1342-1349, 1438-1476, 1545-1563, 1616-1622, 1698-1710, 1876-1970, 1975-1982, 2095-2124 /usr/local/lib/python3.8/dist-packages/scipy/sparse/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/_index.py 221 190 14% 24-27, 35-75, 78-126, 129-150, 157-180, 185-191, 196-202, 205, 208, 211, 214, 217, 220, 223, 226, 229, 232, 235, 238, 242-244, 252-283, 288-322, 326-328 /usr/local/lib/python3.8/dist-packages/scipy/sparse/_matrix_io.py 42 32 24% 16, 63-80, 131-156 /usr/local/lib/python3.8/dist-packages/scipy/sparse/base.py 455 344 24% 71-75, 81-82, 86, 123-131, 157, 182-189, 194-203, 207-208, 212, 223, 239, 250, 254, 257-258, 263-281, 284-287, 295, 313-328, 340, 344, 348, 363, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 407, 410-424, 427, 430-443, 446-455, 466-530, 534, 537, 540, 543, 546-554, 561-564, 567-570, 577-617, 620, 624, 628, 632, 635, 638, 641, 644, 647, 650, 653-675, 678-691, 718, 736-741, 744, 756, 759, 762, 780-782, 791-799, 808-816, 851, 883, 894, 902, 910, 918, 926, 937, 945, 953, 993-1025, 1064-1097, 1124, 1146-1149, 1152-1176, 1179-1189 /usr/local/lib/python3.8/dist-packages/scipy/sparse/bsr.py 315 266 16% 123-214, 225-271, 279, 283-287, 292-293, 299-308, 317, 320, 330, 336, 339, 342-351, 354-364, 367-421, 436-441, 444-463, 468, 479-505, 508, 513-534, 546-561, 568-594, 599-607, 613-627, 635-676, 684-688, 722 /usr/local/lib/python3.8/dist-packages/scipy/sparse/compressed.py 737 654 11% 31-108, 111-123, 130-136, 148-195, 212-215, 219-248, 252-279, 283-313, 316, 322, 328, 334, 344-352, 355, 358, 365-458, 465-475, 478-489, 492-528, 531-539, 548-568, 571, 577, 592-611, 630-635, 642-647, 650-653, 657-668, 672-673, 678-698, 704-730, 736-758, 764-775, 782-793, 797-798, 801-802, 806-823, 826-853, 856-872, 880-911, 918-931, 942-1003, 1010-1017, 1023-1037, 1050-1053, 1068-1074, 1077-1079, 1089-1098, 1110-1113, 1116, 1123-1125, 1135-1138, 1143-1153, 1156-1187, 1201-1207, 1212-1242, 1248-1269, 1273-1290 /usr/local/lib/python3.8/dist-packages/scipy/sparse/construct.py 232 206 11% 62, 138-188, 218, 252-273, 311-355, 384-398, 406-431, 465, 499, 545-623, 668-677, 750-793, 842 /usr/local/lib/python3.8/dist-packages/scipy/sparse/coo.py 294 251 15% 129-198, 201-236, 242-263, 271-291, 294-300, 306-317, 323-330, 352-372, 394-414, 417-420, 425-443, 448-454, 459-475, 480-513, 521-525, 533-537, 541-554, 561-564, 571-579, 583-586, 589-593, 619 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csc.py 87 58 33% 111-119, 125-126, 129-132, 137-155, 164-180, 188-194, 200-206, 209, 212-214, 217-219, 222, 225, 228, 235, 261 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_laplacian.py 51 44 14% 69-81, 85, 89-111, 115-128 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_validation.py 31 25 19% 17-58 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csr.py 135 103 24% 129-137, 143-156, 161-164, 169-186, 191-222, 231, 234-242, 248-256, 263-271, 275, 278-307, 311-313, 316, 319, 322-325, 351 /usr/local/lib/python3.8/dist-packages/scipy/sparse/data.py 184 138 25% 23, 26, 29, 33-35, 38, 41, 44, 47, 50-53, 56-60, 63-68, 71-79, 84-89, 94, 99, 113-119, 126, 135-136, 148-156, 166-187, 191-215, 218-252, 255-289, 321, 353, 376, 399 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dia.py 224 188 16% 79-146, 149-150, 158-164, 167-168, 171-181, 187-225, 230-241, 244, 247-276, 279-282, 287-305, 311-318, 323-343, 349-365, 374-377, 380-392, 420 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dok.py 275 213 23% 23, 79-111, 115, 122, 125-128, 133-136, 139, 145, 151-158, 161, 164, 167, 170-191, 194, 197, 200-201, 204-205, 209-216, 220-227, 230-234, 237-246, 249-277, 280-301, 304-309, 312-316, 320-323, 327-332, 335-338, 341-346, 349-352, 358, 365-374, 380-384, 387-389, 394-404, 409-411, 416, 421-429, 457 /usr/local/lib/python3.8/dist-packages/scipy/sparse/extract.py 22 14 36% 38-42, 101-103, 162-164, 168-171 /usr/local/lib/python3.8/dist-packages/scipy/sparse/lil.py 291 232 20% 89-132, 135-136, 139-140, 143-147, 150-154, 160-172, 175, 181-185, 190-193, 198-206, 210-216, 220-226, 229-231, 234-235, 238, 241, 244-245, 248, 251-252, 255-256, 260-261, 265-271, 289-300, 303, 307-308, 314-324, 328-336, 339-350, 353-360, 363-369, 374-401, 406-426, 431-435, 440, 445-448, 454-484, 512-527, 553 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_expm_multiply.py 255 225 12% 18-23, 28-33, 38-43, 48-55, 140-144, 172-197, 204-223, 313, 342-346, 352, 358-360, 366-369, 375, 399, 414-417, 455-475, 506-511, 556-629, 639-648, 655-677, 684-713 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_norm.py 70 63 10% 15-19, 110-184 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_onenormest.py 199 177 11% 86-119, 130-139, 154-157, 162, 166-174, 178-180, 187-190, 194-197, 204-211, 215, 219, 261-322, 366-468 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/_add_newdocs.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py 197 173 12% 56-59, 63-82, 132-233, 302-324, 386-410, 442-469, 528-607 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py 726 642 12% 280-281, 297-299, 307, 313-364, 367-377, 435-533, 536-573, 576-595, 636-719, 722-759, 762-896, 900-904, 914-917, 922-927, 937-939, 942, 949-951, 961-974, 977-982, 992-1020, 1023-1028, 1032, 1037-1044, 1048-1054, 1058-1089, 1251-1349, 1554-1689, 1694-1713, 1717, 1721, 1804-1910 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py 323 310 4% 34-44, 52-57, 64-72, 77-79, 84-114, 119-125, 287-711 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/interface.py 346 245 29% 140-152, 160-168, 173-175, 184, 196, 222-243, 269-290, 294-298, 324-339, 364-377, 381-384, 387, 390, 407-419, 423-426, 429-432, 435-438, 441-444, 447-450, 453, 456, 459-465, 481, 491, 497, 501, 509-518, 521-524, 527, 530-533, 536-539, 542, 553-556, 559, 562, 565, 568, 573-576, 580, 583, 587, 590, 593-598, 603-610, 613, 616, 619, 622, 625-626, 631-639, 642, 645, 648, 651, 654-655, 660-666, 669, 672, 675, 678, 681-682, 687-695, 698-701, 704, 707, 710, 713, 716-717, 722-725, 728, 731-733, 737-740, 744, 747, 752, 755, 758, 761, 764, 767, 795-823 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/_gcrotmk.py 192 182 5% 66-182, 267-490 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/iterative.py 421 386 8% 73-77, 97-118, 137-198, 209-265, 276-337, 347-414, 514-647, 717-802 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lgmres.py 69 59 14% 128-235 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsmr.py 185 177 4% 197-482 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsqr.py 200 192 4% 81-95, 311-570 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/minres.py 203 196 3% 71-343, 347-363 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/utils.py 56 46 18% 23-27, 31, 65-123 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/matfuncs.py 354 279 21% 77-82, 103-115, 120-130, 155-173, 179-188, 191-193, 196-200, 203-205, 209, 218-235, 238-240, 243-246, 249-251, 255-256, 296, 338, 367-383, 387-390, 394-397, 401-404, 408-411, 415-418, 422-424, 428-430, 434-436, 440-442, 446-454, 458-466, 470-478, 482-490, 493-498, 501-506, 509-514, 517-525, 528-547, 595, 603-677, 700-709, 736-740, 762-764, 783-813, 833-855 /usr/local/lib/python3.8/dist-packages/scipy/sparse/sputils.py 176 146 17% 42-53, 58-63, 70, 80-90, 94, 105-118, 143-171, 176-180, 185, 194-207, 215-225, 229, 235, 241, 245-264, 269-314, 326-331, 338-339, 346-349, 353-356, 360-363 /usr/local/lib/python3.8/dist-packages/scipy/spatial/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/_plotutils.py 78 65 17% 11-27, 31-35, 81-90, 136-148, 212-264 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_procrustes.py 25 20 20% 101-132 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_spherical_voronoi.py 68 57 16% 133-166, 171-206, 217-243, 273-277 /usr/local/lib/python3.8/dist-packages/scipy/spatial/distance.py 633 503 21% 135-165, 170-172, 176-178, 182-183, 194-195, 200-220, 224-240, 244-259, 263-269, 273-286, 290-292, 307-309, 314-329, 334-339, 343-346, 350-357, 451-457, 508-524, 580-581, 620, 661-674, 708-721, 766, 813-820, 883-894, 941-950, 985-991, 1031-1037, 1079-1084, 1122-1130, 1170-1178, 1221-1236, 1286-1296, 1337-1342, 1353, 1397-1412, 1457-1462, 1507-1519, 1565-1578, 1624-1638, 1718, 1722, 1724-1731, 1999, 2006-2012, 2017, 2023, 2027, 2031, 2039-2050, 2069-2094, 2149-2212, 2254-2302, 2334-2339, 2344-2358, 2378-2380, 2403, 2407, 2708-2793 /usr/local/lib/python3.8/dist-packages/scipy/spatial/kdtree.py 419 379 10% 39-55, 78-83, 93-95, 98, 102, 120-126, 140, 154, 168, 182, 243-251, 256, 259, 262, 265, 268, 272-273, 277-281, 284-323, 329-407, 492-547, 550-572, 625-636, 663-705, 731-812, 842-889, 912-942, 978-996 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_groups.py 56 48 14% 6-58, 62-76, 80-90, 94-99, 103-105, 109-140 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_spline.py 176 159 10% 18-25, 30, 48-65, 83-104, 124-151, 168, 188, 220-248, 331-361, 364-404, 424-456 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/rotation.py 470 416 11% 15-17, 29-142, 146-150, 154-158, 162-173, 369-394, 407, 474-479, 568-612, 618, 674-707, 803-860, 907-910, 964-999, 1004, 1052-1073, 1159-1179, 1298-1329, 1397-1406, 1439-1443, 1467-1475, 1510-1527, 1560-1617, 1655, 1703, 1722-1727, 1768-1775, 1783-1838, 1915-1968, 2049-2070, 2090-2114 /usr/local/lib/python3.8/dist-packages/scipy/special/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/_basic.py 524 449 14% 100-112, 179-213, 250-254, 279-285, 305, 325, 345, 365, 397-401, 433-437, 469-473, 481-487, 515-519, 547-551, 602-606, 635-639, 667-671, 699-703, 747-755, 799-807, 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, 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/usr/local/lib/python3.8/dist-packages/scipy/stats/_discrete_distns.py 446 296 34% 43, 46, 49, 52-54, 58, 61-63, 66-67, 70-73, 76-84, 87-89, 119, 122, 126, 129, 134, 137, 140, 143, 146, 149, 193-194, 197, 200, 203-205, 208, 211-230, 263, 266, 270, 273-274, 277-278, 282-283, 286-289, 292-298, 331, 334, 337, 340, 343-344, 347, 350-351, 354-356, 359-364, 433, 436, 439-441, 444-449, 454, 459-471, 474-476, 483-489, 492-501, 504-513, 545, 548, 552, 555-567, 598, 601, 604-605, 609, 612-613, 616-617, 620-623, 626-631, 666, 669, 672-673, 676, 679-680, 683-686, 690-691, 694-698, 701-702, 731, 734, 739-740, 743-744, 747-751, 754-764, 794, 797, 801-802, 805-806, 809-812, 815-821, 825-836, 839, 871, 874, 878-879, 882, 915, 918-921, 924-929, 932-935, 938, 976-977, 981-985, 988-992, 995-999, 1040-1043, 1046, 1049, 1052, 1055, 1058, 1061, 1064-1077 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distn_infrastructure.py 1393 1036 26% 42, 361-363, 367-395, 402-406, 413-417, 424-432, 436, 440, 443, 446, 449, 452, 455, 458, 461-463, 466, 469, 472-474, 477, 480, 483, 486, 489, 492, 495, 498, 501, 508-512, 516, 541-545, 567-571, 575, 579, 607, 611, 614, 617-620, 640-649, 665, 668, 671, 679, 733-734, 742-751, 768, 771, 778, 785-788, 801-859, 871-874, 895, 898-899, 902-903, 912-914, 917-918, 921, 924-925, 928, 931, 961-995, 1024-1110, 1135-1146, 1165-1192, 1219, 1241-1245, 1267-1271, 1293-1295, 1321-1328, 1349-1351, 1362-1368, 1612, 1621, 1629, 1643-1651, 1654, 1657-1678, 1683, 1686-1687, 1692, 1695, 1698, 1702, 1705-1706, 1709, 1736-1752, 1778-1795, 1819-1837, 1861-1880, 1904-1922, 1949-1968, 1992-2014, 2038-2060, 2063, 2066-2072, 2082-2089, 2092-2102, 2109-2114, 2118-2121, 2132-2176, 2276-2314, 2336-2380, 2402-2412, 2415-2438, 2516-2540, 2546-2550, 2554-2609, 2663-2676, 2822, 2851, 2878, 2885, 2893, 2912-2921, 2924, 2927, 2930, 2933-2935, 2938-2939, 2968-2969, 2991-3006, 3028-3044, 3066-3084, 3106-3125, 3147-3164, 3189-3207, 3229-3248, 3270-3294, 3297-3301, 3356-3386, 3394-3428, 3449-3463, 3475-3517, 3532, 3535, 3539-3541, 3544-3546, 3551-3557, 3560, 3563-3564, 3593-3602 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distr_params.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_hypotests.py 43 36 16% 80-132 /usr/local/lib/python3.8/dist-packages/scipy/stats/_multivariate.py 972 690 29% 50-53, 81-88, 108, 156-173, 177-179, 203, 207, 210-213, 223, 227, 361, 373-426, 434-444, 469-471, 493-497, 519-523, 550-555, 587-594, 626-633, 657-661, 681-683, 733-741, 744-747, 750, 753, 756-759, 762, 774-776, 938, 948-1005, 1013-1019, 1050-1055, 1078-1085, 1107, 1131-1142, 1175-1179, 1182-1186, 1189, 1192, 1227-1233, 1237-1277, 1299, 1412, 1429-1430, 1448-1452, 1470-1474, 1490-1493, 1510-1514, 1531-1539, 1559-1561, 1569-1570, 1573, 1576, 1579, 1582, 1585, 1588, 1739, 1742-1768, 1775-1805, 1808-1819, 1851-1864, 1887-1894, 1917, 1933, 1948-1950, 1966-1970, 1988-1990, 2006-2010, 2025-2027, 2051-2072, 2098-2118, 2142-2150, 2169, 2197-2199, 2223-2225, 2250-2253, 2256-2260, 2263, 2266-2267, 2270-2271, 2274-2275, 2278-2281, 2284, 2325-2357, 2471, 2495-2515, 2538-2542, 2566, 2582-2586, 2605-2607, 2623, 2639-2641, 2657-2664, 2682-2684, 2708-2733, 2757-2769, 2773, 2798-2812, 2815-2818, 2821, 2824-2825, 2828-2829, 2832-2833, 2836-2841, 2845, 3002, 3011-3023, 3032-3047, 3050-3058, 3061, 3082-3094, 3115, 3130-3132, 3147-3156, 3182-3196, 3218-3220, 3244-3251, 3254, 3257, 3260, 3263, 3266, 3269, 3348, 3355-3359, 3378-3401, 3430-3431, 3434, 3495-3499, 3518-3538, 3601-3615, 3631-3648, 3657-3689, 3718-3730, 3787-3791, 3810-3824 /usr/local/lib/python3.8/dist-packages/scipy/stats/_rvs_sampling.py 33 28 15% 123-169 /usr/local/lib/python3.8/dist-packages/scipy/stats/_stats_mstats_common.py 107 98 8% 93-144, 232-266, 271-285, 374-404 /usr/local/lib/python3.8/dist-packages/scipy/stats/_tukeylambda_stats.py 54 40 26% 68-102, 164-201 /usr/local/lib/python3.8/dist-packages/scipy/stats/contingency.py 36 28 22% 57-62, 99-109, 243-275 /usr/local/lib/python3.8/dist-packages/scipy/stats/distributions.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/kde.py 183 151 17% 194-209, 231-265, 293-320, 344-355, 375-388, 411-438, 465-475, 485, 495, 548-565, 571-581, 593, 600-632, 636-640, 644-648 /usr/local/lib/python3.8/dist-packages/scipy/stats/morestats.py 829 760 8% 127-136, 194-211, 278-306, 349-358, 416-421, 440-452, 457-471, 585-627, 705-718, 797-812, 894-910, 916-945, 1034-1059, 1129-1165, 1173-1202, 1270, 1345-1363, 1370-1385, 1470-1481, 1533-1536, 1606, 1661-1678, 1771-1825, 1853-1869, 1897-1906, 2014-2070, 2113-2166, 2225-2247, 2307-2371, 2421-2465, 2472-2475, 2548-2600, 2670-2725, 2847-2938, 3080-3162, 3167-3186, 3225-3255, 3295-3307, 3349-3361 /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats_basic.py 947 845 11% 61-67, 71-79, 83-89, 116-128, 151-160, 196-211, 235-259, 299-324, 328-330, 335, 373-399, 459-528, 569-663, 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56% 20-23, 35 /usr/local/lib/python3.8/dist-packages/skimage/io/_io.py 44 22 50% 45, 51, 55-56, 59-61, 92, 126-136, 157-159, 179, 201 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/imageio_plugin.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/matplotlib_plugin.py 86 67 22% 44-58, 70-78, 97-112, 148-165, 176-200, 207-208 /usr/local/lib/python3.8/dist-packages/skimage/io/collection.py 152 119 22% 47-52, 77-78, 85-95, 175-205, 209, 213, 216-239, 258-313, 317-323, 327-328, 332, 335, 347, 366, 390, 435-443, 448 /usr/local/lib/python3.8/dist-packages/skimage/io/manage_plugins.py 136 22 84% 78, 115-116, 123, 188, 192-196, 202-206, 257, 293, 304-306, 330-331, 344-347 /usr/local/lib/python3.8/dist-packages/skimage/io/sift.py 27 20 26% 41-69, 73, 77 /usr/local/lib/python3.8/dist-packages/skimage/io/util.py 27 13 52% 24-41 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/usr/local/lib/python3.8/dist-packages/tensorflow/keras/wrappers/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/keras/wrappers/scikit_learn/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/examples/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/examples/lstm/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/examples/lstm/rnn.py 91 68 25% 161-276, 364-429 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/examples/lstm/rnn_cell.py 165 127 23% 74-85, 89, 93, 104-125, 129-148, 151-157, 243-274, 280, 284, 296-385, 411-512, 515-530 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/experimental/microfrontend/__init__.py 0 0 100% 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464 15% 193-202, 217-227, 254-310, 332-347, 358-377, 382-384, 408, 430, 442-448, 460-466, 494-495, 512-513, 520-522, 525, 528, 532-536, 539, 551-555, 558-559, 563-579, 583, 600-615, 634-659, 662-665, 689-695, 703-709, 712-721, 737-787, 791, 805-825, 843-879, 893-895, 899, 917-927, 955-1060, 1073-1150, 1154-1159, 1165-1171, 1185-1247, 1272-1282, 1286-1294, 1318-1326 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/optimize/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/optimize/calibrator.py 32 20 38% 47-55, 75-78, 102-105, 118-121 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/util.py 175 140 20% 68-71, 83-92, 110-129, 145-160, 172-176, 196-218, 223-229, 252-269, 284-288, 302-327, 341-356, 370-387, 413-507 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/wrap_toco.py 10 2 80% 32, 43 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/model_flags_pb2.py 45 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/toco_flags_pb2.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/types_pb2.