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/28032025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_2/data_log/job/2025/March/28032025/coverage/htmlcov new path : /data_2/data_log/job/2025/March/28032025/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 : 10593 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 : 0.11563730239868164 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 Fri Mar 28 11:20:28 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 : 10593 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 2025-03-28 11:20:32.019770: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-28 11:20:32.047064: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-28 11:20:32.049113: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0e84000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-28 11:20:32.049183: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-28 11:20:32.053290: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-28 11:20:32.197565: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x23b45f50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-28 11:20:32.197610: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-28 11:20:32.199050: 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 2025-03-28 11:20:32.199447: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:20:32.202669: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:20:32.205760: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:20:32.206286: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:20:32.209212: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:20:32.210294: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:20:32.214804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:20:32.216271: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:20:32.216358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:20:32.217094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-28 11:20:32.217110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-28 11:20:32.217119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-28 11:20:32.218385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9671 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 2025-03-28 11:20:32.914738: 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 2025-03-28 11:20:32.914860: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:20:32.914908: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:20:32.914939: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:20:32.914965: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:20:32.914990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:20:32.915014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:20:32.915039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:20:32.916560: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:20:32.917807: 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 2025-03-28 11:20:32.917846: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:20:32.917863: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:20:32.917877: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:20:32.917892: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:20:32.917906: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:20:32.917920: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:20:32.917934: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:20:32.919223: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:20:32.919252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-28 11:20:32.919261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-28 11:20:32.919268: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-28 11:20:32.920585: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9671 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 : [] 2025-03-28 11:20:42.248921: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:20:42.426712: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (480, 640, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 1341828 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1176 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 : 6017 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 : 0.0005502700805664062 nb_pixel_total : 15553 time to create 1 rle with old method : 0.03906369209289551 length of segment : 256 time for calcul the mask position with numpy : 0.0028502941131591797 nb_pixel_total : 145329 time to create 1 rle with old method : 0.3364288806915283 length of segment : 371 time for calcul the mask position with numpy : 0.00025177001953125 nb_pixel_total : 14253 time to create 1 rle with old method : 0.032587528228759766 length of segment : 151 time for calcul the mask position with numpy : 0.00013375282287597656 nb_pixel_total : 5614 time to create 1 rle with old method : 0.013738870620727539 length of segment : 48 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 1824 time to create 1 rle with old method : 0.0046062469482421875 length of segment : 39 time spent for convertir_results : 1.3376281261444092 time spend for datou_step_exec : 21.543537378311157 time spend to save output : 4.458427429199219e-05 total time spend for step 1 : 21.54358196258545 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 3317 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 : 0.011913299560546875 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, 0.99548846, [(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, 137), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,149,56,138,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,219,24,232,24,270,23,273']), (957285035, 492601069, 445, 29, 591, 24, 419, 0.9923747, [(315, 37, 25), (272, 38, 86), (253, 39, 130), (238, 40, 151), (199, 41, 196), (189, 42, 213), (180, 43, 238), (175, 44, 250), (172, 45, 257), (169, 46, 265), (166, 47, 274), (162, 48, 284), (159, 49, 294), (157, 50, 304), (155, 51, 311), (153, 52, 317), (151, 53, 323), (149, 54, 330), (148, 55, 334), (146, 56, 337), (144, 57, 341), (142, 58, 344), (140, 59, 347), (138, 60, 350), (136, 61, 353), (134, 62, 356), (132, 63, 358), (130, 64, 361), (128, 65, 364), (126, 66, 367), (124, 67, 370), (122, 68, 373), (120, 69, 376), (118, 70, 379), (117, 71, 381), (115, 72, 385), (114, 73, 387), (113, 74, 389), (112, 75, 391), (112, 76, 393), (111, 77, 395), (110, 78, 397), (109, 79, 399), (109, 80, 400), (108, 81, 402), (107, 82, 404), (107, 83, 404), (106, 84, 406), (105, 85, 408), (105, 86, 409), (104, 87, 410), (104, 88, 411), (103, 89, 413), (102, 90, 415), (101, 91, 417), (100, 92, 420), (98, 93, 423), (97, 94, 426), (96, 95, 428), (94, 96, 431), (93, 97, 433), (92, 98, 435), (91, 99, 437), (90, 100, 439), (89, 101, 441), (89, 102, 441), (89, 103, 442), (89, 104, 443), (89, 105, 444), (89, 106, 444), (89, 107, 445), (89, 108, 446), (89, 109, 447), (89, 110, 448), (89, 111, 449), (89, 112, 450), (89, 113, 451), (89, 114, 453), (89, 115, 454), (89, 116, 455), (88, 117, 456), (88, 118, 457), (87, 119, 459), (87, 120, 459), (86, 121, 461), (85, 122, 462), (85, 123, 463), (84, 124, 464), (84, 125, 465), (83, 126, 466), (82, 127, 468), (82, 128, 468), (81, 129, 470), (80, 130, 471), (78, 131, 473), (76, 132, 476), (75, 133, 477), (73, 134, 480), (71, 135, 482), (70, 136, 484), (68, 137, 486), (67, 138, 488), (65, 139, 490), (64, 140, 492), (63, 141, 493), (61, 142, 496), (60, 143, 497), (59, 144, 499), (58, 145, 501), (58, 146, 501), (57, 147, 503), (57, 148, 504), (57, 149, 505), (56, 150, 507), (56, 151, 507), (55, 152, 509), (55, 153, 510), (54, 154, 511), (54, 155, 512), (54, 156, 513), (53, 157, 514), (53, 158, 514), (52, 159, 515), (52, 160, 516), (52, 161, 516), (51, 162, 517), (51, 163, 517), (50, 