python /home/admin/mtr/script_for_cron.py -j coverage -m 9 -a '' -s coverage -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded root_folder /data_2/data_log/job/2025/March/01032025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_2/data_log/job/2025/March/01032025/coverage/htmlcov new path : /data_2/data_log/job/2025/March/01032025/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 : 5525 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 : 0.14309096336364746 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 Sat Mar 1 17:20:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 5525 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 /home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp 2025-03-01 17:20:33.625301: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-01 17:20:33.655312: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-01 17:20:33.657262: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f45d0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-01 17:20:33.657294: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-01 17:20:33.660922: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-01 17:20:33.794609: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3336aae0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-01 17:20:33.794653: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-01 17:20:33.795623: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:20:33.796009: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:20:33.798833: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:20:33.801404: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:20:33.801890: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:20:33.804729: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:20:33.805664: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:20:33.809350: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:20:33.810473: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:20:33.810526: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:20:33.811182: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-01 17:20:33.811204: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-01 17:20:33.811213: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-01 17:20:33.812281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4907 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 2025-03-01 17:20:34.498224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:20:34.498294: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:20:34.498312: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:20:34.498328: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:20:34.498343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:20:34.498358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:20:34.498373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:20:34.498389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:20:34.499436: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:20:34.500431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:20:34.500466: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:20:34.500482: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:20:34.500497: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:20:34.500511: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:20:34.500526: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:20:34.500540: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:20:34.500557: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:20:34.501572: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:20:34.501601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-01 17:20:34.501609: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-01 17:20:34.501617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-01 17:20:34.502749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4907 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-03-01 17:20:43.327578: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:20:43.740595: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:20:45.059301: 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 2025-03-01 17:20:45.059973: 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 2025-03-01 17:20:45.060643: 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 2025-03-01 17:20:45.218639: E tensorflow/core/kernels/gpu_utils.cc:85] Detected cudnn out-of-bounds write in convolution buffer! This is likely a cudnn bug. We will skip this algorithm in the future, but your GPU state may already be corrupted, leading to incorrect results. Within Google, no action is needed on your part. Outside of Google, please ensure you're running the latest version of cudnn. If that doesn't fix the problem, please file a bug with this full error message and we'll contact nvidia. 2025-03-01 17:20:45.218692: E tensorflow/core/kernels/gpu_utils.cc:93] Redzone mismatch in LHS redzone of buffer 0x7f43d0b00000 at offset 7603096; expected ffffffffffffffff but was ffffffff3d110158. 2025-03-01 17:20:46.106013: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature. 2025-03-01 17:20:46.179243: 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 2025-03-01 17:20:46.179301: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB 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. 2025-03-01 17:20:46.180675: 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 2025-03-01 17:20:46.180704: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB 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. 2025-03-01 17:20:46.203761: 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 2025-03-01 17:20:46.203799: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.78GiB 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. 2025-03-01 17:20:46.204571: 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 2025-03-01 17:20:46.204587: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.78GiB 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. 2025-03-01 17:20:46.225776: 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 2025-03-01 17:20:46.225846: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 19.91MiB 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. 2025-03-01 17:20:46.225863: 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. 2025-03-01 17:20:46.227182: 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 2025-03-01 17:20:46.227212: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 16.00MiB 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. 2025-03-01 17:20:46.228496: 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 2025-03-01 17:20:46.228522: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 16.00MiB 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. 2025-03-01 17:20:46.241348: 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 2025-03-01 17:20:46.241368: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 63.85MiB 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. 2025-03-01 17:20:46.242129: 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 2025-03-01 17:20:46.242144: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 63.85MiB 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. 2025-03-01 17:20:46.242947: 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 2025-03-01 17:20:46.242964: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.26GiB 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. 2025-03-01 17:20:46.243725: 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 2025-03-01 17:20:46.266356: 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 2025-03-01 17:20:46.267193: 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 2025-03-01 17:20:46.281867: 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 2025-03-01 17:20:46.282858: 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 2025-03-01 17:20:46.283839: 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 2025-03-01 17:20:46.284792: 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 2025-03-01 17:20:46.289067: 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 2025-03-01 17:20:46.289840: 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 2025-03-01 17:20:46.290603: 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 2025-03-01 17:20:46.291452: 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 2025-03-01 17:20:46.292699: 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 2025-03-01 17:20:46.302828: 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 2025-03-01 17:20:46.303608: 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 2025-03-01 17:20:46.314052: 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 2025-03-01 17:20:46.314830: 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 2025-03-01 17:20:46.315641: 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 2025-03-01 17:20:46.316410: 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 2025-03-01 17:20:46.317183: 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 2025-03-01 17:20:46.317948: 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: (480, 640, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 2553619 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 3139 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 : 4332 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl454 Catched exception ! Connect or reconnect ! thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0006537437438964844 nb_pixel_total : 15552 time to create 1 rle with old method : 0.034935712814331055 length of segment : 256 time for calcul the mask position with numpy : 0.002931356430053711 nb_pixel_total : 145329 time to create 1 rle with old method : 0.3173036575317383 length of segment : 371 time for calcul the mask position with numpy : 0.0002830028533935547 nb_pixel_total : 14255 time to create 1 rle with old method : 0.0329132080078125 length of segment : 151 time for calcul the mask position with numpy : 0.0001418590545654297 nb_pixel_total : 5613 time to create 1 rle with old method : 0.013463973999023438 length of segment : 48 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 1825 time to create 1 rle with old method : 0.004414558410644531 length of segment : 39 time spent for convertir_results : 1.2744724750518799 time spend for datou_step_exec : 20.47580623626709 time spend to save output : 6.222724914550781e-05 total time spend for step 1 : 20.475868463516235 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 3305 chid ids of type : 445 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.01305079460144043 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[(957285035, 492601069, 445, 0, 186, 22, 282, 0.9954886, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (126, 30, 28), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 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44), (432, 38, 41), (434, 39, 40), (436, 40, 39), (438, 41, 37), (441, 42, 35), (444, 43, 32), (448, 44, 29), (452, 45, 25), (454, 46, 23), (459, 47, 17), (463, 48, 12), (468, 49, 5)], ['472,49,468,49,467,48,459,47,458,46,454,46,451,44,448,44,447,43,444,43,440,41,438,41,428,36,424,36,423,35,419,35,418,34,409,34,408,33,401,33,400,32,371,32,370,31,337,31,336,30,283,31,281,29,281,6,284,4,291,4,292,3,419,3,420,4,429,4,430,5,432,5,436,7,441,11,445,12,453,16,456,19,457,19,465,27,465,29,472,37,476,44,476,46']), (957285035, 492601069, 445, 456, 547, 6, 45, 0.74149704, [(482, 8, 19), (463, 9, 4), (481, 9, 44), (457, 10, 12), (479, 10, 50), (457, 11, 13), (476, 11, 56), (457, 12, 15), (475, 12, 65), (457, 13, 84), (457, 14, 85), (457, 15, 89), (457, 16, 89), (458, 17, 88), (459, 18, 87), (460, 19, 86), (461, 20, 80), (464, 21, 71), (466, 22, 63), (467, 23, 59), (468, 24, 55), (469, 25, 52), (469, 26, 51), (470, 27, 48), (471, 28, 46), (471, 29, 44), (472, 30, 42), (473, 31, 39), (473, 32, 38), (474, 33, 36), (475, 34, 33), (475, 35, 32), (476, 36, 30), (476, 37, 29), (477, 38, 26), (478, 39, 23), (479, 40, 20), (480, 41, 17), (488, 42, 5)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1740846030_2553429_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4332 ############################### 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 : 0.13657379150390625 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 Sat Mar 1 17:20:52 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 4332 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-01 17:20:55.852248: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-01 17:20:55.887179: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-01 17:20:55.889409: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f45d4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-01 17:20:55.889467: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-01 17:20:55.893388: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-01 17:20:56.011325: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x34362c50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-01 17:20:56.011377: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-01 17:20:56.012449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:20:56.012843: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:20:56.015798: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:20:56.018106: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:20:56.018480: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:20:56.021020: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:20:56.022310: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:20:56.027385: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:20:56.028553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:20:56.028639: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:20:56.029201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-01 17:20:56.029229: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-01 17:20:56.029239: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-01 17:20:56.030161: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3880 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-03-01 17:20:56.145152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:20:56.145239: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:20:56.145259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:20:56.145276: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:20:56.145292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:20:56.145308: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:20:56.145324: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:20:56.145340: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:20:56.146205: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:20:56.147193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:20:56.147252: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:20:56.147271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:20:56.147288: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:20:56.147304: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:20:56.147321: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:20:56.147338: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:20:56.147354: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:20:56.148266: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:20:56.148293: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-01 17:20:56.148302: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-01 17:20:56.148310: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-01 17:20:56.149243: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3880 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-03-01 17:21:04.893720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:21:05.075847: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:21:07.090337: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB 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. 2025-03-01 17:21:07.090387: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.29GiB 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. 