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/__init__.py 116 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/arg_defaults.py 30 7 77% 66-70, 81, 87-88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/asserts.py 18 8 56% 31-47, 51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/break_statements.py 68 47 31% 30-31, 34, 41-48, 52-63, 66-71, 74-105, 108-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/call_trees.py 97 10 90% 102-106, 136, 198-207, 211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/conditional_expressions.py 12 1 92% 29 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/continue_statements.py 91 46 49% 34, 62-74, 79-93, 97-115, 124-128, 131-136, 149-154, 157-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/control_flow.py 208 86 59% 126-128, 144-155, 160-167, 184-195, 200-203, 236, 343-353, 357-378, 381-396, 399-432, 447-510 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/directives.py 92 39 58% 67-87, 94-101, 104-115, 139-140, 153-154, 156-157, 164-168, 171, 174 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/function_scopes.py 57 8 86% 70-88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/lists.py 100 72 28% 46, 53-57, 60-65, 90-107, 110-123, 130-148, 151-180, 190-199, 202, 208-211, 214-217, 220-223, 226-229, 232-234, 238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/logical_expressions.py 66 23 65% 56-59, 72-77, 90-112, 119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/return_statements.py 187 49 74% 87, 99-102, 105-109, 122-126, 130-131, 144, 147, 166, 177, 258-266, 282-293, 296-315, 323-327, 330-331, 376 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/slices.py 35 22 37% 37-45, 49-56, 59-80, 85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/ag_ctx.py 37 3 92% 58, 70, 73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config_lib.py 28 3 89% 48, 60, 64 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785-788, 831-836 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/conversion.py 322 94 71% 87, 117, 134, 151, 210, 308-310, 345, 350, 355, 388, 397-398, 406-410, 418-419, 440, 443-444, 451-453, 459-465, 474-476, 482-484, 509, 514-521, 526, 528-529, 538-632, 638-639, 683-690, 700, 704, 715, 724, 751, 758-759 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/directives.py 16 7 56% 44-46, 95-98 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/special_functions.py 33 20 39% 33-45, 53, 83-88, 113-119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/__init__.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/control_flow.py 449 379 16% 109-119, 128-135, 142-191, 204-234, 241-263, 272-297, 341-372, 377-401, 407-439, 452-485, 497-526, 538-586, 606, 621-677, 684-704, 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428-436, 440, 444-446, 454-462, 466, 470-472, 477-497, 501-507, 514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/slices.py 55 31 44% 55-67, 72, 77-81, 86, 91-92, 97, 117-125, 130, 135, 140, 145-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/special_values.py 27 10 63% 52, 55, 58-63, 66, 81, 93, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/anno.py 59 7 88% 40, 107, 134-138 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/ast_util.py 214 117 45% 101-105, 108-109, 112-113, 130-132, 137-142, 149-151, 154-157, 160-161, 164-170, 173-206, 222-227, 262-275, 294, 299, 309, 315-316, 318-319, 325, 330, 341, 350-351, 354-359, 362-378, 381-388, 392-394 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/cfg.py 416 121 71% 86-93, 132, 136-143, 188, 200, 334-335, 340, 356, 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258, 279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/pretty_printer.py 87 69 21% 30-33, 36-38, 41, 44, 47, 50, 53, 56-57, 62-125, 129-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/qual_names.py 141 45 68% 48, 51, 58, 61, 70, 77, 80, 88, 96, 104, 107, 120-122, 134-138, 153-161, 173, 180, 187-191, 198, 203, 210-215, 244, 252, 265-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/activity.py 341 103 70% 121-124, 134-135, 189, 204-205, 244-249, 261-264, 269-274, 279, 281, 290-300, 324, 327, 330-332, 347, 352-361, 364, 372-382, 387, 400-404, 407, 445-456, 461-463, 466, 469, 472, 475, 481-497, 554-568, 571-579, 586 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/annos.py 17 1 94% 30 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/context_managers.py 18 10 44% 38-49 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/misc.py 26 15 42% 42-52, 57-59, 63-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/py_func.py 48 37 23% 64-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/tensor_list.py 32 16 50% 28-40, 47-49, 52, 55-56, 59, 62, 65, 68 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/tensors.py 16 3 81% 39, 47, 53 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/testing.py 17 8 53% 30-37 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/utils/type_check.py 7 1 86% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/client_lib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/device_lib.py 15 8 47% 34-42 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/pywrap_tf_session.py 31 11 65% 53-59, 66-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py 620 490 21% 57, 62, 66, 70, 74, 78, 84, 141, 192, 201, 206-207, 231, 245, 261-278, 301-316, 319, 322-326, 349-362, 374-379, 382, 386-396, 408-413, 416, 419-422, 434-437, 440, 443-446, 476-498, 501-502, 515, 523, 544-570, 582, 597-600, 604, 608, 612, 616, 619, 650-704, 731-742, 753-755, 759-772, 777, 787, 791, 846, 952-967, 1014, 1038-1089, 1095-1184, 1222-1309, 1340-1361, 1364-1384, 1387-1388, 1396-1411, 1417-1437, 1441, 1446, 1454-1462, 1467-1479, 1485-1487, 1504-1505, 1586-1589, 1592-1601, 1604-1641, 1669-1675, 1738-1769, 1773-1784 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/timeline.py 288 227 21% 60-62, 81-88, 97-102, 112-118, 132-135, 148-150, 163-165, 180-183, 198-200, 215-217, 230-232, 244-246, 257-262, 285-292, 297, 302, 307, 312, 317, 322, 327, 335, 343, 362-372, 376-378, 382-384, 389-397, 401-414, 424-441, 453-455, 459-463, 467, 471-481, 485-512, 517-561, 569-608, 611-617, 633-636 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/compat.py 32 7 78% 49, 54, 119, 162-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/v2_compat.py 60 19 68% 92-113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/mlir.py 9 1 89% 41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/trt_convert.py 455 365 20% 58, 80-81, 86-88, 93-95, 105-108, 199-245, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/batching.py 107 64 40% 81-86, 133-136, 179-192, 243-255, 282-285, 293-310, 315, 323-361, 364, 368, 394-422, 426 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/cardinality.py 27 8 70% 66, 95-98, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/counter.py 23 6 74% 51-54, 60, 66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute.py 70 45 36% 48-67, 71, 75, 88-119, 123, 130-133, 137, 150-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute_options.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/enumerate_ops.py 12 3 75% 55-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/error_ops.py 17 6 65% 50-53, 61-66 /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 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_aggregator.py 23 6 74% 64-77, 128, 140 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_ops.py 32 14 56% 44-48, 67-71, 90-94, 101-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_options.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/take_while_ops.py 25 11 56% 33-49, 52, 69-72 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/threading_options.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/unique.py 20 8 60% 45-48, 56-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/writers.py 22 7 68% 77-79, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/dataset_ops.py 1381 743 46% 203, 227-240, 252-279, 285, 289, 293, 306-318, 329, 354, 357, 367, 385, 406, 420, 423-427, 475-484, 640, 658-665, 668-673, 676, 734, 736, 744, 785-823, 840-846, 913, 958, 987, 1057-1084, 1105, 1137-1138, 1198, 1248, 1267, 1286, 1354, 1387, 1489-1497, 1620-1623, 1748-1751, 1780, 1804-1810, 1889-1891, 1919-1996, 2023-2024, 2051, 2064-2078, 2087, 2103, 2106-2150, 2186, 2189-2204, 2218, 2232, 2246, 2254, 2260, 2265, 2278, 2283, 2289, 2294, 2298, 2302, 2307, 2311, 2315, 2320, 2324, 2328, 2332, 2336, 2345, 2351-2355, 2393-2401, 2412, 2421, 2427, 2446, 2450, 2454, 2459, 2463, 2469, 2476-2477, 2480, 2483, 2486, 2489, 2492, 2496, 2499, 2505-2524, 2540-2545, 2574-2579, 2597-2600, 2619, 2639, 2659, 2750, 2754, 2756, 2758, 2761, 2771-2774, 2782, 2858-2873, 2877, 2885-2902, 2906, 2913-2914, 2917, 2921, 2927-2929, 2933, 2948, 2961, 2985, 2988, 2992, 2995, 2999-3002, 3013, 3016, 3021-3023, 3026-3028, 3031, 3034, 3037, 3088-3092, 3097, 3104, 3144, 3168, 3172-3173, 3181-3202, 3226, 3230-3231, 3244, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py 291 224 23% 43-140, 144, 148, 160-182, 186, 190, 196-208, 232-302, 306-320, 324-332, 335-340, 344-346, 350-365, 370, 381-385, 388-400, 412-414, 418, 421, 425-431, 435-437, 440, 450, 493-566, 574-582, 585, 588, 591-594, 597-602, 606, 610 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/optional_ops.py 77 26 66% 59, 75, 85, 98-103, 121, 132-133, 136, 141-143, 155, 159, 170, 174, 177, 181, 184, 188, 192, 195, 198, 201 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/readers.py 198 115 42% 46-64, 80-90, 114-127, 131, 160-172, 176, 188-190, 196, 200, 216-229, 233, 242-291, 295, 299, 302, 336-349, 356, 362, 366, 378-380, 390, 398, 402, 430-447, 451, 488-505, 509, 524-527, 533, 537, 545-547 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/convert.py 23 13 43% 30-34, 49-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/nest.py 101 35 65% 47-50, 69-70, 75, 88-89, 91, 176, 180, 186, 225-244, 297, 302, 308, 314-320, 462 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/options.py 59 30 49% 23, 38-43, 46-49, 52-55, 81-84, 115, 120, 124, 131-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/random_seed.py 21 10 52% 42-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/structure.py 178 72 60% 45, 51, 57, 64, 100-114, 146-172, 198, 252, 350, 394-404, 435-439, 445-450, 456-462, 476, 486, 489, 493, 496, 499, 502, 506, 509, 512, 515, 518, 521, 524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/traverse.py 21 14 33% 39-56 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/__init__.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/analyzer_cli.py 581 520 10% 84-127, 151-158, 173-418, 444-458, 473-476, 479, 502-598, 616-639, 656-674, 690-738, 757-823, 834, 850-872, 891-908, 925-1050, 1069-1086, 1089-1095, 1107, 1113-1168, 1183-1240, 1244-1271, 1301-1361, 1404-1469, 1495-1520, 1533-1544, 1556-1579, 1601-1659 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_config.py 81 60 26% 41-50, 53-55, 71-97, 114-118, 121, 124-128, 139-147, 150-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_shared.py 177 136 23% 69-82, 99-110, 126-140, 144-147, 185-208, 229, 248-258, 264-273, 301-383, 405-431, 445-493 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/command_parser.py 207 177 14% 37-40, 43-47, 50, 72-101, 118-148, 164-171, 187, 204-216, 234-240, 259-281, 299-310, 328-339, 359-403, 426-439, 454-468, 486-491, 506-550 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/debugger_cli_common.py 451 353 22% 44-45, 49, 73-77, 94-107, 110, 123-132, 145-151, 198-212, 217, 221, 225, 228, 246-267, 285-300, 310-332, 343-345, 348, 359-362, 373-375, 403-431, 456-527, 563-585, 625-655, 682-716, 727, 741-757, 767, 783-790, 793-795, 807-812, 825-843, 850, 883-896, 909-915, 929-934, 948-953, 974-981, 992-1001, 1019-1023, 1026-1040, 1043-1047, 1051, 1063-1075, 1088, 1103-1105, 1125-1127, 1131, 1135, 1139, 1142, 1145, 1148, 1161-1162, 1170, 1173, 1176, 1179, 1194-1199, 1221-1248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/evaluator.py 52 37 29% 69-103, 115-116, 131-152 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/profile_analyzer_cli.py 302 252 17% 58-77, 103-131, 134, 137, 140, 143, 173-195, 209-220, 236-380, 396-439, 447-474, 504-575, 586-592, 613-733, 737-742, 756-762, 765, 786-802 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/tensor_format.py 240 215 10% 67-69, 103-199, 233-279, 321-403, 407-426, 449-481, 485, 503-568 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/ui_factory.py 23 16 30% 51-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/check_numerics_callback.py 122 89 27% 103-106, 114-119, 156-212, 216, 226-232, 242-289, 314-328, 414-419, 437-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/common.py 22 11 50% 44, 59-71, 86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_data.py 530 403 24% 53-56, 70-71, 74-76, 80, 99-102, 121-140, 144-148, 152-156, 160, 164, 168, 182, 199, 219-231, 241-242, 252-254, 266-268, 307-330, 334, 341, 350, 361, 375, 385, 395, 405, 415, 425, 435, 442, 455, 485-498, 502-522, 555-576, 580-581, 584-590, 594-598, 602-606, 621-623, 636-655, 669-673, 684, 717-718, 723-730, 739, 748, 771-799, 802-809, 812-817, 834-886, 906-918, 922, 933-935, 953-956, 970-971, 985-986, 1007-1020, 1036-1046, 1062-1066, 1085-1093, 1121-1137, 1141-1151, 1193-1229, 1249-1258, 1266, 1284-1295, 1313-1322, 1339-1344, 1363-1378, 1397-1415, 1448-1464, 1490-1497, 1522-1528, 1560-1567, 1594-1601, 1618-1625 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_events_writer.py 52 29 44% 51-54, 64-66, 76-79, 89-92, 102-104, 113-115, 125-128, 132, 137, 146, 150, 154, 157-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_gradients.py 120 82 32% 38-39, 53-65, 85-98, 102, 106, 109, 112, 157-169, 215-222, 267-284, 287-290, 304-306, 324-329, 338, 341-346, 353, 360-363, 369, 403-417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_graphs.py 237 183 23% 40-46, 50-51, 66-67, 83, 98, 116-139, 170-178, 191-214, 217, 220, 225-234, 241-266, 277-309, 320-329, 333-337, 346-354, 358-365, 372-393, 401-405, 413-431, 435, 440, 445-446, 450, 454, 458, 462, 466, 470, 474, 478, 503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_utils.py 69 60 13% 61-79, 137-197, 252-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/dumping_callback.py 301 237 21% 61, 67-68, 73, 77, 89-115, 125-133, 137, 141-143, 147, 151, 155-159, 176-189, 202-205, 216-232, 242-267, 288-301, 334-421, 453-514, 526-571, 590-608, 611-617, 632-643, 740-807, 819-826 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/op_callbacks_common.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/profiling.py 41 25 39% 44-56, 62, 76-80, 90-100, 104, 108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/source_utils.py 135 110 19% 44, 48-49, 53-54, 58, 78-84, 112-125, 145-158, 192-225, 262-325, 353-383 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/dumping_wrapper.py 42 28 33% 69-90, 110-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/framework.py 279 191 32% 130-131, 148-149, 177-178, 204-209, 259-270, 298-305, 314, 346-379, 383, 387, 391, 395, 428-517, 530-588, 605-634, 640-641, 645, 650, 654, 658, 661, 667-676, 679-684, 688, 691, 703, 733-734, 753, 806, 809-811, 814, 818-819, 822, 828-831, 867-874, 878, 915-923, 928, 948-951, 975-984, 989 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/grpc_wrapper.py 58 38 34% 57-66, 100-118, 137, 140, 146-151, 155-159, 193-210, 220-224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/hooks.py 95 67 29% 60-65, 83-86, 89, 92-141, 146-148, 176-180, 183, 186-217, 220, 256-264, 277-301, 335-349, 352-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/local_cli_wrapper.py 237 197 17% 80-131, 136, 139-206, 218, 230, 242-277, 280-285, 289-304, 320-374, 377-378, 399-446, 449-453, 462-468, 471-485, 488-513, 525-576, 579-603, 613, 633-642 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/all_reduce.py 392 355 9% 44-57, 71-75, 95-128, 145-157, 176-190, 223-251, 277-294, 317-356, 370-374, 394-423, 469-477, 496-518, 532-555, 582-589, 608-626, 640-645, 666-682, 701-711, 730-762, 767-776, 781-785, 790-791, 797-800, 820-842, 848-853, 860-865 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/central_storage_strategy.py 34 11 68% 56-70, 75, 103, 144, 162, 180, 246, 255-260 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py 173 108 38% 36-39, 44-56, 99, 117, 144-154, 172, 183-198, 202, 218-223, 227, 231, 235, 239, 243, 262-265, 269, 273, 305-321, 347-395, 411-415, 419, 423, 427, 431, 435, 441, 446, 450 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py 77 49 36% 30-31, 83-104, 116-149, 152-162, 166, 170, 174, 180, 184, 188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py 52 34 35% 29, 76-94, 112-120, 135-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py 160 132 18% 39-86, 96-107, 121-125, 135, 146-155, 164, 233-275, 280, 284, 288, 298-301, 321-358, 372, 386-394, 401-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py 78 42 46% 36-39, 43, 47-48, 76-79, 83-87, 91-95, 99, 103, 107, 111-114, 118, 124-126, 135-138, 160-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py 106 67 37% 40, 76-85, 90-95, 150-167, 170, 173, 201-214, 217, 220, 246-256, 278-300, 305, 308-319, 324 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py 228 165 28% 99-110, 116-118, 135, 148-158, 169-176, 180-183, 187-242, 247-344, 356-391, 399-408, 411-412, 421-422, 434-435, 456-470, 473-508, 511-530, 537-540, 545, 549, 553, 557, 561, 565, 577 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_util.py 11 3 73% 62-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_ops.py 452 356 21% 56-59, 64-73, 79-102, 107-116, 122-138, 144-151, 157-161, 165, 169-174, 179-188, 194-211, 219, 224, 251-266, 298-318, 331-332, 359, 387, 402, 426-428, 432-443, 447, 472-479, 504-513, 529-531, 535-583, 587-617, 622-628, 633-635, 652-655, 659-663, 668-672, 679-690, 695-720, 724-730, 760-764, 791-795, 835-861, 867-907, 948-953, 957, 961-989, 993-1004, 1012-1024, 1031-1101, 1106-1137, 1151-1183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_utils.py 400 