164, 518), (50, 165, 518), (49, 166, 519), (49, 167, 520), (48, 168, 521), (48, 169, 521), (47, 170, 522), (47, 171, 522), (46, 172, 523), (46, 173, 523), (46, 174, 523), (45, 175, 524), (45, 176, 523), (44, 177, 524), (44, 178, 524), (44, 179, 524), (43, 180, 525), (43, 181, 525), (42, 182, 525), (42, 183, 525), (42, 184, 525), (41, 185, 526), (41, 186, 526), (40, 187, 526), (39, 188, 526), (39, 189, 525), (38, 190, 526), (38, 191, 525), (37, 192, 525), (37, 193, 523), (36, 194, 523), (36, 195, 522), (36, 196, 522), (35, 197, 522), (35, 198, 521), (34, 199, 521), (34, 200, 521), (34, 201, 520), (34, 202, 520), (34, 203, 520), (34, 204, 519), (34, 205, 519), (33, 206, 520), (33, 207, 519), (33, 208, 519), (33, 209, 519), (33, 210, 518), (33, 211, 518), (33, 212, 518), (33, 213, 517), (32, 214, 518), (32, 215, 517), (32, 216, 517), (32, 217, 516), (32, 218, 515), (32, 219, 514), (32, 220, 513), (32, 221, 512), (32, 222, 511), (32, 223, 510), (32, 224, 508), (32, 225, 507), (32, 226, 505), (32, 227, 504), (32, 228, 503), (32, 229, 502), (32, 230, 502), (32, 231, 501), (32, 232, 500), (32, 233, 499), (32, 234, 498), (32, 235, 497), (31, 236, 496), (31, 237, 495), (31, 238, 494), (31, 239, 493), (31, 240, 491), (31, 241, 490), (31, 242, 488), (31, 243, 487), (31, 244, 486), (31, 245, 485), (31, 246, 483), (31, 247, 482), (31, 248, 480), (31, 249, 479), (31, 250, 477), (31, 251, 475), (31, 252, 473), (31, 253, 472), (31, 254, 470), (31, 255, 468), (31, 256, 467), (31, 257, 465), (31, 258, 464), (31, 259, 463), (31, 260, 462), (31, 261, 461), (31, 262, 459), (31, 263, 458), (31, 264, 456), (31, 265, 455), (31, 266, 453), (31, 267, 451), (31, 268, 449), (31, 269, 448), (31, 270, 447), (31, 271, 445), (31, 272, 444), (31, 273, 443), (32, 274, 441), (32, 275, 440), (32, 276, 438), (32, 277, 437), (32, 278, 435), (32, 279, 434), (32, 280, 432), (33, 281, 429), (33, 282, 427), (33, 283, 426), (33, 284, 424), (33, 285, 423), (34, 286, 421), (34, 287, 420), (34, 288, 419), (35, 289, 416), (35, 290, 415), (35, 291, 414), (36, 292, 411), (36, 293, 410), (37, 294, 407), (37, 295, 406), (38, 296, 403), (38, 297, 401), (39, 298, 399), (39, 299, 397), (41, 300, 394), (42, 301, 392), (43, 302, 389), (44, 303, 387), (45, 304, 385), (46, 305, 382), (47, 306, 380), (47, 307, 378), (48, 308, 376), (49, 309, 373), (50, 310, 370), (51, 311, 368), (51, 312, 367), (52, 313, 365), (54, 314, 362), (55, 315, 360), (56, 316, 359), (58, 317, 356), (61, 318, 352), (64, 319, 349), (67, 320, 345), (70, 321, 341), (73, 322, 338), (75, 323, 335), (78, 324, 332), (80, 325, 329), (82, 326, 327), (84, 327, 324), (86, 328, 322), (88, 329, 320), (90, 330, 317), (93, 331, 314), (96, 332, 311), (99, 333, 307), (102, 334, 304), (105, 335, 300), (108, 336, 297), (111, 337, 294), (113, 338, 291), (115, 339, 289), (117, 340, 286), (119, 341, 283), (121, 342, 281), (123, 343, 278), (125, 344, 275), (127, 345, 272), (129, 346, 269), (132, 347, 266), (135, 348, 262), (138, 349, 258), (141, 350, 255), (143, 351, 252), (145, 352, 250), (147, 353, 247), (149, 354, 245), (151, 355, 242), (152, 356, 241), (154, 357, 239), (156, 358, 237), (159, 359, 233), (161, 360, 231), (163, 361, 229), (165, 362, 227), (167, 363, 224), (169, 364, 222), (170, 365, 221), (172, 366, 219), (173, 367, 218), (174, 368, 216), (175, 369, 215), (177, 370, 213), (178, 371, 212), (180, 372, 209), (183, 373, 206), (185, 374, 204), (188, 375, 200), (191, 376, 197), (194, 377, 193), (196, 378, 191), (199, 379, 188), (201, 380, 185), (203, 381, 183), (205, 382, 180), (207, 383, 178), (208, 384, 176), (210, 385, 174), (212, 386, 171), (213, 387, 169), (215, 388, 166), (218, 389, 162), (221, 390, 158), (225, 391, 153), (228, 392, 149), (232, 393, 144), (235, 394, 140), (238, 395, 136), (241, 396, 133), (245, 397, 128), (248, 398, 124), (252, 399, 119), (257, 400, 113), (263, 401, 105), (272, 402, 94), (283, 403, 82), (297, 404, 65), (306, 405, 53), (313, 406, 38), (321, 407, 23)], ['321,407,296,403,263,401,215,388,206,382,178,371,168,363,140,349,110,336,90,330,77,323,56,316,39,299,31,273,31,236,34,199,58,145,79,131,89,116,89,101,104,88,115,72,159,49,180,43,199,41,237,41,272,38,339,37,382,39,402,43,417,43,481,55,543,116,556,143,566,156,568,167,566,186,554,199,548,216,528,235,496,256,471,275,460,281,414,315,403,339,392,355,389,371,383,385,369,400,358,405']), (957285035, 492601069, 445, 485, 636, 23, 174, 0.9711558, [(540, 24, 21), (626, 24, 3), (531, 25, 49), (594, 25, 40), (527, 26, 107), (523, 27, 111), (520, 28, 114), (517, 29, 118), (516, 30, 119), (515, 31, 120), (513, 32, 122), (512, 33, 123), (510, 34, 125), (509, 35, 126), (507, 36, 128), (506, 37, 129), (504, 38, 131), (503, 39, 132), (501, 40, 134), (500, 41, 135), (499, 42, 136), (498, 43, 137), (497, 44, 138), (496, 45, 139), (496, 46, 139), (495, 47, 140), (495, 48, 140), (494, 49, 141), (493, 50, 142), (492, 51, 143), (491, 52, 144), (491, 53, 144), (490, 54, 145), (490, 55, 145), (490, 56, 145), (490, 57, 146), (490, 58, 146), (490, 59, 146), (491, 60, 145), (491, 61, 145), (491, 62, 145), (492, 63, 144), (493, 64, 143), (494, 65, 142), (495, 66, 141), (496, 67, 139), (497, 68, 138), (498, 69, 138), (499, 70, 137), (500, 71, 136), (501, 72, 135), (503, 73, 133), (503, 74, 133), (505, 75, 131), (506, 76, 130), (507, 77, 129), (508, 78, 128), (509, 79, 127), (510, 80, 126), (511, 81, 125), (512, 82, 124), (513, 83, 123), (514, 84, 122), (515, 85, 121), (516, 86, 120), (517, 87, 119), (518, 88, 118), (519, 89, 117), (521, 90, 115), (521, 91, 115), (522, 92, 114), (523, 93, 113), (524, 94, 112), (525, 95, 111), (526, 96, 110), (527, 97, 109), (529, 98, 107), (530, 99, 106), (532, 100, 104), (533, 101, 103), (534, 102, 102), (535, 103, 101), (536, 104, 100), (538, 105, 98), (540, 106, 96), (541, 107, 95), (543, 108, 93), (546, 109, 90), (548, 110, 88), (549, 111, 87), (551, 112, 84), (552, 113, 83), (553, 114, 82), (555, 115, 80), (556, 116, 79), (556, 117, 79), (557, 118, 78), (558, 119, 77), (559, 120, 76), (560, 121, 75), (560, 122, 75), (561, 123, 74), (561, 124, 74), (561, 125, 74), (562, 126, 73), (562, 127, 73), (563, 128, 72), (563, 129, 72), (564, 130, 70), (564, 131, 70), (565, 132, 69), (565, 133, 68), (565, 134, 68), (565, 135, 67), (566, 136, 65), (566, 137, 64), (566, 138, 64), (566, 139, 62), (566, 140, 61), (566, 141, 59), (566, 142, 57), (566, 143, 56), (566, 144, 55), (566, 145, 54), (567, 146, 53), (567, 147, 52), (567, 148, 51), (568, 149, 50), (568, 150, 49), (568, 151, 48), (568, 152, 47), (569, 153, 45), (569, 154, 44), (570, 155, 42), (570, 156, 42), (570, 157, 41), (571, 158, 39), (571, 159, 39), (572, 160, 37), (572, 161, 37), (573, 162, 35), (573, 163, 34), (573, 164, 34), (574, 165, 32), (575, 166, 30), (577, 167, 28), (578, 168, 26), (581, 169, 22), (585, 170, 18), (587, 171, 15), (591, 172, 8)], ['598,172,591,172,584,169,578,168,573,164,573,162,568,152,568,149,566,145,566,136,565,132,561,125,560,121,556,116,547,109,543,108,536,104,531,99,527,97,491,62,490,54,495,48,496,45,501,40,514,32,517,29,531,25,539,25,540,24,560,24,561,25,579,25,580,26,593,26,594,25,633,25,634,29,634,56,635,57,635,111,634,112,634,129,632,134,629,138,623,141,619,145,617,149,611,155,608,161,604,166']), (957285035, 492601069, 445, 280, 481, 2, 55, 0.8297637, [(291, 3, 129), (284, 4, 146), (282, 5, 151), (281, 6, 154), (281, 7, 156), (281, 8, 157), (281, 9, 158), (281, 10, 160), (281, 11, 162), (281, 12, 165), (281, 13, 167), (281, 14, 169), (281, 15, 171), (281, 16, 173), (281, 17, 174), (281, 18, 175), (281, 19, 177), (281, 20, 178), (281, 21, 179), (281, 22, 180), (281, 23, 181), (281, 24, 182), (281, 25, 183), (281, 26, 184), (281, 27, 185), (281, 28, 185), (281, 29, 185), (282, 30, 