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 2554575 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 962 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 : 5525 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0006210803985595703 nb_pixel_total : 16902 time to create 1 rle with old method : 0.051247596740722656 length of segment : 107 time for calcul the mask position with numpy : 0.08385181427001953 nb_pixel_total : 480739 time to create 1 rle with new method : 0.028749465942382812 length of segment : 632 time for calcul the mask position with numpy : 0.00046372413635253906 nb_pixel_total : 36582 time to create 1 rle with old method : 0.08474349975585938 length of segment : 132 time for calcul the mask position with numpy : 0.00011229515075683594 nb_pixel_total : 4794 time to create 1 rle with old method : 0.011600971221923828 length of segment : 51 time spent for convertir_results : 0.5372662544250488 time spend for datou_step_exec : 18.535053968429565 time spend to save output : 3.528594970703125e-05 total time spend for step 1 : 18.535089254379272 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 400 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.013619422912597656 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, 0.99883837, [(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, 0.9977465, [(710, 22, 23), (926, 22, 46), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (589, 25, 428), (582, 26, 444), (574, 27, 459), (569, 28, 466), (565, 29, 472), (560, 30, 480), (555, 31, 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[(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (419, 25, 100), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['450,47,449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,419,25,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46,451,46'])], 'temp/1740846052_2553429_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 : 0.13777732849121094 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 Sat Mar 1 17:21:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 5525 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-01 17:21:15.945600: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-01 17:21:15.971184: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-01 17:21:15.972876: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f45d4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-01 17:21:15.972929: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-01 17:21:15.975947: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-01 17:21:16.225754: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3488b980 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-01 17:21:16.225791: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-01 17:21:16.226650: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:21:16.226976: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:21:16.229161: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:21:16.231274: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:21:16.231615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:21:16.234037: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:21:16.235059: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:21:16.239597: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:21:16.240812: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:21:16.240908: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:21:16.241512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-01 17:21:16.241529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-01 17:21:16.241538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-01 17:21:16.242572: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-03-01 17:21:16.356704: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:21:16.356814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:21:16.356835: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:21:16.356853: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:21:16.356870: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:21:16.356887: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:21:16.356904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:21:16.356921: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:21:16.357950: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:21:16.359167: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-03-01 17:21:16.359256: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-01 17:21:16.359278: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:21:16.359297: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-01 17:21:16.359316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-01 17:21:16.359335: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-01 17:21:16.359353: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-01 17:21:16.359372: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-01 17:21:16.360374: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-01 17:21:16.360410: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-01 17:21:16.360421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-01 17:21:16.360429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-01 17:21:16.361530: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5051 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-03-01 17:21:26.852691: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-01 17:21:27.037786: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 2555302 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 236 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.1447277069091797 nb_pixel_total : 3693251 time to create 1 rle with new method : 0.4687333106994629 length of segment : 2041 time spent for convertir_results : 1.9086670875549316 time spend for datou_step_exec : 22.37371015548706 time spend to save output : 4.00543212890625e-05 total time spend for step 1 : 22.37375020980835 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 719 chid ids of type : 445 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++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++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 : 0.014729022979736328 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, 0.9850381, [(675, 120, 111), (520, 121, 481), (1052, 121, 379), (503, 122, 946), (486, 123, 981), (471, 124, 1013), (456, 125, 1045), (442, 126, 1091), (429, 127, 1136), (417, 128, 1168), (405, 129, 1187), (394, 130, 1205), (383, 131, 1222), (373, 132, 1239), (368, 133, 1250), (366, 134, 1258), (364, 135, 1265), (361, 136, 1274), (359, 137, 1281), (357, 138, 1288), (355, 139, 1295), (353, 140, 1302), (351, 141, 1309), (349, 142, 1315), (347, 143, 1320), (345, 144, 1326), (343, 145, 1331), (342, 146, 1335), (340, 147, 1340), (338, 148, 1345), (337, 149, 1349), (335, 150, 1354), (334, 151, 1358), (333, 152, 1362), (331, 153, 1366), (330, 154, 1370), (328, 155, 1374), (327, 156, 1378), (326, 157, 1381), (325, 158, 1385), (324, 159, 1388), (322, 160, 1393), (321, 161, 1397), (320, 162, 1401), (318, 163, 1406), (317, 164, 1410), (315, 165, 1415), (314, 166, 1419), (312, 167, 1424), (311, 168, 1428), (309, 169, 1434), (307, 170, 1439), (305, 171, 1444), (304, 172, 1448), (302, 173, 1453), (300, 174, 1458), (298, 175, 1463), (296, 176, 1469), (294, 177, 1474), (292, 178, 1480), (289, 179, 1487), (286, 180, 1493), (284, 181, 1499), (281, 182, 1507), (278, 183, 1514), (275, 184, 1521), (272, 185, 1529), (269, 186, 1536), (266, 187, 1544), (263, 188, 1552), (260, 189, 1561), (257, 190, 1569), (254, 191, 1579), (251, 192, 1588), (248, 193, 1597), (245, 194, 1606), (243, 195, 1614), (240, 196, 1623), (237, 197, 1631), (234, 198, 1640), (231, 199, 1648), (228, 200, 1657), (225, 201, 1665), (222, 202, 1673), (219, 203, 1682), (216, 204, 1689), (213, 205, 1694), (210, 206, 1699), (208, 207, 1702), (206, 208, 1706), (204, 209, 1710), (203, 210, 1712), (201, 211, 1716), (199, 212, 1719), (198, 213, 1722), (196, 214, 1725), (195, 215, 1727), (193, 216, 1730), (192, 217, 1733), (191, 218, 1735), (189, 219, 1738), (188, 220, 1740), (187, 221, 1742), (186, 222, 1744), (185, 223, 1746), (184, 224, 1748), (182, 225, 1751), (181, 226, 1753), (180, 227, 1755), (179, 228, 1757), (178, 229, 1759), (177, 230, 1761), (176, 231, 1762), (176, 232, 1763), (175, 233, 1765), (174, 234, 1767), (173, 235, 1768), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1776), (168, 240, 1777), (167, 241, 1779), (166, 242, 1781), (165, 243, 1783), (164, 244, 1785), (163, 245, 1787), (162, 246, 1789), (161, 247, 1791), (159, 248, 1794), (158, 249, 1796), (157, 250, 1798), (156, 251, 1800), (155, 252, 1802), (153, 253, 1805), (152, 254, 1807), (151, 255, 1809), (149, 256, 1812), (148, 257, 1815), (146, 258, 1818), (145, 259, 1820), (143, 260, 1824), (142, 261, 1826), (140, 262, 1829), (139, 263, 1832), (137, 264, 1835), 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['936,2144,775,2093,694,2075,586,2031,288,1973,215,1963,128,1971,54,1825,39,1677,39,1455,29,1242,27,757,21,696,27,543,39,458,93,308,116,278,210,206,291,179,373,132,520,121,1430,121,1584,128,1663,142,1768,178,1904,204,1993,277,2094,411,2148,535,2168,614,2165,833,2128,914,2112,994,2081,1068,2031,1132,2009,1191,1958,1273,1931,1368,1879,1444,1846,1670,1760,1913,1719,1973,1662,2015,1581,2015,1496,2039,1420,2046,1318,2074,1177,2101,1097,2141,1027,2150'])], 'temp/1740846072_2553429_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3690248 proportion of common points : 0.9994456022609245 #&_# 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 : 0.21274971961975098 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 Sat Mar 1 17:21:40 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0019729137420654297 nb_pixel_total : 2351 time to create 1 rle with old method : 0.005515575408935547 time for calcul the mask position with numpy : 0.0013713836669921875 nb_pixel_total : 3757 time to create 1 rle with old method : 0.008785486221313477 time for calcul the mask position with numpy : 0.0013937950134277344 nb_pixel_total : 6640 time to create 1 rle with old method : 0.015494823455810547 time for calcul the mask position with numpy : 0.0018551349639892578 nb_pixel_total : 83636 time to create 1 rle with old method : 0.18328356742858887 time for calcul the mask position with numpy : 0.001607656478881836 nb_pixel_total : 14624 time to create 1 rle with old method : 0.033179283142089844 time for calcul the mask position with numpy : 0.0013933181762695312 nb_pixel_total : 11760 time to create 1 rle with old method : 0.02660822868347168 time for calcul the mask position with numpy : 0.0015404224395751953 nb_pixel_total : 16445 time to create 1 rle with old method : 0.037342071533203125 time for calcul the mask position with numpy : 0.001483917236328125 nb_pixel_total : 7642 time to create 1 rle with old method : 0.017151594161987305 time for calcul the mask position with numpy : 0.0013580322265625 nb_pixel_total : 4261 time to create 1 rle with old method : 0.010061025619506836 time for calcul the mask position with numpy : 0.0013840198516845703 nb_pixel_total : 9630 time to create 1 rle with old method : 0.02151775360107422 time for calcul the mask position with numpy : 0.0013530254364013672 nb_pixel_total : 5615 time to create 1 rle with old method : 0.013297319412231445 time for calcul the mask position with numpy : 0.0016145706176757812 nb_pixel_total : 29471 time to create 1 rle with old method : 0.06658720970153809 time for calcul the mask position with numpy : 0.0013737678527832031 nb_pixel_total : 2940 time to create 1 rle with old method : 0.006961345672607422 time for calcul the mask position with numpy : 0.0014295578002929688 nb_pixel_total : 10806 time to create 1 rle with old method : 0.024377822875976562 time for calcul the mask position with numpy : 0.0013365745544433594 nb_pixel_total : 2780 time to create 1 rle with old method : 0.0061337947845458984 time for calcul the mask position with numpy : 0.0013549327850341797 nb_pixel_total : 5252 time to create 1 rle with old method : 0.012291431427001953 time for calcul the mask position with numpy : 0.0015003681182861328 nb_pixel_total : 13913 time to create 1 rle with old method : 0.031154155731201172 time for calcul the mask position with numpy : 0.0013239383697509766 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0028183460235595703 time for calcul the mask position with numpy : 0.0017049312591552734 nb_pixel_total : 1227 time to create 1 rle with old method : 0.002744913101196289 time for calcul the mask position with numpy : 0.0013458728790283203 nb_pixel_total : 4360 time to create 1 rle with old method : 0.010007858276367188 time for calcul the mask position with numpy : 0.0014009475708007812 nb_pixel_total : 1020 time to create 1 rle with old method : 0.0022971630096435547 time for calcul the mask position with numpy : 0.0014066696166992188 nb_pixel_total : 3966 time to create 1 rle with old method : 0.008669614791870117 time for calcul the mask position with numpy : 0.0013878345489501953 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007133007049560547 time for calcul the mask position with numpy : 0.0013322830200195312 nb_pixel_total : 2077 time to create 1 rle with old method : 0.004626035690307617 time for calcul the mask position with numpy : 0.0014767646789550781 nb_pixel_total : 16367 time to create 1 rle with old method : 0.03713798522949219 time for calcul the mask position with numpy : 0.001361846923828125 nb_pixel_total : 1208 time to create 1 rle with old method : 0.002880573272705078 time for calcul the mask position with numpy : 0.0013189315795898438 nb_pixel_total : 860 time to create 1 rle with old method : 0.0024788379669189453 time for calcul the mask position with numpy : 0.0015070438385009766 nb_pixel_total : 13083 time to create 1 rle with old method : 0.029816865921020508 time for calcul the mask position with numpy : 0.0013849735260009766 nb_pixel_total : 1501 time to create 1 rle with old method : 0.003542661666870117 time for calcul the mask position with numpy : 0.0013785362243652344 nb_pixel_total : 2787 time to create 1 rle with old method : 0.006600856781005859 time for calcul the mask position with numpy : 0.001468658447265625 nb_pixel_total : 5523 time to create 1 rle with old method : 0.01251983642578125 time for calcul the mask position with numpy : 0.0014836788177490234 nb_pixel_total : 7652 time to create 1 rle with old method : 0.017697572708129883 time for calcul the mask position with numpy : 0.0015187263488769531 nb_pixel_total : 12982 time to create 1 rle with old method : 0.030978918075561523 time for calcul the mask position with numpy : 0.0014028549194335938 nb_pixel_total : 9918 time to create 1 rle with old method : 0.0222012996673584 time for calcul the mask position with numpy : 0.001470327377319336 nb_pixel_total : 27570 time to create 1 rle with old method : 0.06207728385925293 time for calcul the mask position with numpy : 0.0014846324920654297 nb_pixel_total : 8609 time to create 1 rle with old method : 0.019362926483154297 time for calcul the mask position with numpy : 0.0014655590057373047 nb_pixel_total : 2460 time to create 1 rle with old method : 0.005841970443725586 time for calcul the mask position with numpy : 0.0014691352844238281 nb_pixel_total : 3530 time to create 1 rle with old method : 0.00825953483581543 time for calcul the mask position with numpy : 0.0014672279357910156 nb_pixel_total : 4172 time to create 1 rle with old method : 0.009713888168334961 time for calcul the mask position with numpy : 0.0013842582702636719 nb_pixel_total : 4109 time to create 1 rle with old method : 0.00947117805480957 time for calcul the mask position with numpy : 0.0014331340789794922 nb_pixel_total : 2743 time to create 1 rle with old method : 0.009598255157470703 time for calcul the mask position with numpy : 0.0014753341674804688 nb_pixel_total : 3866 time to create 1 rle with old method : 0.008975982666015625 time for calcul the mask position with numpy : 0.0013461112976074219 nb_pixel_total : 1670 time to create 1 rle with old method : 0.00398707389831543 time for calcul the mask position with numpy : 0.001607656478881836 nb_pixel_total : 27282 time to create 1 rle with old method : 0.06164216995239258 time for calcul the mask position with numpy : 0.001527547836303711 nb_pixel_total : 1630 time to create 1 rle with old method : 