345 14% 44-53, 80-135, 158-170, 188-197, 214-229, 270-275, 280-282, 293-306, 318-319, 323-325, 329-331, 365-387, 414-433, 465-535, 545-576, 594-618, 635-655, 675-690, 705-713, 743-769, 785-801, 806-809, 813-818, 822-830, 835-842, 846-849, 863-874, 887-897, 910-916, 937-958, 976-986 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/device_util.py 47 29 38% 47-67, 72, 79-80, 87-90, 93, 96, 102-108, 113-114, 121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_coordinator.py 315 243 23% 74-79, 83-92, 97-99, 137-146, 149-153, 156-162, 167, 172-192, 196-205, 213-216, 244-260, 268, 273, 278, 283, 288, 293, 298, 303, 308, 313, 318, 323, 336-360, 373-382, 395-451, 457-494, 500-526, 538-552, 590-625, 752-868 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_coordinator_context.py 11 4 64% 28-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py 687 352 49% 149-152, 159-160, 163-164, 167-168, 186-194, 206, 209-214, 220-225, 232-240, 249, 254-258, 277-285, 289-300, 303-332, 374-376, 381, 386, 391, 407-411, 414, 463-464, 469, 474, 477, 507, 617-625, 640-644, 661, 666, 673-677, 705, 711-713, 874, 943, 957, 998-1056, 1078, 1097, 1102, 1107, 1126, 1132, 1136-1144, 1147, 1201, 1264, 1314, 1388, 1429, 1470, 1501, 1536, 1540, 1559, 1754-1777, 1785, 1789, 1818, 1857-1865, 1869, 1872, 1877, 1881, 1885, 1888, 1891, 1894, 1897, 1901, 1922-1931, 1934, 1953-1959, 1964, 2007-2013, 2016, 2033-2039, 2042, 2045, 2060, 2064-2072, 2077, 2082, 2089, 2095, 2110, 2118, 2121, 2140, 2165-2166, 2169, 2182-2186, 2189, 2233-2235, 2240, 2293, 2308, 2317, 2322, 2327, 2332, 2374, 2415-2420, 2424-2429, 2434, 2447-2448, 2458-2459, 2489-2511, 2526-2530, 2544, 2550-2551, 2571, 2583-2584, 2603-2604, 2613, 2616, 2619, 2624-2625, 2628-2637, 2640-2643, 2653-2654, 2659, 2664-2669, 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870-957, 966, 975-977, 982-990, 994, 1001-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/tpu_values.py 143 89 38% 41-46, 51-61, 68-75, 78-81, 85-88, 92, 95-98, 101-104, 108, 114-118, 124, 127-129, 132-135, 138-141, 144-147, 151, 159-166, 171-183, 190-196, 200-202, 205-207, 210-212, 215, 218, 221, 224, 227, 230, 233, 236, 243-246, 251-254, 259-262, 266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/values.py 749 470 37% 48-55, 125, 129-133, 136, 142-151, 156, 160, 163-165, 168-170, 191-198, 203, 212, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300-304, 307-311, 314-318, 321-325, 335, 341, 352, 355, 359, 362-367, 370, 380, 383-387, 391-392, 396-397, 401-402, 416-433, 445-454, 458-465, 468, 472, 476, 480, 484, 488, 493, 497, 501, 505, 509, 513-518, 521, 525, 528, 531-532, 536, 540, 544, 547, 550, 557-560, 564, 567-568, 571, 575, 582-584, 591-595, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/dense_features.py 36 18 50% 92, 102, 111-113, 116, 137-150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/dense_features_v2.py 18 6 67% 82-87, 90-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/feature_column.py 877 622 29% 182-231, 297, 322-329, 332, 342, 346, 350, 354, 358, 362, 366, 492-504, 516-526, 543-548, 551-566, 569-577, 590-592, 595-601, 604, 608-612, 616, 633-661, 669, 672-700, 706-708, 744-752, 801-814, 904-929, 997-1009, 1083-1097, 1144-1155, 1242-1268, 1357-1385, 1444-1453, 1488, 1558-1560, 1670-1688, 1718-1722, 1731, 1734-1742, 1745, 1768, 1790, 1810, 1815, 1840, 1861, 1885, 1910, 1921-1931, 1947-1963, 1981, 2008, 2046-2067, 2085, 2128-2129, 2149-2171, 2191-2217, 2226-2233, 2254-2274, 2297-2323, 2335, 2339, 2346-2353, 2357, 2374-2378, 2388, 2392, 2395-2396, 2402, 2406-2409, 2418, 2423-2448, 2470, 2484-2486, 2490, 2493, 2497-2499, 2507-2531, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/callbacks_v1.py 218 186 15% 130-163, 167-175, 180-230, 235-310, 313-314, 325-343, 351-367, 370-372, 378-385, 394-451, 454-457 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/constraints.py 102 47 54% 38, 41, 68-69, 72-75, 78, 87, 109, 112, 118, 152-155, 158-163, 166, 204-216, 221-249, 268, 273, 284-292 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/boston_housing.py 25 16 36% 58-79 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar.py 19 12 37% 37-51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar10.py 31 19 39% 50-82 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar100.py 27 15 44% 59-84 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/fashion_mnist.py 26 15 42% 66-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/imdb.py 56 42 25% 99-158, 171-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/mnist.py 15 6 60% 57-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/reuters.py 47 33 30% 106-151, 164-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/distributed_training_utils.py 473 386 18% 68-80, 113-137, 142-166, 193-213, 236, 253-272, 308-322, 343-359, 363-366, 372-375, 381-399, 404-410, 424-426, 435, 447-449, 459-469, 502-573, 577-581, 585-588, 592-595, 600-620, 636-669, 684-689, 694-703, 732-773, 779-783, 790-825, 830-835, 840-849, 854-880, 885-902, 907-913, 918-947, 953-983, 993-1025, 1033-1040, 1045-1049, 1054-1062, 1066-1071, 1075-1076, 1080-1081, 1085-1086, 1090-1091, 1095-1096, 1101-1102, 1121-1133, 1137, 1151-1169, 1176-1192, 1197-1201 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_preprocessing_layer_v1.py 26 14 46% 51-54, 58-61, 66-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/compile_utils.py 318 253 20% 41-44, 63-68, 85-98, 101, 104, 126-132, 136-149, 153-161, 183-247, 261-269, 272, 275, 295-297, 301-335, 342-370, 375-383, 387-416, 420-421, 434-477, 481-484, 488-490, 495, 500, 528-545, 575-592, 597-602, 607-617, 623-632 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/data_adapter.py 639 339 47% 56-57, 61-62, 115, 140, 156, 172, 185, 198, 203, 211, 216, 220-225, 229, 241, 249, 264-371, 388-408, 411, 414, 417, 420, 423, 427, 452, 467, 470-476, 493-523, 533, 539, 542, 546-548, 562-593, 596, 599, 602, 605, 608, 611, 622, 628, 630, 641-650, 660, 663, 666, 669, 672, 675, 691-699, 702, 705, 708, 711, 714, 720, 726-735, 762, 765, 779-780, 789, 801-802, 823, 830-831, 836-849, 855, 858, 861, 864, 867, 870, 891, 894, 917-922, 925-932, 944, 960, 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123-336, 349-362, 398-418, 449-484, 493-509, 514-530, 535-538, 571-574, 604-606, 626-627, 659-666, 692-695, 706-707, 738-766, 791-800, 816-819 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_utils.py 834 682 18% 79-83, 92, 105, 110, 124, 131, 135-140, 143-145, 156-157, 161, 168-174, 178-192, 209-212, 241-244, 253-258, 262-286, 289-300, 303-310, 321-342, 345-347, 350-353, 358-364, 385-400, 428-436, 441-457, 486-583, 601-626, 633, 638, 657-697, 713-750, 771-808, 844-891, 907-915, 944-1042, 1046-1048, 1052-1056, 1069-1091, 1106-1133, 1142-1157, 1162-1186, 1210-1213, 1216-1228, 1233-1235, 1256-1270, 1278-1289, 1297-1306, 1333-1355, 1359-1364, 1380-1391, 1405, 1420-1421, 1435-1461, 1482-1503, 1525-1544, 1552, 1556, 1584-1613, 1639-1670, 1682-1689, 1694, 1700-1705, 1709-1711, 1723-1725, 1737-1759, 1787-1817, 1827-1840, 1850, 1857-1887, 1891-1892, 1896, 1923-1925, 1929, 1947, 1951, 1965-1970, 1975-1977, 1997-2007, 2012-2030, 2049-2078, 2110, 2123, 2133 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_v1.py 1067 853 20% 71-72, 143-163, 167, 175-180, 228-233, 301-463, 475-476, 481-486, 494-506, 522-545, 549, 556-586, 754-766, 874-879, 953-957, 970-976, 1025-1070, 1110-1146, 1166-1192, 1217, 1249-1251, 1276, 1288-1299, 1313-1337, 1348-1350, 1362-1380, 1386-1423, 1427-1437, 1460-1468, 1471-1478, 1494-1515, 1528, 1532, 1546-1627, 1632-1642, 1646-1650, 1673-1742, 1747-1756, 1760-1768, 1791-1799, 1805, 1818-1827, 1831-1856, 1876-1882, 1914-1938, 1949-1953, 1959-2003, 2006-2030, 2033-2041, 2049-2057, 2099-2170, 2242-2302, 2312-2436, 2440-2498, 2501-2534, 2572-2591, 2596-2636, 2643-2647, 2652, 2660, 2668, 2676, 2684, 2692, 2696-2702, 2706, 2710, 2714, 2732-2735, 2743-2746, 2753-2754, 2774-2775, 2781-2787, 2791, 2794-2802, 2806, 2813-2814, 2817, 2820, 2825-2829, 2832-2835, 2842-2846, 2886-2893, 2897, 2901, 2905, 2909, 2913, 2917, 2921, 2925, 2942-2972, 2979, 2983, 2987, 2991, 2995, 2999, 3002, 3005, 3009, 3012, 3016-3018, 3023-3042, 3048, 3054-3075, 3097-3099, 3103, 3107, 3111, 3115, 3135-3151, 3165-3175, 3179-3180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/estimator/__init__.py 23 12 48% 115-122, 212-219 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/initializers.py 89 8 91% 93, 118, 142, 166, 181, 194, 202, 207 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/__init__.py 180 21 88% 54-59, 157-159, 214-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/advanced_activations.py 135 78 42% 68-70, 73, 76-78, 82, 126-136, 140-157, 160-162, 165-172, 176, 203-205, 208, 211-213, 217, 244-246, 249-250, 253-255, 259, 279-281, 284, 287-289, 293, 345-358, 363, 369-375, 379 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py 730 587 20% 121-149, 152-191, 194-222, 225-250, 254-273, 277-282, 285-288, 291-295, 298-304, 319-325, 429, 582, 726, 874-898, 903-934, 937-994, 997-1027, 1030-1032, 1167-1190, 1195-1226, 1229-1295, 1298-1332, 1335-1338, 1430-1453, 1456-1496, 1499, 1502-1543, 1656, 1680-1719, 1841, 1866-1887, 1985-2001, 2004-2036, 2039-2056, 2060-2078, 2081-2093, 2134-2136, 2139-2141, 2144-2145, 2148-2150, 2216-2223, 2226-2239, 2243, 2248-2254, 2302-2305, 2308-2325, 2329, 2333-2335, 2384-2386, 2389-2393, 2396, 2399-2401, 2472-2492, 2495-2516, 2520, 2524-2526, 2584-2610, 2613-2642, 2646, 2650-2652, 2692-2694, 2697-2702, 2705-2708, 2711-2713, 2768-2788, 2791-2802, 2813-2833, 2838-2840, 2899-2925, 2928-2958, 2964-3014, 3020-3022 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional_recurrent.py 345 261 24% 165-181, 185-222, 228-275, 279-292, 302-350, 353-420, 510-537, 541-584, 587-644, 647-654, 657-660, 663-692, 842-869, 872-873, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 963-995, 999 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/core.py 458 303 34% 101-104, 107, 110-115, 118, 121-123, 179-183, 189-196, 199-212, 215, 218-224, 260-261, 264-266, 311-318, 321-325, 369-376, 379-383, 415-417, 420, 423, 426-428, 472-473, 495-515, 518-527, 530, 534-536, 571-578, 581-586, 589, 592-594, 630-632, 635-667, 670-679, 682-684, 714-716, 719-720, 723, 726-728, 825-841, 845-870, 874-890, 893-932, 937, 940-942, 945-966, 969-985, 989-1016, 1022-1048, 1129, 1150, 1154, 1176, 1183-1188, 1192, 1199, 1202-1208, 1211-1225, 1246-1250, 1253, 1256-1258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/cudnn_recurrent.py 189 143 24% 65-81, 84-121, 124-133, 137, 141-143, 147-149, 153, 156, 215-236, 240, 243-269, 272-316, 319-337, 400-422, 426, 429-465, 468-518, 521-540 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/dense_attention.py 132 95 28% 77-80, 92, 119-134, 138-167, 170-176, 180-196, 201-206, 307-308, 312-321, 332-335, 338-340, 443-444, 447-460, 473-480, 484-486, 491-495, 499-503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/embeddings.py 71 46 35% 101-123, 133-148, 151-154, 158-178, 181-185, 188-203 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/kernelized.py 80 56 30% 138-154, 157-200, 203-207, 210-216, 219-228, 234-251, 255-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/local.py 209 172 18% 151-171, 175-259, 263-274, 277-298, 301-334, 465-485, 489-581, 585-600, 603-625, 628-661, 704-724, 770-778, 807-816, 834-841 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/merge.py 342 244 29% 49, 67-87, 92-118, 122, 124-181, 187-202, 205-217, 251-254, 284-286, 290-293, 320-323, 357-360, 387-390, 417-420, 493, 496, 504-519, 526-536, 541-564, 567-571, 639-654, 659-674, 680-700, 704-722, 725, 728-733, 767, 796, 810, 846, 878, 892, 927, 947 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/noise.py 80 47 41% 60-62, 66-73, 76-78, 82, 111-113, 116-127, 130-132, 136, 170-174, 177, 180-203, 206-208, 212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization.py 511 442 14% 199-247, 258-271, 275-279, 283, 287-289, 292-295, 300-303, 306, 311-503, 506-516, 519-521, 525-635, 640-693, 696, 699-708, 711-722, 725-890, 893, 896-928, 1010-1034, 1042-1055, 1058-1105, 1109-1193, 1196, 1199-1212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization_v2.py 43 23 47% 136, 161-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/pooling.py 256 162 37% 60-70, 73-81, 84-98, 101-108, 193, 235, 272-282, 285-297, 300-315, 319-326, 458, 508, 544-554, 557-574, 577-596, 600-607, 654, 704, 714-717, 720-724, 727, 730-732, 775-777, 780-789, 792, 840-841, 849-852, 855-859, 862, 865-867, 906-909, 947-950, 957-960, 963-967, 970, 973-975, 1008-1011, 1043-1046 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding.py 218 164 25% 83-129, 136, 139-145, 163-170, 173-178, 181-187, 190-193, 196-205, 208-217, 220-234, 237-292, 317-318, 322-341, 345-364, 379, 394-412, 416, 421-429, 433-447, 452-458 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding_v1.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py 485 384 21% 78-83, 86-89, 92-96, 99-100, 104-110, 135-138, 141-144, 147-171, 174-175, 179-184, 214-219, 222-277, 280-281, 285-291, 315-316, 319-320, 323, 326-330, 367-383, 386-402, 405, 408-413, 467-506, 509-541, 544, 547-555, 570-578, 647-670, 696-704, 768-790, 793-816, 819, 822-829, 879-918, 921-952, 955, 958-966, 987-997, 1043-1054, 1057-1067, 1070, 1073-1078, 1118-1132, 1135-1157, 1160-1161, 1165-1171, 1213-1227, 1230-1252, 1255-1256, 1260-1266, 1270-1273, 1277-1282 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup.py 230 173 25% 104-190, 193-208, 211-212, 215, 218-222, 225-231, 234-235, 239-241, 245-246, 250-257, 260, 263-268, 283-285, 288-294, 297-305, 312, 333-353, 356-358, 361-389, 392-401, 404-429, 451, 455-465, 469-477, 489-495, 499, 504-507, 511-518, 523-524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup_v1.py 38 22 42% 63-66, 69, 72-76, 79-81, 84-85, 89-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization.py 95 56 41% 63-71, 75-104, 109-111, 114, 117, 120-122, 126-128, 149, 155-173, 178-197, 202, 212-220, 225-230, 234-235, 241 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization_v1.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py 309 235 24% 214-320, 325, 328-329, 332, 336-338, 342-343, 347, 350-359, 362-364, 379-404, 407, 410-420, 427, 461-521, 528-534, 537-543, 546-592, 595-633, 663-665, 669-694, 698-715, 730, 745-762, 766, 771-781, 785-802, 807-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization_v1.py 26 9 65% 84, 87, 91-97 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent.py 1046 802 23% 85-104, 108, 113-118, 121-131, 135-160, 165-180, 183-191, 195-200, 397-439, 444-447, 454, 457-505, 513-519, 522-592, 606-621, 625-645, 648-707, 717-814, 820-854, 857-866, 871-873, 890-943, 946-965, 969-974, 978, 1052, 1061, 1066, 1069, 1096-1099, 1110, 1121, 1124, 1131, 1153-1156, 1174-1177, 1266-1290, 1294-1319, 1322-1340, 1343, 1346-1378, 1489-1526, 1529-1530, 1535, 1539, 1543, 1547, 1551, 1555, 1559, 1563, 1567, 1571, 1575, 1579, 1583, 1587, 1590-1625, 1629-1631, 1709-1740, 1744-1778, 1781-1879, 1882-1907, 1910, 2032-2071, 2074-2075, 2080, 2084, 2088, 2092, 2096, 2100, 2104, 2108, 2112, 2116, 2120, 2124, 2128, 2132, 2136, 2140, 2144, 2147-2188, 2192-2194, 2275-2312, 2316-2353, 2357-2367, 2371-2376, 2379-2436, 2439-2477, 2480, 2518-2530, 2536-2549, 2552-2559, 2678-2717, 2720-2721, 2726, 2730, 2734, 2738, 2742, 2746, 2750, 2754, 2758, 2762, 2766, 2770, 2774, 2778, 2782, 2786, 2790, 2793-2834, 2838-2840, 2844-2852, 2877-2913, 2918, 2923-2926, 2931-2944, 2964-2991, 3008-3011 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent_v2.py 321 259 19% 165, 345-385, 388-398, 403-454, 462-503, 547-587, 593-673, 713-791, 895, 1059-1102, 1107-1207, 1233-1238, 1288-1321, 1359-1444, 1485-1569, 1596-1602, 1625-1626, 1631-1635, 1642-1645, 1649-1650 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/rnn_cell_wrapper_v2.py 43 17 60% 42-43, 66, 71-72, 75-82, 86-89, 98-100, 113, 124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/serialization.py 57 18 68% 68, 84-105 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/wrappers.py 370 318 14% 52-54, 57-60, 64-67, 70-77, 81-86, 126-137, 163-172, 175-184, 187-195, 199-251, 290-327, 400-456, 460-469, 480-495, 499-518, 522-593, 602-677, 680-681, 684-688, 691-707, 711-715, 718-728, 733-745 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/losses.py 266 127 52% 105, 107, 140-144, 157, 161, 177, 181-201, 243-246, 249-253, 312, 372, 433, 494, 570-576, 729, 793, 855, 916, 974, 1032, 1093, 1162, 1196-1198, 1228-1230, 1262-1266, 1300-1304, 1309-1319, 1347-1350, 1379-1382, 1411-1415, 1438-1444, 1481-1487, 1517-1527, 1555-1557, 1585-1594, 1632-1636, 1667-1669, 1714-1716, 1782, 1797-1803, 1816, 1831, 1853-1863 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/metrics.py 759 457 40% 151, 164-167, 186-207, 212, 216, 224, 244, 253, 268, 274, 290, 328-374, 377-385, 425, 516-518, 533-547, 551-554, 595-604, 608-618, 624-628, 665, 710, 765, 809, 889, 913-918, 936, 944-948, 951-952, 956-958, 1004, 1054, 1104, 1154, 1225-1237, 1256, 1269-1271, 1274-1275, 1279-1285, 1351-1363, 1382, 1395-1397, 1400-1401, 1405-1411, 1423-1450, 1465, 1478-1479, 1537-1541, 1546-1556, 1561-1566, 1623-1627, 1632-1642, 1647-1652, 1701-1705, 1713-1723, 1728-1730, 1782-1786, 1796-1807, 1810-1813, 1920-1981, 1985-2023, 2038-2067, 2129-2164, 2167-2214, 2219-2223, 2228-2244, 2295, 2326, 2359, 2390, 2423, 2456, 2491, 2523, 2555, 2570-2575, 2579, 2610, 2641, 2672, 2729-2734, 2754-2776, 2780-2799, 2803, 2806-2808, 2840-2844, 2847-2857, 2861, 2865, 2877-2908, 2911-2916, 2919-2920, 2973, 3112, 3136, 3155-3157, 3160-3166, 3170-3174, 3178-3184, 3200-3202, 3219, 3239-3252, 