185), (283, 31, 27), (337, 31, 131), (371, 32, 97), (401, 33, 68), (409, 34, 61), (419, 35, 52), (424, 36, 48), (429, 37, 44), (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)], ['472,49,468,49,467,48,459,47,458,46,454,46,451,44,448,44,447,43,444,43,440,41,438,41,428,36,424,36,423,35,419,35,418,34,409,34,408,33,401,33,400,32,371,32,370,31,337,31,336,30,283,31,281,29,281,6,284,4,290,4,291,3,419,3,420,4,429,4,430,5,432,5,436,7,441,11,445,12,453,16,456,19,457,19,465,27,465,29,472,37,476,44,476,46']), (957285035, 492601069, 445, 456, 547, 6, 45, 0.73998404, [(482, 8, 19), (464, 9, 3), (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)], ['492,42,488,42,487,41,480,41,476,37,475,34,473,32,469,25,465,21,461,20,457,16,457,10,463,10,464,9,466,9,470,12,474,13,476,11,480,10,482,8,500,8,501,9,524,9,525,10,528,10,532,12,539,12,542,15,545,15,545,19,535,20,534,21,529,21,525,23,523,23,513,30,512,30,504,37,496,41,493,41'])], 'temp/1743157228_1341668_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 6017 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### 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 : 0.1842479705810547 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 Fri Mar 28 11:20:52 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 : 6017 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-28 11:20:55.507481: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-28 11:20:55.535178: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-28 11:20:55.537354: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0e94000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-28 11:20:55.537407: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-28 11:20:55.541421: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-28 11:20:55.691455: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x23314370 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-28 11:20:55.691513: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-28 11:20:55.692744: 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 2025-03-28 11:20:55.693171: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:20:55.699777: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:20:55.702637: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:20:55.703236: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:20:55.706089: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:20:55.707400: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:20:55.713382: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:20:55.715135: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:20:55.715265: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:20:55.716242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-28 11:20:55.716269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-28 11:20:55.716284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-28 11:20:55.718203: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5145 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. 2025-03-28 11:20:55.837031: 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 2025-03-28 11:20:55.837157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:20:55.837176: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:20:55.837193: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:20:55.837209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:20:55.837226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:20:55.837241: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:20:55.837258: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:20:55.838177: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:20:55.839249: 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 2025-03-28 11:20:55.839301: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:20:55.839321: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:20:55.839341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:20:55.839367: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:20:55.839386: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:20:55.839405: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:20:55.839423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:20:55.840389: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:20:55.840425: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-28 11:20:55.840434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-28 11:20:55.840441: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-28 11:20:55.841393: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5145 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 : [] 2025-03-28 11:21:06.907618: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:21:07.116704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:21:08.553614: 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 2025-03-28 11:21:08.554150: 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 2025-03-28 11:21:08.554648: 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 2025-03-28 11:21:08.555145: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-28 11:21:08.555645: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-28 11:21:08.556141: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-28 11:21:08.556641: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2282521344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-03-28 11:21:08.556662: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.557151: 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 2025-03-28 11:21:08.557166: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.564207: 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 2025-03-28 11:21:08.564235: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.564746: 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 2025-03-28 11:21:08.564761: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.571326: 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 2025-03-28 11:21:08.571351: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.571858: 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 2025-03-28 11:21:08.571873: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.602723: 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 2025-03-28 11:21:08.602760: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.603277: 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 2025-03-28 11:21:08.603295: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.609062: 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 2025-03-28 11:21:08.609084: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.609623: 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 2025-03-28 11:21:08.609641: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-03-28 11:21:08.643687: 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 2025-03-28 11:21:08.644221: 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 2025-03-28 11:21:08.645990: 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 2025-03-28 11:21:08.646471: 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 2025-03-28 11:21:08.690273: 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 2025-03-28 11:21:08.690797: 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 2025-03-28 11:21:08.692954: 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 2025-03-28 11:21:08.693481: 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 2025-03-28 11:21:08.701405: 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 2025-03-28 11:21:08.702027: 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 2025-03-28 11:21:08.707130: 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 2025-03-28 11:21:08.707725: 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 2025-03-28 