0.003996610641479492 time for calcul the mask position with numpy : 0.0014595985412597656 nb_pixel_total : 1035 time to create 1 rle with old method : 0.002579927444458008 time for calcul the mask position with numpy : 0.0014619827270507812 nb_pixel_total : 419 time to create 1 rle with old method : 0.0010449886322021484 time for calcul the mask position with numpy : 0.0014660358428955078 nb_pixel_total : 813 time to create 1 rle with old method : 0.0020411014556884766 time for calcul the mask position with numpy : 0.0014798641204833984 nb_pixel_total : 3329 time to create 1 rle with old method : 0.007988214492797852 time for calcul the mask position with numpy : 0.0017244815826416016 nb_pixel_total : 38893 time to create 1 rle with old method : 0.08987116813659668 time for calcul the mask position with numpy : 0.001483917236328125 nb_pixel_total : 1253 time to create 1 rle with old method : 0.002971649169921875 time for calcul the mask position with numpy : 0.0015141963958740234 nb_pixel_total : 10570 time to create 1 rle with old method : 0.024477720260620117 time for calcul the mask position with numpy : 0.0014319419860839844 nb_pixel_total : 342 time to create 1 rle with old method : 0.0008666515350341797 time for calcul the mask position with numpy : 0.0013988018035888672 nb_pixel_total : 2264 time to create 1 rle with old method : 0.0052030086517333984 time for calcul the mask position with numpy : 0.001482248306274414 nb_pixel_total : 18416 time to create 1 rle with old method : 0.04195761680603027 time for calcul the mask position with numpy : 0.0014028549194335938 nb_pixel_total : 691 time to create 1 rle with old method : 0.0016224384307861328 time for calcul the mask position with numpy : 0.0014057159423828125 nb_pixel_total : 594 time to create 1 rle with old method : 0.0014033317565917969 time for calcul the mask position with numpy : 0.00133514404296875 nb_pixel_total : 886 time to create 1 rle with old method : 0.002203702926635742 time for calcul the mask position with numpy : 0.0014235973358154297 nb_pixel_total : 2329 time to create 1 rle with old method : 0.005354881286621094 time for calcul the mask position with numpy : 0.0013632774353027344 nb_pixel_total : 877 time to create 1 rle with old method : 0.0022673606872558594 time for calcul the mask position with numpy : 0.0013842582702636719 nb_pixel_total : 2385 time to create 1 rle with old method : 0.005653858184814453 time for calcul the mask position with numpy : 0.001455068588256836 nb_pixel_total : 2429 time to create 1 rle with old method : 0.00601959228515625 time for calcul the mask position with numpy : 0.0013322830200195312 nb_pixel_total : 335 time to create 1 rle with old method : 0.0008096694946289062 time for calcul the mask position with numpy : 0.0013687610626220703 nb_pixel_total : 8722 time to create 1 rle with old method : 0.019707679748535156 time for calcul the mask position with numpy : 0.0014624595642089844 nb_pixel_total : 1659 time to create 1 rle with old method : 0.0038442611694335938 time for calcul the mask position with numpy : 0.0014202594757080078 nb_pixel_total : 1703 time to create 1 rle with old method : 0.0040090084075927734 time for calcul the mask position with numpy : 0.0013737678527832031 nb_pixel_total : 585 time to create 1 rle with old method : 0.0014073848724365234 time for calcul the mask position with numpy : 0.0014333724975585938 nb_pixel_total : 8632 time to create 1 rle with old method : 0.019534826278686523 time for calcul the mask position with numpy : 0.001331329345703125 nb_pixel_total : 1075 time to create 1 rle with old method : 0.002557992935180664 time for calcul the mask position with numpy : 0.0013360977172851562 nb_pixel_total : 1044 time to create 1 rle with old method : 0.0025496482849121094 time for calcul the mask position with numpy : 0.0013375282287597656 nb_pixel_total : 894 time to create 1 rle with old method : 0.0022373199462890625 time for calcul the mask position with numpy : 0.0013527870178222656 nb_pixel_total : 2652 time to create 1 rle with old method : 0.0063114166259765625 time for calcul the mask position with numpy : 0.0014226436614990234 nb_pixel_total : 563 time to create 1 rle with old method : 0.0013818740844726562 time for calcul the mask position with numpy : 0.0014240741729736328 nb_pixel_total : 1448 time to create 1 rle with old method : 0.003567218780517578 time for calcul the mask position with numpy : 0.0013852119445800781 nb_pixel_total : 1512 time to create 1 rle with old method : 0.003576993942260742 time for calcul the mask position with numpy : 0.0015070438385009766 nb_pixel_total : 16670 time to create 1 rle with old method : 0.03908824920654297 time for calcul the mask position with numpy : 0.0013427734375 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006783008575439453 time for calcul the mask position with numpy : 0.0014538764953613281 nb_pixel_total : 1788 time to create 1 rle with old method : 0.004004955291748047 time for calcul the mask position with numpy : 0.0014586448669433594 nb_pixel_total : 7523 time to create 1 rle with old method : 0.017301321029663086 time for calcul the mask position with numpy : 0.0013706684112548828 nb_pixel_total : 941 time to create 1 rle with old method : 0.002401590347290039 time for calcul the mask position with numpy : 0.0014178752899169922 nb_pixel_total : 9077 time to create 1 rle with old method : 0.02092146873474121 time for calcul the mask position with numpy : 0.0014829635620117188 nb_pixel_total : 3176 time to create 1 rle with old method : 0.007322549819946289 time for calcul the mask position with numpy : 0.0014231204986572266 nb_pixel_total : 713 time to create 1 rle with old method : 0.001828908920288086 time for calcul the mask position with numpy : 0.0014476776123046875 nb_pixel_total : 1336 time to create 1 rle with old method : 0.003340005874633789 time for calcul the mask position with numpy : 0.0013997554779052734 nb_pixel_total : 1011 time to create 1 rle with old method : 0.002599954605102539 time for calcul the mask position with numpy : 0.0014319419860839844 nb_pixel_total : 989 time to create 1 rle with old method : 0.0024008750915527344 time for calcul the mask position with numpy : 0.0013949871063232422 nb_pixel_total : 616 time to create 1 rle with old method : 0.0014350414276123047 time for calcul the mask position with numpy : 0.001394033432006836 nb_pixel_total : 252 time to create 1 rle with old method : 0.0006670951843261719 time for calcul the mask position with numpy : 0.0013165473937988281 nb_pixel_total : 221 time to create 1 rle with old method : 0.0005609989166259766 time for calcul the mask position with numpy : 0.0013957023620605469 nb_pixel_total : 889 time to create 1 rle with old method : 0.002084016799926758 time for calcul the mask position with numpy : 0.001367330551147461 nb_pixel_total : 734 time to create 1 rle with old method : 0.0018589496612548828 time for calcul the mask position with numpy : 0.0014042854309082031 nb_pixel_total : 1638 time to create 1 rle with old method : 0.0038411617279052734 time for calcul the mask position with numpy : 0.0014619827270507812 nb_pixel_total : 5013 time to create 1 rle with old method : 0.01159214973449707 time for calcul the mask position with numpy : 0.001346588134765625 nb_pixel_total : 3238 time to create 1 rle with old method : 0.007460832595825195 time for calcul the mask position with numpy : 0.001439809799194336 nb_pixel_total : 299 time to create 1 rle with old method : 0.0008223056793212891 time for calcul the mask position with numpy : 0.0014390945434570312 nb_pixel_total : 943 time to create 1 rle with old method : 0.0021772384643554688 time for calcul the mask position with numpy : 0.0014488697052001953 nb_pixel_total : 596 time to create 1 rle with old method : 0.001443624496459961 time for calcul the mask position with numpy : 0.0013756752014160156 nb_pixel_total : 1438 time to create 1 rle with old method : 0.0035409927368164062 time for calcul the mask position with numpy : 0.0014693737030029297 nb_pixel_total : 1128 time to create 1 rle with old method : 0.002546072006225586 time for calcul the mask position with numpy : 0.0015037059783935547 nb_pixel_total : 1530 time to create 1 rle with old method : 0.003693103790283203 time for calcul the mask position with numpy : 0.0014548301696777344 nb_pixel_total : 2625 time to create 1 rle with old method : 0.006612539291381836 time for calcul the mask position with numpy : 0.0015959739685058594 nb_pixel_total : 3278 time to create 1 rle with old method : 0.010887861251831055 time for calcul the mask position with numpy : 0.0015616416931152344 nb_pixel_total : 886 time to create 1 rle with old method : 0.0029959678649902344 time for calcul the mask position with numpy : 0.0015826225280761719 nb_pixel_total : 1603 time to create 1 rle with old method : 0.005187034606933594 time for calcul the mask position with numpy : 0.001589059829711914 nb_pixel_total : 2183 time to create 1 rle with old method : 0.007246494293212891 time for calcul the mask position with numpy : 0.0015707015991210938 nb_pixel_total : 1321 time to create 1 rle with old method : 0.004416465759277344 time for calcul the mask position with numpy : 0.0015559196472167969 nb_pixel_total : 481 time to create 1 rle with old method : 0.001628875732421875 batch 1 Loaded 107 chid ids of type : 4677 Number RLEs to save : 9751 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 : 0.0120391845703125 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.845596075057983 time spend to save output : 0.012393712997436523 total time spend for step 1 : 10.85798978805542 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1740846100_2553429_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 107 ############################### 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 : 0.09869670867919922 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 Sat Mar 1 17:21:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! 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 : [] WARNING: Logging before InitGoogleLogging() is written to STDERR E0301 17:21:52.507537 2553429 common.cpp:114] Cannot create Cublas handle. Cublas won't be available. E0301 17:21:52.514330 2553429 common.cpp:121] Cannot create Curand generator. Curand won't be available. F0301 17:21:52.527683 2553429 syncedmem.hpp:22] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1950: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1957: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2141: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2148: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None Name Stmts Miss Cover Missing -------------------------------------------------------------------------------------------------------------------------------------- /home/admin/.local/lib/python3.8/site-packages/PIL/BmpImagePlugin.py 218 181 17% 52, 56, 76-264, 276-284, 291-355, 366, 384, 388-449 /home/admin/.local/lib/python3.8/site-packages/PIL/ExifTags.py 340 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/GifImagePlugin.py 585 527 10% 55, 71-74, 77-80, 84-108, 112-120, 124-139, 142-155, 158-410, 413-430, 433-456, 459, 480-491, 506-543, 547-564, 568-574, 578-649, 653, 658-670, 674-680, 684-746, 756-793, 812-844, 849-854, 865-872, 882, 886-905, 914-967, 971-983, 1002-1015, 1036-1048 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpGradientFile.py 68 53 22% 32-43, 47, 51, 55, 59, 70-98, 105-137 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/home/admin/.local/lib/python3.8/site-packages/cffi/error.py 19 8 58% 8-15 /home/admin/.local/lib/python3.8/site-packages/cffi/lock.py 10 6 40% 4-7, 11-12 /home/admin/.local/lib/python3.8/site-packages/cffi/model.py 389 251 35% 16, 21, 30-45, 48, 51, 54, 57-63, 66, 69, 75, 79, 82, 92, 99, 166, 168, 170, 172, 175, 182-183, 186, 189, 196-197, 200, 208-220, 232, 236, 243-254, 258, 269, 273-274, 288-290, 303-306, 311, 314, 317-322, 332-333, 336-337, 340-341, 351-356, 359-362, 365-376, 382-394, 397-401, 404-464, 467, 470-471, 474-477, 495-499, 502-505, 508-509, 512-514, 520-557, 561-566, 569-572, 582-587, 590-610, 613, 616-617 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/api.py 195 181 7% 62-497, 515, 543-544 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/assets/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/cd.py 189 164 13% 24-50, 63-71, 80-91, 100-112, 120-129, 138-164, 175-244, 253-283, 291-311, 319-338, 350-388 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/constant.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/legacy.py 19 14 26% 22-50 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/models.py 174 110 37% 20-34, 37-43, 49-62, 66, 70-72, 75, 78-86, 90, 97-103, 107, 111, 119, 127-147, 151, 155-157, 161, 165, 172, 176, 180, 184-192, 201, 208-212, 219, 229, 232, 239-246, 249, 252, 259-272, 278-280, 286, 308-318, 322, 337 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/utils.py 214 158 26% 24-28, 40-46, 54-60, 65-69, 74-78, 83-93, 98-108, 113-118, 123-128, 133, 137-139, 144-149, 154-159, 164-169, 174-179, 184-189, 194, 199, 212-237, 245, 266-276, 280, 284-296, 300-310, 314-334, 342, 353-358, 372-414 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/__about__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/cryptography/__init__.py 7 1 86% 18 /home/admin/.local/lib/python3.8/site-packages/cryptography/utils.py 75 45 40% 29-30, 34-37, 41, 47-50, 59-61, 66-67, 70-74, 77, 80-84, 87, 97-104, 108-119, 126, 129 /home/admin/.local/lib/python3.8/site-packages/cv2/__init__.py 16 2 88% 18-19 /home/admin/.local/lib/python3.8/site-packages/cv2/data/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/__init__.py 13 4 69% 6-7, 17, 24 /home/admin/.local/lib/python3.8/site-packages/dateutil/_common.py 25 15 40% 14-17, 20-25, 28, 34, 37-41 /home/admin/.local/lib/python3.8/site-packages/dateutil/_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/__init__.py 33 4 88% 31-32, 47-48 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/_parser.py 812 687 15% 63-75, 91-184, 187, 190-194, 197, 201, 206, 211, 216, 222-223, 226-231, 234, 238, 320, 323-327, 330-334, 337-340, 343-346, 349, 352, 355-358, 367-378, 382-391, 396-400, 404, 408, 412, 415-426, 429-454, 461-472, 475-565, 636-659, 707-873, 877-1004, 1007-1038, 1042-1054, 1057, 1070-1090, 1093-1097, 1103-1109, 1116-1127, 1135-1139, 1142-1152, 1160-1175, 1178-1215, 1218-1240, 1243-1248, 1257-1264, 1365-1368, 1383, 1386-1388, 1391-1579, 1586, 1598-1601, 1604-1605 /home/admin/.local/lib/python3.8/site-packages/dateutil/parser/isoparser.py 185 150 19% 26-37, 51-55, 134-146, 159-163, 176-179, 199, 207-210, 213-251, 254-295, 316-328, 331-381, 384-412 /home/admin/.local/lib/python3.8/site-packages/dateutil/relativedelta.py 241 206 15% 112-229, 232-262, 266, 270, 273-280, 294-308, 318-402, 405, 408, 411-413, 440, 458, 476, 496-501, 521-531, 548, 568, 571-576, 581-592, 597 /home/admin/.local/lib/python3.8/site-packages/dateutil/rrule.py 979 862 12% 26-27, 72, 87-89, 96-103, 106-111, 114-122, 125-147, 150-169, 172-180, 186-189, 195-210, 216-228, 249-269, 276-302, 434-698, 707-760, 765-774, 777-1030, 1062-1077, 1103-1109, 1119-1121, 1125-1251, 1254, 1257-1261, 1265-1276, 1279-1282, 1285-1292, 1295-1300, 1303, 1317-1323, 1326-1333, 1338, 1341, 1344, 1347, 1350-1354, 1360, 1366, 1374, 1381, 1384-1413, 1475, 1478, 1493, 1497-1504, 1507, 1513-1533, 1542-1561, 1566-1613, 1625-1725, 1732 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_common.py 161 124 23% 20-28, 55-129, 139-144, 169-177, 196-202, 205, 222-242, 258-264, 290, 293-300, 303-310, 314-317, 321-350, 366-372, 375-393, 396-405, 409, 414, 417 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/_factories.py 49 21 57% 22, 34-52, 64-79 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/tz.py 803 644 20% 75, 78, 82, 98, 106, 109-112, 118, 121, 144-152, 155, 158, 162, 166, 180, 183-186, 191, 194, 206-216, 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71 53 25% 18, 40, 59-69, 79, 90-92, 107-116, 130-154, 168-191 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/point.