3268, 3285-3294, 3310-3312, 3327-3330, 3335, 3340, 3345, 3355-3364, 3368 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py 243 135 44% 63-69, 73-75, 82-85, 90, 93-96, 99-100, 104-105, 109-110, 113, 117-127, 131, 134-143, 161, 165, 169, 173, 176, 179, 183, 187, 190-191, 194-195, 198-199, 202-203, 206-207, 210-211, 214-215, 218-219, 222-223, 226-227, 230-231, 234-235, 238-239, 242-243, 246, 250, 254, 258, 262, 266, 270, 274, 277, 284, 289, 292, 304, 308, 312, 316, 325, 328, 331, 334, 337, 340, 343, 346, 349, 352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388-392, 395-399, 402-406, 409-413, 439-462, 481-483 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/device_compatibility_check.py 66 48 27% 54-61, 73-128, 154-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/get_layer_policy.py 11 3 73% 38-41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale.py 15 4 73% 29, 33-38, 48 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale_optimizer.py 140 81 42% 48, 123-155, 160, 182-189, 212-214, 218-224, 227-229, 232, 238-245, 251-272, 279, 284-286, 293-298, 301-303, 313, 317, 320, 323, 327, 330, 333, 336, 345, 349, 353, 357, 366, 372, 395-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/policy.py 133 60 55% 328, 331, 339, 341, 348, 361-371, 374, 376, 378, 382-388, 460, 463-471, 475-479, 508-509, 519-520, 549-558, 573-578, 582-586, 606, 612-617, 621-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/models.py 277 231 17% 57, 61, 67-74, 90-129, 162-215, 234-247, 265-276, 307-381, 419-426, 452-533, 550-557, 570-589, 638-723 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adadelta.py 49 28 43% 100-104, 108-111, 114-115, 121-127, 130-136, 147-153, 165-172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adagrad.py 60 35 42% 91-100, 103-107, 110-111, 118-124, 143-147, 150-155, 164-169, 179-186 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adam.py 84 61 27% 144-150, 155-161, 164-173, 185-192, 195-217, 232-269, 272-281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adamax.py 63 41 35% 103-108, 112-115, 118-126, 137-144, 157-181, 184-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/ftrl.py 60 42 30% 108-136, 141-146, 149-150, 162-181, 194-214, 228-245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/gradient_descent.py 53 24 55% 112, 118-120, 123-124, 128-143, 148-156, 161-166, 177-184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/learning_rate_schedule.py 249 184 26% 44, 48, 60, 135-140, 143-154, 158, 225-233, 236-256, 259, 360-367, 370-392, 399, 480-486, 489-502, 505, 573-578, 581-594, 597, 668-675, 678-716, 719, 803-810, 813-831, 835, 925-934, 937-965, 969, 983, 988 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/nadam.py 96 72 25% 92-105, 108-124, 127-146, 164-165, 168-188, 191-228, 231-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py 448 310 31% 72-76, 268, 272, 293, 301, 333-336, 340-356, 385-402, 418-430, 472-504, 520-545, 550-604, 607-613, 618, 622-630, 633-643, 646, 661-663, 675, 677, 684, 686, 692, 696-730, 733-735, 738-754, 757-759, 763-765, 768-782, 787-795, 799-803, 807-815, 829-834, 853-859, 863-870, 874, 879, 908-909, 941-958, 969-1000, 1004, 1019-1023, 1034, 1041, 1055, 1080-1082, 1104, 1107-1109, 1112-1114, 1119, 1124, 1132-1140, 1163-1199, 1206-1224, 1243-1247, 1253-1254, 1269-1270, 1274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/rmsprop.py 101 76 25% 133-146, 149-156, 159-162, 171-214, 217-272, 275-281, 284-293 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizers.py 434 336 23% 62-72, 88, 91, 107-121, 137-151, 159, 162-167, 171, 189-196, 199-202, 205-230, 233-240, 258-267, 270-272, 275-297, 300-307, 332-340, 343-346, 349-371, 374-380, 411-420, 423-427, 430-459, 462-469, 495-506, 509-516, 519-555, 558-567, 590-600, 604-610, 613-644, 647-655, 681-691, 694-699, 702-742, 745-753, 760-767, 772, 775, 778, 781-806, 810, 813, 816, 832, 849-865, 892-902 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/linear.py 59 39 34% 88-96, 99-127, 130-147, 150-160, 164-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/wide_deep.py 98 75 23% 87-91, 94-109, 113-136, 140-194, 197-205, 209-215 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/image.py 95 29 69% 27-28, 80-87, 152-154, 230-238, 299-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/sequence.py 18 1 94% 156 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/text.py 19 2 89% 42-43 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/regularizers.py 65 23 65% 152, 172, 192, 215-222, 225, 244, 280, 285, 290, 302, 304-311, 315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/hdf5_format.py 354 269 24% 43-44, 82-130, 158-217, 251-258, 272, 287-310, 316, 318, 320, 323-393, 397-404, 446, 468-473, 483-519, 527-572, 585-598, 610-613, 623-644, 661, 665, 682, 699, 729-789, 809-831, 851-856, 880 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/model_config.py 28 14 50% 29-30, 50-55, 86-90, 114-116 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/save.py 45 21 53% 39-40, 113-137, 181-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/base_serialization.py 33 10 70% 34, 43, 54, 74, 87-95, 106, 122, 172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/constants.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/json_utils.py 32 19 41% 38-41, 44, 48-56, 60, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/layer_serialization.py 74 40 46% 36, 41, 48-69, 72, 76, 81-96, 100-105, 111-119, 127, 131, 141, 144, 152, 155-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/load.py 460 367 20% 116-137, 143-148, 175-202, 209-215, 219, 223-234, 239-291, 295-313, 317-344, 348-363, 367-393, 401-431, 434-450, 454-466, 470-472, 486-510, 514-520, 523-539, 544-572, 577-579, 609-623, 627-646, 650-654, 662-687, 694-719, 723-725, 730-738, 745-753, 758-778, 786-791, 802-831, 835, 838-841, 848-863, 872-882, 885, 890-902, 915-922, 931-948, 953-958, 966-968, 972 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/metric_serialization.py 18 7 61% 30, 33-40, 43 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/model_serialization.py 27 11 59% 32, 35-39, 42-55, 62 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/network_serialization.py 14 5 64% 30, 33-39 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/save.py 29 14 52% 59-81 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/save_impl.py 271 210 23% 72-78, 97-116, 146-197, 201-205, 233-290, 296-302, 309-316, 320-323, 338-363, 375-397, 406-422, 427-434, 437-441, 445-448, 452, 457-491, 495-504, 509-527, 534-537, 540-542, 545-547, 564-567, 572-576, 582-586, 590-594, 603-607, 614-616, 624-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/serialized_attributes.py 78 34 56% 145-154, 158-160, 165, 171, 177, 183-186, 190-203, 207-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/utils.py 112 88 21% 56-96, 101-113, 117-120, 125-128, 150-199, 214-220, 224-230, 234-239, 243-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model_experimental.py 135 89 34% 133-145, 151-155, 160-163, 168-221, 226-227, 231, 255-326, 331-361, 371, 416-430 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saving_utils.py 138 110 20% 48-53, 77-87, 91, 112-142, 147-191, 197-199, 204-233, 245-264, 270-275, 280-285, 291-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/all_utils.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/conv_utils.py 172 152 12% 29-48, 68-87, 103-113, 128-137, 160-186, 190-197, 201-208, 227-233, 279-309, 358-400, 439-456, 475-482 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/data_utils.py 425 321 24% 56-57, 62-64, 69-104, 111-114, 133-160, 214-282, 286-294, 316-325, 342-350, 357-367, 370, 373, 376-384, 389-393, 453, 462, 467, 471-472, 485-486, 506-514, 519-525, 531-533, 538, 559-645, 662, 689-715, 718, 728-738, 743, 753-759, 762-763, 768, 780, 791, 807-808, 819-826, 831-833, 837-860, 872-880, 894-907, 923, 946-947, 958-964, 968-974, 987-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/generic_utils.py 363 275 24% 72-73, 76-79, 82-83, 114, 135, 140, 167-189, 207-210, 216-221, 251-257, 263-296, 301-305, 314-347, 356, 360-382, 386, 388, 392, 397-402, 415-426, 441-474, 490-493, 518-540, 553-675, 678, 691-692, 717-739, 754-756, 766, 774, 779-781, 792, 797 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/io_utils.py 84 52 38% 32-33, 84-99, 102, 105-134, 143, 152, 161, 170, 182-185, 201-209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/layer_utils.py 202 85 58% 50, 55, 62-69, 79-90, 136, 152-162, 205-208, 222-223, 228, 262, 287-294, 310-326, 345-357, 382-396, 400-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/losses_utils.py 58 39 33% 48-49, 54-55, 61-67, 91-112, 117-121, 137-148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/metrics_utils.py 216 159 26% 75-93, 119-149, 157-166, 170-173, 179-182, 199-204, 227-234, 296-456, 472-475, 495-539 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/mode_keys.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/multi_gpu_utils.py 82 65 21% 31, 35-36, 157-266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/np_utils.py 26 16 38% 49-61, 76-78 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/tf_utils.py 203 105 48% 61-64, 83-91, 95-99, 116-152, 178, 181, 226, 231, 248, 251, 266-295, 319, 345, 352, 355-356, 359, 392, 397-404, 408, 427-428, 452, 458-462, 467-472, 478, 481, 485, 490-496, 521 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/version_utils.py 34 4 88% 69, 79-85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/vis_utils.py 150 125 17% 45-53, 57-59, 64-65, 98-249, 278-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/scikit_learn.py 106 77 27% 75-77, 88-106, 117-119, 130-132, 150-168, 181-187, 214-223, 240-242, 263-270, 293-308, 332-333, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/base.py 218 171 22% 105-110, 148, 153, 195-234, 244-246, 250-258, 262-269, 273-279, 282-294, 300-302, 305-314, 376-481, 507-552, 555-569, 573, 578, 582-593 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/convolutional.py 84 21 75% 98, 198-218, 297, 404-424, 504, 612-632, 717, 829, 947-971, 1072-1096, 1164, 1260-1279, 1344, 1434-1453 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/core.py 39 9 77% 98, 173-187, 219, 226, 270-271, 331-332 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/layers.py 46 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/normalization.py 22 4 82% 147, 172, 312-336 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/pooling.py 78 30 62% 50-52, 90-95, 120-122, 160-165, 194-196, 235-238, 267-269, 308-311, 342-344, 385-388, 419-421, 460-463 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/utils.py 129 109 16% 28-47, 67-86, 90-95, 99-103, 119-129, 144-153, 168-175, 197-200, 219-227, 232-233, 238-243, 260-285 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/file_io.py 257 108 58% 58, 66, 71, 76, 84, 120, 139-165, 169-170, 174-181, 189-191, 202, 205-208, 211, 220-221, 232, 316-320, 333-334, 350, 366-374, 396, 412, 458, 474, 526-535, 589-590, 610-614, 653, 681, 701-729, 782-790, 808-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/python_io.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/tf_record.py 81 48 41% 90-99, 114-125, 129-149, 170-171, 212, 294-298, 313, 317, 321 /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/module.py 96 58 40% 107-121, 130, 135-139, 154, 169, 193, 249-252, 287-291, 295, 299, 303, 310, 314, 326-378 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_grad.py 573 409 29% 44, 50, 72-212, 218, 228, 247-260, 266-285, 301-305, 319, 324-329, 337, 342, 347, 352, 358, 364-368, 374-382, 389-399, 407-424, 430-460, 466-500, 505-507, 516, 525, 531-538, 552-564, 569-585, 593-615, 627-687, 692-700, 705-713, 719, 728, 737, 742, 747, 755, 767, 773, 778, 784-785, 791-792, 810-832, 842-854, 864-865, 876-877, 882-883, 889-890, 898, 906-907, 915, 923-928, 934-939, 947-948, 953-954, 959, 964, 970, 975-1027, 1032-1090, 1095-1097, 1102-1107, 1112-1115, 1120-1123, 1128-1130, 1135-1150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_ops.py 1121 797 29% 193-195, 277, 341-344, 426, 475, 503, 530, 576, 602, 620-630, 649, 683, 715, 732-753, 787, 802-811, 825-836, 840, 902-973, 1037, 1137-1178, 1224, 1270-1277, 1328-1342, 1357-1393, 1407-1414, 1419-1425, 1446-1455, 1501-1511, 1594, 1601-1605, 1659-1693, 1746, 1786-1790, 1832, 1882, 1943-1961, 2042, 2112-2129, 2190-2210, 2368-2371, 2514-2517, 2653, 2661-2667, 2710, 2712, 2721-2723, 2728-2730, 2732, 2774, 2819, 2825-2850, 2883, 2918, 2923-2931, 2959-2981, 3023, 3092-3139, 3199, 3260-3294, 3311-3316, 3362-3392, 3404-3438, 3443-3456, 3519-3527, 3541, 3553, 3565, 3607-3650, 3661-3670, 3678, 3687, 3695, 3704, 3712, 3720-3729, 3845-3848, 3966-4009, 4016-4025, 4064-4090, 4141-4145, 4197, 4243-4251, 4340-4348, 4406-4410, 4424, 4512-4524, 4535, 4554-4560, 4589-4682, 4835-4846, 4852, 4860-4934, 4956-4975, 5007-5019, 5043-5060, 5106-5113, 5172-5184, 5307, 5350-5352, 5405, 5410-5416, 5441-5455, 5499-5565, 5571-5578, 5583-5591, 5641-5644 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/batch_ops.py 25 13 48% 79-111 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bitwise_ops.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/boosted_trees_ops.py 144 76 47% 63-66, 74-89, 93-95, 111-126, 129, 133, 138-140, 143, 147, 150, 153-156, 159, 162, 176-190, 203-204, 214-227, 234, 238, 245-247, 250, 254-255, 259-262, 271-276, 290, 303 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/candidate_sampling_ops.py 37 12 68% 83-84, 148-149, 208-209, 299-300, 337-338, 386-387 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/check_ops.py 590 425 28% 73-78, 83-85, 91, 238-271, 285-297, 327-372, 392-402, 435, 442-455, 487, 494-506, 539, 547-560, 593, 601-614, 648, 654-658, 696, 705, 758, 810-838, 873, 879, 915, 923, 959, 966, 1003, 1012, 1037-1061, 1094, 1124-1156, 1189, 1222-1255, 1259, 1263-1269, 1294-1321, 1353, 1386-1416, 1436, 1462-1475, 1494, 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870, 873-942, 947-976, 986-1064, 1088-1124, 1138-1148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/confusion_matrix.py 62 42 32% 59-92, 152-201, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_grad.py 121 89 26% 42-88, 98-136, 143, 149-182, 194, 199, 209-232, 237, 243 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_ops.py 1318 1123 15% 86-104, 148-176, 189-198, 202-211, 240-259, 274-283, 305-317, 341-362, 390-424, 431-434, 438-442, 450-457, 473-492, 506-529, 545-562, 578-591, 596-618, 641-654, 663-679, 684, 689, 693, 697, 707, 718-724, 727, 732-734, 738-740, 744-745, 749-750, 754-758, 762-764, 768-785, 791-792, 796, 799, 802, 805, 808, 833-849, 858-867, 872, 876, 880, 884-886, 890-892, 895, 906-922, 927-933, 936, 940-972, 975, 979-1032, 1036-1050, 1053-1059, 1063-1083, 1086, 1090-1093, 1175-1296, 1310-1314, 1392, 1397-1401, 1433-1440, 1456-1475, 1484-1529, 1534, 1539, 1544, 1549, 1554, 1559, 1564, 1569, 1580-1609, 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/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 25% 50-56, 64-88, 92-99, 104-112, 160-182, 200-219, 228, 233-235, 240, 245, 250, 272-293, 304-309, 334-347, 377-395, 412-419, 441-457, 484-500, 528-541, 565-573, 590-595, 606-611, 614-616, 683-706, 754-765, 818-829, 834, 840, 904-919, 974-987, 1055-1075, 1080, 1085-1087, 1106-1108, 1153-1178, 1202-1204, 1219-1221, 1233-1235, 1259-1268, 1273, 1278, 1283, 1294-1297, 1313, 1346-1356, 1375-1379, 1404-1407, 1438-1444, 1464, 1508-1509, 1540, 1564-1566, 1581-1584, 1600, 1618-1647, 1652, 1657, 1662, 1667, 1672, 1677, 1707-1757, 1764-1772, 1790-1800, 1811-1816, 1894, 1915-1933, 1936-1940, 1964-1974, 1994-2004, 2015-2018, 2034-2037, 2140-2161, 2190-2205, 2208-2235, 2255-2270, 2295-2298, 2316-2331, 2351-2370, 2381-2384, 2400-2403, 2419-2422, 2477-2489, 2496-2516 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/default_gradient.py 37 24 35% 28-35, 40-49, 54-57, 62-65, 79-84 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/bernoulli.py 70 32 54% 83-90, 101, 106, 111, 114, 117, 120, 123, 126-130, 133-152, 155, 159, 162, 166, 182-186 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/beta.py 114 53 54% 192-203, 216, 223, 228, 233, 236, 239, 242, 245, 248-263, 267, 271, 275, 279, 282-283, 287, 292, 300, 303, 311-320, 333-335, 343-345, 368-379, 395-407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/categorical.py 108 69 36% 38-59, 193-234, 247, 252, 257, 260, 263, 266, 269, 272-282, 285-304, 307-314, 318, 322-325, 341-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/dirichlet.py 93 44 53% 196-202, 215, 220, 223, 226, 229, 232, 235-240, 244, 248, 251-252, 255, 258-259, 268, 271-272, 278-280, 284, 292-302, 311-313, 327-329, 351-410 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/dirichlet_multinomial.py 87 43 51% 203-223, 236, 241, 246, 249, 252, 255, 259, 262-278, 282-286, 291, 294, 316-317, 323-325, 331-332, 336-340, 348-351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/distribution.py 405 242 40% 91, 135-140, 170, 179, 186, 189, 203, 245, 259, 463-477, 481, 493-495, 515-516, 540-555, 559, 564, 569, 577, 591, 608, 613, 630-631, 634, 649-654, 657, 671, 674, 686-691, 694, 705, 716-717, 730-731, 736, 740-750, 766, 769, 773-782, 795, 798, 802-811, 824, 827, 831-840, 863, 866, 870-879, 898, 901, 906-915, 939, 942, 946-955, 976, 979, 984-985, 988, 993-994, 997, 1001-1004, 1023, 1026, 1048-1055, 1058, 1081-1088, 1091, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/multinomial.py 88 46 48% 194-208, 222, 227, 232, 235, 238, 241, 244, 247-277, 281, 284-285, 288-289, 292, 295-297, 303-305, 309-312 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/normal.py 100 