11:21:08.720506: 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 2025-03-28 11:21:08.721042: 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 2025-03-28 11:21:08.722757: 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 2025-03-28 11:21:08.723314: 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 2025-03-28 11:21:08.729781: 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 2025-03-28 11:21:08.730310: 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 2025-03-28 11:21:08.732143: 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 2025-03-28 11:21:08.732657: 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 2025-03-28 11:21:08.738999: 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 2025-03-28 11:21:08.739532: 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 2025-03-28 11:21:08.741043: 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 2025-03-28 11:21:08.741554: 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 2025-03-28 11:21:08.768218: 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 2025-03-28 11:21:08.768760: 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 2025-03-28 11:21:08.769269: 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 2025-03-28 11:21:08.769776: 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 2025-03-28 11:21:08.773284: 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 2025-03-28 11:21:08.773806: 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 2025-03-28 11:21:08.789220: 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 2025-03-28 11:21:08.789762: 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 2025-03-28 11:21:08.790271: 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 2025-03-28 11:21:08.790779: 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 2025-03-28 11:21:08.803129: 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 2025-03-28 11:21:08.803672: 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 2025-03-28 11:21:08.804180: 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 2025-03-28 11:21:08.804689: 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 2025-03-28 11:21:08.808925: 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 2025-03-28 11:21:08.809449: 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 2025-03-28 11:21:08.813920: 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 2025-03-28 11:21:08.814442: 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 2025-03-28 11:21:08.826539: 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 2025-03-28 11:21:08.827106: 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 2025-03-28 11:21:08.831210: 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 2025-03-28 11:21:08.831727: 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 2025-03-28 11:21:08.832235: 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 2025-03-28 11:21:08.832742: 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 2025-03-28 11:21:08.854521: 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 2025-03-28 11:21:08.855096: 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 2025-03-28 11:21:08.855659: 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 2025-03-28 11:21:08.856197: 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 2025-03-28 11:21:08.856752: 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 2025-03-28 11:21:08.857288: 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 2025-03-28 11:21:08.857825: 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 2025-03-28 11:21:08.858457: 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 2025-03-28 11:21:08.867939: 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 2025-03-28 11:21:08.868570: 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 2025-03-28 11:21:08.875410: 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 2025-03-28 11:21:08.875975: 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 2025-03-28 11:21:08.901576: 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 2025-03-28 11:21:08.901648: 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. 2025-03-28 11:21:08.902352: 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 2025-03-28 11:21:08.903088: 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 2025-03-28 11:21:08.910325: 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 2025-03-28 11:21:08.911025: 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 2025-03-28 11:21:08.911734: 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 2025-03-28 11:21:08.912421: 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 2025-03-28 11:21:08.920733: 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 2025-03-28 11:21:08.921307: 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 2025-03-28 11:21:08.937703: 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 2025-03-28 11:21:08.938454: 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 2025-03-28 11:21:08.939187: 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 2025-03-28 11:21:08.939850: 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 2025-03-28 11:21:08.943966: 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 2025-03-28 11:21:08.944494: 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 2025-03-28 11:21:08.944998: 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 2025-03-28 11:21:08.945497: 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 2025-03-28 11:21:08.946563: 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 2025-03-28 11:21:08.956677: 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 2025-03-28 11:21:08.957197: 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 2025-03-28 11:21:08.967783: 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 2025-03-28 11:21:08.968362: 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 2025-03-28 11:21:08.968931: 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 2025-03-28 11:21:08.969490: 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 2025-03-28 11:21:08.970020: 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 2025-03-28 11:21:08.970535: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 1344053 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 334 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 : 1527 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 : 0.0007488727569580078 nb_pixel_total : 16902 time to create 1 rle with old method : 0.04140448570251465 length of segment : 107 time for calcul the mask position with numpy : 0.016720294952392578 nb_pixel_total : 480742 time to create 1 rle with new method : 0.033323049545288086 length of segment : 632 time for calcul the mask position with numpy : 0.0004558563232421875 nb_pixel_total : 36642 time to create 1 rle with old method : 0.0832676887512207 length of segment : 133 time for calcul the mask position with numpy : 0.00012540817260742188 nb_pixel_total : 4793 time to create 1 rle with old method : 0.011312723159790039 length of segment : 51 time spent for convertir_results : 0.4550790786743164 time spend for datou_step_exec : 20.621204614639282 time spend to save output : 6.341934204101562e-05 total time spend for step 1 : 20.621268033981323 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 404 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 : 0.01168370246887207 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, 0.99883693, [(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, 0.99774593, [(711, 22, 21), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 234), (850, 24, 158), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (489, 47, 589), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (476, 56, 608), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 