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/publicKey.py 48 32 33% 11-12, 15-23, 26-32, 35, 39, 43-76, 80-97 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/signature.py 35 23 34% 10-12, 15-18, 21, 25-40, 44-45 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/base.py 8 2 75% 8, 12 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/binary.py 15 5 67% 15, 26, 36, 48-49 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/compatibility.py 24 13 46% 13, 19, 22-39 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/der.py 149 110 26% 27-28, 32-43, 47-53, 57, 61, 65, 69-74, 78-89, 93-111, 115-121, 125-131, 135-144, 148-152, 156-164, 168-180, 184-194, 198-207, 211-227, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm.py 141 12 91% 59-64, 145-152 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm_listed.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_color_data.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_constrained_layout.py 373 352 6% 102-149, 162-194, 202-240, 247-260, 264-297, 303-335, 347-440, 447-479, 507-576, 583-596, 615-624, 632-665, 689-751, 761-768, 772-783 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_docstring.py 39 4 90% 35, 53, 59-60 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_enums.py 57 36 37% 24, 89-111, 161-177 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_fontconfig_pattern.py 46 7 85% 89-91, 97, 101-105, 114-118 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_layoutgrid.py 208 174 16% 40-103, 106-118, 126-128, 132-137, 144-162, 166, 173-206, 213-245, 266-267, 287-288, 303-304, 322-323, 339-347, 352, 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81-82, 88, 91, 102, 129-149, 178-181, 200-210, 252-264, 285-293, 341-343, 364-371, 396-403, 415-425, 458-495, 509-518, 527, 531, 537, 555-563, 573, 583-587, 591, 595-620, 636, 643-647, 654-658, 665-666, 718-724 /home/admin/.local/lib/python3.8/site-packages/matplotlib/collections.py 835 666 20% 156-202, 205, 208, 211, 215-221, 232, 251-300, 305, 310-341, 345-419, 431, 434, 443-471, 484-485, 494, 529-531, 535, 545-553, 558, 562, 574-582, 610-623, 635, 638, 649, 652, 676-690, 700-703, 707, 722-723, 727, 730-734, 748-751, 754, 757-760, 764, 767-783, 798-801, 815-817, 822, 825, 841-859, 868-897, 901, 906-925, 943, 957-967, 971-972, 994-997, 1000-1001, 1004, 1069-1144, 1170-1173, 1189-1215, 1221-1226, 1238-1244, 1264-1271, 1315-1320, 1323, 1326, 1330-1337, 1408-1412, 1415-1421, 1434-1448, 1451, 1454, 1457, 1460, 1473, 1478, 1541-1549, 1555-1556, 1560-1571, 1575-1580, 1585, 1591, 1598-1603, 1613-1618, 1622, 1626-1635, 1639, 1643-1652, 1656, 1659, 1663, 1680-1683, 1711-1718, 1723-1760, 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1118-1124, 1132, 1140-1165 /home/admin/.local/lib/python3.8/site-packages/matplotlib/figure.py 1041 867 17% 69-70, 83-84, 88, 92, 96-98, 102-103, 107, 110, 116-118, 152-154, 161-178, 187-214, 218-239, 262-282, 286, 304-308, 314, 357-394, 401-404, 411-414, 421-425, 429, 433, 441, 451, 457, 467, 477, 489-490, 516-527, 615-641, 744-770, 774-783, 904-919, 926-957, 969-992, 1009, 1128-1150, 1188-1200, 1277-1315, 1346-1357, 1400-1418, 1460-1478, 1501-1502, 1543-1545, 1587-1614, 1639-1641, 1645-1647, 1659-1660, 1680-1693, 1705-1729, 1732-1737, 1766-1808, 1812-1827, 1831-1837, 1949-2148, 2151-2154, 2219-2252, 2256, 2260, 2266, 2276-2277, 2280, 2292-2308, 2316, 2332, 2335, 2350, 2358-2373, 2399, 2402, 2505-2597, 2600-2601, 2610-2620, 2652-2684, 2689, 2698-2700, 2736-2744, 2758, 2763-2766, 2769, 2780-2787, 2793, 2814-2819, 2827, 2851-2856, 2889-2890, 2909-2923, 2933, 3007-3024, 3056-3066, 3087, 3091, 3095, 3099, 3109-3110, 3127, 3144, 3148-3153, 3161-3185, 3192-3194, 3200, 3203-3220, 3223-3247, 3253, 3366-3378, 3429-3474, 3484-3494, 3507-3509, 3539-3549, 3600-3629 /home/admin/.local/lib/python3.8/site-packages/matplotlib/font_manager.py 563 423 25% 135-136, 177, 190-191, 207-212, 217-244, 250-258, 269-291, 295-301, 305-307, 347-456, 474-524, 594-608, 622-631, 634-642, 645, 648, 658, 664, 670, 676, 685, 693, 699, 705, 715, 725-729, 739-742, 752-755, 768-781, 794-807, 820-837, 844, 853-857, 865, 885-892, 896, 907-920, 938, 958-962, 991-1024, 1037-1047, 1053, 1060, 1067, 1073, 1077-1079, 1094-1111, 1123-1128, 1136-1139, 1149-1157, 1171-1175, 1188-1199, 1257-1265, 1269, 1316-1359, 1365-1444, 1454-1458, 1463-1464, 1490, 1515-1523, 1539-1540, 1545-1548 /home/admin/.local/lib/python3.8/site-packages/matplotlib/gridspec.py 277 216 22% 48-56, 59-63, 79, 83, 97-99, 108-113, 121, 130-135, 143, 167-205, 213-226, 230-263, 273-316, 371-379, 400-410, 425-434, 443, 467-474, 501-505, 511-521, 529, 553-555, 558, 570-605, 612, 616, 619, 629-630, 635-636, 641-645, 648, 651, 654, 657, 663-673, 679-683, 691, 697, 739 /home/admin/.local/lib/python3.8/site-packages/matplotlib/hatch.py 143 103 28% 16-17, 20-28, 33-34, 37-45, 50-55, 58-64, 69-75, 78-84, 91-97, 102-121, 126-129, 136-137, 144-145, 153-154, 162-168, 183-189, 205-225 /home/admin/.local/lib/python3.8/site-packages/matplotlib/image.py 760 661 13% 83-110, 123-157, 171-213, 221-227, 259-274, 277-281, 285, 289-292, 302-306, 318, 325-326, 358-587, 607, 615, 620-646, 650-677, 681-683, 695-731, 743, 754, 771-776, 788-792, 796-797, 811-814, 818, 830-831, 835, 846-850, 854, 920-922, 936-938, 942-949, 954, 977-1002, 1006-1014, 1025-1041, 1058-1059, 1063, 1067-1133, 1148-1165, 1168, 1177-1180, 1183-1185, 1188, 1191, 1194-1196, 1199-1201, 1245-1248, 1252-1281, 1284, 1304-1338, 1341, 1345-1354, 1379-1389, 1393-1394, 1399-1410, 1416-1417, 1440-1451, 1454-1462, 1466-1476, 1480-1486, 1530-1564, 1619-1689, 1708-1724, 1734-1754, 1796-1818 /home/admin/.local/lib/python3.8/site-packages/matplotlib/layout_engine.py 69 39 43% 63-64, 70, 78-80, 88-90, 96, 103, 122-124, 130, 158-162, 181-189, 207-209, 249-259, 269-274, 303-305 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend.py 470 385 18% 69-74, 77-80, 83-90, 93-94, 343, 416-657, 666-671, 677-680, 684, 687, 694-706, 711-737, 764, 769, 774, 778-779, 797-806, 816-906, 921-941, 945, 949, 953, 957, 963, 977-979, 983, 1001-1012, 1016, 1020-1022, 1026, 1030, 1040-1041, 1047-1050, 1072-1093, 1110, 1121-1158, 1161-1164, 1189-1198, 1202, 1209-1238, 1243-1250, 1298-1348 /home/admin/.local/lib/python3.8/site-packages/matplotlib/legend_handler.py 343 255 26% 41-43, 79-82, 85, 89-93, 98-102, 125-139, 164, 189-192, 195-206, 231-236, 249-273, 290-312, 343-350, 355-359, 369, 375-384, 389-396, 404-407, 410-415, 420-428, 440-443, 447-464, 468-473, 477, 487-502, 510, 521, 538-545, 551-629, 659-664, 670-712, 719-720, 748-773, 782-807, 813-817 /home/admin/.local/lib/python3.8/site-packages/matplotlib/lines.py 679 562 17% 36-60, 64-69, 78-106, 118-201, 262-271, 310-414, 440-484, 492, 506-508, 518, 537-538, 596-597, 605, 616-618, 622-624, 627-635, 645-651, 654, 657-699, 708-714, 718-720, 724-726, 732-878, 882, 890, 898, 906, 914, 922, 930, 938-948, 956, 959-965, 973, 981, 989, 997, 1006-1010, 1019-1023, 1027-1029, 1035-1037, 1047-1049, 1059-1061, 1089-1096, 1116-1119, 1130-1134, 1167-1179, 1192-1193, 1196-1207, 1217, 1227, 1237, 1248-1252, 1263-1266, 1276-1287, 1297-1308, 1329-1332, 1336-1355, 1368-1371, 1384-1387, 1395, 1403, 1416-1419, 1432-1435, 1443, 1451, 1462, 1472-1481, 1484-1521, 1525-1526, 1566-1575, 1590, 1594-1599 /home/admin/.local/lib/python3.8/site-packages/matplotlib/markers.py 427 328 23% 253-272, 278-291, 294, 297, 300, 312-316, 319, 322, 325, 340-367, 376, 383-386, 395, 402-405, 408, 412-413, 424-429, 445-458, 474-480, 483, 486-488, 491, 494, 497-515, 523-541, 544, 547-556, 559, 562-573, 584-611, 614, 617, 620, 623, 626-641, 644-655, 658-659, 662-682, 685-704, 707-728, 731-754, 757-775, 780-783, 786-787, 792-795, 798-801, 806-809, 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250, 254, 261, 265, 272, 279, 287-289, 308-319, 328-345, 348, 351, 394-417, 441-464, 477-483, 495, 537-546, 587-590, 599-601, 619-642, 651, 662, 671-682, 704-725, 735-738, 749-762, 772-788, 796, 807-810, 834-878, 889-922, 942-1001, 1019, 1029-1030, 1043-1045, 1077-1083 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/__init__.py 28 8 71% 75, 95, 104-110 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/geo.py 273 183 33% 24, 27-28, 33-34, 41-57, 61-106, 111-114, 119-120, 123, 126, 129-130, 133, 136, 139, 142, 145-146, 152, 159-162, 170-172, 179-181, 187-188, 195, 205, 213, 216, 219, 222, 236-237, 240, 244-245, 256-267, 271, 278, 282, 285-288, 291, 302-309, 313, 319-323, 327, 330-333, 336, 347-377, 381, 387-393, 397, 400-403, 406, 421-423, 427-442, 446, 454-456, 460-473, 477, 483-488, 492-493, 496, 502 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/polar.py 719 577 20% 50-54, 63, 68-77, 81-131, 135, 165-169, 175-184, 207-210, 219-231, 235, 246-253, 259, 262, 265, 268, 271, 274, 277, 290-291, 294-295, 298-302, 305-306, 324-332, 337-344, 347-352, 355-396, 413-416, 420-422, 425-433, 437-445, 458-459, 462, 466-471, 478-479, 483-487, 490-494, 514-518, 523-544, 559-561, 566-615, 618-695, 710-711, 714-716, 720-722, 725-726, 735, 744, 761-765, 771-800, 815-821, 825-844, 848-849, 857-951, 955-956, 959, 962, 965-970, 973-982, 985-991, 994-1037, 1040, 1043-1053, 1057, 1061, 1065, 1069, 1087-1098, 1104-1106, 1112, 1129-1138, 1150-1159, 1171, 1181, 1190, 1200, 1209, 1219, 1227, 1230, 1244-1256, 1266, 1277, 1280-1281, 1285, 1288, 1340-1349, 1402-1415, 1419-1441, 1453, 1463, 1473, 1476-1486, 1496, 1499-1523 /home/admin/.local/lib/python3.8/site-packages/matplotlib/pyplot.py 860 526 39% 119-120, 135-157, 163-167, 175, 180-182, 186-193, 204-209, 231-356, 360-375, 383-384, 397, 445-446, 476, 512-516, 552-556, 576-584, 589, 594, 599-601, 609, 614, 619, 658-686, 803-869, 882-890, 902-906, 911, 916, 921-923, 940, 945, 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3267, 3278, 3289, 3300, 3311, 3322 /home/admin/.local/lib/python3.8/site-packages/matplotlib/quiver.py 390 338 13% 291-314, 318, 322-345, 348, 357-362, 365, 373-374, 377-385, 407-437, 441-443, 477-506, 516-527, 530-536, 540-544, 549-571, 575-577, 592-596, 599-605, 608-663, 670-723, 897-941, 967-973, 1024-1117, 1122-1162, 1173-1180 /home/admin/.local/lib/python3.8/site-packages/matplotlib/rcsetup.py 414 127 69% 68-69, 75-82, 99, 110, 127, 135-136, 159, 169-170, 185, 188-189, 218, 230-234, 238, 260, 282, 288, 290, 292, 294, 296-300, 304, 344-347, 354, 366-367, 381-384, 395-398, 411, 415, 427-428, 438, 457-483, 506-524, 534, 537-541, 549-552, 560, 568, 583-589, 683, 686, 689-692, 695-698, 705, 716-718, 738-739, 746, 750, 758, 761, 783, 786-792 /home/admin/.local/lib/python3.8/site-packages/matplotlib/scale.py 274 155 43% 69, 76, 86, 105-113, 120, 145-150, 153, 156, 180-182, 186, 190-198, 205-209, 213, 218-236, 239, 246-247, 250, 253, 256, 281-282, 288-291, 297, 301-304, 333-335, 339, 343, 350-361, 364-371, 374, 382-388, 391-398, 401, 440-441, 449-453, 457, 465-469, 472, 475, 483-484, 487, 490, 551-557, 562, 565-574, 581-584, 588-593, 596, 599, 606-607, 611, 614, 617, 646-648, 652, 657-665, 677-679, 708-709, 726 /home/admin/.local/lib/python3.8/site-packages/matplotlib/spines.py 315 261 17% 33, 54-86, 90-99, 103-109, 113-114, 126-131, 136-140, 153-197, 200, 203-206, 216-219, 223-225, 230-282, 287-290, 313-325, 329-330, 334-386, 408-419, 423, 429-442, 448-451, 456-460, 476-477, 491, 494-505, 508-512, 539, 543, 546, 549, 552-555, 559-574, 578, 582, 585, 588 /home/admin/.local/lib/python3.8/site-packages/matplotlib/stackplot.py 42 37 12% 71-127 /home/admin/.local/lib/python3.8/site-packages/matplotlib/streamplot.py 370 328 11% 91-241, 247-248, 274-284, 288, 291, 294, 297, 300-301, 304-305, 308-311, 314, 321-362, 366, 372, 386-396, 399, 403-404, 408-409, 417-426, 443-502, 535-602, 607-624, 633-667, 678-707 /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/core.py 92 45 51% 22, 127-180, 220-224, 242, 256, 262-266 /home/admin/.local/lib/python3.8/site-packages/matplotlib/table.py 335 272 19% 94-103, 108-110, 113-114, 118, 122-123, 127, 131-138, 142-149, 153-164, 170, 176-177, 188-189, 202, 206-218, 222-228, 298-321, 342-345, 351-361, 365, 384, 388-389, 392, 401-415, 423-427, 431-444, 448, 452-457, 464-488, 500-508, 512-516, 520-521, 525-539, 543-545, 568-570, 574-577, 584-635, 650, 737-830 /home/admin/.local/lib/python3.8/site-packages/matplotlib/texmanager.py 151 103 32% 48-49, 105-106, 110-115, 120-130, 134-171, 178-187, 194-195, 200, 205-207, 246-249, 253-275, 284-305, 314-329, 334-344, 357-361, 366-373 /home/admin/.local/lib/python3.8/site-packages/matplotlib/text.py 812 676 17% 41-49, 67-90, 97, 105, 130, 165-183, 201-219, 223-233, 236-239, 246-268, 274-275, 278-281, 292-313, 317-321, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/ticker.py 1228 996 19% 165-167, 170, 173, 176, 179, 182, 186, 193, 196-197, 213, 217-218, 225, 233, 236, 245, 255, 261, 269, 283-284, 294-297, 300, 303, 316-317, 325, 328, 331, 346, 354, 365, 374, 428-439, 452, 481-486, 498, 509-512, 520, 531, 544-564, 572-578, 588, 620-622, 626-650, 654-666, 672-694, 698-703, 706-740, 748-776, 780-809, 874-883, 893, 902, 914, 925, 933-984, 987-993, 997-1016, 1019-1020, 1024, 1028-1046, 1054-1062, 1072, 1076-1110, 1120-1125, 1167-1172, 1184, 1193, 1205, 1218, 1221-1260, 1263-1286, 1289-1292, 1295-1313, 1318-1322, 1388-1392, 1395, 1398-1401, 1406, 1409-1412, 1417-1421, 1438-1473, 1503-1506, 1510-1512, 1536-1556, 1559, 1570-1579, 1583, 1616, 1623, 1631, 1644-1649, 1665, 1673, 1685-1686, 1690-1693, 1697-1698, 1701, 1717-1719, 1723-1724, 1727, 1739-1747, 1756, 1767, 1791-1795, 1800, 1804, 1808-1811, 1815-1816, 1819-1830, 1835-1850, 1860, 1864-1865, 1869-1870, 1873-1879, 1886-1896, 1900-1907, 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/home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/art3d.py 488 390 20% 28-31, 36-39, 62-73, 97-98, 102, 116-119, 129-130, 144-146, 150-159, 164, 179-180, 208-209, 224-229, 248-254, 265, 269-273, 289-290, 296-300, 306-314, 320-329, 337-344, 354-355, 361-362, 368-377, 382-384, 404-405, 421-422, 426, 429-434, 455-456, 472-473, 476-481, 486-489, 494-496, 501-506, 529-531, 534, 546-547, 551-552, 570-580, 583-592, 595-602, 605, 611-613, 636-640, 643-645, 649-650, 668-695, 698-700, 703-705, 708, 720-721, 724-754, 758-768, 771-778, 781, 787-789, 809-816, 871-896, 914-916, 920-928, 944-947, 953-955, 960-966, 970-971, 977-1040, 1044-1045, 1049-1050, 1054-1065, 1070-1073, 1078-1081, 1097-1101, 1111-1118, 1127-1132, 1141-1146, 1173-1189, 1198-1227 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py 1305 1135 13% 122-180, 183-184, 187-188, 195, 200-207, 211-213, 217, 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534-576 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/proj3d.py 101 81 20% 20-29, 39-48, 58-68, 95-107, 125-130, 148-150, 154-162, 167-173, 177-181, 185-192, 199-206, 210, 217-218, 230-231, 235, 239-240, 244-249 /home/admin/.local/lib/python3.8/site-packages/numpy/__config__.py 30 16 47% 12-16, 27-28, 69-78 /home/admin/.local/lib/python3.8/site-packages/numpy/__init__.py 142 52 63% 124, 128-132, 279-313, 317-325, 351-358, 368-374, 378-391, 410-417 /home/admin/.local/lib/python3.8/site-packages/numpy/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/_globals.py 19 2 89% 26, 85 /home/admin/.local/lib/python3.8/site-packages/numpy/_pytesttester.py 51 43 16% 38-44, 128-201 /home/admin/.local/lib/python3.8/site-packages/numpy/_version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/compat/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/compat/_inspect.py 67 17 75% 75, 86, 107, 122-123, 126-129, 136, 182-191 /home/admin/.local/lib/python3.8/site-packages/numpy/compat/py3k.py 59 25 58% 39-41, 44-46, 49-51, 54, 57, 60, 65, 68-71, 74-77, 85, 103, 106, 109, 134-135 /home/admin/.local/lib/python3.8/site-packages/numpy/core/__init__.py 85 16 81% 23-48, 62-68, 125-126, 135, 142, 148-151 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs.py 261 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_add_newdocs_scalars.py 48 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/core/_asarray.py 34 26 24% 19, 94-135 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype.py 157 137 13% 27-28, 35-42, 46-49, 60, 65, 95-100, 104-156, 163-175, 180-186, 191-230, 245-253, 257-279, 286-296, 300-301, 308-318, 324-342 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_dtype_ctypes.py 54 36 33% 33, 37-68, 84-93, 