46 54% 146-153, 164, 171, 176, 179, 184, 189, 192, 195-198, 201, 204, 207, 210, 213, 216, 219, 223-224, 227, 230, 233, 236, 240-241, 245-246, 263-271, 287-294 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/special_math.py 113 89 21% 137-143, 148-156, 179-185, 195-279, 332-365, 376-379, 384-398, 415-421, 426, 458-474 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/student_t.py 103 52 50% 191-200, 211, 219, 224, 229, 232, 238, 244, 247, 256-266, 269, 272-273, 276, 284-287, 290-294, 306-316, 337-358, 368, 386-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/uniform.py 69 31 55% 114-123, 135, 142, 147, 151-152, 155, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_bitwise_ops.py 319 274 14% 56-95, 101-111, 147-186, 192-202, 238-277, 283-293, 348-387, 393-402, 449-488, 494-504, 524-550, 556-565, 615-654, 660-670 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_boosted_trees_ops.py 1223 1116 9% 46-80, 86-101, 121-162, 168-192, 241-285, 291-310, 362-409, 415-439, 484-532, 538-566, 588-619, 625-639, 657-681, 686-695, 715-744, 749-761, 781-805, 810-819, 833-869, 875-890, 913-953, 959-977, 996-1028, 1034-1044, 1068-1097, 1103-1113, 1134-1167, 1173-1189, 1215-1259, 1265-1286, 1308-1346, 1352-1369, 1389-1416, 1421-1434, 1452-1480, 1485-1498, 1523-1551, 1556-1567, 1586-1619, 1625-1635, 1649-1685, 1691-1706, 1727-1756, 1762-1772, 1816-1858, 1864-1881, 1934-1981, 1987-2008, 2045-2089, 2095-2115, 2156-2229, 2234-2291, 2338-2442, 2447-2533, 2547-2575, 2581-2591, 2607-2635, 2641-2651 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_candidate_sampling_ops.py 423 383 9% 65-108, 114-133, 169-209, 215-232, 314-400, 406-451, 499-549, 555-576, 624-673, 679-700, 748-799, 805-826, 874-922, 928-949 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_checkpoint_ops.py 103 84 18% 77-119, 125-142, 215-259, 265-284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_clustering_ops.py 116 94 19% 42-68, 74-85, 113-145, 151-164, 194-221, 227-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_collective_ops.py 215 191 11% 38-85, 91-109, 127-173, 179-197, 215-260, 266-284, 305-368, 374-406 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_control_flow_ops.py 434 371 15% 40-68, 73-84, 98-117, 122-127, 151-190, 196-213, 228-254, 260-269, 286-312, 318-327, 354-386, 392-406, 419-445, 451-460, 474-505, 510-515, 539-564, 570, 585-599, 605, 631-650, 656, 669-683, 689, 704-723, 729, 756-770, 776, 802-829, 835-845 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ctc_ops.py 214 186 13% 57-101, 107-127, 164-198, 204-217, 260-315, 321-345, 389-443, 449-472 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_cudnn_rnn_ops.py 910 857 6% 83-145, 151-184, 262-329, 335-364, 446-514, 520-550, 640-721, 727-765, 818-892, 898-948, 1003-1081, 1087-1137, 1183-1247, 1253-1289, 1349-1414, 1420-1456, 1519-1596, 1602-1644, 1711-1773, 1779-1812, 1886-1961, 1967-2007 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py 3989 3627 9% 39-47, 52, 64-78, 84, 101-110, 115, 138-154, 160, 195-236, 242, 265-277, 282, 294-308, 314, 338-347, 352, 364-378, 384, 425-462, 468, 497-528, 534, 547-566, 571-577, 634-678, 684-695, 774-827, 833-857, 885-926, 932, 959-1015, 1021-1051, 1064-1078, 1084, 1096-1122, 1128-1137, 1150-1176, 1182-1191, 1206-1233, 1239-1249, 1266-1306, 1311-1334, 1351-1401, 1407-1433, 1455-1508, 1514-1541, 1558-1608, 1614-1640, 1666-1708, 1713-1740, 1762-1815, 1821-1848, 1876-1930, 1936-1965, 1982-2022, 2027-2050, 2067-2119, 2125-2152, 2175-2229, 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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, 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829-849, 883-914, 920-934, 960-986, 992-1001, 1029-1073, 1079-1098 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_image_ops.py 1972 1820 8% 36-64, 70-82, 108-135, 141-151, 173-199, 205-215, 238-264, 270-280, 343-387, 393-414, 470-507, 513-532, 566-596, 602-618, 654-688, 694-711, 764-825, 831-861, 884-913, 919-931, 953-979, 985-994, 1045-1105, 1111-1140, 1172-1206, 1212-1227, 1256-1282, 1288-1298, 1329-1356, 1362-1373, 1423-1498, 1504-1543, 1560-1587, 1593-1604, 1631-1661, 1667-1679, 1736-1782, 1788-1812, 1830-1860, 1866-1878, 1926-1967, 1973-1992, 2014-2053, 2059-2068, 2097-2134, 2140-2157, 2197-2230, 2236-2250, 2292-2322, 2328-2340, 2384-2416, 2422-2435, 2489-2529, 2535-2552, 2616-2657, 2663-2677, 2719-2751, 2757-2771, 2803-2843, 2849-2869, 2904-2943, 2949-2958, 2987-3021, 3027-3043, 3074-3105, 3111-3124, 3146-3184, 3190-3207, 3228-3266, 3272-3289, 3311-3349, 3355-3372, 3393-3432, 3438-3455, 3475-3514, 3520-3537, 3557-3596, 3602-3620, 3709-3789, 3795-3838, 3927-4002, 4008-4048, 4066-4104, 4110-4128, 4146-4185, 4191-4209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_io_ops.py 1163 1009 13% 46-85, 91, 119-181, 187-217, 238-260, 266, 286-320, 326-340, 358-379, 385, 405-444, 450-459, 486-512, 517-527, 543-582, 588-597, 613-627, 633, 648-674, 680-690, 703-717, 723, 735-763, 769-779, 805-820, 826, 854-870, 876, 904-933, 939-950, 976-1004, 1010-1020, 1033-1040, 1045, 1057-1076, 1081-1087, 1106-1114, 1119, 1137-1157, 1162-1169, 1185-1199, 1205, 1220-1246, 1252-1262, 1301-1335, 1341-1355, 1387-1422, 1428-1443, 1487, 1493-1515, 1522, 1534, 1559-1579, 1584-1592, 1635-1657, 1662-1671, 1696-1718, 1723-1732, 1749-1776, 1782-1793, 1807-1834, 1840-1850, 1869-1895, 1901, 1919-1959, 1965-1983, 2003-2030, 2036, 2055-2096, 2102-2120, 2141-2163, 2169, 2189-2223, 2229-2243, 2264-2296, 2301-2308 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_linalg_ops.py 1226 1097 11% 33-59, 65-74, 88-114, 120-130, 143-169, 175-185, 199-229, 235-247, 262-293, 299-312, 328-360, 366-380, 396-431, 437-454, 467-493, 499-508, 529-560, 566-578, 601-639, 645-661, 692-731, 737-746, 769-795, 801-811, 848-880, 886-899, 991-1024, 1030-1045, 1076-1103, 1109-1118, 1161-1205, 1211-1223, 1244-1283, 1289-1298, 1311-1337, 1343-1352, 1382-1425, 1431-1443, 1473-1499, 1505-1514, 1543-1587, 1593-1606, 1663-1694, 1700-1714, 1746-1785, 1791-1800, 1873-1907, 1913-1929, 1968-2012, 2018-2030, 2052-2078, 2084-2093, 2129-2160, 2166-2178, 2221-2258, 2264-2280, 2306-2333, 2339-2349, 2379-2412, 2418-2431 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_list_ops.py 716 641 10% 42-74, 80-91, 119-155, 161-174, 189-218, 224-235, 270-304, 310-322, 339-367, 373-384, 403-431, 437-448, 471-502, 508-520, 540-571, 577-589, 605-631, 637-646, 675-707, 713-724, 744-771, 777-787, 801-828, 834-845, 865-897, 903-914, 932-958, 964-974, 998-1026, 1032-1044, 1067-1096, 1102-1114, 1142-1171, 1177-1190, 1210-1237, 1243-1254, 1277-1305, 1311-1323, 1345-1381, 1387-1401 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py 492 436 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, 1355-1364, 1403-1437, 1443-1459, 1502-1536, 1542-1558, 1578-1617, 1623-1632, 1652-1691, 1697-1706, 1738-1777, 1783-1793, 1819-1845, 1851-1862, 1889-1921, 1927-1941, 1956-1988, 1994-2007, 2020-2046, 2052-2061, 2086-2113, 2119-2129, 2169-2195, 2201-2211, 2241-2271, 2277-2290, 2309-2339, 2345-2357, 2384-2410, 2416-2425, 2450-2489, 2495-2504, 2528-2567, 2573-2582, 2605-2644, 2650-2660, 2712-2748, 2754-2771, 2823-2859, 2865-2882, 2922-2958, 2964-2981, 2999-3038, 3044-3053, 3070-3096, 3102-3112, 3130-3156, 3162-3172, 3200-3233, 3239-3253, 3269-3308, 3314-3323, 3339-3378, 3384-3393, 3406-3432, 3438-3447, 3471-3502, 3508-3521, 3561-3587, 3593-3602, 3632-3671, 3677-3686, 3701-3740, 3746-3755, 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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, 8259-8299, 8305-8315, 8357-8397, 8403-8413, 8455-8495, 8501-8511, 8567-8593, 8599-8610, 8625-8651, 8657-8668, 8683-8709, 8715-8724, 8741-8767, 8773-8783, 8804-8830, 8836-8845, 8869-8908, 8914-8923, 8947-8986, 8992-9001, 9026-9056, 9062-9076, 9104-9151, 9157-9181, 9203-9231, 9237-9248, 9271-9300, 9306-9318, 9344-9376, 9382-9396, 9417-9445, 9451-9462, 9485-9514, 9520-9533, 9561-9593, 9599-9613, 9660-9688, 9694-9705, 9752-9784, 9790-9804, 9819-9845, 9851-9860, 9877-9903, 9909-9919, 9939-9978, 9984-9993, 10013-10052, 10058-10068, 10093-10111, 10117-10127, 10151-10182, 10188-10201, 10226-10265, 10271-10280, 10304-10343, 10349-10358, 10375-10401, 10407-10417, 10441-10480, 10486-10496, 10518-10557, 10563-10573, 10623-10667, 10673-10685, 10731-10775, 10781-10793, 10838-10882, 10888-10900, 10946-10990, 10996-11008, 11024-11063, 11069-11079, 11093-11119, 11125-11135, 11151-11190, 11196-11206, 11228-11267, 11273-11283 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nccl_ops.py 124 103 17% 49-85, 91-104, 127-154, 160-170, 193-227, 233-249 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py 5462 5063 7% 49-95, 101-125, 153-199, 205-229, 259-306, 312-337, 366-413, 419-444, 479-517, 523-537, 580-619, 625-639, 669-681, 690-693, 702-715, 741-772, 778-790, 811-837, 843-853, 917-983, 989-1027, 1072-1142, 1148-1187, 1232-1302, 1308-1347, 1387-1436, 1442-1469, 1495-1541, 1547-1571, 1613-1687, 1693-1722, 1748-1794, 1800-1824, 1863-1916, 1922-1950, 1971-2008, 2014-2029, 2049-2086, 2092-2107, 2160-2228, 2234-2261, 2306-2388, 2394-2423, 2467-2549, 2555-2584, 2632-2674, 2680-2702, 2728-2772, 2778-2801, 2827-2870, 2876-2899, 2917-2956, 2962-2971, 2987-3013, 3019-3029, 3088-3148, 3154-3185, 3224-3262, 3268-3283, 3366-3426, 3432-3463, 3498-3537, 3543-3558, 3603-3655, 3661-3685, 3735-3782, 3788-3808, 3858-3905, 3911-3934, 3988-4036, 4042-4065, 4110-4162, 4168-4193, 4239-4291, 4297-4322, 4359-4398, 4404-4423, 4465-4512, 4518-4542, 4574-4601, 4607-4618, 4651-4677, 4683-4694, 4714-4753, 4759-4768, 4803-4861, 4867-4889, 4913-4958, 4964-4987, 5001-5030, 5036-5048, 5066-5096, 5102-5115, 5133-5159, 5165-5174, 5201-5247, 5253-5277, 5305-5351, 5357-5381, 5413-5460, 5466-5492, 5524-5572, 5578-5603, 5634-5681, 5687-5712, 5743-5791, 5797-5822, 5853-5889, 5895-5911, 5939-5991, 5997-6025, 6056-6091, 6097-6113, 6141-6193, 6199-6226, 6253-6288, 6294-6309, 6350-6406, 6412-6439, 6467-6497, 6503-6516, 6550-6594, 6600-6623, 6688-6742, 6748-6773, 6807-6840, 6846-6861, 6909-6964, 6970-7001, 7032-7098, 7104-7144, 7177-7251, 7257-7299, 7332-7403, 7409-7451, 7489-7545, 7551-7583, 7615-7682, 7688-7729, 7761-7831, 7837-7878, 7912-7989, 7995-8038, 8072-8148, 8154-8197, 8234-8317, 8323-8370, 8403-8474, 8480-8522, 8559-8642, 8648-8695, 8733-8789, 8795-8827, 8866-8925, 8931-8964, 9004-9076, 9082-9123, 9168-9245, 9251-9294, 9343-9396, 9402-9432, 9457-9513, 9519-9549, 9599-9653, 9659-9689, 9743-9804, 9810-9842, 9875-9934, 9940-9972, 10006-10050, 10056-10079, 10105-10140, 10146-10160, 10186-10221, 10227-10241, 10268-10304, 10310-10325, 10345-10384, 10390-10399, 10412-10438, 10444-10453, 10470-10496, 10502-10512, 10529-10555, 10561-10571, 10594-10633, 10639-10648, 10664-10690, 10696-10706, 10724-10750, 10756-10765, 10792-10821, 10827-10838, 10853-10892, 10898-10907, 10923-10949, 10955-10965, 10980-11019, 11025-11034, 11050-11076, 11082-11092, 11123-11153, 11159-11170, 11210-11242, 11248-11261, 11299-11330, 11336-11349 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_parsing_ops.py 1066 994 7% 52-103, 109-137, 164-209, 215-227, 252-291, 297-306, 326-361, 367-381, 400-434, 440-453, 520-582, 588-628, 708-782, 788-834, 925-1095, 1101-1211, 1314-1499, 1505-1632, 1689-1755, 1761-1800, 1886-2018, 2024-2118, 2138-2179, 2185-2195, 2211-2250, 2256-2265, 2289-2319, 2325-2337 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_array_ops.py 58 42 28% 76-119, 125-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_conversion_ops.py 189 163 14% 61-106, 112-130, 157-192, 198-214, 279-325, 331-355, 386-425, 431-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_math_ops.py 53 37 30% 63-95, 101-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_random_ops.py 511 460 10% 43-83, 89-109, 141-180, 186-204, 232-267, 273-289, 303-329, 335-345, 361-396, 402-418, 453-493, 499-519, 548-581, 587-602, 626-662, 668-684, 717-745, 751-767, 799-835, 841-858, 884-920, 926-942 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_resource_variable_ops.py 631 526 17% 38-57, 62-69, 87-106, 111-118, 144-155, 160-167, 189-208, 213-219, 247-264, 269-278, 330-356, 362-371, 389-422, 428-442, 467-479, 486-488, 497-507, 538-579, 585-603, 618-647, 653-664, 701-721, 726-734, 771-791, 796-804, 841-861, 866-874, 911-931, 936-944, 981-1001, 1006-1014, 1051-1071, 1076-1084, 1111-1131, 1136-1144, 1173-1203, 1209-1226, 1247-1265, 1271-1280, 1303-1332, 1338-1350 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_rnn_ops.py 350 306 13% 84-130, 136-157, 220-257, 263-275, 338-375, 381-393, 460-501, 507-525, 597-625, 631-641, 753-782, 788-798, 866-912, 918-938, 987-1022, 1028-1039 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_script_ops.py 136 104 24% 39-80, 86-104, 128-140, 144, 152, 154-157, 165-179, 194-226, 232-247 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sdca_ops.py 322 287 11% 37-76, 82-91, 170-329, 335-421, 497-617, 623-709, 730-751, 756 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sendrecv_ops.py 97 79 19% 42-91, 97-116, 138-173, 178-193 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_set_ops.py 187 160 14% 56-94, 100-116, 167-208, 214-232, 258-292, 298-312, 378-423, 429-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sparse_ops.py 1272 1150 10% 68-108, 114-132, 169-209, 215-232, 298-327, 333-343, 409-439, 445-455, 484-519, 525-539, 559-594, 600-614, 663-695, 701-716, 750-781, 787-799, 870-924, 930-965, 1040-1103, 1109-1143, 1173-1200, 1206-1218, 1242-1269, 1275-1287, 1315-1342, 1348-1360, 1427-1456, 1462-1474, 1505-1533, 1539-1550, 1586-1621, 1627-1642, 1686-1723, 1729-1744, 1780-1815, 1821-1836, 1880-1917, 1923-1938, 1973-2002, 2008-2019, 2061-2089, 2095-2106, 2153-2181, 2187-2200, 2225-2256, 2262-2274, 2309-2336, 2342-2353, 2387-2418, 2424-2438, 2472-2503, 2509-2523, 2572-2607, 2613-2630, 2652-2680, 2686-2698, 2734-2773, 2779-2799, 2845-2882, 2888-2904, 2985-3025, 3031-3049 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_special_math_ops.py 172 145 16% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_spectral_ops.py 714 630 12% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290, 303-329, 335-344, 364-403, 409-418, 438-477, 483-492, 512-551, 557-566, 586-625, 631-640, 660-699, 705-714, 734-773, 779-788, 819-849, 855-868, 900-930, 936-949, 981-1011, 1017-1030, 1058-1089, 1095-1108, 1137-1168, 1174-1187, 1216-1247, 1253-1266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_state_ops.py 513 432 16% 46-69, 75, 96-114, 120, 141-159, 165, 181-197, 203, 226-242, 248, 263-277, 283, 300-329, 335-346, 403-427, 432-444, 501-525, 530-542, 602-627, 632-645, 689-709, 715, 756-776, 782, 825-845, 851, 894-914, 920, 961-981, 987, 1044-1064, 1070, 1129-1149, 1155, 1213-1233, 1239, 1282-1302, 1308, 1355-1375, 1381, 1413-1435, 1441, 1456-1482, 1488, 1512-1538, 1544 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateful_random_ops.py 353 314 11% 37-67, 73-85, 106-126, 131-139, 157-191, 197-214, 233-264, 270-284, 304-335, 341-356, 378-409, 415-430, 451-482, 488-502, 522-553, 559-574, 601-630, 636-649 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateless_random_ops.py 354 315 11% 40-75, 81-95, 123-155, 161-177, 200-228, 234-246, 268-299, 305-318, 343-373, 379-393, 416-447, 453-467, 489-521, 527-541, 565-593, 599-612, 636-667, 673-687 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_string_ops.py 1301 1175 10% 64-128, 134-159, 178-217, 223-232, 257-299, 305-317, 363-399, 405-421, 451-477, 483-493, 518-550, 556-570, 592-619, 625-635, 655-691, 697-712, 734-775, 781-800, 826-861, 867-884, 912-941, 947-959, 980-1022, 1028-1040, 1083-1137, 1143-1166, 1212-1243, 1249-1262, 1313-1343, 1349-1362, 1378-1417, 1423-1432, 1453-1481, 1487-1497, 1526-1569, 1575-1586, 1625-1675, 1681-1697, 1718-1760, 1766-1778, 1884-1913, 1919-1933, 1989-2043, 2049-2073, 2134-2190, 2196-2221, 2271-2313, 2319-2337, 2363-2402, 2408-2417, 2495-2570, 2576-2598, 2647-2699, 2705-2720 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_summary_ops.py 386 335 13% 33-52, 57-63, 80-104, 109-119, 136-160, 165-176, 189-208, 213-219, 233-252, 257-264, 278-312, 318-332, 350-376, 381-394, 409-429, 434-442, 458-478, 483-492, 510-536, 541-554, 569-589, 594-602, 618-638, 643-652, 669-689, 694-704 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_tpu_ops.py 3018 2798 7% 59-98, 104-118, 141-169, 175-185, 206-267, 273-300, 315-335, 340-346, 370-397, 403-413, 436-468, 473-489, 537-598, 603-647, 701-779, 784-841, 855-884, 890-900, 916-954, 960-978, 1002-1037, 1042-1062, 1079-1104, 1109-1119, 1144-1181, 1186-1208, 1237-1278, 1283-1304, 1335-1379, 1384-1406, 1435-1477, 1482-1503, 1534-1578, 1583-1605, 1632-1673, 1678-1698, 1727-1770, 1775-1796, 1827-1868, 1873-1895, 1924-1965, 1970-1991, 2022-2066, 2071-2093, 2124-2167, 2172-2194, 2221-2261, 2266-2286, 2315-2358, 2363-2384, 2411-2452, 2457-2477, 2506-2549, 2554-2575, 2604-2643, 2648-2669, 2700-2743, 2748-2770, 2795-2836, 2841-2860, 2880-2916, 2922-2935, 2958-3003, 3009-3031, 3044-3063, 3068-3074, 3089-3108, 3113-3119, 3138-3175, 3181-3200, 3221-3261, 3267-3288, 3311-3342, 3348-3358, 3388-3436, 3442-3463, 3494-3543, 3549-3570, 3600-3648, 3654-3675, 3706-3755, 3761-3782, 3811-3859, 3865-3886, 3916-3965, 3971-3992, 4023-4072, 4078-4099, 4129-4177, 4183-4204, 4235-4284, 4290-4311, 4342-4391, 4397-4418, 4447-4495, 4501-4522, 4552-4601, 4607-4628, 4657-4706, 4712-4733, 4763-4812, 4818-4839, 4869-4917, 4923-4944, 4975-5024, 5030-5051, 5073-5121, 5127-5148, 5175-5207, 5212-5231, 5245-5264, 5269-5275, 5291-5317, 5323-5331, 5359-5393, 5399-5412, 5428-5454, 5460-5468, 5486-5525, 5531-5550, 5580-5663, 5668-5722, 5749-5793, 5799-5820, 5844-5872, 5878-5888, 5905-5931, 5937-5946 