92, 658), (431, 93, 659), (430, 94, 660), (429, 95, 661), (428, 96, 662), (427, 97, 663), (425, 98, 665), (423, 99, 667), (421, 100, 669), (419, 101, 671), (417, 102, 673), (413, 103, 677), (410, 104, 680), (405, 105, 685), (401, 106, 689), (397, 107, 693), (392, 108, 698), (387, 109, 703), (382, 110, 708), (377, 111, 713), (373, 112, 717), (369, 113, 721), (365, 114, 725), (362, 115, 728), (358, 116, 732), (356, 117, 734), (353, 118, 737), (351, 119, 739), (349, 120, 741), (346, 121, 744), (344, 122, 746), (341, 123, 749), (338, 124, 752), (335, 125, 755), (331, 126, 759), (327, 127, 763), (323, 128, 767), (319, 129, 770), (314, 130, 775), (308, 131, 781), (303, 132, 786), (294, 133, 795), (287, 134, 802), (279, 135, 810), (273, 136, 816), (267, 137, 822), (262, 138, 827), (258, 139, 831), (255, 140, 834), (252, 141, 837), (250, 142, 839), (247, 143, 842), (245, 144, 844), (242, 145, 847), (240, 146, 849), (237, 147, 852), (234, 148, 855), (230, 149, 859), (226, 150, 863), (220, 151, 869), (213, 152, 876), (207, 153, 882), (200, 154, 889), (193, 155, 896), (187, 156, 902), (184, 157, 905), (181, 158, 908), (178, 159, 911), (176, 160, 913), (174, 161, 915), (172, 162, 917), (170, 163, 919), (168, 164, 921), (167, 165, 922), (165, 166, 924), (164, 167, 925), (162, 168, 927), (161, 169, 928), (159, 170, 930), (157, 171, 932), (155, 172, 934), (153, 173, 935), (151, 174, 937), (148, 175, 940), (146, 176, 942), (144, 177, 944), (142, 178, 946), (140, 179, 948), (139, 180, 949), (137, 181, 951), (136, 182, 952), (134, 183, 954), (133, 184, 955), (132, 185, 956), (131, 186, 957), (130, 187, 958), (129, 188, 959), (128, 189, 960), (127, 190, 960), (126, 191, 961), (126, 192, 961), (125, 193, 962), (124, 194, 963), (123, 195, 964), (122, 196, 965), (122, 197, 965), (121, 198, 966), (120, 199, 967), (119, 200, 968), (118, 201, 969), (117, 202, 970), (116, 203, 971), (114, 204, 973), (113, 205, 973), (112, 206, 974), (111, 207, 975), (109, 208, 977), (108, 209, 978), (107, 210, 979), (106, 211, 980), (105, 212, 981), (104, 213, 982), (103, 214, 983), (102, 215, 984), (101, 216, 985), (101, 217, 984), (100, 218, 985), (100, 219, 985), (99, 220, 986), (98, 221, 987), (98, 222, 987), (97, 223, 988), (97, 224, 987), (96, 225, 988), (96, 226, 988), (95, 227, 989), (95, 228, 989), (94, 229, 990), (94, 230, 990), (94, 231, 990), (93, 232, 990), (93, 233, 990), (92, 234, 991), (92, 235, 991), (92, 236, 991), (91, 237, 992), (91, 238, 991), (91, 239, 991), (91, 240, 991), (91, 241, 990), (90, 242, 991), (90, 243, 990), (90, 244, 990), (90, 245, 989), (90, 246, 989), (89, 247, 990), (89, 248, 989), (89, 249, 989), (89, 250, 988), (89, 251, 988), (88, 252, 988), (88, 253, 988), (88, 254, 987), (88, 255, 986), (88, 256, 986), (87, 257, 986), (87, 258, 986), (87, 259, 985), (87, 260, 984), (87, 261, 983), (86, 262, 983), (86, 263, 983), (86, 264, 982), (86, 265, 981), (85, 266, 981), (85, 267, 980), (85, 268, 980), (84, 269, 980), (84, 270, 979), (84, 271, 979), (84, 272, 978), (83, 273, 979), (83, 274, 978), (83, 275, 977), (82, 276, 978), (82, 277, 977), (82, 278, 977), (81, 279, 977), (81, 280, 977), (81, 281, 977), (80, 282, 977), (80, 283, 977), (80, 284, 976), (79, 285, 977), (79, 286, 976), (79, 287, 976), (78, 288, 976), (78, 289, 976), (78, 290, 975), (77, 291, 976), (77, 292, 975), (77, 293, 975), (76, 294, 975), (76, 295, 975), (76, 296, 974), (75, 297, 975), (75, 298, 974), (74, 299, 975), (74, 300, 974), (74, 301, 974), (73, 302, 974), (73, 303, 974), (72, 304, 974), (72, 305, 974), (71, 306, 974), (71, 307, 973), (71, 308, 972), (70, 309, 972), (70, 310, 971), (70, 311, 970), (70, 312, 968), (69, 313, 968), (69, 314, 966), (69, 315, 964), (69, 316, 962), (68, 317, 961), (68, 318, 959), (68, 319, 958), (68, 320, 956), (67, 321, 955), (67, 322, 954), (67, 323, 952), (67, 324, 951), (66, 325, 951), (66, 326, 950), (66, 327, 948), (66, 328, 947), (65, 329, 947), (65, 330, 946), (65, 331, 946), (65, 332, 945), (65, 333, 944), (65, 334, 942), (65, 335, 941), (65, 336, 940), (65, 337, 939), (65, 338, 938), (64, 339, 937), (64, 340, 936), (64, 341, 934), (64, 342, 932), (64, 343, 930), (64, 344, 928), (64, 345, 926), (64, 346, 925), (64, 347, 923), (64, 348, 922), (64, 349, 920), (64, 350, 919), (63, 351, 919), (63, 352, 918), (63, 353, 917), (63, 354, 916), (63, 355, 915), (63, 356, 914), (63, 357, 912), (63, 358, 911), (63, 359, 910), (63, 360, 909), (63, 361, 908), (63, 362, 906), (63, 363, 905), (63, 364, 904), (63, 365, 902), (63, 366, 901), (63, 367, 899), (63, 368, 898), (63, 369, 896), (62, 370, 895), (62, 371, 893), (62, 372, 891), (62, 373, 890), (62, 374, 888), (62, 375, 887), (62, 376, 886), (62, 377, 885), (62, 378, 884), (62, 379, 883), (63, 380, 880), (63, 381, 879), (63, 382, 878), (63, 383, 877), (63, 384, 876), (63, 385, 875), (63, 386, 874), (63, 387, 873), (63, 388, 872), (64, 389, 870), (64, 390, 869), (64, 391, 868), (64, 392, 867), (64, 393, 865), (64, 394, 864), (64, 395, 863), (65, 396, 861), (65, 397, 860), (65, 398, 859), (65, 399, 858), (65, 400, 857), (65, 401, 856), (65, 402, 854), (65, 403, 853), (65, 404, 851), (65, 405, 850), (65, 406, 848), (66, 407, 846), (66, 408, 844), (66, 409, 843), (66, 410, 842), (66, 411, 841), (66, 412, 840), (66, 413, 838), (66, 414, 837), (66, 415, 836), (66, 416, 835), (66, 417, 835), (66, 418, 834), (66, 419, 833), (67, 420, 831), (67, 421, 830), (67, 422, 829), (67, 423, 829), (67, 424, 828), (67, 425, 827), (67, 426, 826), (67, 427, 825), (67, 428, 824), (68, 429, 822), (68, 430, 820), (68, 431, 819), (68, 432, 818), (68, 433, 816), (68, 434, 815), (68, 435, 813), (68, 436, 811), (69, 437, 809), (69, 438, 807), (69, 439, 806), (69, 440, 804), (69, 441, 803), (69, 442, 802), (69, 443, 800), (70, 444, 798), (70, 445, 797), (70, 446, 796), (70, 447, 796), (71, 448, 794), (71, 449, 794), (72, 450, 792), (72, 451, 792), (73, 452, 790), (73, 453, 789), (74, 454, 788), (74, 455, 787), (75, 456, 786), (75, 457, 785), (76, 458, 784), (76, 459, 783), (77, 460, 782), (77, 461, 781), (77, 462, 781), (78, 463, 779), (78, 464, 779), (79, 465, 777), (79, 466, 777), (79, 467, 776), (80, 468, 775), (80, 469, 774), (80, 470, 774), (81, 471, 772), (81, 472, 771), (82, 473, 770), (82, 474, 769), (83, 475, 767), (83, 476, 766), (83, 477, 766), (84, 478, 764), (84, 479, 763), (85, 480, 761), (85, 481, 760), (85, 482, 759), (86, 483, 757), (86, 484, 755), (87, 485, 753), (87, 486, 752), (87, 487, 751), (88, 488, 748), (88, 489, 747), (88, 490, 746), (89, 491, 744), (89, 492, 743), (90, 493, 741), (90, 494, 741), (91, 495, 739), (91, 496, 738), (92, 497, 737), (93, 498, 735), (94, 499, 733), (94, 500, 733), (95, 501, 731), (96, 502, 729), (97, 503, 728), (98, 504, 726), (99, 505, 724), (99, 506, 724), (100, 507, 722), (101, 508, 721), (102, 509, 719), (104, 510, 717), (105, 511, 715), (106, 512, 714), (107, 513, 712), (108, 514, 711), (110, 515, 708), (111, 516, 707), (113, 517, 704), (114, 518, 703), (115, 519, 701), (117, 520, 698), (118, 521, 697), (119, 522, 695), (121, 523, 693), (122, 524, 691), (124, 525, 689), (125, 526, 687), (126, 527, 685), (128, 528, 683), (129, 529, 681), (131, 530, 678), (132, 531, 676), (134, 532, 673), (135, 533, 672), (137, 534, 669), (138, 535, 667), (140, 536, 664), (141, 537, 662), (143, 538, 659), (144, 539, 657), (146, 540, 654), (148, 541, 651), (149, 542, 649), (151, 543, 645), (153, 544, 642), (154, 545, 640), (156, 546, 638), (158, 547, 635), (159, 548, 633), (161, 549, 630), (162, 550, 628), (164, 551, 626), (166, 552, 623), (167, 553, 621), (169, 554, 618), (170, 555, 617), (171, 556, 615), (173, 557, 613), (174, 558, 611), (176, 559, 608), (177, 560, 607), (178, 561, 