106, 108, 110, 112, 116 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_exceptions.py 98 57 42% 11-14, 35, 42-44, 47, 58-59, 62, 75-78, 85-86, 90-91, 103-104, 108-109, 128-138, 145-146, 150-153, 160-190, 193-194 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_internal.py 430 327 24% 16-17, 24, 27-51, 57-76, 89-133, 141, 158-203, 207, 209, 211, 213, 215, 220, 222-223, 227, 230-233, 242, 246, 251-265, 282-284, 291-293, 300-302, 320, 332, 343, 352, 361-363, 370-372, 379-381, 388-392, 400-416, 431-434, 457-466, 490-495, 561-562, 565-567, 570-573, 576-585, 589, 592, 596-598, 601-742, 746-757, 761-779, 782-785, 789-791, 794, 798-803, 810-811, 830, 834, 852, 871, 877-878, 907, 909 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_methods.py 155 118 24% 40, 44, 48, 52, 58, 62-64, 68-86, 93-99, 102-104, 108-123, 126-159, 163-193, 197-258, 262-272, 275, 282-287, 290 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_string_helpers.py 15 5 67% 68-69, 97-100 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_type_aliases.py 122 13 89% 47-53, 108, 224-230 /home/admin/.local/lib/python3.8/site-packages/numpy/core/_ufunc_config.py 87 23 74% 192-203, 217, 302-310, 356, 432, 437 /home/admin/.local/lib/python3.8/site-packages/numpy/core/arrayprint.py 550 435 21% 29-30, 66-98, 252-265, 295, 325-330, 340-350, 355-359, 362, 365, 372-414, 420-452, 472, 490-511, 520, 673-698, 702-712, 719-740, 751-858, 861-865, 872-894, 898-978, 981-1001, 1081-1087, 1169-1178, 1186-1191, 1194, 1201, 1204, 1212-1224, 1230-1237, 1242-1253, 1257, 1260-1263, 1270-1284, 1287-1289, 1292, 1300, 1305, 1308-1310, 1322, 1330-1336, 1339-1346, 1355, 1360, 1362, 1390-1398, 1411-1423, 1430-1470, 1475, 1523, 1530, 1540, 1551, 1556, 1595, 1658-1664 /home/admin/.local/lib/python3.8/site-packages/numpy/core/defchararray.py 438 243 45% 57-61, 68, 79-84, 92-94, 98, 124, 150, 177, 203, 229, 255, 259, 283, 307-311, 315, 339-344, 349, 376, 415-416, 420, 451-456, 461, 507, 511, 556, 592, 597, 640, 645, 680, 716, 745, 772, 798, 824, 851, 878, 904, 931, 935, 960, 966, 996-1001, 1036-1037, 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334-337 /home/admin/.local/lib/python3.8/site-packages/numpy/core/multiarray.py 104 23 78% 145, 244-245, 338, 492-495, 610, 661, 822, 880, 957, 1018, 1068, 1148, 1206, 1290, 1365, 1406, 1460, 1554, 1622, 1690 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numeric.py 495 346 30% 73, 138-142, 146, 202, 215, 280-282, 286, 337-345, 354, 416-418, 422, 485-496, 564, 568, 614-618, 622, 663, 667, 741, 745, 837-844, 848, 934-936, 940, 1072-1133, 1137, 1211-1238, 1242, 1317-1332, 1379-1391, 1395, 1447-1466, 1471, 1475, 1592-1673, 1764-1779, 1783, 1842-1846, 1855, 2011-2051, 2093-2108, 2116-2123, 2127, 2164, 2176, 2249-2250, 2254, 2337-2378, 2382, 2439-2453, 2457, 2496-2505 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numerictypes.py 150 71 53% 173-181, 219-227, 270-281, 355, 420, 436, 500-506, 566-572, 576-588, 651-672 /home/admin/.local/lib/python3.8/site-packages/numpy/core/overrides.py 58 9 84% 102, 107, 175-181 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/home/admin/.local/lib/python3.8/site-packages/numpy/fft/_pocketfft.py 164 120 27% 50-75, 79-88, 93-102, 111-114, 119, 211-216, 312-317, 405-410, 509-514, 607-612, 674-679, 683-698, 702-708, 712, 815, 918, 1014, 1107, 1200-1205, 1257, 1362-1367, 1424 /home/admin/.local/lib/python3.8/site-packages/numpy/fft/helper.py 46 33 28% 16, 64-73, 111-120, 160-169, 216-221 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/__init__.py 39 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_datasource.py 177 139 21% 59-66, 104-128, 146-147, 150-151, 192-193, 248-254, 258-261, 267-268, 274-278, 288-291, 295-300, 306-315, 325-342, 357-373, 399-415, 421-429, 463-485, 521-533, 578-579, 582, 586-591, 595, 618, 652, 683, 700-704 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_iotools.py 352 300 15% 30-35, 42-46, 53-57, 81-84, 120-131, 168, 172-197, 201-206, 209-216, 219-224, 227, 288-310, 339-380, 383, 413-419, 505, 524, 529, 539-541, 568-582, 587-596, 601-669, 672-675, 678-700, 703, 707-723, 746-751, 754-763, 796-820, 861-898 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_version.py 75 61 19% 56-76, 80-97, 101-112, 115-134, 137, 140, 143, 146, 149, 152, 155 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraypad.py 218 200 8% 29-30, 55, 81-83, 109-126, 146-151, 175-183, 208-227, 257-293, 321-378, 401-451, 482-518, 522, 736-876 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py 197 166 16% 34, 82-122, 127-130, 135, 270-317, 325-359, 364, 430-463, 467, 500-510, 514, 584-631, 635, 732-733, 738, 775, 779, 819-824 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arrayterator.py 71 58 18% 85-90, 93, 101-125, 132-134, 161-162, 172, 177-219 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/format.py 266 234 12% 192-194, 212-216, 230-235, 238-245, 270-281, 304-336, 352-364, 371-389, 396-413, 430-440, 452, 468, 499, 532, 552-567, 576-625, 663-696, 731-789, 842-890, 902-918 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/function_base.py 1153 979 15% 56, 114-143, 147, 231-241, 277, 377-419, 486-490, 494-497, 588-618, 622-623, 665-719, 723, 804, 810-811, 989-1157, 1161, 1249-1294, 1298, 1406-1439, 1443, 1485-1496, 1500, 1569-1596, 1600, 1627-1637, 1641, 1679-1694, 1698, 1750, 1754, 1794-1798, 1833-1840, 1866-1869, 1887-1905, 1926-1934, 1939, 1945-1948, 2113, 2118, 2120, 2122, 2131, 2140-2163, 2168-2232, 2236-2253, 2257-2316, 2321, 2448-2543, 2548, 2679-2701, 2796-2801, 2905-2910, 3009-3014, 3109-3114, 3182-3190, 3194, 3198, 3202, 3256-3262, 3388-3392, 3396, 3475-3477, 3481, 3508-3510, 3539-3565, 3570, 3655-3660, 3666-3716, 3721, 3863-3867, 3873, 3976-3979, 3986-3992, 3997-4004, 4009-4015, 4020-4122, 4126, 4215-4240, 4244, 4353-4375, 4379, 4447-4560, 4564, 4656-4755, 4759, 4811-4817, 4821, 4915-4932 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/histograms.py 287 254 11% 29, 49-50, 72-73, 96-97, 118-119, 146-161, 182-196, 224-226, 263-270, 285-301, 309-331, 342-357, 382-451, 460, 467, 668-670, 675, 791-929, 934-940, 1014-1129 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/index_tricks.py 258 185 28% 32, 93-107, 149-207, 325-420, 423, 593, 607, 610, 657-661, 665, 678-681, 695-696, 758-761, 775, 891-909, 977-978, 982, 1006-1013 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/mixins.py 59 12 80% 10-13, 19-21, 29-31, 39, 54 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/nanfunctions.py 279 219 22% 61-66, 96-110, 135-139, 164-180, 209-221, 225, 313-336, 340, 428-451, 455, 494-500, 504, 544-550, 554, 647-648, 652, 717-718, 722, 787-788, 792, 854-855, 859, 937-957, 965-974, 984-1000, 1010-1020, 1025, 1113-1124, 1129, 1245-1249, 1255, 1358-1362, 1371-1381, 1391-1405, 1413-1418, 1424, 1518-1567, 1572, 1670-1676 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/npyio.py 854 780 9% 34-37, 83, 86-89, 97, 108-112, 189-202, 205, 208, 215-221, 224, 228, 231, 242-260, 269-273, 277-281, 396-450, 455, 519-529, 534-535, 618, 622-623, 689, 695-726, 732-758, 768, 903-1188, 1199, 1326-1447, 1510-1541, 1557, 1751-2284, 2311-2317, 2339-2345, 2369-2377, 2403-2415 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/polynomial.py 438 345 21% 41, 138-165, 169, 229-261, 265, 342-366, 370, 432-446, 450, 620-689, 693, 764-772, 776, 830-844, 884-898, 956-961, 965, 1020-1038, 1042-1065, 1180, 1185-1186, 1191, 1197, 1202, 1208, 1211, 1219-1243, 1246-1249, 1252-1254, 1257, 1260-1314, 1317, 1320, 1323, 1326-1330, 1333-1337, 1340-1341, 1344-1345, 1348-1353, 1356-1357, 1360-1361, 1364-1368, 1373-1377, 1382-1386, 1389-1391, 1395-1400, 1403-1411, 1414, 1427, 1440 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/scimath.py 70 38 46% 106-110, 135-138, 162-165, 189-192, 196, 239-240, 287-288, 337-338, 342, 376-378, 425-426, 430, 473-475, 519-520, 565-566, 616-617 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/shape_base.py 263 196 25% 31-49, 53, 161-170, 174, 251-260, 264, 359-414, 418, 487-505, 509, 588-602, 648, 660, 716-723, 727-732, 736, 766-792, 796, 866-874, 878, 937-942, 989-991, 1034-1036, 1043-1048, 1055-1060, 1064, 1136-1164, 1168, 1238-1260 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py 91 70 23% 21-22, 26-35, 97-114, 119, 301-335, 340-359, 363, 411, 420-428, 470-471, 475, 536-544 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/twodim_base.py 177 114 36% 34-40, 44, 95-98, 151-154, 158, 211, 216, 220, 231, 289-303, 346-363, 367, 412-424, 433, 469-472, 498-501, 505, 582-597, 602-615, 741-752, 821-823, 904-906, 911, 938-940, 1023-1025, 1057-1059 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/type_check.py 138 90 35% 70-77, 81, 112-114, 118, 157-160, 164, 200-203, 207, 240-244, 300, 336-341, 390, 395-397, 401, 498-521, 526, 574-583, 588-590, 618, 696, 713, 753-769 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/ufunclike.py 57 30 47% 24-36, 50-53, 65, 70, 117-124, 188-196, 260-268 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/utils.py 454 415 9% 38-46, 50-51, 67-69, 76-127, 134-141, 188-194, 221, 260-277, 329-379, 391-407, 415-431, 452-482, 537-628, 672-677, 737-812, 835-947, 950-956, 1003-1004, 1028-1043, 1056-1070 /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py 678 574 15% 88, 91, 94, 97, 100, 103-105, 108-110, 113, 126, 129, 133, 137-157, 165-174, 177-185, 188-190, 194-196, 200-203, 206-208, 212, 230, 235, 286-306, 310, 378-395, 399, 456-467, 473, 538-546, 550, 618-666, 756-764, 770, 890-982, 1059-1077, 1081, 1159-1176, 1179-1181, 1314-1333, 1453-1473, 1479, 1617-1674, 1678, 1761-1797, 1801, 1898-1906, 1912, 1995-2011, 2092-2101, 2153-2160, 2166, 2266-2328, 2354-2356, 2360, 2514-2611, 2617-2618, 2707-2739, 2750-2760, 2780-2801, 2806-2812 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ma/core.py 2405 1774 26% 102-113, 122, 124, 204-212, 217-222, 270-277, 282-291, 342, 393, 414-425, 439-469, 532-534, 543-547, 575-579, 627-636, 646-663, 708-714, 768-776, 779, 804, 812-813, 831-832, 848-853, 868-869, 884-885, 895, 928-969, 1011-1050, 1057-1080, 1087-1105, 1112-1116, 1155-1192, 1284-1291, 1295-1297, 1305, 1466-1469, 1534-1537, 1544-1547, 1621-1636, 1682-1686, 1726-1753, 1788-1809, 1814-1817, 1924-1943, 1969, 1995, 2021, 2047, 2073, 2101-2103, 2139-2143, 2179-2183, 2244-2251, 2323-2331, 2361-2373, 2400, 2407, 2414, 2421, 2424, 2439-2447, 2489-2495, 2525-2550, 2581-2590, 2647-2653, 2656, 2659-2670, 2674-2676, 2696-2703, 2834, 2840, 2845-2847, 2855-2882, 2887, 2889, 2895-2896, 2900-2909, 2915-2932, 2938, 2943, 2956, 3012, 3030-3042, 3047, 3054-3058, 3062, 3065, 3074-3121, 3180-3183, 3190-3194, 3204-3210, 3224-3337, 3347-3402, 3411-3419, 3427-3431, 3438-3500, 3512, 3516, 3532-3535, 3539, 3554-3555, 3570-3571, 3576, 3591-3594, 3599, 3629-3630, 3635, 3652, 3660, 3664-3665, 3697-3706, 3710-3725, 3776-3809, 3833-3836, 3898-3908, 3915-3939, 3942, 3949-4026, 4031-4040, 4053-4104, 4117, 4130, 4137-4139, 4148, 4155-4157, 4164, 4168-4170, 4179, 4186-4188, 4195-4197, 4204, 4211-4213, 4220, 4227-4229, 4236, 4243-4253, 4260-4269, 4276-4285, 4292-4303, 4310-4321, 4328-4339, 4346-4360, 4367-4373, 4380-4385, 4407-4409, 4434-4436, 4497-4538, 4585-4591, 4652-4658, 4673-4676, 4739-4761, 4785-4787, 4815, 4841-4855, 4871-4885, 4983, 4990-4997, 5037, 5078-5099, 5133-5140, 5160-5181, 5206-5213, 5241-5260, 5293-5300, 5317-5363, 5380-5388, 5401-5413, 5479-5493, 5535-5538, 5572-5575, 5648-5658, 5692-5724, 5788-5792, 5826-5859, 5936-5947, 5950-5953, 5956-5959, 5964-5985, 6024-6046, 6057-6061, 6101, 6116, 6163-6175, 6184-6186, 6200-6203, 6209, 6214-6221, 6229-6231, 6241-6257, 6262, 6269-6284, 6287-6291, 6294-6302, 6308-6316, 6319, 6343, 6356-6366, 6422, 6462-6469, 6472, 6475, 6478, 6481-6485, 6491-6500, 6505, 6510, 6524, 6527, 6530, 6536-6542, 6559, 6611-6616, 6640-6647, 6651-6679, 6683-6695, 6698-6705, 6710-6717, 6723-6729, 6766-6777, 6812-6813, 6833-6865, 6872-6882, 6898-6908, 6923, 6967-6983, 6998-7001, 7016-7022, 7037-7043, 7059-7062, 7083-7101, 7136-7139, 7154-7158, 7216-7222, 7230, 7237, 7243, 7308-7340, 7388-7415, 7442-7448, 7527-7540, 7605-7625, 7636-7642, 7650-7660, 7670-7682, 7710, 7738, 7783-7796, 7871-7904, 7951-7952, 8000-8002, 8011, 8017, 8082, 8116-8127, 8186 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/extras.py 560 468 16% 48, 101-102, 152-154, 207-209, 259, 262, 272-280, 290-293, 306-318, 333-341, 364-369, 376-451, 459-477, 587-631, 700-714, 719-799, 823-842, 894-896, 911-914, 928-931, 975-981, 1024-1030, 1049-1063, 1078-1087, 1113-1119, 1133-1146, 1168-1188, 1209-1210, 1225, 1248-1253, 1267-1301, 1358-1374, 1425-1461, 1486-1487, 1491-1494, 1569-1576, 1621-1626, 1674-1684, 1744-1760, 1769-1789, 1825-1828, 1864-1867, 1880-1884, 1894-1921 /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py 238 178 25% 15-33, 69, 116-165, 168-187, 190-213, 216-221, 224, 227-228, 231, 234-235, 238, 244-251, 257-260, 284, 319, 372, 411, 445, 479, 513, 546, 569, 609, 644, 683, 718, 757, 790, 830-835, 865, 894, 933, 966, 998-1001, 1011-1032, 1089-1111 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/__init__.py 18 7 61% 171-180 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/_polybase.py 419 296 29% 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 193-198, 216, 234, 252, 280-288, 291-304, 307-311, 314-324, 327-329, 337-367, 375-380, 389-394, 398-403, 409, 413-462, 469-473, 476, 481-483, 486, 489, 494, 497, 500-505, 508-513, 516-521, 527-532, 535-538, 541-544, 547-556, 559-561, 564-568, 571-575, 578-582, 586, 591, 594-597, 600-603, 606-614, 617-622, 625, 640, 653, 678, 700-701, 723-730, 761-767, 796, 823-829, 849-851, 865-866, 894-898, 971-986, 1014-1027, 1054-1060, 1091-1099, 1137-1141 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/chebyshev.py 357 294 18% 152-155, 177-180, 207, 243-274, 302-306, 333-340, 389-394, 441-455, 508-511, 566, 608, 652, 686-698, 742-747, 797-814, 855-872, 935-964, 1052-1091, 1153-1175, 1224, 1277, 1328, 1384, 1422-1437, 1490, 1544, 1670, 1700-1715, 1766-1776, 1827-1843, 1881-1888, 1915-1916, 1946-1953, 1979-1986, 2065-2069 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite.py 267 214 20% 134-139, 180-197, 251-254, 310, 350, 390, 432-443, 485-509, 557, 594, 652-677, 763-799, 871-895, 944, 997, 1048, 1104, 1151-1165, 1218, 1272, 1403, 1433-1448, 1502-1512, 1544-1555, 1594-1622, 1649-1650 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite_e.py 264 211 20% 135-140, 181-197, 250-253, 309, 349, 389, 427-438, 480-504, 550, 587, 645-670, 756-792, 864-887, 936, 989, 1040, 1096, 1143-1156, 1209, 1263, 1395, 1426-1441, 1495-1505, 1537-1548, 1587-1615, 1641-1642 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/laguerre.py 252 200 21% 134-138, 179-193, 245-248, 304, 345, 385, 427-439, 481-505, 551, 588, 646-674, 761-798, 870-893, 942, 995, 1046, 1102, 1149-1162, 1215, 1269, 1400, 1429-1444, 1498-1508, 1547-1572, 1598-1599 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/legendre.py 261 209 20% 140-145, 193-207, 261-264, 319, 361, 405, 447-461, 505-529, 578, 609, 672-701, 789-829, 891-914, 963, 1016, 1067, 1123, 1161-1176, 1229, 1283, 1411, 1441-1455, 1506-1516, 1555-1584, 1611-1612 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polynomial.py 221 166 25% 145-148, 212, 248, 285, 317-325, 361-363, 400-421, 460, 515-542, 623-661, 745-757, 835-845, 895, 948, 999, 1055, 1096-1109, 1157, 1211, 1361, 1390-1401, 1454-1464, 1514, 1518, 1522-1529 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polyutils.py 229 204 11% 71-77, 130-153, 200-208, 248-254, 297-301, 366-368, 372-374, 422-443, 452-453, 469-483, 497-513, 527-529, 547-565, 571-578, 584-592, 606-680, 697-713, 732-750 /home/admin/.local/lib/python3.8/site-packages/numpy/random/__init__.py 17 1 94% 210 /home/admin/.local/lib/python3.8/site-packages/numpy/random/_pickle.py 22 12 45% 31-37, 54-60, 77-83 /home/admin/.local/lib/python3.8/site-packages/numpy/version.