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_user_ops.py 43 28 35% 32-57, 63-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker.py 145 117 19% 39-45, 49-54, 83-132, 160-193, 202-208, 221-242, 254-268, 321-335, 339-345, 393-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker_v2.py 140 116 17% 37-43, 59-64, 78-93, 110-127, 150-197, 221-261, 266-281, 287-293, 332-335, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_impl.py 59 25 58% 168-169, 298-299, 342-357, 392-424, 433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_util.py 446 380 15% 61-69, 97-133, 137, 162-227, 231-234, 250-254, 279-289, 295-299, 303, 308-318, 323-355, 362-374, 383, 388-392, 405-411, 428, 444-457, 471-476, 490-716, 721-728, 734-766, 771-781, 786-805, 810-813, 817-821, 826-838, 846-867, 932-987 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/histogram_ops.py 28 13 54% 77-100, 146-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_grad.py 91 69 24% 39-50, 64-69, 84-91, 105-113, 131-156, 167, 188-381 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops.py 51 32 37% 195-203, 228-239, 245-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops_impl.py 1010 802 21% 75-81, 93, 108-113, 133-150, 171, 193, 212-236, 258, 277-301, 315-320, 360, 401, 422-447, 481, 515, 537-546, 581-594, 610-625, 641-657, 706-715, 772-847, 907-956, 990-1036, 1070-1155, 1183-1255, 1315-1333, 1470-1511, 1523-1590, 1628-1631, 1668-1671, 1698-1717, 1748-1752, 1784-1792, 1830-1843, 1885-1899, 1945-1963, 2012-2062, 2086-2097, 2122-2132, 2168-2175, 2230-2241, 2277-2289, 2327-2339, 2377-2385, 2426-2437, 2455-2457, 2471-2473, 2540, 2586-2656, 2694-2728, 2823-2824, 2930-2931, 2988-2992, 3064-3079, 3129-3133, 3175-3179, 3211-3215, 3238-3242, 3278-3282, 3305-3309, 3329-3354, 3393-3408, 3444-3466, 3471-3483, 3519-3565, 3627-3640, 3690-3771, 3826-3844, 3861-3885, 3893, 3906, 3919, 4036, 4053-4055, 4137, 4222, 4295-4300, 4354-4356, 4408, 4427 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops.py 509 330 35% 68, 76, 97, 113, 117, 132-134, 137, 215-222, 225-229, 237, 260-263, 266-268, 272, 300-303, 306-308, 312, 346-349, 352-354, 358, 407-409, 412-427, 431, 480, 482, 487, 495-518, 522, 563-565, 568-595, 598, 626-628, 631-663, 666, 688-690, 693, 696, 708-715, 726-733, 758-771, 785, 803-813, 828-845, 860-877, 903-913, 926, 939-944, 959-973, 988-1002, 1028-1041, 1056, 1075-1093, 1109-1129, 1144-1165, 1185-1186, 1189-1201, 1204, 1236, 1265, 1269, 1318, 1345, 1370, 1394, 1410-1425, 1443 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops_v2.py 241 105 56% 59, 67, 88-89, 168-171, 239-243, 257-259, 263, 300-303, 316-319, 323, 364-367, 380-381, 385, 429-432, 445-446, 450, 509, 511, 515, 518, 541, 544, 546, 551-552, 554-555, 561, 612-614, 628-650, 653, 684, 698-708, 711, 757, 796, 803, 864, 907, 947, 987, 1004, 1006, 1013-1017, 1037, 1048, 1054-1058, 1064, 1072-1076 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/initializers_ns.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/inplace_ops.py 41 16 61% 53-64, 90, 116, 142, 160-161, 191, 221, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/io_ops.py 120 54 55% 68-71, 93-94, 131-137, 142, 161-171, 193-206, 224-228, 240-244, 259-262, 278-282, 287, 298-301, 337-338, 369-371, 410-417, 446-451, 479-481, 511-512 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/adjoint_registrations.py 50 18 64% 37, 48, 54, 60-64, 76-80, 92, 106, 118-123, 134 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/cholesky_registrations.py 31 6 81% 36, 46, 57, 70, 83, 96 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/inverse_registrations.py 68 31 54% 40, 51, 57, 68, 74, 87, 132-185, 200, 213, 225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg_impl.py 400 323 19% 95-97, 126-128, 135-145, 150-161, 166-178, 183-203, 208-229, 262-339, 439-490, 496-538, 591-618, 623-635, 657-673, 746-802, 848-896, 949-959, 1000-1036, 1041-1068, 1073-1094 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator.py 387 259 33% 188-213, 218-222, 227, 232, 238, 242, 246, 250, 257-264, 269, 282, 288, 303-308, 322, 338-339, 344-349, 365-366, 381-382, 387-391, 404-407, 424-425, 430-435, 448-451, 468-469, 474-479, 483-493, 500-508, 527-528, 532-544, 560-561, 564-568, 586-587, 591-592, 598, 629-653, 656, 659-661, 690-696, 699-704, 718-723, 726-733, 747-752, 756-763, 768-771, 814-846, 850-852, 893-900, 914-917, 937-945, 966-970, 974-986, 990-991, 995, 1022-1023, 1026, 1039-1040, 1044, 1056-1059, 1062, 1078-1081, 1084-1093, 1105-1106, 1109, 1122-1127, 1137, 1142, 1155, 1161, 1166-1169, 1174-1176, 1190-1202, 1212-1215, 1220 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_addition.py 160 105 34% 99-138, 144-149, 168-187, 195-211, 217-223, 233-235, 253, 258, 264, 280-290, 301-302, 307-317, 330-331, 334, 346-347, 350-355, 367, 371-375, 412-424 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_adjoint.py 72 39 46% 116-155, 160, 163, 166, 169, 173-174, 178-179, 183, 187, 190-192, 195, 198-200, 203, 207, 210-212, 215, 218-221, 224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_algebra.py 112 49 56% 37-48, 53, 58, 63, 68, 73, 90-96, 113-119, 138-144, 163-169, 186-192, 228, 231, 270, 273, 315, 318, 361, 364, 404, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_diag.py 153 119 22% 156-215, 219, 223-238, 242-260, 263-273, 276-279, 282-285, 288-299, 302-308, 311-314, 317-341, 344, 348, 352, 356-362, 378-386 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_lower_triangular.py 191 152 20% 224-241, 250-252, 259-283, 290-301, 304-310, 314-318, 324, 328-344, 348-367, 370-417, 420-425, 428-431, 460-510, 513-520, 523-526, 529-550, 553, 557-563, 579-587 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_circulant.py 204 144 29% 93-126, 130-138, 171, 177, 180-186, 190, 194, 210-224, 239-260, 272-273, 285-286, 300-302, 305-321, 324-331, 345-350, 356, 366-368, 373, 381-402, 405-425, 428-430, 433-436, 439-450, 455-478, 490-512, 747, 758, 927, 1077, 1089-1095 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_composition.py 86 62 28% 147-187, 191, 195-211, 215-233, 239-248, 251-254, 257-260, 270-279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_diag.py 83 49 41% 143-168, 172-173, 178-179, 182-184, 188, 191, 196-205, 210, 217-220, 223-224, 227, 230-234, 237-240, 243, 246, 249-251, 254, 257-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_full_matrix.py 38 17 55% 137-151, 155-172, 177, 180, 183, 187, 190 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_householder.py 86 52 40% 126-156, 160-162, 168-169, 172-174, 177, 180, 185, 200-207, 211-212, 219, 223, 228, 231-236, 240-243, 247-254, 258, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_identity.py 234 171 27% 48-74, 79-83, 87, 91-98, 255-292, 295-301, 304-308, 311, 314, 317, 322-350, 354-358, 361, 364, 367, 371-380, 384, 396-400, 403, 406, 411-437, 442-470, 599-630, 634-638, 641-644, 647, 651, 656-657, 664-668, 671-675, 678, 681, 685-689, 693-702, 706, 718-731, 734, 739, 747 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_inversion.py 52 27 48% 117-168, 173, 176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 206 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_kronecker.py 205 169 18% 38, 46, 55-62, 171-231, 235, 239-257, 260-277, 317-387, 399-405, 409-415, 419-422, 433-504, 507-522, 525-544, 550-563, 566-570, 575-579 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py 148 108 27% 185-263, 268-281, 286-294, 300, 305, 310, 315, 320, 325, 328-331, 334-340, 344-360, 363-374, 380-394, 397-435, 442-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py 49 22 55% 141-159, 164-165, 171, 175, 178, 181, 184, 189, 193, 196, 200-201, 205, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_permutation.py 81 48 41% 143-156, 166-178, 183-184, 187-189, 192, 195-196, 199-220, 226, 231, 234-235, 240-241, 247, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_toeplitz.py 82 50 39% 142-159, 163-170, 176-178, 181-187, 190, 210-224, 229, 234-235, 239-263, 267, 271, 275-278 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_tridiag.py 122 87 29% 175-190, 199-213, 216-230, 236-266, 270-287, 290-294, 299-331, 334-341, 344-359, 369, 373 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_util.py 150 122 19% 105-116, 122-125, 130-135, 151, 160-161, 182-187, 201-209, 228-236, 241-243, 251-255, 311-354, 359-373, 383-467, 491-502 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_zeros.py 157 118 25% 179-232, 235-241, 244-248, 251, 256, 261, 266-294, 297-320, 323-326, 330-333, 336, 348, 353-399, 404-432, 437-441, 445, 449-456, 459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/matmul_registrations.py 65 28 57% 37-50, 65-66, 73-74, 82, 102-105, 112-115, 125, 142, 159, 175, 191, 209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/registrations_util.py 29 21 28% 27-44, 49-62, 71-78, 86-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/solve_registrations.py 55 22 60% 37-50, 67, 75-76, 83-84, 92, 112, 129, 146, 162, 181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/conjugate_gradient.py 52 37 29% 76-136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/gen_sparse_csr_matrix_ops.py 624 562 10% 48-77, 83-95, 109-137, 143-154, 177-207, 213-224, 239-266, 272-283, 303-329, 335-347, 399-459, 465-495, 518-544, 550-560, 573-599, 605-614, 677-703, 709-719, 739-766, 772-782, 797-825, 831-843, 937-965, 971-983, 1083-1133, 1139-1164, 1183-1215, 1221-1234, 1248-1275, 1281-1291, 1307-1335, 1341-1353 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_grad.py 127 101 20% 30-34, 40-41, 65-66, 73, 80-81, 95-169, 175-222, 231-233 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_ops.py 157 96 39% 56, 61-69, 74, 80-87, 105-116, 124-144, 181-241, 253, 257, 261, 265, 269, 273, 277, 281, 286, 289, 294, 301, 307, 310, 330-352, 357, 360-370, 373, 376-377 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_grad.py 389 345 11% 53-54, 61-366, 372-378, 392-443, 449-454, 462-480, 486-510, 516-524, 537-601, 609-633, 639-675, 690-811, 816-819, 824-827, 833-848, 854-867, 881-898, 917-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops.py 166 121 27% 65-79, 139-140, 181-186, 227, 293-367, 393-398, 419-424, 446-447, 469-470, 535-540, 607, 682-761 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops_impl.py 35 24 31% 42-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/list_ops.py 141 87 38% 49-52, 60, 68, 76, 85, 97, 110-114, 127, 139, 148, 161-170, 176, 184-189, 194, 201-209, 214-217, 227-243, 249-259, 265-274, 279-281, 287-294, 301-311, 317-325, 350-371 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/logging_ops.py 147 89 39% 48, 112, 120-124, 129, 258-375, 383, 387-390, 426-429, 487-491, 542-552, 583-586, 606-610, 624-634, 667-670 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/lookup_ops.py 638 452 29% 63, 81-84, 99-103, 117-119, 122, 127, 132, 137, 141, 145, 165-179, 182, 187, 198-199, 217-236, 243, 279-291, 294-303, 307, 319-325, 364, 372, 385-386, 391, 396, 400, 405-411, 428-445, 461-467, 587-626, 641-654, 658-669, 712, 765, 807-814, 818-820, 896-933, 936-938, 941-944, 948-951, 956, 960-962, 966, 970-975, 979-988, 1005-1034, 1106-1144, 1147-1149, 1152-1155, 1159-1161, 1165, 1169-1174, 1191-1215, 1223-1226, 1311-1347, 1408-1446, 1520-1537, 1588-1599, 1645-1667, 1673-1694, 1698, 1709-1711, 1729-1737, 1756-1762, 1781-1789, 1801-1806, 1810, 1820-1826, 1829-1833, 1903-1930, 1936-1950, 1954, 1965-1967, 1986-1993, 2012-2020, 2039, 2057-2066, 2084, 2096-2102, 2106, 2116-2122, 2125-2129 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/loss_reduction.py 16 1 94% 68 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/losses.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/losses_impl.py 237 180 24% 61, 71-72, 86-87, 112-129, 134-135, 174-203, 245-255, 296-311, 348-361, 414-437, 480-493, 548-591, 633-643, 690-707, 756-778, 805-830, 873-887 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/losses/util.py 88 59 33% 58-121, 139-145, 158-175, 189-190, 204, 217, 231-235, 264-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/manip_grad.py 12 4 67% 28-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/manip_ops.py 12 1 92% 30 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/map_fn.py 85 63 26% 149-287, 417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/math_grad.py 1177 911 23% 37, 42-43, 48-49, 56-64, 91-136, 144, 152-214, 219-235, 241, 246, 252-266, 277-315, 321, 327-334, 340-341, 349-350, 358-359, 366-367, 374-375, 382-383, 389-399, 405, 411, 426-445, 452-464, 470, 476, 482, 504-530, 535-536, 542, 548-549, 555-556, 561-566, 571-576, 581-586, 591-592, 597-605, 611-612, 618-625, 631-637, 643-650, 656-662, 668-674, 680-690, 697-707, 714-724, 731-734, 740-743, 749-752, 758-761, 767-773, 779-785, 790-793, 799-803, 809-814, 820-822, 829-831, 838-844, 850-857, 863-866, 872-874, 880-882, 888-890, 896-901, 907-914, 920-938, 944-963, 970-971, 978-1004, 1014-1032, 1040-1058, 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510-511, 522-536, 548-562, 568-582, 608, 630, 644-648, 656, 667, 678, 690-692, 704-705, 717, 733-735, 751, 782, 804, 830-833, 861-908, 913, 918, 923, 954-1002, 1020-1040, 1045, 1050-1051, 1065, 1081-1105, 1126-1138 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn_impl.py 390 293 25% 86-109, 161-186, 240, 299-315, 382-383, 419-439, 471-477, 495-500, 520-536, 584-589, 616-617, 642-646, 661-667, 690-708, 763-794, 852, 920-957, 1021, 1061-1088, 1116, 1137-1149, 1187-1214, 1250, 1271-1328, 1347, 1415-1421, 1498-1545, 1594-1597, 1641, 1660-1663, 1726-1848, 1941, 2046-2063, 2142, 2240-2258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/nn_ops.py 1086 861 21% 62-81, 130-142, 169-214, 220, 231, 296-299, 317-319, 468-482, 512-588, 593-635, 638, 646-658, 686-717, 739-760, 887-890, 909, 935-1001, 1030-1075, 1085, 1095-1106, 1200-1274, 1371, 1523, 1545-1566, 1637-1664, 1721, 1783-1817, 1910, 1997-2006, 2071-2072, 2133-2136, 2202-2206, 2274-2283, 2343-2448, 2460-2462, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/control_flow_ops.py 165 134 19% 64-106, 111-114, 122-132, 182-201, 206-219, 224-317, 398-407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/gradients.py 57 45 21% 48-80, 112-147 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/pfor.py 1990 1462 27% 75-80, 103-111, 126-225, 229, 234, 239-242, 247, 252, 257, 261-265, 269, 274, 297-340, 344-354, 369-396, 400-464, 474-483, 487-514, 520-559, 604-679, 694-696, 705-711, 721-742, 746, 750, 753-754, 757-768, 771-781, 785, 789, 792, 796, 799-800, 906, 915-919, 946-953, 957-982, 990-993, 997, 1001, 1022-1060, 1075, 1088, 1101, 1105-1106, 1167-1184, 1188-1192, 1224-1239, 1243-1257, 1262-1266, 1279-1292, 1296-1297, 1300-1302, 1306-1329, 1332-1516, 1521, 1526, 1530, 1534, 1544, 1556-1558, 1563-1565, 1570-1572, 1580-1582, 1587-1589, 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3455-3459, 3464-3485, 3492-3503, 3511-3520, 3532-3548, 3553-3580, 3587-3610, 3616-3628, 3632-3638, 3642-3662, 3668-3715, 3728, 3738-3741 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_config.py 305 240 21% 207-228, 303, 323, 358, 412-434, 454-463, 467, 471, 475, 491-516, 520-531, 535-537, 541-548, 553-568, 572-589, 593-603, 607-619, 623-627, 632-665, 704-758, 778-803, 827-885, 893-901 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_ops.py 165 110 33% 62-76, 304-313, 318, 339-370, 406, 442-447, 545-567, 597-689, 779-801, 830-833, 867-875, 907-918, 965, 1013-1020, 1033-1047 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/partitioned_variables.py 68 48 29% 104-154, 181-218, 233-237, 288-311 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/proto_ops.