605), (180, 562, 603), (181, 563, 601), (183, 564, 599), (185, 565, 597), (186, 566, 595), (189, 567, 592), (192, 568, 589), (195, 569, 585), (198, 570, 582), (201, 571, 579), (204, 572, 575), (206, 573, 573), (209, 574, 569), (212, 575, 566), (215, 576, 563), (218, 577, 559), (221, 578, 556), (223, 579, 553), (226, 580, 550), (228, 581, 547), (230, 582, 545), (232, 583, 542), (234, 584, 540), (235, 585, 539), (237, 586, 536), (238, 587, 534), (240, 588, 531), (242, 589, 528), (243, 590, 526), (245, 591, 523), (247, 592, 520), (249, 593, 516), (251, 594, 513), (253, 595, 510), (256, 596, 505), (258, 597, 501), (261, 598, 497), (263, 599, 493), (267, 600, 488), (271, 601, 482), (274, 602, 478), (278, 603, 473), (281, 604, 468), (284, 605, 464), (287, 606, 460), (290, 607, 456), (292, 608, 453), (295, 609, 449), (298, 610, 445), (300, 611, 442), (303, 612, 438), (305, 613, 434), (308, 614, 430), (310, 615, 427), (312, 616, 423), (315, 617, 418), (317, 618, 415), (320, 619, 410), (322, 620, 406), (325, 621, 401), (328, 622, 395), (330, 623, 390), (333, 624, 384), (335, 625, 379), (338, 626, 374), (341, 627, 369), (345, 628, 362), (349, 629, 356), (353, 630, 350), (357, 631, 344), (360, 632, 340), (364, 633, 334), (368, 634, 327), (373, 635, 320), (378, 636, 313), (383, 637, 305), (389, 638, 295), (395, 639, 282), (401, 640, 270), (408, 641, 256), (416, 642, 240), (432, 643, 216), (448, 644, 193), (465, 645, 169), (480, 646, 148), (495, 647, 126), (511, 648, 104), (526, 649, 82), (565, 650, 9)], ['526,649,416,642,341,627,297,609,263,599,220,577,186,566,102,509,91,496,70,447,63,351,65,329,86,265,91,237,101,216,134,183,187,156,225,151,252,141,302,133,343,123,358,116,416,103,493,45,527,36,608,23,753,23,754,24,893,24,925,22,996,23,1032,27,1066,41,1082,52,1089,72,1088,172,1082,237,1045,305,1002,338,950,373,885,432,865,446,851,473,822,505,810,528,786,554,773,585,725,621,683,638,607,649']), (917855882, 492601069, 445, 0, 440, 0, 116, 0.99194324, [(127, 1, 141), (94, 2, 206), (384, 2, 2), (59, 3, 273), (340, 3, 57), (22, 4, 381), (19, 5, 387), (16, 6, 392), (15, 7, 394), (14, 8, 396), (14, 9, 397), (13, 10, 399), (12, 11, 400), (12, 12, 400), (11, 13, 402), (10, 14, 403), (11, 15, 403), (11, 16, 404), (12, 17, 403), (12, 18, 404), (12, 19, 405), (12, 20, 405), (12, 21, 406), (12, 22, 406), (12, 23, 407), (12, 24, 407), (12, 25, 408), (12, 26, 408), (12, 27, 408), (12, 28, 408), (12, 29, 409), (12, 30, 409), (12, 31, 409), (12, 32, 409), (12, 33, 409), (12, 34, 410), (12, 35, 410), (12, 36, 410), (12, 37, 410), (12, 38, 410), (12, 39, 410), (12, 40, 410), (12, 41, 411), (12, 42, 411), (12, 43, 411), (12, 44, 411), (12, 45, 411), (12, 46, 410), (12, 47, 410), (12, 48, 410), (12, 49, 410), (12, 50, 410), (12, 51, 410), (12, 52, 409), (12, 53, 408), (12, 54, 408), (12, 55, 407), (12, 56, 406), (12, 57, 404), (12, 58, 403), (11, 59, 403), (11, 60, 402), (11, 61, 401), (11, 62, 400), (11, 63, 400), (11, 64, 399), (11, 65, 398), (11, 66, 397), (11, 67, 397), (11, 68, 396), (11, 69, 395), (11, 70, 395), (11, 71, 394), (11, 72, 394), (11, 73, 394), (11, 74, 393), (11, 75, 393), (11, 76, 393), (11, 77, 393), (11, 78, 393), (11, 79, 393), (11, 80, 392), (10, 81, 394), (10, 82, 394), (10, 83, 395), (9, 84, 396), (9, 85, 262), (279, 85, 126), (9, 86, 75), (98, 86, 28), (142, 86, 117), (292, 86, 112), (9, 87, 71), (152, 87, 103), (294, 87, 110), (8, 88, 68), (161, 88, 91), (296, 88, 107), (8, 89, 63), (176, 89, 73), (297, 89, 106), (7, 90, 61), (205, 90, 40), (298, 90, 104), (7, 91, 57), (299, 91, 103), (6, 92, 54), (300, 92, 102), (6, 93, 50), (301, 93, 100), (7, 94, 46), (303, 94, 97), (7, 95, 44), (306, 95, 92), (7, 96, 42), (308, 96, 89), (7, 97, 40), (310, 97, 86), (7, 98, 38), (312, 98, 83), (8, 99, 34), (314, 99, 79), (8, 100, 32), (317, 100, 75), (8, 101, 29), (319, 101, 71), (13, 102, 19), (324, 102, 63), (20, 103, 6), (330, 103, 51), (337, 104, 37), (344, 105, 22), (352, 106, 3)], ['344,105,319,101,301,93,291,85,259,85,244,90,205,90,204,89,176,89,161,88,141,85,126,85,125,86,98,86,84,85,56,92,36,101,26,102,8,101,6,92,11,80,11,59,12,58,12,17,10,14,16,6,22,4,58,4,59,3,93,3,94,2,126,2,127,1,267,1,268,2,331,3,396,3,407,6,411,10,419,25,421,34,421,51,410,62,404,71,402,80,404,85,401,92,394,98,386,102,365,105']), (917855882, 492601069, 445, 390, 550, 0, 54, 0.9391703, [(414, 0, 7), (441, 0, 60), (508, 0, 28), (402, 1, 142), (401, 2, 146), (402, 3, 145), (404, 4, 143), (406, 5, 140), (408, 6, 137), (410, 7, 134), (411, 8, 132), (412, 9, 130), (413, 10, 127), (414, 11, 125), (415, 12, 123), (415, 13, 122), (416, 14, 120), (417, 15, 117), (417, 16, 116), (418, 17, 114), (418, 18, 113), (418, 19, 111), (418, 20, 109), (419, 21, 107), (419, 22, 105), (419, 23, 103), (419, 24, 102), (420, 25, 99), (420, 26, 97), (420, 27, 95), (420, 28, 94), (421, 29, 91), (421, 30, 90), (422, 31, 88), (422, 32, 88), (422, 33, 87), (423, 34, 84), (423, 35, 82), (423, 36, 81), (424, 37, 79), (424, 38, 77), (424, 39, 75), (424, 40, 73), (424, 41, 71), (425, 42, 67), (425, 43, 66), (426, 44, 62), (426, 45, 6), (433, 45, 52), (443, 46, 30), (450, 47, 1)], ['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1743157251_1341668_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 : 0.2081587314605713 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 Fri Mar 28 11:21:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6816 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-03-28 11:21:17.558549: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-28 11:21:17.583125: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-28 11:21:17.584655: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0e8c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-28 11:21:17.584710: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-28 11:21:17.587584: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-28 11:21:17.732430: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x24143d50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-28 11:21:17.732469: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-28 11:21:17.733325: 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 2025-03-28 11:21:17.733617: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:21:17.739678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:21:17.741801: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:21:17.742125: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:21:17.744283: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:21:17.745318: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:21:17.749732: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:21:17.751028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:21:17.751107: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:21:17.751767: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-28 11:21:17.751784: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-28 11:21:17.751793: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-28 11:21:17.752930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6264 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. 