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/packaging/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/packaging/_structures.py 36 16 56% 8, 11, 14, 17, 20, 23, 26, 29, 37, 40, 43, 46, 49, 52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/packaging/version.py 163 67 59% 69, 76, 81-84, 87-90, 93-96, 99-102, 105-108, 198, 228, 236-261, 272-273, 289-290, 305-306, 317, 328, 339-342, 355, 371-380, 397, 408, 419, 428, 439, 450, 461, 470, 472, 474, 476, 482-484, 497, 527, 533, 547, 560 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/home/admin/.local/lib/python3.8/site-packages/pygit2/credentials.py 56 18 68% 52, 56, 60, 63, 74-75, 79, 83, 86, 113-116, 120, 124, 127, 132, 138 /home/admin/.local/lib/python3.8/site-packages/pygit2/errors.py 26 20 23% 34-65, 70 /home/admin/.local/lib/python3.8/site-packages/pygit2/ffi.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/pygit2/index.py 226 171 24% 44-50, 54-59, 63, 66, 69, 72-77, 80-94, 97, 110-111, 115-116, 119-120, 129-144, 157-167, 172-173, 178-180, 188-190, 202-212, 232-249, 272-295, 322-331, 338-343, 348, 353, 356, 359-360, 365-369, 377-384, 388-396, 402, 405-417, 420-421, 424, 430-434, 437, 440, 443-457, 460 /home/admin/.local/lib/python3.8/site-packages/pygit2/packbuilder.py 40 23 42% 37-43, 47, 50, 53, 57-59, 62-64, 67-69, 72, 75-77, 81 /home/admin/.local/lib/python3.8/site-packages/pygit2/refspec.py 37 17 54% 37-38, 43, 48, 53, 58, 63, 69, 74, 77-84, 90, 96 /home/admin/.local/lib/python3.8/site-packages/pygit2/remote.py 155 112 28% 41-60, 69-71, 74, 80, 86, 92, 97-101, 106-107, 122-130, 138-164, 169-171, 177, 181-182, 188-192, 198-202, 220-224, 241-249, 252-265, 268-275, 284-296, 306-319, 326-327, 332-333, 338-339, 345-346, 352-353 /home/admin/.local/lib/python3.8/site-packages/pygit2/repository.py 552 422 24% 77, 85, 105-117, 121, 139-163, 169-174, 184-198, 204-205, 208-211, 214, 217, 223-224, 237-241, 250-254, 281-291, 308-316, 324-346, 353-354, 361-362, 369-373, 410-429, 444-453, 459-479, 543-568, 575, 615-636, 644-648, 690-698, 704-729, 743-762, 818-838, 894-919, 983-1039, 1074-1091, 1095-1104, 1128-1129, 1141, 1148-1149, 1187-1224, 1249-1262, 1288-1303, 1310-1316, 1326-1327, 1347-1363, 1372-1375, 1383-1393, 1396-1399, 1402-1404, 1407, 1410, 1413-1416, 1423-1424, 1427, 1439-1442, 1445-1446, 1449, 1452, 1455, 1459, 1462, 1491, 1493, 1497, 1501-1506 /home/admin/.local/lib/python3.8/site-packages/pygit2/settings.py 74 25 66% 40, 43, 65-71, 81, 86, 90, 98, 102, 110, 120, 128, 138, 148, 153, 158, 163, 168, 173, 178 /home/admin/.local/lib/python3.8/site-packages/pygit2/submodule.py 37 19 49% 35-40, 43, 47-51, 56-57, 62-63, 68-69, 74-75, 80-81 /home/admin/.local/lib/python3.8/site-packages/pygit2/utils.py 60 46 23% 33-36, 40-49, 53-62, 66-70, 85-102, 105, 108, 119-121, 124, 127-132 /home/admin/.local/lib/python3.8/site-packages/python_http_client/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/python_http_client/client.py 106 76 28% 11-15, 29-31, 38, 45, 52, 59-62, 92-99, 109, 118-136, 145, 154-155, 174-184, 196, 207-290, 293, 296 /home/admin/.local/lib/python3.8/site-packages/python_http_client/exceptions.py 46 16 65% 8-17, 20, 30, 93-97 /home/admin/.local/lib/python3.8/site-packages/pytz/__init__.py 198 125 37% 56-75, 87-108, 113-124, 167-190, 195, 204-206, 226-228, 231, 234, 237, 240, 244-246, 250-254, 257, 260, 295, 307, 347, 350-366, 379-390, 403-406, 409, 412, 415, 418, 421, 425-427, 431-435, 491-502, 509-512, 516 /home/admin/.local/lib/python3.8/site-packages/pytz/exceptions.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pytz/lazy.py 100 59 41% 4-8, 21-28, 31-38, 41-48, 51-58, 61-68, 87, 98-106, 142, 151-160 /home/admin/.local/lib/python3.8/site-packages/pytz/tzfile.py 76 66 13% 21, 25-123, 126-133 /home/admin/.local/lib/python3.8/site-packages/pytz/tzinfo.py 178 126 29% 7-8, 34-41, 49-58, 66, 76, 87-89, 97, 105, 113, 117-119, 144-148, 151, 156, 183-194, 198-204, 251-259, 320-397, 422-428, 461-467, 499-505, 508-517, 524, 542-580 /home/admin/.local/lib/python3.8/site-packages/requests/__init__.py 68 27 60% 49-50, 54-55, 64, 80-86, 91-100, 108-109, 123-124, 127-136 /home/admin/.local/lib/python3.8/site-packages/requests/__version__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/_internal_utils.py 21 10 52% 30-35, 45-50 /home/admin/.local/lib/python3.8/site-packages/requests/adapters.py 194 147 24% 61, 74, 93, 97, 142-155, 158, 163-169, 188-192, 211-235, 249-290, 304-329, 340-358, 366-368, 384-397, 411, 426-432, 453-538 /home/admin/.local/lib/python3.8/site-packages/requests/api.py 19 10 47% 58-59, 73, 85, 99-100, 115, 130, 145, 157 /home/admin/.local/lib/python3.8/site-packages/requests/auth.py 173 141 18% 35-66, 73, 80-81, 84, 92, 95-96, 103-104, 111-114, 118-124, 131-234, 238-239, 250-284, 288-304, 307, 315 /home/admin/.local/lib/python3.8/site-packages/requests/certs.py 4 1 75% 17 /home/admin/.local/lib/python3.8/site-packages/requests/compat.py 30 5 83% 12-13, 36-37, 42 /home/admin/.local/lib/python3.8/site-packages/requests/cookies.py 239 176 26% 19-20, 36-38, 41, 44, 47, 52-58, 70, 73, 76, 80, 85, 88, 92, 96, 100, 115, 118, 121, 131-137, 146-148, 156-167, 201-204, 212-223, 231-232, 240, 248-249, 257, 265-266, 275, 279-283, 287-291, 299-304, 313-319, 322-325, 334, 341, 347, 350-356, 360-364, 378-384, 398-413, 417-420, 424-426, 430-433, 437, 441-452, 461-489, 495-504, 530-539, 549-561 /home/admin/.local/lib/python3.8/site-packages/requests/exceptions.py 37 8 78% 19-24, 41-42 /home/admin/.local/lib/python3.8/site-packages/requests/hooks.py 14 11 21% 16, 24-33 /home/admin/.local/lib/python3.8/site-packages/requests/models.py 455 368 19% 89-104, 115-134, 146-203, 210-216, 223-227, 273-291, 294, 298-311, 337-350, 367-378, 381, 384-392, 396-398, 402-408, 417-482, 487-493, 502-571, 575-587, 593-609, 622-629, 636-638, 660-704, 707, 710, 715-718, 721-726, 729, 739, 749, 753, 764-768, 775, 780, 788, 793, 812-851, 863-885, 891-904, 920-942, 952-975, 981-992, 997-1021, 1029-1034 /home/admin/.local/lib/python3.8/site-packages/requests/packages.py 17 4 76% 5-10 /home/admin/.local/lib/python3.8/site-packages/requests/sessions.py 268 219 18% 56, 67-88, 97-103, 115-125, 129-157, 173-281, 288-301, 315-332, 338-354, 396-451, 454, 457, 469-500, 563-591, 601-602, 612-613, 623-624, 637, 649, 661, 671, 680-749, 758-780, 788-794, 798-799, 806-810, 813-814, 817-818, 833 /home/admin/.local/lib/python3.8/site-packages/requests/status_codes.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/structures.py 39 19 51% 41-44, 49, 52, 55, 58, 61, 65, 68-73, 77, 80, 91, 96, 99 /home/admin/.local/lib/python3.8/site-packages/requests/utils.py 485 411 15% 76-121, 127-130, 134-196, 202-253, 258-260, 268-297, 303-310, 331-337, 357-366, 393-398, 424-433, 445-459, 469-474, 485, 493-506, 521-535, 545-560, 566-577, 582-587, 602-626, 641-656, 667-678, 689-693, 703-704, 711-715, 724-739, 750-761, 772-821, 830-833, 842-859, 873-886, 902, 920-946, 962-984, 993-1013, 1022-1029, 1038-1040, 1044-1056, 1068-1076, 1083-1094 /home/admin/.local/lib/python3.8/site-packages/sendgrid/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/base_interface.py 22 15 32% 23-30, 38-46, 49, 59-62 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/endpoints/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/__init__.py 14 6 57% 19, 30, 46-50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/eventwebhook_header.py 5 1 80% 10 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/__init__.py 63 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/amp_html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/asm.py 33 20 39% 16-23, 31, 40-43, 52, 62-65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/attachment.py 75 47 37% 43-62, 70, 79-82, 90, 99-102, 110, 119-122, 137, 162-165, 176, 191-194, 203-218 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/batch_id.py 15 7 53% 14-17, 25, 34, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings.py 27 16 41% 16-23, 31, 40, 48, 57, 66-72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings_email.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_bounce_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_list_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_spam_management.py 16 9 44% 15-18, 26, 35, 44-47 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_unsubscribe_management.py 16 9 44% 17-20, 28, 37, 46-49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/category.py 13 6 54% 10-13, 21, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/cc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/click_tracking.py 27 16 41% 12-19, 27, 36, 45, 56, 65-71 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content.py 30 18 40% 19-27, 36, 48, 56, 65-66, 75-81 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content_id.py 13 6 54% 13-16, 27, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/custom_arg.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/disposition.py 13 6 54% 21-24, 39, 63, 72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/dynamic_template_data.py 24 12 50% 16-22, 30, 39, 47, 57, 64, 73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/email.py 79 43 46% 41-60, 68, 77-80, 92, 108, 120, 137, 145, 154, 162, 171, 179, 189, 198-213, 222-228 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/exceptions.py 22 10 55% 24-31, 39, 48, 56, 65 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_content.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_name.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_type.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_html.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_settings.py 38 23 39% 14-25, 33, 42, 50, 59, 67, 76, 85-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_text.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/from_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ganalytics.py 63 35 44% 26-38, 48-49, 57, 66, 75, 86, 94, 103, 111, 120, 128, 137, 145, 154, 163-176 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/group_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/groups_to_display.py 15 8 47% 13-16, 25, 37-39, 48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/header.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ip_pool_name.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail.py 470 334 29% 50-80, 88, 100-102, 112-114, 122-124, 133, 150-190, 198, 208, 213, 229-241, 258-276, 280, 296-308, 324-330, 335, 356-368, 388-394, 406, 415-433, 441, 445, 454-458, 466-490, 494, 503-507, 515-535, 543, 547, 557-561, 569-592, 601, 612-629, 633, 642-653, 662, 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/home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/plain_text_content.py 25 14 44% 15-19, 27, 35, 44-45, 54-60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/reply_to.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/sandbox_mode.py 16 9 44% 12-15, 23, 32, 41-44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/section.py 25 14 44% 12-18, 26, 35, 43, 52, 61-64 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/send_at.py 24 12 50% 22-28, 36, 45, 53, 63, 70, 79 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_check.py 44 27 39% 18-27, 35, 44, 54, 68-71, 80, 91-94, 103-112 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_threshold.py 13 6 54% 15-18, 29, 44, 53 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_url.py 13 6 54% 12-15, 24, 35, 44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subject.py 23 11 52% 13-18, 26, 35, 43, 53, 60, 69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_html.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_substitution_tag.py 13 6 54% 18-21, 32, 48, 58 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_text.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_tracking.py 49 30 39% 21-33, 41, 50, 59, 71, 80, 92, 103, 120, 129-142 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/substitution.py 34 19 44% 17-26, 34, 43, 51, 60, 68, 78, 87-90 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/template_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/to_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/tracking_settings.py 49 30 39% 30-45, 53, 63, 71, 81, 89, 98, 106, 115, 124-134 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_campaign.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_content.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_medium.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_source.py 13 6 54% 11-14, 23, 34, 43 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_term.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/validators.py 27 21 22% 18-28, 42-55, 66-69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/stats.py 166 108 35% 12-22, 29, 38-53, 61, 70, 78, 87, 95, 104, 112, 121, 129, 138, 146, 155, 163, 172, 187-194, 202-220, 228, 236-238, 253-260, 268-286, 294, 302-304, 317-319, 327, 336, 344, 357-359, 367, 376, 384 /home/admin/.local/lib/python3.8/site-packages/sendgrid/sendgrid.py 7 3 57% 55-58 /home/admin/.local/lib/python3.8/site-packages/sendgrid/twilio_email.py 9 4 56% 63-73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/zipp.py 123 73 41% 28, 47-50, 62, 73-75, 78-79, 82, 89-92, 100-111, 121-124, 127-130, 223-224, 232-242, 246, 250, 254, 258, 262, 265-266, 269-270, 273, 276, 279, 282, 285, 288-291, 294, 297, 300-301, 307-312 /home/admin/mtr/.credentials/credentials.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelConfig.py 63 45 29% 15-18, 23-26, 30, 35-48, 54-68, 73-77, 81-85, 88-95, 98, 101 /home/admin/workarea/git/Velours/python/mtr/database_queries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/admin_queries.py 457 392 14% 32-39, 44-50, 56-66, 71-87, 92, 96-99, 102-116, 120-135, 138-143, 146-148, 156-165, 168-177, 180-187, 192-206, 211-227, 232-250, 254-271, 275-291, 294-295, 298-299, 302-308, 317-323, 326-331, 334-337, 340-349, 353-357, 360-367, 370-376, 379-389, 392-399, 402-404, 407-414, 417-430, 433-444, 447-469, 473-485, 488-495, 498-504, 507-510, 514-518, 522-540, 543-548, 551-556, 559-564, 568-577, 580-588, 591-600, 603-612, 615-621, 625-648 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_admin_tools.py 87 53 39% 27-28, 30-34, 45, 61, 64, 76-92, 97-105, 110-137, 142, 147-163, 166-171 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_queries.py 291 256 12% 22-42, 45-49, 52-71, 74-82, 85-91, 94-98, 101-106, 109-117, 124-134, 139-148, 152-159, 162-172, 176-197, 200-220, 223-233, 236-248, 253-261, 267-283, 301-363, 368-397, 402-429, 436-450, 455-485, 489-511, 514-528 /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/objet_thcl.py 146 114 22% 32-50, 56-65, 70-77, 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, 120, 123, 126, 129-132, 138-139, 143-147, 152-171, 177-196, 200-202, 205-212, 226-232, 237-269 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_queries.py 1475 1135 23% 44, 57, 69, 87, 95-97, 100-104, 117-135, 149-153, 162-170, 186-295, 303-308, 311-316, 319-326, 329-348, 351-359, 362-369, 378-402, 406-409, 420-461, 472-492, 503-523, 526-531, 534-541, 551-572, 576-597, 608, 620, 626-627, 630, 646, 651, 658-659, 662-663, 682, 689, 696, 712, 719, 722, 726, 743-783, 801, 808-811, 831, 851-923, 934-1044, 1083, 1090, 1123-1124, 1127-1129, 1135-1138, 1141-1146, 1150-1153, 1158, 1164, 1179, 1191-1195, 1204-1208, 1217, 1228-1232, 1250-1310, 1337, 1339, 1342-1349, 1366-1374, 1384-1396, 1400-1407, 1410-1416, 1419-1427, 1432-1439, 1443-1456, 1460-1473, 1476-1483, 1486-1493, 1498-1519, 1524-1534, 1541, 1548-1554, 1556, 1568-1573, 1585-1590, 1602-1607, 1613-1617, 1624-1630, 1635-1649, 1655-1663, 1668-1687, 1691-1710, 1714-1729, 1733-1753, 1757-1775, 1781-1803, 1808-1818, 1821-1829, 1835-1852, 1858-1868, 1873-1908, 1912-1920, 1923-1927, 1930-1952, 1957-1970, 1975-1983, 1986-1994, 2001-2026, 2029-2069, 2075-2083, 2087-2095, 2100-2109, 2112-2121, 2124-2129, 2137, 2144-2212, 2257-2281, 2286-2309, 2313-2331, 2334-2342, 2352-2377, 2385-2386, 2394, 2413-2444, 2449-2477, 2481-2487, 2491-2510, 2514-2523, 2527-2531, 2535-2547, 2551-2578, 2582-2609, 2612-2650, 2654-2690, 2693-2708, 2713-2728, 2739-2775, 2784-2820 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_utils/util_portfolio_hashtag_ids.py 213 183 14% 18-19, 23-24, 29-128, 143, 146-147, 150, 154, 160-161, 169, 181-232, 237-350 /home/admin/workarea/git/Velours/python/mtr/database_queries/descriptor_queries.py 354 327 8% 22-79, 82-103, 106-145, 160-264, 270-301, 304-321, 328-352, 360-387, 390-400, 404-407, 412-435, 444-471, 474-477, 480-495, 499-556 /home/admin/workarea/git/Velours/python/mtr/database_queries/general_queries.py 148 98 34% 12-13, 33-34, 36-37, 40-46, 49, 54-61, 70-80, 83-95, 103-114, 117-133, 137-140, 147-160, 163-167, 182 /home/admin/workarea/git/Velours/python/mtr/database_queries/graph_nodes_queries.py 77 64 17% 22-34, 38-54, 59-130 /home/admin/workarea/git/Velours/python/mtr/database_queries/hashtag_queries.py 158 110 30% 46-50, 64-65, 72, 80-91, 94-110, 113-125, 128-133, 136-142, 145-155, 158-165, 168-183, 186-193, 196-207, 211-218, 221-226, 229-235 /home/admin/workarea/git/Velours/python/mtr/database_queries/mission_queries.py 520 478 8% 26-38, 42-250, 255-272, 275-314, 317-414, 418-430, 433-445, 448-460, 463-475, 479-491, 495-507, 510-522, 525-548, 551-552, 555-567, 570-582, 586-622, 625-644, 647-662, 665-671, 674-681, 697-741, 747-756, 773-799, 803-810, 815-822, 828-838, 841-843, 848-855, 859-873 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_insert_queries.py 105 84 20% 30-71, 74-81, 84-91, 94-103, 106-113, 118-138, 141-145, 149-163, 173-192, 203-218 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_retrieval_queries.py 558 469 16% 12, 50-75, 81-101, 107-123, 129-142, 148-161, 180-181, 188, 199-200, 212, 217, 221, 224-226, 229-231, 234-237, 248, 254, 261-266, 269, 271, 274, 277-278, 288-326, 332-348, 351-425, 428-475, 481-492, 495-544, 547-548, 555-605, 608-631, 634-668, 674-687, 694-721, 724-742, 749-802, 805-826, 832-849, 852-864, 868-922, 928-948, 968, 975-986, 989-1010 /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py 511 407 20% 39, 41, 44, 47, 56-72, 75-94, 97-114, 118-124, 127-136, 140-158, 163-180, 184-199, 202-212, 215-222, 225-235, 240-255, 258-263, 266-271, 274-284, 287-299, 302-311, 315-327, 332-341, 344-354, 357-365, 369-375, 378-383, 386-390, 393-397, 400-410, 415-468, 473-497, 513-519, 522-526, 535-541, 548-571, 576-584, 587-594, 598-608, 615-616, 620-622, 630, 634-638, 642-662, 675, 684, 717-748, 750-759, 764-801 /home/admin/workarea/git/Velours/python/mtr/datou/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py 1753 1190 32% 43-44, 75-101, 108-109, 112-113, 117-118, 153, 158, 172-174, 188-189, 199, 206-209, 212-213, 227-229, 241-243, 246-247, 277, 302-306, 311, 328-332, 335, 344-406, 434, 462-463, 481-495, 509-516, 522-573, 580-624, 652-653, 660-663, 674-677, 691, 696, 700-702, 721, 729-730, 738-739, 773-776, 788-797, 807-810, 818, 821, 823-825, 829-853, 863-870, 874, 877, 886-942, 968-1161, 1165, 1171-1174, 1178-1180, 1185-1186, 1191-1192, 1196-1198, 1201-1281, 1326-1334, 1348, 1351-1353, 1365-1376, 1379-1397, 1401-1450, 1454-1500, 1505-1545, 1550-1556, 1561, 1564, 1567, 1570-1571, 1574-1577, 1580, 1583, 1586-1588, 1596, 1603-1609, 1612-1617, 1621, 1634-1653, 1656-1659, 1662-1672, 1675-1678, 1682-1735, 1739-1742, 1747-1785, 1790-1791, 1794-1806, 1809-1813, 1819-1822, 1827-1871, 1874-1875, 1878-1894, 1903-1919, 1924-1941, 1945-1957, 1966-1969, 1975-1985, 1993-1996, 2002-2006, 2009-2012, 2015-2018, 2021-2024, 2027-2034, 2037-2041, 2045-2068, 2086-2110, 2113-2122, 2125-2151, 2154-2203, 2206-2241, 2258-2264, 2267-2277, 2280-2282, 2299, 2313, 2316-2328, 2331-2332, 2334-2369, 2371, 2375-2381, 2422, 2426, 2428, 2430, 2432, 2434, 2436, 2438, 2440, 2442, 2444, 2446, 2448, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2511, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2528, 2530, 2532, 2534, 2536, 2538, 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2555, 2559, 2562, 2564, 2566, 2568, 2570, 2572, 2574, 2577, 2580, 2582, 2584, 2586, 2588, 2590, 2592, 2594, 2596, 2598, 2600, 2603, 2605, 2607, 2609, 2611, 2617, 2619, 2621, 2623, 2625, 2630-2667, 2682, 2688, 2701-2703, 2718-2720, 2731, 2736, 2742-2744, 2746, 2751, 2760-2762, 2771-2774, 2783-2787, 2803-2808, 2811, 2819-2833, 2837-2843, 2855-2873 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_object.py 478 208 56% 16-23, 35-37, 46, 51-62, 73-84, 101-122, 135-149, 178, 194, 200, 212, 215, 222-246, 252-290, 302-321, 335, 358-382, 385, 389, 392, 396, 399-402, 412, 417-418, 439-440, 456, 469-470, 495, 499-500, 506, 512-513, 522-523, 570, 577-580, 589, 616, 635-652, 660, 665, 675-679, 684-685, 687-691, 694, 723-743, 747-771 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_data_increase.py 204 197 3% 7-121, 125-162, 167-218, 221-294, 297-339 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_save.py 1287 1137 12% 10-15, 18-183, 191-369, 374-436, 445-486, 489-553, 559-633, 638-744, 747-761, 764-779, 783-809, 813-864, 873-902, 907-936, 943-1043, 1047-1078, 1086-1087, 1089-1091, 1095, 1098, 1102, 1106-1116, 1138-1140, 1155-1157, 1165, 1172-1175, 1194-1213, 1223-1253, 1257-1279, 1295-1332, 1338-1357, 1362-1387, 1393-1457, 1472-1500, 1503-1519, 1523-1534, 1538-1619, 1625, 1631-1632, 1654-1655, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1683, 1685, 1687, 1689-1690, 1692, 1694, 1696, 1698, 1700, 1703, 1705, 1708, 1710, 1712, 1714, 1716, 1719, 1721, 1725-1726, 1730-1739, 1743-1745, 1749-1769, 1775-1783, 1786-1818, 1821-1836, 1840-1875 /home/admin/workarea/git/Velours/python/mtr/datou/datou_local_cache_db.py 157 135 14% 11-32, 35-36, 40-56, 62-70, 73-84, 88-102, 105-113, 117-122, 126-136, 139-143, 167-175, 178-194, 197-201, 204-205, 214-218, 233-257, 287-301, 304-307, 311 /home/admin/workarea/git/Velours/python/mtr/datou/datou_step_finale.py 325 254 22% 9-77, 82-130, 135-351, 370-372, 375-376, 395, 413-426, 430, 435, 439-443, 449, 473 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_deprecated.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_end_or_aggreg.py 484 469 3% 17, 19, 21, 24, 27, 34-274, 288-767, 908-918 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_initialisation.py 372 364 2% 15-244, 249-267, 271-372, 376-558 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_post_processing.py 1061 868 18% 28-121, 138, 141-142, 149-153, 161, 163, 179, 187-189, 197-198, 209-215, 226-293, 310-739, 744-867, 874-924, 932-1006, 1011-1081, 1099-1269, 2533, 2536-2538, 2541-2542, 2545-2552, 2562, 2569-2581, 2584, 2588-2592, 2599-2606, 2621-2638, 2655-2659, 2667-2669, 2709-2735, 2754-2767, 2770-2783, 2789-2791, 2798-2808, 2816, 2832, 2848, 2859 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py 1402 1368 2% 34-89, 92-196, 200-500, 506, 510-696, 703-838, 843-885, 889-968, 975-1356, 1362-1459, 1463-1503, 1508-1551, 1555-1630, 1634-1715, 1719-1733, 1739-1892, 1895-1898, 1905-1986, 1989-2177 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py 1963 1930 2% 35-308, 313-424, 427-556, 560-628, 632-779, 783-1028, 1032-1077, 1081-1187, 1196-1272, 1279-1466, 1470-1499, 1503-1579, 1586-1674, 1678-1855, 1859-2027, 2031-2078, 2088-2369, 2373-2418, 2422-2481, 2485-2511, 2515-2619, 2626-2814, 2995-3193, 3452-3526, 3530-3569 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_send_or_copy.py 554 540 3% 19-195, 200-268, 273-332, 336-379, 383-488, 493-623, 628-790, 795-841 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_sort.py 193 188 3% 12-115, 119-171, 178-183, 189-287, 291-305 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_util.py 298 281 6% 6-55, 62-134, 143-155, 162-209, 213-219, 224-231, 236-315, 319-324, 327-333, 337-398, 405, 409-411, 423-467 /home/admin/workarea/git/Velours/python/mtr/datou/merge_rubbia.py 50 46 8% 12-36, 40-86 /home/admin/workarea/git/Velours/python/mtr/lib/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/fotonower_connect.py 322 307 5% 23-46, 52-92, 96-119, 123-184, 187-213, 218-335, 338-384, 389-412, 415-433, 436-461 /home/admin/workarea/git/Velours/python/mtr/mem_info.py 76 30 61% 33-34, 41, 49, 59-63, 72, 95-124 /home/admin/workarea/git/Velours/python/mtr/monitor_sys.py 131 88 33% 40, 44, 47-50, 52, 54, 59, 61, 65-68, 91-134, 137-150, 162, 164-167, 170-194 /home/admin/workarea/git/Velours/python/mtr/ses_mailer.py 55 44 20% 16, 20-44, 47-85 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/image_utils.py 328 255 22% 21-28, 37-52, 72-85, 88, 91-113, 118-122, 129, 138-162, 166, 174, 181-191, 194-236, 239, 242-253, 256, 259, 262, 265-298, 301-314, 343, 346-354, 363-365, 368-381, 385-397, 401-441, 446-465, 470-473, 476-484 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/simple_image_editor.py 2091 1975 6% 24-25, 43-51, 60-81, 86-126, 131-134, 140-324, 329-332, 335-359, 365-387, 391-422, 429-446, 451-469, 475-485, 492-598, 605-613, 619-793, 798-815, 821-853, 859-907, 910-911, 916-936, 942-972, 979-1100, 1109-1145, 1151-1183, 1189-1227, 1232-1251, 1259-1567, 1575-1639, 1643-1654, 1660-1683, 1690-1756, 1762-1828, 1832-1907, 1913-1990, 1993-2006, 2014-2389, 2395-2421, 2431-2465, 2479-2741, 2752-2799, 2804-2840, 2846-2881, 2894, 2900, 2906, 2912, 2918, 2924, 2929-2966, 2973-3014, 3020-3044, 3052-3129, 3140-3156, 3164-3189, 3200-3304, 3336-3465, 3508-3540, 3562, 3579-3590, 3594-3600, 3603-3682, 3685-3688, 3691-3723, 3726-3752, 3757-3819, 3825-3877 /home/admin/workarea/git/Velours/python/mtr/utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cd.py 11 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cdn/swift_upload_manager.py 151 131 13% 15-29, 32-48, 51-54, 57-79, 92-99, 103-111, 118-127, 130, 133-142, 145-156, 160-174, 180-184, 187-193, 197-210, 213, 216-217, 223-239 /home/admin/workarea/git/Velours/python/mtr/utils/general_util.py 57 32 44% 11-12, 20-27, 30, 33-57, 61-63, 69-70, 75, 86-90 /home/admin/workarea/git/Velours/python/mtr/utils/upload_batch.py 58 53 9% 21-95 /home/admin/workarea/git/Velours/python/mtr/utils/utils_timer.py 11 8 27% 12-20 /home/admin/workarea/git/Velours/python/tests/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/tests/cod_main_test.py 75 67 11% 8-59, 69-124, 128, 131, 134, 138 /home/admin/workarea/git/Velours/python/tests/datou_test.py 1923 1825 5% 24-72, 83-130, 140-232, 236-277, 280-366, 378-416, 428-472, 482-518, 523-588, 594-681, 692-752, 762-837, 851-889, 900-938, 949-1000, 1011-1073, 1084-1176, 1269-1339, 1350-1811, 1822-1879, 1889-1949, 1959-2067, 2081-2133, 2144-2215, 2230-2332, 2343-2401, 2411-2461, 2472-2558, 2563-2681, 2693-2748, 2759-2809, 2819-2869, 2881-2935, 2960, 2966-2968, 2981-2983, 2985-2987, 2996, 3010-3056, 3066-3121, 3131-3186, 3196-3217, 3227-3270, 3280-3328, 3338-3371, 3381-3436, 3446-3492, 3503-3542, 3553-3592, 3674-3676, 3682-3684, 3688, 3700-3701, 3704 /home/admin/workarea/git/Velours/python/tests/python_tests.py 221 61 72% 41, 94-95, 99, 102, 105, 107, 112, 122, 124, 128, 130, 132, 134, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 172, 188, 195-202, 208, 222-225, 245, 248-254, 259, 271-272, 275-278, 288-289, 370, 377 /usr/lib/python3/dist-packages/babel/__init__.py 3 0 100% /usr/lib/python3/dist-packages/babel/_compat.py 51 26 49% 34-56, 63-67, 77-80 /usr/lib/python3/dist-packages/babel/core.py 329 213 35% 27, 70-77, 102-105, 157-168, 192-193, 216-219, 261-331, 334-337, 343, 346, 349-355, 358, 363-365, 378-393, 418-421, 432-435, 446-449, 468, 482, 494, 506, 515, 531, 542, 554, 566, 580, 592, 604, 615-618, 626, 632, 641, 650, 659, 673, 687, 704, 720, 731, 740, 749, 759, 773, 787, 801, 814, 836, 851, 867, 884, 896, 907, 918, 932, 962-977, 1026-1040, 1083-1115, 1131-1133 /usr/lib/python3/dist-packages/babel/localedata.py 112 86 23% 34-39, 49-55, 66, 96-123, 138-156, 167, 170, 181-189, 198-201, 204, 207, 210-221, 224, 227, 230 /usr/lib/python3/dist-packages/babel/plural.py 280 181 35% 43-73, 112-119, 122-123, 137-139, 149-150, 158, 161, 164-166, 184-189, 211-229, 242-252, 272, 292, 306-315, 334-349, 353, 358-359, 363, 367, 371, 375, 413-421, 425-432, 435-438, 441-444, 447-461, 464-471, 474-478, 481-484, 487-495, 498, 520-521, 538, 550-553, 567-583, 598-603, 620, 623-629 /usr/lib/python3/dist-packages/certifi/__init__.py 2 0 100% /usr/lib/python3/dist-packages/certifi/core.py 5 0 100% /usr/lib/python3/dist-packages/chardet/__init__.py 11 7 36% 31-39 /usr/lib/python3/dist-packages/chardet/big5freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/big5prober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/chardistribution.py 117 83 29% 49-59, 65-68, 72-82, 88-98, 103, 110, 115-118, 125-129, 134-137, 144-148, 153-156, 163-167, 172-175, 182-189, 194-197, 204-214, 219-222, 229-233 /usr/lib/python3/dist-packages/chardet/charsetgroupprober.py 72 61 15% 34-37, 40-47, 51-55, 59-63, 66-83, 86-106 /usr/lib/python3/dist-packages/chardet/charsetprober.py 55 36 35% 40-42, 45, 49, 52, 56, 59, 63-64, 81-101, 115-145 /usr/lib/python3/dist-packages/chardet/codingstatemachine.py 28 18 36% 56-61, 64, 69-78, 81, 84, 88 /usr/lib/python3/dist-packages/chardet/compat.py 10 4 60% 26-29 /usr/lib/python3/dist-packages/chardet/cp949prober.py 16 6 62% 36-41, 45, 49 /usr/lib/python3/dist-packages/chardet/enums.py 35 1 97% 62 /usr/lib/python3/dist-packages/chardet/escprober.py 58 45 22% 43-56, 59-67, 71, 75, 78-81, 84-101 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85-87, 90-91, 99, 108, 119, 133-140, 151, 157, 165, 168, 196-198 /usr/lib/python3/dist-packages/keyring/backends/OS_X.py 46 25 46% 30, 33-43, 47-58, 62-69 /usr/lib/python3/dist-packages/keyring/backends/SecretService.py 62 40 35% 10, 13-15, 31-39, 46-59, 64-72, 77-84, 89-94 /usr/lib/python3/dist-packages/keyring/backends/Windows.py 95 59 38% 16-18, 22-24, 56, 60, 64-71, 74-84, 87-94, 97-103, 106-114, 117-126, 129-138, 151, 155, 159, 163-165 /usr/lib/python3/dist-packages/keyring/backends/_OS_X_API.py 151 136 10% 24-345 /usr/lib/python3/dist-packages/keyring/backends/__init__.py 0 0 100% /usr/lib/python3/dist-packages/keyring/backends/chainer.py 36 18 50% 47-50, 53-57, 60-64, 67-70 /usr/lib/python3/dist-packages/keyring/backends/fail.py 8 2 75% 19-25 /usr/lib/python3/dist-packages/keyring/backends/kwallet.py 95 55 42% 14-18, 35, 38-39, 46-48, 51-52, 55-75, 78-95, 100-107, 112-115, 121-126 /usr/lib/python3/dist-packages/keyring/core.py 80 42 48% 27, 34, 41-51, 57, 63, 69, 75, 79, 124-127, 135-138, 159-176, 181-185 /usr/lib/python3/dist-packages/keyring/credentials.py 37 14 62% 16, 20, 28-29, 33, 37, 46-47, 52-55, 59, 63 /usr/lib/python3/dist-packages/keyring/errors.py 26 1 96% 60 /usr/lib/python3/dist-packages/keyring/py27compat.py 35 6 83% 7-8, 17, 42, 49-50 /usr/lib/python3/dist-packages/keyring/py32compat.py 5 2 60% 3-4 /usr/lib/python3/dist-packages/keyring/py33compat.py 10 4 60% 28-31 /usr/lib/python3/dist-packages/keyring/util/__init__.py 13 2 85% 34-35 /usr/lib/python3/dist-packages/keyring/util/platform_.py 31 7 77% 8, 12, 16-18, 47-50 /usr/lib/python3/dist-packages/keyring/util/properties.py 14 6 57% 51-53, 56-58 /usr/lib/python3/dist-packages/keystoneauth1/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneauth1/_fair_semaphore.py 43 33 23% 35-42, 47-54, 58-60, 63-77, 87-88, 93-104 /usr/lib/python3/dist-packages/keystoneauth1/_utils.py 33 18 45% 30-33, 38-43, 57-59, 73-75, 82-83 /usr/lib/python3/dist-packages/keystoneauth1/adapter.py 167 135 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/usr/lib/python3/dist-packages/keystoneauth1/exceptions/connection.py 16 2 88% 50-51 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/discovery.py 16 1 94% 39 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/http.py 147 55 63% 72-83, 254-259, 394-460 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/oidc.py 14 0 100% /usr/lib/python3/dist-packages/keystoneauth1/exceptions/response.py 7 2 71% 24-25 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/service_providers.py 7 3 57% 22-24 /usr/lib/python3/dist-packages/keystoneauth1/plugin.py 48 29 40% 33, 62, 95-100, 124-131, 149-154, 176-180, 193, 209, 224, 239, 254, 268, 287, 304, 315 /usr/lib/python3/dist-packages/keystoneauth1/session.py 539 443 18% 36-37, 65-70, 81-86, 91-106, 114, 118, 124-131, 138-139, 142-153, 162-195, 208-222, 238-240, 247-248, 251-254, 257-260, 352-388, 392-399, 403, 407, 410, 413-429, 434-441, 452-459, 464-517, 522-580, 593-623, 751-983, 1001-1107, 1115, 1123, 1131, 1139, 1147, 1155, 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125, 134-180, 183-186, 190-192 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/password.py 33 19 42% 23, 46-52, 55-67, 77-79, 83-86 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/token.py 21 10 52% 22, 34-35, 38-42, 46-48 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v2.py 108 62 43% 41-49, 56-61, 66, 71, 74-94, 131-144, 149, 154, 159, 164, 167-174, 178-181, 186-197, 213-214, 219, 224, 227-229, 233-239 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/__init__.