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_array_ops.py 187 158 16% 92-201, 230-243, 273-305, 332-373, 431-448, 474-477, 502-506, 520-528, 574-634, 660-689 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_ops.py 46 34 26% 64-124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py 61 44 28% 69-141, 147-183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_concat_ops.py 110 87 21% 67-70, 115-118, 136-202, 219-238, 253-289, 295-297, 302-310, 315-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_config.py 6 1 83% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_conversion_ops.py 58 39 33% 36-39, 49-52, 57, 64-106, 113-137, 141, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_dispatch.py 243 116 52% 82, 90-102, 114, 119-160, 175, 182-231, 246, 252-253, 258-261, 264-284, 417, 427, 435-437, 441, 445-447, 452-456, 515, 548, 550, 559-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_factory_ops.py 121 99 18% 78-84, 133-143, 168-240, 260-271, 277-310, 336-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_functional_ops.py 39 25 36% 70-91, 112-128 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_gather_ops.py 98 80 18% 88-118, 167-261, 270-295 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_getitem.py 161 136 16% 97-103, 124-187, 200-226, 244-340, 360-361, 381-389, 394-406, 437-456, 462-471 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_map_ops.py 132 103 22% 169-331, 360-364, 372-386, 395-403, 409-418, 423-427, 434-459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_math_ops.py 172 110 36% 88-105, 112-114, 193-256, 262, 271, 280, 289, 298-308, 313-324, 469-542, 551, 561, 572, 583, 594-608, 612, 618-619, 626-627 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_operators.py 44 1 98% 74 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_squeeze_op.py 53 40 25% 52-122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_string_ops.py 192 153 20% 59-78, 121-175, 219-220, 280-281, 322-325, 385-394, 400-452, 493-512, 563-575, 626-643, 648, 721-803 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_tensor.py 823 674 18% 268-311, 363-428, 472-497, 531-553, 587-608, 637-659, 718-795, 837-858, 888-897, 927-936, 959-983, 992, 1011-1020, 1031-1032, 1056, 1079, 1105, 1131-1134, 1161-1166, 1192-1196, 1227-1233, 1255-1266, 1290-1291, 1315-1316, 1345-1360, 1377-1383, 1410-1435, 1456-1466, 1491-1494, 1509-1533, 1570-1583, 1652-1793, 1828-1839, 1878-1905, 1927-1930, 1985-2001, 2030-2031, 2038-2041, 2075-2086, 2096-2099, 2105-2108, 2112-2117, 2140-2143, 2151, 2158, 2162, 2167, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/segment_id_ops.py 50 34 32% 55-71, 98-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_grad.py 32 18 44% 29-32, 58-71, 99-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py 147 77 48% 86-96, 136-153, 187-197, 276, 278-280, 292, 298-299, 340-341, 371-386, 415-416, 443-444, 449-451, 459-465, 544-558, 598, 637-643 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/resource_variable_ops.py 686 328 52% 65-70, 76-81, 97-102, 126-143, 151, 161-163, 184-191, 254-255, 270, 291-300, 318-320, 447-452, 464-468, 471, 474, 477, 480-491, 506, 519, 522-524, 527-530, 535-538, 548, 554, 559, 564-566, 576, 581, 589, 593, 597-599, 602-604, 628, 641-643, 665-669, 673-688, 692-698, 713-743, 747-749, 765, 786-792, 810-816, 851, 855, 878-880, 898-900, 919-921, 940-942, 960-962, 980-982, 1000-1002, 1050-1052, 1101, 1150, 1199, 1206-1207, 1222, 1225, 1228, 1231, 1236, 1240, 1245, 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170-179, 206-212, 232-244, 247-260, 269, 274, 279, 282-309, 328-340, 344, 385, 420-436, 440, 444, 448-462, 466-471, 474-480, 526-544, 548, 552, 556-582, 586-603, 606-614, 633-637, 697-719, 723, 728, 732-746, 762-794, 797-805, 896-941, 945, 949, 953-993, 1019-1071, 1074-1089, 1100-1104, 1123, 1147, 1151-1158, 1162-1168, 1179, 1190, 1200, 1234-1255, 1261-1264, 1268, 1271-1277, 1281-1287, 1291-1301, 1305-1327, 1345-1346, 1350-1354 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell_wrapper_impl.py 224 164 27% 109-173, 179-183, 187, 191, 195, 198-199, 202-203, 209-215, 225-250, 271-289, 293-308, 312-319, 337-338, 342, 346, 349-350, 369-381, 385-396, 400-407, 424-425, 429, 433, 436-438, 442-443, 446-448, 453-465, 471-494, 498-504, 508-515 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_grad.py 14 5 64% 26-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/script_ops.py 174 76 56% 59-67, 83-85, 102-119, 124-150, 171-172, 183, 203-212, 230-253, 257, 282, 295, 319, 336, 347, 357-364, 452-457, 517-524, 536, 555 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sdca_ops.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/session_ops.py 128 85 34% 38, 56-60, 63-64, 67, 71-76, 85, 90, 94-99, 103-108, 117-118, 123-124, 129-130, 135, 173-178, 214-219, 239-243, 247, 251, 256-267, 272-288, 293-302 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets_impl.py 55 31 44% 51-57, 81-90, 118-133, 200-201, 277-280, 356-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/dct_ops.py 77 60 22% 33-47, 97-179, 223-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/fft_ops.py 217 146 33% 35-42, 48-60, 65-108, 116-140, 150-170, 198, 203-204, 209-212, 217-218, 223-226, 231-232, 237-240, 250-320, 336-360, 393-403, 434-444 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mel_ops.py 57 39 32% 46-48, 64-66, 73-89, 161-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mfcc_ops.py 20 9 55% 89-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/reconstruction_ops.py 60 48 20% 54-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/shape_ops.py 82 68 17% 33-54, 107-214 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/signal.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/spectral_ops.py 131 103 21% 70-94, 120-155, 224-276, 281-285, 331-365, 426-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/util_ops.py 33 20 39% 47-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/window_ops.py 77 52 32% 47-51, 70-90, 110-117, 135-141, 165, 192, 217-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sort_ops.py 53 33 38% 65-66, 107-109, 128-140, 155-204, 209-211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_grad.py 130 88 32% 50-60, 83-96, 101-103, 109-115, 136-145, 166-201, 206, 212-241, 247, 253, 273-286, 291, 297, 306-312, 317-322 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_ops.py 516 356 31% 66-70, 87-91, 95-101, 118-124, 147-179, 202-211, 324-331, 336-366, 430-434, 490-517, 550, 595, 614-655, 678-680, 720-731, 784-830, 840, 889-914, 955, 995-1008, 1062, 1129-1144, 1211-1216, 1259-1269, 1319-1333, 1387-1392, 1435-1445, 1490-1494, 1552-1564, 1663, 1672-1718, 1751-1765, 1829-1871, 1925-1935, 1958, 1977-1979, 2013, 2041-2043, 2107-2114, 2179-2187, 2401-2419, 2476-2480, 2508-2519, 2546-2557, 2595-2619, 2643-2645, 2683-2685, 2756-2777, 2795-2806 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/special_math_ops.py 357 299 16% 87-101, 129-130, 157-158, 186-187, 214-215, 242-243, 266-267, 290-291, 296-303, 319-324, 404, 409-468, 490-551, 580-679, 684-687, 693-700, 707-714, 723-726, 731-802, 807-853, 861-869, 881-973 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/standard_ops.py 83 1 99% 115 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_grad.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_ops.py 131 81 38% 44-52, 74, 101-114, 130-133, 161-164, 192-195, 224-228, 249-251, 301-304, 363-366, 415-418, 478-481, 532-535, 596-599, 648-651, 700-703, 755-758, 810-813, 872-914 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/stateful_random_ops.py 279 177 37% 90, 95-97, 110-136, 140-145, 149-151, 155, 168-182, 206-207, 212-216, 220-223, 230-231, 235, 239, 243-250, 254, 257, 270-278, 369-387, 410, 439-444, 467-473, 500-507, 517-519, 529-530, 541-548, 552, 557, 562, 565, 585-589, 600, 621-626, 629, 657-664, 667, 705-721, 737-741, 789-794, 810, 832-840, 880-890, 913-916, 939 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/build_info.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/device_context.py 6 1 83% 22 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/flags.py 59 18 69% 52-54, 56, 78, 81-86, 89, 92, 95, 98, 101, 104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/gfile.py 29 1 97% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/googletest.py 103 67 35% 56, 61-66, 72-94, 107, 112, 134-135, 143, 147, 150, 154-155, 184-218, 229-232, 251-258, 271-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/remote_utils.py 6 1 83% 22 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/resource_loader.py 47 22 53% 47-48, 60, 72-100, 121-125, 136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/self_check.py 22 12 45% 26-27, 44-53 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/sysconfig.py 35 17 51% 42-43, 53-54, 64-67, 77-86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/test.py 43 9 79% 41, 57-58, 70, 84, 90, 96, 102, 108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/tf_logging.py 158 67 58% 49-58, 65-90, 106, 129-130, 148, 158, 163, 168, 173, 178, 201, 206, 226-227, 243-244, 259-260, 266-267, 272-275, 285-309, 315, 321, 327-329 /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/internal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/internal/flops_registry.py 179 93 48% 48-49, 54-57, 66-68, 74, 80, 87, 93, 99, 105, 111, 117-121, 133, 142-144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234, 243-249, 256, 263, 270, 277, 285, 296-297, 303-321, 327, 333, 339-349, 356-378, 396-407, 420-430, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/traceme.py 27 16 41% 35-41, 44-45, 48-49, 53-60 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_mlir.py 14 4 71% 27, 34, 41, 47 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow.py 30 6 80% 38, 51, 61, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py 65 39 40% 19-21, 31, 35-36, 40-55, 59, 63-71, 74, 78-82, 87-90 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tfe.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder_impl.py 231 176 24% 93-112, 122-131, 145-153, 168-182, 201-214, 220-228, 267-300, 345-393, 412-428, 437, 442-446, 456-465, 478-492, 507-511, 525-555, 570-615, 638-667, 688-706, 710-716, 731-740, 752-754, 759-774, 786-792, 796-797 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/constants.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_deserialization.py 202 39 81% 74, 80-86, 94-95, 104-105, 110, 112, 114, 116, 118, 121, 132, 190, 243-255, 364-365, 376, 380, 387-392, 398-403, 441, 461 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_serialization.py 71 56 21% 31-49, 54-75, 81-83, 92-103, 122-161 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py 270 68 75% 55-64, 93, 98, 128-130, 205, 212-219, 231, 234-241, 256, 263, 273-279, 308, 326-333, 342-346, 369, 391-394, 409, 435-442, 445, 453-454, 457, 460, 464, 468-470, 476-482, 586, 595, 612-614 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load_v1_in_v2.py 137 105 23% 55-57, 60, 64, 72-83, 89-92, 95-102, 109-126, 133-184, 188-257, 262-263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader_impl.py 157 93 41% 63-67, 100-110, 135-161, 182-190, 194, 199-204, 208, 215-219, 244-246, 265, 298-299, 312-314, 319, 324, 329, 343-358, 379-381, 401-409, 425-433, 450-455 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/main_op.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/main_op_impl.py 23 7 70% 43-46, 70-72 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/method_name_updater.py 42 26 38% 71-72, 92-113, 130-148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/export_output.py 155 93 40% 53, 57-63, 86-99, 134-148, 152, 156, 159-166, 182-185, 189, 192-199, 223, 228, 231, 269-278, 294-298, 301-303, 325-361, 365, 369, 373, 378, 381-382, 395, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/export_utils.py 115 84 27% 93-140, 154-182, 211-226, 243-247, 277-290, 309-325, 342-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/mode_keys.py 43 5 88% 87, 100, 103, 106, 109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/nested_structure_coder.py 250 95 62% 70, 81, 96, 107-111, 132, 135-139, 152, 164-166, 175, 178-182, 198, 201-205, 225, 228-236, 256, 259-262, 268-269, 279, 282-285, 291-292, 306, 309-312, 329, 332-335, 352, 355-358, 375, 378-382, 399, 402-405, 422, 425-431, 482, 486-494, 497, 501-511 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/revived_types.py 70 21 70% 78, 92, 94, 96, 98-99, 122, 128, 133, 142-147, 171, 175-181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/save.py 392 316 19% 88-99, 103, 108-133, 136, 140-145, 171-210, 214, 218-234, 254-335, 339, 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100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer.py 128 95 26% 69-82, 90-96, 100, 110-112, 120-121, 132-137, 145-153, 160-163, 166-168, 193-204, 207-232, 244-254, 264-269, 285-294, 298-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer_v2.py 44 27 39% 74-96, 100, 110-112, 120-122, 131, 138-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer.py 133 90 32% 79-99, 119-142, 155-156, 159-161, 179-214, 217-227, 244-249, 263-273, 276-279, 360-372, 376, 380, 384, 387-388, 393-394, 402-403, 413-414, 421-422, 432-433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer_cache.py 24 8 67% 43-48, 60-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tf2.py 14 1 93% 40 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/module_util.py 31 12 61% 24, 47, 50-56, 60-61, 63 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/api.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/bfloat16.py 26 13 50% 49-68, 78-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/client.py 162 118 27% 34-35, 46, 50-54, 59-68, 75-81, 85-87, 106-141, 157-176, 181, 186-191, 196-200, 203, 206, 213-216, 220, 224, 228, 232, 236, 240, 244, 248-258, 270-281, 286-315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/device_assignment.py 180 148 18% 36-56, 81-102, 108, 113, 118, 129, 133, 148-151, 157-158, 162-163, 167-168, 175, 198-213, 257-413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/feature_column.py 220 158 28% 106-155, 219-285, 295-304, 310, 314, 318, 322, 330, 334, 338, 341, 344, 347, 351, 373, 397-402, 405, 409, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/preempted_hook.py 46 27 41% 40, 43-45, 48, 61-72, 75-77, 80-93 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/session_support.py 205 148 28% 45, 50, 71-74, 79-91, 95, 104-106, 111-124, 128-140, 144, 150-160, 165-175, 211-222, 226-247, 253-259, 262-265, 268, 271, 277-284, 293-298, 304-306, 325-333, 338-360, 364-381, 384-406, 417, 420-424, 435, 438, 449, 452 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer.py 795 657 17% 113-144, 158-179, 197-200, 217-231, 237-249, 274, 280-281, 293-295, 300, 314, 333, 340-346, 350-354, 361-373, 378-380, 386, 393-399, 402, 418-443, 448-455, 460-462, 479-509, 514-520, 524-531, 534, 538-552, 556, 571-585, 596-599, 612-745, 762-857, 871-909, 926-983, 989-1022, 1043-1055, 1060-1080, 1093-1122, 1125, 1140-1194, 1209-1213, 1219-1233, 1237-1252, 1256-1266, 1279-1283, 1294-1399, 1402, 1406, 1435-1600, 1630-1659, 1680-1701 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_flags.py 250 163 35% 103-152, 155, 160-170, 174-183, 186-194, 199-211, 214, 219-229, 245-259, 263-290, 299-315, 319, 335-340, 353-363, 376-385, 401-418, 423-431, 436-444, 449-454, 468 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_pb2.py 28 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tensor_tracer_report.py 242 183 24% 68-119, 126-130, 137-139, 143-160, 166-183, 193-199, 202, 205-206, 213-214, 217, 220, 223, 243-277, 281-284, 289-296, 300-304, 310-314, 319-335, 340-343, 348-361, 366-382, 387-396, 402-408 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/topology.py 90 60 33% 29-32, 37-40, 75-94, 98-129, 133-140, 145, 150, 164, 169, 182, 195, 199, 204, 211, 216, 220-228 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/tpu.py 696 574 18% 90-93, 116-119, 145-147, 160-162, 175, 180-190, 196-198, 202-207, 216-221, 232-233, 235, 271-286, 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196-212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/training_loop.py 82 70 15% 56-177, 201-222 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adadelta.py 46 28 39% 59-67, 70-72, 75-81, 84-86, 97-99, 110-112, 124-126 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad.py 48 26 46% 63-70, 73-80, 84-90, 93-94, 98-99, 107-108, 116-117, 126-127 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad_da.py 59 39 34% 75-88, 91-100, 104-109, 112-116, 128-132, 144-148, 161-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adam.py 93 67 28% 101-111, 114-119, 127-136, 139-147, 150-153, 167-170, 184-207, 210, 221-223, 226, 231-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_loops.py 21 13 38% 51-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_session_run_hooks.py 491 361 26% 61, 65, 69, 83, 87, 99-108, 111-112, 125-139, 142-152, 155, 162-163, 166-167, 170, 212-231, 234-237, 243-247, 250-263, 266-270, 273-275, 306-315, 352-360, 363-366, 369-371, 374-377, 382-386, 412-417, 420-422, 425-427, 430, 433-442, 500, 503, 506, 509, 546-557, 560, 563-569, 572-588, 591, 594-602, 605-609, 613-634, 637-656, 669-678, 681, 684-690, 693, 696-702, 705-736, 743, 760-761, 764, 767-775, 809-817, 822-827, 831-839, 842-861, 864-865, 873-884, 903, 906-909, 913-933, 949-951, 955, 958-977, 991, 994, 1033-1037, 1041-1044, 1047-1055, 1058-1072, 1075-1078, 1085-1104 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_management.py 266 201 24% 45-47, 61-63, 96-128, 167, 213-247, 270-305, 322-324, 351-364, 381-388, 410, 438-456, 476-484, 489-490, 506-508, 614-664, 668, 672, 686, 698, 702-717, 721-722, 743, 748, 771-824, 844-852 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_ops.py 76 60 21% 123-203, 332-416, 467-473 /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, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/momentum.py 40 22 45% 80-83, 86-87, 90-98, 101-102, 111-112, 121-122, 131-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/monitored_session.py 481 348 28% 153-188, 192-247, 251, 255, 259, 263, 267, 271, 275, 279, 283, 288-300, 315, 337-427, 512-601, 614, 639-644, 648-657, 660-661, 690-694, 698-707, 710-711, 734-751, 756-758, 774, 819-834, 852-853, 857, 861, 871, 874, 877, 880, 883-887, 893-897, 902-910, 915-929, 939, 950, 1034, 1118-1124, 1132, 1154-1155, 1159, 1163, 1173-1177, 1185, 1188-1197, 1200, 1206-1207, 1230-1231, 1235-1238, 1249-1272, 1276-1295, 1299-1316, 1342-1344, 1349-1351, 1354-1365, 1368-1384, 1412-1414, 1418, 1422-1453, 1458-1481, 1484-1486, 1501-1514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/moving_averages.py 131 103 21% 87-114, 152-178, 220-266, 378-382, 387, 421-473, 485, 509-511, 545-561 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/optimizer.py 403 311 23% 58-61, 76-80, 85-87, 97, 102, 109, 112, 115, 118-132, 139, 142, 146-151, 158, 161, 165-176, 188, 191, 194, 199-213, 327-343, 353, 399-412, 458-519, 523-529, 561-640, 667-735, 755-773, 783, 794-811, 816-843, 848-857, 862-866, 869-875, 883, 894-898, 914, 924, 932, 944, 957, 980-982, 1002, 1032-1038, 1057, 1074, 1090-1094, 1109-1116, 1134-1142, 1156-1163, 1171-1179, 1202-1239, 1245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_adagrad.py 44 25 43% 63-74, 77-82, 85-90, 95-96, 103-104, 111-112, 120-121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_gradient_descent.py 30 13 57% 57-62, 65, 74, 83, 93, 103-107 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/py_checkpoint_reader.py 40 17 58% 31-48, 61, 73-74, 