2025-03-28 11:21:17.867911: 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 2025-03-28 11:21:17.868053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:21:17.868077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:21:17.868101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:21:17.868136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:21:17.868156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:21:17.868174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:21:17.868193: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:21:17.869233: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:21:17.870340: 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 2025-03-28 11:21:17.870377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:21:17.870394: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:21:17.870411: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:21:17.870427: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:21:17.870444: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:21:17.870460: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:21:17.870476: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:21:17.871547: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:21:17.871584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-28 11:21:17.871593: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-28 11:21:17.871600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-28 11:21:17.872644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6264 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 : [] 2025-03-28 11:21:29.842247: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:21:30.016265: 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 1346173 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5304 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 : 10593 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 : 0.8196218013763428 nb_pixel_total : 3690455 time to create 1 rle with new method : 0.5366966724395752 length of segment : 2036 time spent for convertir_results : 2.890254259109497 time spend for datou_step_exec : 24.812392234802246 time spend to save output : 4.38690185546875e-05 total time spend for step 1 : 24.8124361038208 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 721 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 : 0.012439966201782227 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, 122, 2237, 0.9851282, [(523, 124, 448), (1138, 124, 289), (501, 125, 950), (480, 126, 994), (461, 127, 1036), (443, 128, 1093), (426, 129, 1152), (411, 130, 1179), (396, 131, 1202), (381, 132, 1225), (370, 133, 1244), (367, 134, 1254), (364, 135, 1264), (361, 136, 1274), (359, 137, 1283), (356, 138, 1292), (354, 139, 1300), (352, 140, 1308), (349, 141, 1316), (347, 142, 1322), (345, 143, 1328), (343, 144, 1334), (341, 145, 1340), (339, 146, 1346), (338, 147, 1351), (336, 148, 1356), (334, 149, 1361), (332, 150, 1367), (331, 151, 1371), (329, 152, 1376), (328, 153, 1380), (326, 154, 1385), (325, 155, 1389), (323, 156, 1393), (322, 157, 1397), (321, 158, 1401), (319, 159, 1405), (318, 160, 1409), (317, 161, 1412), (316, 162, 1416), (315, 163, 1419), (313, 164, 1424), (312, 165, 1428), (311, 166, 1432), (309, 167, 1436), (308, 168, 1439), (306, 169, 1443), (305, 170, 1446), (303, 171, 1451), (302, 172, 1454), (300, 173, 1459), (298, 174, 1463), (297, 175, 1467), (295, 176, 1471), (293, 177, 1476), (291, 178, 1481), (289, 179, 1486), (286, 180, 1492), (284, 181, 1497), (281, 182, 1503), (278, 183, 1510), (276, 184, 1515), (273, 185, 1522), (270, 186, 1528), (268, 187, 1534), (265, 188, 1541), (262, 189, 1548), (260, 190, 1555), (257, 191, 1562), (254, 192, 1570), (251, 193, 1579), (248, 194, 1588), (246, 195, 1597), (243, 196, 1606), (240, 197, 1615), (237, 198, 1624), (234, 199, 1633), (231, 200, 1642), (228, 201, 1650), (226, 202, 1658), (223, 203, 1667), (220, 204, 1676), (217, 205, 1684), (214, 206, 1691), (211, 207, 1696), (208, 208, 1701), (206, 209, 1705), (204, 210, 1709), (202, 211, 1712), (201, 212, 1715), (199, 213, 1719), (197, 214, 1722), (196, 215, 1724), (194, 216, 1728), (193, 217, 1730), (192, 218, 1732), (190, 219, 1736), (189, 220, 1738), (188, 221, 1740), (186, 222, 1743), (185, 223, 1745), (184, 224, 1748), (183, 225, 1750), (182, 226, 1752), (180, 227, 1755), (179, 228, 1757), (178, 229, 1758), (177, 230, 1760), (176, 231, 1762), (175, 232, 1764), (174, 233, 1766), (174, 234, 1767), (173, 235, 1769), (172, 236, 1770), (171, 237, 1772), (170, 238, 1774), (169, 239, 1775), (168, 240, 1777), (167, 241, 1779), (166, 242, 1781), (165, 243, 1783), (164, 244, 1784), (163, 245, 1786), (162, 246, 1788), (161, 247, 1790), (160, 248, 1792), (159, 249, 1794), (158, 250, 1796), (157, 251, 1798), (156, 252, 1800), (155, 253, 1802), (153, 254, 1805), (152, 255, 1807), (151, 256, 1809), (149, 257, 1812), (148, 258, 1814), (147, 259, 1816), (145, 260, 1820), (144, 261, 1822), (142, 262, 1825), (141, 263, 1828), (139, 264, 1831), (138, 265, 1833), (136, 266, 1837), (135, 267, 1839), (133, 268, 1843), (131, 269, 1846), (129, 270, 1850), (128, 271, 1853), (126, 272, 1857), (125, 273, 1859), (123, 274, 1863), (122, 275, 1866), (121, 276, 1868), (120, 277, 1871), (118, 278, 1875), (117, 279, 1877), (116, 280, 1879), (115, 281, 1882), (114, 282, 1884), (113, 283, 1886), (112, 284, 1889), (111, 285, 1891), (110, 286, 1893), (109, 287, 1895), (108, 288, 1897), (107, 289, 1899), (106, 290, 1901), (106, 291, 1902), (105, 292, 1904), (104, 293, 1906), (103, 294, 1908), (102, 295, 1910), (102, 296, 1911), (101, 297, 1913), (100, 298, 1915), (100, 299, 1916), (99, 300, 1917), (98, 301, 1919), (98, 302, 1920), (97, 303, 1922), (96, 304, 1924), (96, 305, 1924), (95, 306, 1926), (95, 307, 1927), (94, 308, 1928), (93, 309, 1930), (93, 310, 1931), (93, 311, 1931), (92, 312, 1933), (92, 313, 1933), (92, 314, 1934), (92, 315, 1934), (91, 316, 1936), (91, 317, 1936), (91, 318, 1937), (90, 319, 1938), (90, 320, 1939), (90, 321, 1939), (90, 322, 1940), (89, 323, 1941), (89, 324, 1942), (89, 325, 1942), (89, 326, 1943), (88, 327, 1945), (88, 328, 1945), (88, 329, 1946), (88, 330, 1946), (87, 331, 1948), (87, 332, 1949), (87, 333, 1949), (86, 334, 1951), (86, 335, 1952), (86, 336, 1952), (86, 337, 1953), (85, 338, 1955), (85, 339, 1955), (85, 340, 1956), (85, 341, 1957), (84, 342, 1958), (84, 343, 1959), (84, 344, 1960), (83, 345, 1962), (83, 346, 1962), (83, 347, 1963), (83, 348, 1964), (82, 349, 1966), (82, 350, 1967), (82, 351, 1968), (81, 352, 1969), (81, 353, 1970), (81, 354, 1971), (80, 355, 1973), (80, 356, 1974), (80, 357, 1975), (80, 358, 1976), (79, 359, 1978), (79, 360, 1979), (79, 361, 1980), (78, 362, 1982), (78, 363, 1983), (78, 364, 1984), (77, 365, 1986), (77, 366, 1987), (77, 367, 1988), (77, 368, 1989), (76, 369, 1991), (76, 370, 1993), (76, 371, 1994), (75, 372, 1996), (75, 373, 1997), (75, 374, 1997), (74, 375, 1999), (74, 376, 2000), (74, 377, 2001), (73, 378, 2003), (73, 379, 2004), (73, 380, 2005), (72, 381, 2007), (72, 382, 2007), (72, 383, 2008), (71, 384, 2010), (71, 385, 2011), (71, 386, 2011), (70, 387, 2013), (70, 388, 2013), (70, 389, 2014), (70, 390, 2014), (69, 391, 2016), (69, 392, 2017), (69, 393, 2017), (68, 394, 2019), (68, 395, 2019), (68, 396, 2020), (67, 397, 2021), (67, 398, 2022), (67, 399, 2022), (66, 400, 2024), (66, 401, 2024), (66, 402, 2025), (65, 403, 2026), (65, 404, 2026), (64, 405, 2028), (64, 406, 2028), (64, 407, 2029), (63, 408, 2030), (63, 409, 2031), (63, 410, 2031), (62, 411, 2033), (62, 412, 2033), (61, 413, 2035), (61, 414, 2035), (61, 415, 2035), (60, 416, 2037), (60, 417, 2037), (59, 418, 2039), (59, 419, 2039), (59, 420, 2040), (58, 421, 2041), (58, 422, 2041), (57, 423, 2043), (57, 424, 2043), (56, 425, 2045), (56, 426, 2045), (55, 427, 2046), (55, 428, 2047), (54, 429, 2048), (54, 430, 2049), (54, 431, 2049), (53, 432, 2050), (53, 433, 2051), (52, 434, 2052), (52, 435, 2053), (51, 436, 2054), (51, 437, 2054), (50, 438, 2056), (49, 439, 2057), (49, 440, 2057), (48, 441, 2059), (48, 442, 2059), (47, 443, 2061), (47, 444, 2061), (46, 445, 2062), (46, 446, 2063), (45, 447, 2064), (45, 448, 2064), (44, 449, 2066), (44, 450, 2066), (43, 451, 2067), (43, 452, 2068), (42, 453, 2069), (42, 454, 2069), (41, 455, 2071), (41, 456, 