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/base.py 107 68 36% 59-68, 73, 78, 83, 91-105, 130-132, 135-197, 217-222, 227, 261-263 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/federated.py 35 17 51% 46-48, 52-65, 70-79, 82, 106-116 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/password.py 29 16 45% 39-51, 78-89, 93-96 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/v3/token.py 17 6 65% 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/usr/lib/python3/dist-packages/keystoneclient/v3/access_rules.py 29 14 52% 58-61, 73-76, 87-90, 104-107, 111, 116 /usr/lib/python3/dist-packages/keystoneclient/v3/application_credentials.py 49 32 35% 72-98, 121-124, 136-139, 150-153, 166-169, 173 /usr/lib/python3/dist-packages/keystoneclient/v3/auth.py 22 10 55% 42-48, 60-66 /usr/lib/python3/dist-packages/keystoneclient/v3/client.py 104 64 38% 218-267, 270, 277-286, 313-352 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/endpoint_filter.py 82 62 24% 34-47, 50-64, 68-73, 77-82, 86-91, 95-99, 106-110, 117-122, 126-131, 135-140, 144-149, 156-160 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/endpoint_policy.py 59 39 34% 28-38, 42, 47, 52, 56-66, 70, 75, 80, 85-97, 102, 108, 114, 125-134, 146-153 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/base.py 19 8 58% 30, 33-40 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/core.py 16 7 56% 24-31 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/domains.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/identity_providers.py 22 8 64% 36-38, 54, 69, 82, 96, 109 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/mappings.py 22 8 64% 36-38, 76, 89, 99, 136, 149 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/projects.py 5 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/protocols.py 31 16 48% 38-49, 52-54, 72, 90, 105, 123, 141 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/saml.py 16 9 44% 37-40, 56-59, 62-79 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/federation/service_providers.py 22 8 64% 38-40, 52, 65, 75, 89, 102 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/__init__.py 1 0 100% /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/access_tokens.py 20 9 55% 23-24, 38-51 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/consumers.py 17 4 76% 38, 43, 47, 53 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/core.py 20 11 45% 22-31, 36-38, 62-65 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/request_tokens.py 33 20 39% 24-25, 30-36, 54-57, 60-73 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/oauth1/utils.py 14 10 29% 28-38 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/simple_cert.py 11 6 45% 21-22, 31-33, 42-44 /usr/lib/python3/dist-packages/keystoneclient/v3/contrib/trusts.py 33 17 48% 59-74, 85, 90-92, 98, 102 /usr/lib/python3/dist-packages/keystoneclient/v3/credentials.py 17 5 71% 62, 80, 93, 119, 138 /usr/lib/python3/dist-packages/keystoneclient/v3/domain_configs.py 28 13 54% 37, 63-65, 78-79, 105-107, 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293-294, 297, 300-301, 308, 312 /usr/lib/python3/dist-packages/netaddr/ip/nmap.py 64 55 14% 22-45, 51-62, 69-87, 96-101, 115-117 /usr/lib/python3/dist-packages/netaddr/ip/rfc1924.py 28 18 36% 32-42, 49-61 /usr/lib/python3/dist-packages/netaddr/ip/sets.py 350 300 14% 27-53, 65-81, 105-122, 126, 133, 145-210, 216-217, 226, 238-245, 249, 257, 263, 281-296, 317-350, 361, 371-372, 376-378, 392-413, 417, 426-429, 438-441, 450-453, 462-465, 476-479, 488-494, 505-507, 518-551, 566-619, 631-673, 683-688, 696, 700, 711-718, 729-735, 744-748 /usr/lib/python3/dist-packages/netaddr/strategy/__init__.py 113 90 20% 44-56, 70-83, 97-106, 121-138, 154-160, 177-194, 207-226, 238-257, 270-273 /usr/lib/python3/dist-packages/netaddr/strategy/eui48.py 135 70 48% 144-152, 163-197, 209-216, 226, 237-245, 249-251, 255-257, 261-263, 267-269, 273-275, 279-281, 286-288, 292, 296 /usr/lib/python3/dist-packages/netaddr/strategy/eui64.py 122 66 46% 121-124, 133-139, 149-176, 187-192, 202-203, 214-222, 226-228, 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/usr/lib/python3/dist-packages/rfc3986/abnf_regexp.py 63 0 100% /usr/lib/python3/dist-packages/rfc3986/api.py 15 6 60% 38, 52, 77, 92-93, 106 /usr/lib/python3/dist-packages/rfc3986/compat.py 23 10 57% 20-21, 25-26, 45-47, 52-54 /usr/lib/python3/dist-packages/rfc3986/exceptions.py 45 24 47% 18, 28, 37, 52-59, 71-81, 89-95, 103-110 /usr/lib/python3/dist-packages/rfc3986/iri.py 50 34 32% 49-57, 61-73, 76, 86-89, 111-142 /usr/lib/python3/dist-packages/rfc3986/misc.py 31 5 84% 116-121 /usr/lib/python3/dist-packages/rfc3986/normalizers.py 79 63 20% 24, 29-37, 42, 47, 52-67, 72-76, 81-83, 88-90, 101-105, 114-139, 144-167 /usr/lib/python3/dist-packages/rfc3986/parseresult.py 166 124 25% 34-46, 50, 55, 60, 65, 81-92, 98-112, 134-139, 152, 159-172, 176-181, 193-198, 208-220, 227-244, 267-273, 287, 294-314, 327-337, 342-364, 368-385 /usr/lib/python3/dist-packages/rfc3986/uri.py 30 17 43% 88-96, 102-115, 128, 144-147 /usr/lib/python3/dist-packages/rfc3986/validators.py 129 99 23% 60-73, 87-89, 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/usr/lib/python3/dist-packages/stevedore/exception.py 3 0 100% /usr/lib/python3/dist-packages/stevedore/extension.py 104 71 32% 46-49, 58, 99-107, 141-146, 150-152, 155-156, 160-165, 176-179, 183, 187-214, 220-230, 237, 259-265, 269, 290, 294-301, 309, 317, 326, 331 /usr/lib/python3/dist-packages/stevedore/hook.py 11 6 45% 59, 74-78, 87-89 /usr/lib/python3/dist-packages/stevedore/named.py 34 24 29% 74-89, 123-129, 134-140, 143-146, 154-156 /usr/lib/python3/dist-packages/swiftclient/__init__.py 7 2 71% 31-32 /usr/lib/python3/dist-packages/swiftclient/client.py 959 847 12% 53-63, 71-72, 75-76, 86-89, 128-135, 146-156, 160-190, 194-222, 230-232, 236-245, 250-260, 275-276, 279, 282, 285-288, 291, 294, 320-328, 331-368, 401-441, 445-450, 454, 458-472, 481, 485-521, 524-526, 531-532, 536-567, 573-574, 584-661, 683-745, 749-753, 765-768, 794-839, 857-875, 896-921, 952-1008, 1027-1048, 1068-1092, 1111-1133, 1155-1180, 1210-1242, 1262-1285, 1329-1400, 1420-1439, 1465-1503, 1529-1557, 1568-1578, 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334-354 /usr/lib/python3/dist-packages/urllib3/connection.py 173 116 33% 17-21, 27-30, 105-115, 134, 144, 151-175, 178-184, 187-188, 192-199, 206-234, 256-266, 297-310, 314-402, 409-420, 428 /usr/lib/python3/dist-packages/urllib3/connectionpool.py 318 257 19% 75-80, 83, 86, 89-91, 97, 182-215, 221-236, 250-275, 291-303, 309, 313, 317-325, 330-348, 369-451, 454, 460-472, 479-493, 601-854, 904-927, 935-947, 954-955, 961-991, 997-1004, 1035-1040, 1048-1058 /usr/lib/python3/dist-packages/urllib3/contrib/__init__.py 0 0 100% /usr/lib/python3/dist-packages/urllib3/contrib/_appengine_environ.py 11 1 91% 36 /usr/lib/python3/dist-packages/urllib3/contrib/socks.py 75 66 12% 55-210 /usr/lib/python3/dist-packages/urllib3/exceptions.py 96 21 78% 21-22, 26, 33-34, 38, 79-83, 90-92, 147-150, 222, 225, 241-242, 249-250 /usr/lib/python3/dist-packages/urllib3/fields.py 90 70 22% 18-20, 38-61, 82-91, 113-118, 150-156, 176-192, 205, 218-227, 233-246, 263-273 /usr/lib/python3/dist-packages/urllib3/filepost.py 43 30 30% 19-22, 33-42, 57-60, 74-98 /usr/lib/python3/dist-packages/urllib3/packages/__init__.py 8 2 75% 10-11 /usr/lib/python3/dist-packages/urllib3/packages/ssl_match_hostname/__init__.py 11 6 45% 7, 10-16 /usr/lib/python3/dist-packages/urllib3/poolmanager.py 172 132 23% 89-114, 160-167, 170, 173-175, 187-202, 211, 224-234, 243-247, 257-271, 284-285, 297-307, 318-372, 411-431, 434-439, 448-456, 460-469, 473 /usr/lib/python3/dist-packages/urllib3/request.py 39 28 28% 42, 54, 70-79, 88-97, 144-171 /usr/lib/python3/dist-packages/urllib3/response.py 399 322 19% 34-36, 39, 42-61, 73-74, 77, 80-98, 103-118, 131, 134, 137-139, 143-152, 190, 214-258, 268-271, 274-278, 283-287, 291, 294, 302, 308-354, 362-373, 377, 383-399, 406-410, 421-467, 490-541, 559-567, 578-599, 603, 606, 610, 614-621, 625-634, 637-642, 648-653, 657, 661-666, 675, 680-689, 692-711, 727-781, 789-792, 795-809 /usr/lib/python3/dist-packages/urllib3/util/__init__.py 10 0 100% /usr/lib/python3/dist-packages/urllib3/util/connection.py 66 45 32% 17-26, 51-86, 90-94, 102-105, 118, 130-131 /usr/lib/python3/dist-packages/urllib3/util/queue.py 14 5 64% 7, 12, 15, 18, 21 /usr/lib/python3/dist-packages/urllib3/util/request.py 50 25 50% 13, 63, 65, 71, 74, 77, 80, 85, 95-105, 119-133 /usr/lib/python3/dist-packages/urllib3/util/response.py 35 29 17% 15-35, 54-71, 83-86 /usr/lib/python3/dist-packages/urllib3/util/retry.py 150 102 32% 186-187, 202-218, 223-232, 240-249, 253-265, 270-275, 278-283, 286-289, 300-305, 311, 317, 323-326, 335-341, 350-355, 376-442, 445 /usr/lib/python3/dist-packages/urllib3/util/ssl_.py 148 112 24% 31-34, 43-44, 50-56, 61-63, 104-149, 162-174, 192-201, 208-217, 256-293, 327-383, 393-396, 401-407 /usr/lib/python3/dist-packages/urllib3/util/timeout.py 63 42 33% 96-99, 102, 120-153, 169, 183, 191-194, 204-208, 220-226, 245-258 /usr/lib/python3/dist-packages/urllib3/util/url.py 205 152 26% 101-105, 112, 117-122, 127-129, 150-169, 172, 193-207, 214-241, 246-271, 275-299, 303-317, 322-327, 352-416, 431-432 /usr/lib/python3/dist-packages/urllib3/util/wait.py 76 58 24% 8-9, 43-68, 72-87, 91-107, 111, 118-124, 133-139, 146, 153 /usr/local/lib/python3.8/dist-packages/MySQLdb/__init__.py 46 13 72% 21, 36-37, 44-46, 63, 66, 69, 72, 75-76, 79 /usr/local/lib/python3.8/dist-packages/MySQLdb/_exceptions.py 12 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/compat.py 12 5 58% 4-8 /usr/local/lib/python3.8/dist-packages/MySQLdb/connections.py 146 34 77% 42, 138, 140, 143, 150, 161, 197, 204, 245, 249, 260, 262, 265, 269, 282, 286-293, 304-308, 314-317, 324-328 /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/CLIENT.py 18 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FIELD_TYPE.py 29 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FLAG.py 16 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/converters.py 35 13 63% 47-48, 52, 56, 63-68, 79, 82, 85 /usr/local/lib/python3.8/dist-packages/MySQLdb/cursors.py 261 96 63% 83-90, 93, 96-97, 107, 109, 114-120, 127, 135, 142-144, 160, 171, 187, 194-206, 227, 239-240, 259-260, 296-310, 328, 358-363, 368-372, 378, 391-400, 403-405, 417, 421-426, 431-434, 438-441, 444, 447-450 /usr/local/lib/python3.8/dist-packages/MySQLdb/release.py 3 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/times.py 76 49 36% 21, 25, 29, 34-37, 43-47, 53, 60-64, 75-76, 79-99, 102-113, 116-123, 127, 131 /usr/local/lib/python3.8/dist-packages/cycler.py 177 107 40% 73, 110, 118, 122, 127, 131, 157-178, 185-189, 218-223, 229, 240-243, 255-262, 265, 268-273, 284-292, 303-311, 317-322, 325-333, 337-347, 396-397, 425, 454-465, 509, 513-516, 519-526, 548-556 /usr/local/lib/python3.8/dist-packages/defusedxml/ElementTree.py 64 25 61% 21-23, 52, 79-105, 108, 113, 120 /usr/local/lib/python3.8/dist-packages/defusedxml/__init__.py 21 15 29% 25-51 /usr/local/lib/python3.8/dist-packages/defusedxml/common.py 65 42 35% 23, 31-34, 37-38, 46-52, 55-56, 64-68, 71-72, 81-90, 98-105, 115-122, 125-132 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1950: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1957: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2141: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2148: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /usr/local/lib/python3.8/dist-packages/pyparsing.py 3062 1772 42% 122-123, 127-128, 134-137, 141-145, 149-150, 191-194, 233-266, 274-278, 295-296, 307-308, 321, 329-336, 339-346, 349, 354-359, 361, 410-453, 487, 490, 498, 500, 563, 567, 576, 580, 588-591, 606-611, 616-634, 650, 653-656, 659, 662, 675-694, 738-754, 794-798, 813, 829-832, 838-839, 844-845, 848-850, 869-874, 877, 880, 883-891, 908, 930-944, 961-1016, 1019-1023, 1050-1063, 1086-1128, 1155, 1159, 1166-1173, 1176, 1179, 1188-1207, 1222-1223, 1235, 1240-1245, 1248, 1251, 1254, 1258, 1297-1302, 1319-1337, 1344-1345, 1370, 1392, 1396-1398, 1464, 1504-1516, 1559, 1562, 1612-1613, 1616-1626, 1630, 1636, 1641, 1653-1674, 1694-1712, 1717-1720, 1729-1730, 1735-1738, 1741-1746, 1750-1768, 1783-1786, 1792, 1800-1826, 1853-1856, 1896, 1939, 1945-1955, 1990-2031, 2053-2079, 2102-2111, 2129-2136, 2166, 2171-2173, 2181, 2186-2188, 2195-2201, 2207-2213, 2236, 2238, 2246, 2248-2253, 2258-2260, 2263, 2265, 2267, 2276-2279, 2284, 2290, 2297, 2300, 2302-2304, 2311-2317, 2323-2329, 2335-2341, 2347-2353, 2359-2365, 2376, 2398-2412, 2465-2466, 2482-2490, 2496-2500, 2539-2543, 2549, 2557, 2563, 2571-2585, 2589, 2591, 2593, 2597, 2603, 2606, 2622-2626, 2724-2788, 2795-2799, 2802-2815, 2818, 2821, 2846-2850, 2853, 2876-2879, 2891-2893, 2932-2950, 2953-2969, 2972-2974, 2980, 2994-2998, 3001-3003, 3016, 3052-3058, 3061-3084, 3146, 3159, 3164, 3181, 3187, 3191-3192, 3208, 3213-3218, 3223, 3248-3253, 3262-3267, 3304, 3313-3324, 3335, 3337, 3348-3349, 3353-3359, 3362-3368, 3392-3408, 3451-3512, 3515-3547, 3550-3558, 3587, 3593, 3610-3620, 3630, 3682, 3687-3688, 3691-3703, 3718-3719, 3722-3728, 3731-3736, 3766-3768, 3780-3788, 3799-3803, 3814, 3817-3820, 3832-3834, 3837-3841, 3852-3855, 3858-3863, 3873, 3876, 3878, 3883, 3886-3889, 3893-3895, 3907-3916, 3919-3926, 3963-3966, 3975-3977, 4007-4009, 4014-4024, 4036-4043, 4056-4057, 4059-4067, 4075-4077, 4080-4084, 4088, 4114-4118, 4121-4124, 4127-4184, 4188-4190, 4193-4199, 4202-4204, 4207-4216, 4241, 4260-4263, 4271, 4274-4276, 4280, 4288-4290, 4293-4302, 4363-4367, 4370-4372, 4375-4421, 4424-4430, 4433-4435, 4448, 4464, 4474-4483, 4492-4496, 4499-4504, 4540-4541, 4546-4549, 4581-4601, 4604-4624, 4658-4660, 4664, 4677, 4682, 4691, 4696, 4702, 4704, 4716-4726, 4757, 4787, 4797, 4800, 4852-4856, 4863, 4936, 4942-4986, 5016, 5019-5029, 5032, 5035-5036, 5039-5043, 5046-5053, 5056-5073, 5076-5081, 5084-5092, 5131-5135, 5138-5145, 5213-5234, 5263, 5270-5271, 5273-5277, 5279, 5306-5324, 5346, 5373-5384, 5387-5393, 5410-5423, 5440-5452, 5494-5547, 5586, 5617-5628, 5634, 5661-5662, 5708-5709, 5715-5718, 5733, 5748, 5787, 5792-5793, 5811-5812, 5818-5819, 5873, 5931-5943, 5981-5982, 6060-6115, 6192-6229, 6312-6355, 6365, 6622-6627, 6647-6652, 6680, 6705-6713, 6734-6740, 6745, 6750, 6755, 6760, 6886, 6889-6902, 6906-6921, 6924, 6927, 6940-6943, 6952-6955, 6964-6967, 6981-7028, 7034-7035, 7040-7105 /usr/local/lib/python3.8/dist-packages/wrapt/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/wrapt/decorators.py 186 91 51% 11-23, 40-41, 55-56, 60, 64, 68, 72, 76, 86, 91, 95, 99-102, 105-106, 112, 117-120, 123, 138, 142, 146, 149-150, 154, 158, 162-163, 165, 205, 208-212, 253-279, 292-294, 322, 343-390, 411, 444-445, 450-451, 454, 464-514 /usr/local/lib/python3.8/dist-packages/wrapt/importer.py 102 75 26% 12, 37-45, 52-98, 103-109, 112-119, 128-135, 145-148, 153, 156-159, 164, 172-221, 227-230 /usr/local/lib/python3.8/dist-packages/wrapt/wrappers.py 472 304 36% 11, 32, 36, 40, 44, 51, 60, 78-87, 91, 95, 99, 103, 107, 111, 114, 117, 121, 124, 130, 134, 138, 141, 144, 147, 150, 153, 156, 159, 162, 165, 168-190, 196-199, 202-216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300, 303-304, 307-308, 311-312, 315-316, 319-320, 323-324, 327-328, 331-332, 335-336, 339-340, 343-344, 347-348, 351-352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 406, 409, 412, 415, 418, 421, 424, 427, 431, 437, 442-453, 456-461, 471-477, 505-533, 542-566, 578-624, 704-719, 727-728, 733-771, 774, 777-780, 791-794, 797-798, 801, 804, 807-811, 819-828, 831, 834-836, 839-858, 870-880, 899-928, 936-947 -------------------------------------------------------------------------------------------------------------------------------------- TOTAL 130913 96901 26% ret : 34304 command : coverage3 html -i --omit=/usr/local/lib/python3.8/dist-packages/*,/home/admin/.local/lib/python3.8/site-packages/*,/usr/lib/python3/dist-packages/* -d htmlcov ret : 0 command : coverage3 report -i -m ret : 0 59.17user 23.30system 1:38.95elapsed 83%CPU (0avgtext+0avgdata 2958336maxresident)k 2090960inputs+5608outputs (4425major+3121483minor)pagefaults 0swaps