98-99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/quantize_training.py 15 4 73% 45-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner_impl.py 176 128 27% 97-118, 138-165, 175-192, 196, 200, 204, 208, 212, 231, 236, 248-283, 293-298, 327-356, 368-385, 390, 411, 451-480 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/rmsprop.py 70 50 29% 107-118, 121-131, 134-142, 145-161, 173-189, 201-218, 231-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saver.py 480 383 20% 84, 104-124, 144-154, 173-179, 194, 206-207, 254-285, 298-311, 335-361, 379-390, 409-417, 462, 484-557, 574-583, 598-610, 800-843, 846-848, 851, 856-902, 906-914, 926-927, 931-942, 957-973, 981, 992-1008, 1021, 1032, 1045-1049, 1061-1062, 1075-1080, 1139-1217, 1253, 1283-1334, 1346, 1460, 1471-1490, 1496-1514, 1580-1599, 1603-1607, 1626-1634, 1679-1728 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/functional_saver.py 119 48 60% 51, 64-73, 118, 148, 153-154, 165-169, 179-182, 188-191, 202-263, 284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_hook.py 16 4 75% 44, 51, 55, 59 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object.py 34 8 76% 41, 47-51, 55, 78, 101 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object_util.py 161 98 39% 54-56, 63-64, 67-70, 84-85, 90-95, 101, 111, 120, 139, 143, 145-169, 175-183, 190, 196-209, 230-303, 319, 342 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/server_lib.py 200 151 24% 57-95, 146-150, 153-164, 173, 184, 194, 213, 235, 285-313, 318, 324, 327, 330-334, 348-361, 365, 374, 388-392, 407-411, 427-434, 457-464, 473-491, 527-530, 534-538, 542-546, 555-573 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_manager.py 149 120 19% 42-47, 144-155, 189-227, 288-319, 353-383, 411-442, 453-459, 473, 487, 502-512, 529-551, 557-558, 561-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_run_hook.py 41 13 68% 110, 127, 150, 169, 186, 211, 226-228, 240, 245, 255, 263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/slot_creator.py 62 48 23% 55-101, 124-135, 161-173, 190-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/summary_io.py 12 1 92% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/supervisor.py 339 239 29% 308-357, 360-369, 380-390, 405-416, 427-433, 444-455, 464-469, 478-484, 493-500, 509, 518, 530, 539, 548, 557, 561, 570, 579, 588, 597, 606, 615, 624, 628-636, 661-688, 720-745, 772-780, 801-808, 831-847, 859, 869, 879, 883, 898-902, 910-918, 928-931, 999-1023, 1038-1040, 1043-1051, 1066-1073, 1076-1077, 1081-1098, 1112-1114, 1117-1122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/sync_replicas_optimizer.py 150 114 24% 181-205, 223, 248-358, 375-378, 392, 403, 417, 439-458, 462, 476-478, 481-497, 501-513 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/base.py 342 122 64% 71-76, 79-82, 86, 93-95, 98, 112, 126, 143-155, 161, 165, 170-173, 181-183, 187, 211, 233, 241-253, 266, 277-278, 291-307, 320, 323-324, 338-348, 352-354, 364-367, 369, 373, 376, 411, 414, 489-494, 521-526, 544-546, 550, 558, 562, 566, 570, 601, 624, 629, 633, 637-640, 726, 736-737, 754, 780-790, 822, 825, 829-839, 876, 883, 923-925, 933, 1004-1005, 1022-1023 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/data_structures.py 536 236 56% 28-30, 70, 72, 74, 92, 94, 97, 144, 167, 171, 184, 187, 193, 205, 214, 221, 228, 232, 236, 240, 248-252, 257-261, 266, 271, 314, 318, 321, 324, 328, 336, 349-350, 353, 356-366, 369, 372, 375, 381, 387, 390, 444-445, 454-455, 459-462, 465-468, 472, 482, 484-485, 494, 501, 510, 522-523, 532-545, 550, 568-574, 577, 580, 583, 586, 589, 592, 597, 600-601, 604-605, 608, 611-612, 626, 629, 646-648, 654, 657, 660, 666-669, 672-676, 679-683, 689-690, 693, 696, 699, 702, 717, 720, 732, 747, 751-754, 757-760, 770, 777, 785, 806, 808-809, 815, 823-824, 828-832, 845-859, 862-864, 867, 870, 875, 878-879, 882, 892-938, 943, 947-954, 957, 962, 967, 970, 973, 976, 983, 987, 991-998, 1001-1009, 1013, 1048-1055 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/graph_view.py 201 126 37% 78-86, 97-135, 177-179, 192, 211-312, 319-330, 335-356, 379-380, 385-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/layer_utils.py 110 13 88% 33-34, 190, 231, 242, 268, 293-296, 301-304 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/python_state.py 17 1 94% 87 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/tracking.py 131 52 60% 82, 92-94, 98, 102-113, 119-125, 140, 177-178, 185-186, 190-191, 221, 226, 231-234, 237-252, 274-276, 323-324, 330 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py 602 378 37% 69-72, 90-115, 129, 133, 136, 143-145, 147-149, 151, 229, 244, 248-249, 252-254, 272-277, 285, 291-294, 302-308, 324-348, 355-379, 392-418, 433, 465-476, 493, 511-512, 537-607, 616, 621, 626, 631, 636, 640, 651-664, 710-740, 763, 780, 788-806, 810-814, 831-845, 849-850, 864-867, 871, 876, 881, 893, 911-921, 941-945, 959, 963-982, 990, 998, 1002-1014, 1018-1024, 1029, 1036-1037, 1040-1045, 1094-1107, 1125-1140, 1164-1198, 1259, 1263, 1268-1281, 1286-1290, 1333-1335, 1343, 1468-1481, 1485-1490, 1520-1529, 1540-1541, 1568-1601, 1705-1712, 1808-1821, 1825-1830, 1857-1866, 1877-1878, 1902-1933, 2009-2016 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training.py 105 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_ops.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_util.py 95 65 32% 66-68, 88-103, 120-137, 158-162, 172-184, 201-209, 222-239, 243-253 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/warm_starting_util.py 150 123 18% 122-126, 152-156, 173-189, 240-311, 340-372, 396-411, 465-549 /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/user_ops.py 10 1 90% 32 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/all_util.py 36 6 83% 78-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat.py 51 9 82% 60-61, 80, 86, 111, 116, 178-180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat_internal.py 9 3 67% 34-36 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/decorator_utils.py 52 11 79% 26-32, 51, 71, 100, 108, 119, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py 208 81 61% 95, 97, 102-110, 129-131, 188-239, 264, 314-317, 379, 381-382, 390, 439-442, 463-471, 485, 487, 489, 495, 497-500, 553, 565-568, 596-601, 635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py 58 21 64% 69, 81, 100-104, 120-125, 128-131, 156, 170, 181-188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/function_utils.py 59 38 36% 31-32, 36, 51-63, 78-86, 95-101, 106-119, 127-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/is_in_graph_mode.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lazy_loader.py 27 4 85% 50-52, 66-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lock_util.py 43 3 93% 68, 92, 112 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/memory.py 11 4 64% 40-45 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/module_wrapper.py 132 60 55% 37, 44-48, 52, 68-78, 104-105, 108, 133-140, 144-152, 161-162, 170-174, 193-206, 219-227, 230-233, 236, 239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py 303 121 60% 91-93, 100-101, 150, 157-162, 165, 167-171, 178-179, 181, 185, 220-221, 223-224, 231, 256, 332, 335, 379-382, 418-441, 485-486, 489, 496, 508-512, 596, 599, 605, 610, 653-657, 696, 777, 782, 789-804, 809-822, 827-828, 833, 837, 844, 1032-1037, 1189, 1249-1285, 1347-1351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/object_identity.py 114 27 76% 44, 47-48, 51-52, 56, 61, 70, 114, 141, 148, 155, 159, 162-168, 179-181, 190, 199, 202, 205, 232 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/compare.py 87 69 21% 94-118, 139-186, 190, 194-200, 216-255, 274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/serialization.py 29 19 34% 43-76 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_contextlib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_decorator.py 98 16 84% 172-173, 180-181, 188-193, 218, 222, 224, 257, 260, 272, 276 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_export.py 145 47 68% 114, 118, 132, 154, 174, 178, 180, 194-207, 220-228, 241-249, 274, 302, 307, 329-331, 343-344, 388-393 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_inspect.py 138 55 60% 34, 43, 79-90, 95, 118, 126, 131-147, 190-235, 282, 291-293, 298, 327, 342, 347, 362, 377, 382, 397, 402, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_should_use.py 98 67 32% 45-61, 64-68, 72-86, 95, 100-101, 105-107, 113-117, 122-129, 148-161, 179-202, 235 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_stack.py 70 6 91% 37-38, 61, 76, 88, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/all_renames_v2.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/renames_v2.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/doc_controls.py 49 30 39% 258-261, 276-322 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/baseline.py 124 78 37% 72-79, 83-90, 95-112, 128-154, 187-197, 220-227, 264-283, 381-398, 414-427, 497-506, 516-525, 607-621, 636-650 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees.py 715 607 15% 61-81, 95-99, 111-115, 130-147, 168, 201-275, 280-287, 317-388, 393-414, 443-471, 476-489, 497, 505-570, 597-621, 625-639, 643-654, 674-680, 686, 690, 703-706, 709, 717-724, 730-731, 761-776, 824, 830-836, 841-889, 898, 906-911, 916-920, 926, 938-982, 986-1010, 1017-1052, 1056-1089, 1093, 1147-1407, 1414, 1417, 1440-1479, 1489-1506, 1513-1521, 1531-1551, 1556, 1565-1578, 1601-1615, 1641-1686, 1691-1700, 1739-1751, 1770-1781, 1840-1889, 1894-1903, 2038-2063, 2190-2214, 2315-2343, 2357-2360 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees_utils.py 42 27 36% 33-37, 45-57, 61, 66, 73-82, 87-94 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn.py 224 162 28% 46-48, 73-102, 125-153, 157-158, 174-230, 233-254, 260, 272, 287-344, 347-359, 363-364, 371-379, 431-456, 489-504, 552-579, 737-759, 787-807, 946-962, 986-1003, 1151-1172, 1199-1221 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn_linear_combined.py 187 140 25% 43-44, 64-65, 69-71, 76-82, 141-226, 288-383, 554-585, 615-643, 657-682, 820-843, 864-888, 1048-1077, 1106-1136 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/head.py 497 410 18% 58, 78-89, 155, 166, 193, 230-240, 274, 299-354, 382-443, 448-472, 487-494, 510-520, 536-559, 563-567, 571-573, 579-584, 590-598, 615-617, 622-628, 632-637, 646-650, 658-666, 671-679, 738-748, 767-774, 778, 782, 787-808, 812-825, 829-852, 893-994, 1062-1077, 1096-1101, 1105, 1109, 1113-1195, 1199-1223, 1265-1373, 1435-1440, 1460-1467, 1471, 1475, 1479-1505, 1514-1532, 1571-1652, 1661-1664, 1668-1676, 1704-1715 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/kmeans.py 110 69 37% 46-48, 51-52, 55-62, 82-84, 87-99, 119-127, 137-145, 173-224, 404-415, 424-425, 436-438, 454, 470-475, 479 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py 283 217 23% 128-133, 138-174, 181-239, 243-244, 248-249, 262-270, 283-308, 326-375, 394-448, 470-539, 547-548, 555, 559, 583-619, 652-678, 719-748, 759-767, 920-945, 968-991, 1107-1119, 1158-1171, 1178-1186, 1325-1349, 1371-1396 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sdca_ops.py 291 245 16% 95-104, 114, 123, 132, 200-251, 254, 257, 261, 265, 270-271, 280-298, 305-307, 310-312, 316-318, 322-333, 337-348, 356-364, 368-371, 379-402, 420-433, 440-449, 464-629, 644-670, 679-699, 715-760, 775-781 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sharded_mutable_dense_hashtable.py 139 101 27% 73-99, 106-120, 124, 135-138, 158-165, 184-192, 204-210, 214, 223-229, 232-235, 262-280, 285, 289, 293, 296-298, 301-307, 310-311, 316-335, 339-352, 364-370 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/metric_keys.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/optimizers.py 46 27 41% 78-91, 95-97, 129-146 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/parsing_utils.py 44 25 43% 138-140, 254-257, 288-312, 321-324, 342-346 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/prediction_keys.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/early_stopping.py 179 131 27% 84-104, 153, 210, 268, 326, 346-356, 365-388, 401-430, 446-450, 454-458, 471-479, 482-484, 487-488, 491-498, 505, 508, 511-512, 515-518, 525-529, 540-549, 552-566, 570-571, 577-592 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/estimator.py 729 611 16% 176-203, 209, 213, 217, 228-231, 246-248, 259-261, 270-271, 321-351, 365-376, 390, 445-462, 478-511, 525-543, 596-639, 646, 717-722, 800, 817-889, 926-1010, 1014-1017, 1020-1021, 1027-1039, 1043, 1048-1054, 1058-1072, 1086, 1097-1100, 1123-1137, 1154-1176, 1179-1182, 1197-1213, 1231-1241, 1250-1354, 1361-1385, 1390-1523, 1527-1563, 1567-1575, 1581-1631, 1636-1658, 1661-1664, 1735-1738, 1756, 1760-1763, 1769-1786, 1793-1805, 1822-1849, 1854-1861, 1871-1941, 1945-1947, 1953-1957, 1962-1968, 1985-1998, 2003-2016, 2021-2026, 2031-2040, 2052, 2065-2110, 2123-2134, 2141-2148, 2338-2341, 2366-2385 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export.py 128 88 31% 65-72, 77-95, 99-103, 139-157, 206-215, 238, 271-277, 302-313, 327-343, 348, 370-376, 404-440, 470-474 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export_lib.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export_output.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/exporter.py 151 106 30% 41, 62, 97-100, 104, 108-117, 136-145, 150-160, 255-271, 277, 281-307, 319-337, 350-365, 400, 406, 410-416, 457-462, 467, 471-477, 489-507 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/extenders.py 38 25 34% 82-94, 104-107, 113-123 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/gc.py 63 46 27% 87-95, 110-127, 140-147, 161-166, 179-184, 207-217 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/base_head.py 290 222 23% 110, 122, 133, 163, 178, 192, 224, 227, 281-295, 347, 406-469, 498-582, 587-624, 639-648, 652-654, 660-661, 666-667, 673-677, 695-705, 726-759, 763-765, 771-773, 779-784, 790, 798-817, 821-824, 844-855, 896-918, 925-934 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py 205 164 20% 151-193, 198, 203, 208, 229-232, 245-250, 254-259, 263-275, 284-300, 318-362, 366-392, 421-427, 430-433, 443-487, 540-597 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/head_utils.py 30 17 43% 55-66, 70-77, 92-94, 101-102 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_class_head.py 151 113 25% 143-168, 173, 178, 183, 204-207, 220-225, 229-242, 246-262, 271-287, 305-345, 349-359, 368-385, 436-492 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_head.py 182 149 18% 34-35, 40-45, 178-200, 205, 210, 215-220, 250-282, 286-306, 315-342, 346-353, 357-367, 376-395, 454-512, 528-548 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_label_head.py 217 181 17% 161-243, 248, 253, 258, 277-280, 284-315, 322-339, 348-364, 381-399, 403-427, 436-480, 532-587 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/regression_head.py 124 85 31% 143-159, 164, 169, 174, 177-182, 186-203, 212-228, 242-254, 258-269, 279-297, 351-402, 483-484, 493, 568-570, 577 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/basic_session_run_hooks.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/hooks.py 92 67 27% 103-120, 124-141, 148-176, 179-201, 205-207, 211, 237-244, 247-249, 253, 256-266, 275-277 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/session_run_hook.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/numpy_io.py 70 56 20% 50-53, 70-86, 141-224 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/pandas_io.py 61 46 25% 32-36, 50-52, 91-158 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py 208 177 15% 34-38, 51-60, 78-96, 126-145, 158-169, 172-181, 197-212, 215-230, 243-255, 258-271, 285-297, 300-330, 376-504 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py 68 56 18% 57-73, 87-122, 150-174, 177, 182 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/mode_keys.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/model_fn.py 176 139 21% 152-164, 180-184, 213-221, 237-242, 283-295, 316-331, 353-377, 405-408, 424-430, 459-507, 512-516, 534-538, 542-543, 548-550, 577-598, 607-620 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/run_config.py 315 208 34% 95-113, 118-121, 126-130, 135-139, 145-149, 154-199, 211-226, 236-248, 254-328, 337-341, 536-586, 596-597, 609-627, 635-689, 697-724, 728, 738, 742, 746, 750, 754, 758, 762, 802, 806, 810, 814, 818, 822, 826, 830, 834, 838, 842, 846, 850, 855, 860, 865, 870, 907, 933-946, 957-975 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tools/analytics.py 8 2 75% 28, 37 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/_tpu_estimator_embedding.py 227 180 21% 58, 62, 66-68, 73-94, 103-122, 137-174, 270-315, 337-358, 362-363, 368, 372-412, 415-418, 424-429, 434-490, 495-505, 510-519, 524-541, 553-557 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/error_handling.py 65 44 32% 56-58, 73-105, 115-117, 122-125, 136-154 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/iteration_count_estimator.py 64 46 28% 68, 72-81, 84, 87, 90, 107-111, 122-123, 132-150, 169-201 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_config.py 99 60 39% 141-189, 227-265, 272, 276, 280, 284, 288, 291-297, 304-309 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/usr/local/lib/python3.8/dist-packages/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 839385 582474 31% ret : 34304 command : coverage3 html -i --omit=/usr/local/lib/python3.8/dist-packages/*,/home/admin/.local/lib/python3.8/site-packages/*,/usr/lib/python3/dist-packages/* -d htmlcov ret : 0 command : coverage3 report -i -m ret : 0 175.84user 48.52system 11:35.46elapsed 32%CPU (0avgtext+0avgdata 5845780maxresident)k 7232976inputs+40552outputs (20608major+5831708minor)pagefaults 0swaps