2071), (40, 457, 2072), (40, 458, 2073), (40, 459, 2073), (39, 460, 2074), (39, 461, 2075), (38, 462, 2076), (38, 463, 2076), (38, 464, 2077), (38, 465, 2077), (38, 466, 2077), (38, 467, 2078), (37, 468, 2079), (37, 469, 2079), (37, 470, 2080), (37, 471, 2080), (37, 472, 2080), (37, 473, 2081), (36, 474, 2082), (36, 475, 2082), (36, 476, 2083), (36, 477, 2083), (36, 478, 2083), (36, 479, 2084), (36, 480, 2084), (35, 481, 2085), (35, 482, 2086), (35, 483, 2086), (35, 484, 2086), (35, 485, 2087), (35, 486, 2087), (34, 487, 2089), (34, 488, 2089), (34, 489, 2089), (34, 490, 2090), (34, 491, 2090), (34, 492, 2091), (34, 493, 2091), (33, 494, 2092), (33, 495, 2093), (33, 496, 2093), (33, 497, 2094), (33, 498, 2094), (33, 499, 2095), (33, 500, 2095), (32, 501, 2097), (32, 502, 2097), (32, 503, 2097), (32, 504, 2098), (32, 505, 2098), (32, 506, 2099), (32, 507, 2099), (31, 508, 2101), (31, 509, 2101), (31, 510, 2102), (31, 511, 2102), (31, 512, 2103), (31, 513, 2104), (31, 514, 2104), (31, 515, 2105), (30, 516, 2106), (30, 517, 2107), (30, 518, 2107), (30, 519, 2108), (30, 520, 2109), (30, 521, 2109), (30, 522, 2110), (29, 523, 2111), (29, 524, 2112), (29, 525, 2113), (29, 526, 2113), (29, 527, 2114), (29, 528, 2115), (29, 529, 2115), (29, 530, 2116), (28, 531, 2118), (28, 532, 2119), (28, 533, 2119), (28, 534, 2120), (28, 535, 2121), (28, 536, 2121), (28, 537, 2122), (28, 538, 2122), (28, 539, 2123), (28, 540, 2123), (28, 541, 2123), (27, 542, 2125), (27, 543, 2125), (27, 544, 2125), (27, 545, 2126), (27, 546, 2126), (27, 547, 2126), (27, 548, 2127), (27, 549, 2127), (27, 550, 2127), (27, 551, 2127), (27, 552, 2128), (27, 553, 2128), (27, 554, 2128), (27, 555, 2129), (27, 556, 2129), (27, 557, 2129), (27, 558, 2129), (27, 559, 2130), (27, 560, 2130), (26, 561, 2131), (26, 562, 2132), (26, 563, 2132), (26, 564, 2132), (26, 565, 2132), (26, 566, 2133), (26, 567, 2133), (26, 568, 2133), (26, 569, 2133), (26, 570, 2134), (26, 571, 2134), (26, 572, 2134), (26, 573, 2134), (26, 574, 2135), (26, 575, 2135), (26, 576, 2135), (26, 577, 2135), (26, 578, 2136), (26, 579, 2136), (25, 580, 2137), (25, 581, 2137), (25, 582, 2138), (25, 583, 2138), (25, 584, 2138), (25, 585, 2138), (25, 586, 2138), (25, 587, 2139), (25, 588, 2139), (25, 589, 2139), (25, 590, 2139), (25, 591, 2140), (25, 592, 2140), (25, 593, 2140), (25, 594, 2140), (25, 595, 2140), (25, 596, 2141), (25, 597, 2141), (25, 598, 2141), (25, 599, 2141), (24, 600, 2143), (24, 601, 2143), (24, 602, 2143), (24, 603, 2143), (24, 604, 2143), (24, 605, 2144), (24, 606, 2144), (24, 607, 2144), (24, 608, 2144), (24, 609, 2144), (24, 610, 2145), (24, 611, 2145), (24, 612, 2145), (24, 613, 2145), (24, 614, 2145), (24, 615, 2145), (24, 616, 2145), (24, 617, 2145), (24, 618, 2145), (24, 619, 2145), (24, 620, 2145), (24, 621, 2146), (23, 622, 2147), (23, 623, 2147), (23, 624, 2147), (23, 625, 2147), (23, 626, 2147), (23, 627, 2147), (23, 628, 2147), (23, 629, 2147), (23, 630, 2147), (23, 631, 2147), (23, 632, 2147), (23, 633, 2147), (23, 634, 2147), (23, 635, 2147), (23, 636, 2147), (23, 637, 2147), (23, 638, 2148), (23, 639, 2148), (23, 640, 2148), (23, 641, 2148), (23, 642, 2148), (23, 643, 2148), (23, 644, 2148), (23, 645, 2148), (22, 646, 2149), (22, 647, 2149), (22, 648, 2149), (22, 649, 2149), (22, 650, 2149), (22, 651, 2149), (22, 652, 2149), (22, 653, 2149), (22, 654, 2149), (22, 655, 2150), (22, 656, 2150), (22, 657, 2150), (22, 658, 2150), (22, 659, 2150), (22, 660, 2150), (22, 661, 2150), (22, 662, 2150), (22, 663, 2150), (22, 664, 2150), (22, 665, 2150), (22, 666, 2150), (22, 667, 2150), (22, 668, 2150), (22, 669, 2150), (22, 670, 2150), (21, 671, 2151), (21, 672, 2151), (21, 673, 2152), (21, 674, 2152), (21, 675, 2152), (21, 676, 2152), (21, 677, 2152), (21, 678, 2152), (21, 679, 2152), (21, 680, 2152), (21, 681, 2152), (21, 682, 2152), (21, 683, 2152), (21, 684, 2152), (21, 685, 2152), (21, 686, 2152), (21, 687, 2152), (21, 688, 2152), (21, 689, 2152), (21, 690, 2152), (21, 691, 2152), (21, 692, 2152), (21, 693, 2152), (21, 694, 2152), (21, 695, 2152), (21, 696, 2152), (21, 697, 2152), (22, 698, 2151), (22, 699, 2151), (22, 700, 2151), (22, 701, 2151), (22, 702, 2151), (22, 703, 2151), (22, 704, 2151), (22, 705, 2151), (22, 706, 2151), (22, 707, 2150), (22, 708, 2150), (22, 709, 2150), (23, 710, 2149), (23, 711, 2149), (23, 712, 2149), (23, 713, 2149), (23, 714, 2149), (23, 715, 2149), (23, 716, 2149), (23, 717, 2149), (23, 718, 2149), (23, 719, 2149), (23, 720, 2149), (23, 721, 2149), (23, 722, 2149), (24, 723, 2148), (24, 724, 2148), (24, 725, 2148), (24, 726, 2148), (24, 727, 2148), (24, 728, 2148), (24, 729, 2147), (24, 730, 2147), (24, 731, 2147), (24, 732, 2147), (24, 733, 2147), (24, 734, 2147), (25, 735, 2146), (25, 736, 2146), (25, 737, 2146), (25, 738, 2146), (25, 739, 2146), (25, 740, 2146), (25, 741, 2146), (25, 742, 2146), (25, 743, 2146), (25, 744, 2146), (25, 745, 2146), (26, 746, 2145), (26, 747, 2145), (26, 748, 2145), (26, 749, 2145), (26, 750, 2144), (26, 751, 2144), (26, 752, 2144), (26, 753, 2144), (26, 754, 2144), (26, 755, 2144), (26, 756, 2144), (27, 757, 2143), (27, 758, 2143), (27, 759, 2143), (27, 760, 2143), (27, 761, 2143), (27, 762, 2143), (27, 763, 2143), (27, 764, 2143), (27, 765, 2143), (27, 766, 2143), (27, 767, 2143), (27, 768, 2143), (27, 769, 2143), (27, 770, 2143), (27, 771, 2142), (27, 772, 2142), (27, 773, 2142), (27, 774, 2142), (27, 775, 2142), (27, 776, 2142), (27, 777, 2142), (27, 778, 2142), (27, 779, 2142), (27, 780, 2142), (27, 781, 2142), (27, 782, 2142), (27, 783, 2142), (27, 784, 2142), (27, 785, 2142), (27, 786, 2142), (27, 787, 2142), (27, 788, 2142), (27, 789, 2142), (27, 790, 2141), (27, 791, 2141), (27, 792, 2141), (27, 793, 2141), (27, 794, 2141), (27, 795, 2141), (27, 796, 2141), (27, 797, 2141), (27, 798, 2141), (27, 799, 2141), (27, 800, 2141), (26, 801, 2142), (26, 802, 2142), (26, 803, 2142), (26, 804, 2142), (26, 805, 2142), (26, 806, 2142), (26, 807, 2142), (26, 808, 2142), (26, 809, 2141), (26, 810, 2141), (26, 811, 2141), (26, 812, 2141), (26, 813, 2141), (26, 814, 2141), (26, 815, 2141), (26, 816, 2141), (26, 817, 2141), (26, 818, 2141), (26, 819, 2141), (26, 820, 2141), (26, 821, 2141), (26, 822, 2141), (26, 823, 2141), (26, 824, 2141), (26, 825, 2141), (26, 826, 2141), (26, 827, 2140), (26, 828, 2140), (26, 829, 2140), (26, 830, 2140), (26, 831, 2140), (26, 832, 2140), (26, 833, 2140), (26, 834, 2140), (26, 835, 2140), (26, 836, 2140), (26, 837, 2140), (26, 838, 2140), (26, 839, 2140), (26, 840, 2139), (26, 841, 2139), (26, 842, 2139), (26, 843, 2138), (26, 844, 2138), (26, 845, 2138), (26, 846, 2137), (26, 847, 2137), (26, 848, 2136), (26, 849, 2136), (26, 850, 2136), (26, 851, 2135), (27, 852, 2134), (27, 853, 2133), (27, 854, 2133), (27, 855, 2133), (27, 856, 2132), (27, 857, 2132), (27, 858, 2131), (27, 859, 2131), (27, 860, 2130), (27, 861, 2130), (27, 862, 2129), (27, 863, 2129), (27, 864, 2128), (27, 865, 2128), (27, 866, 2127), (27, 867, 2127), (27, 868, 2126), (27, 869, 2126), (27, 870, 2125), (27, 871, 2125), (28, 872, 2123), (28, 873, 2123), (28, 874, 2122), (28, 875, 2122), (28, 876, 2121), (28, 877, 2121), (28, 878, 2120), (28, 879, 2119), (28, 880, 2119), (28, 881, 2118), (28, 882, 2118), (28, 883, 2117), (28, 884, 2116), (28, 885, 2116), (28, 886, 2115), (28, 887, 2115), (28, 888, 2114), (28, 889, 2114), (28, 890, 2113), (29, 891, 2112), (29, 892, 2111), (29, 893, 2111), (29, 894, 2110), (29, 895, 2110), (29, 896, 2109), (29, 897, 2109), (29, 898, 2108), (29, 899, 2108), (29, 900, 2107), (29, 901, 2107), (29, 902, 2106), (29, 903, 2106), (29, 904, 2105), (29, 905, 2105), (29, 906, 2105), (29, 907, 2104), (29, 908, 2104), (30, 909, 2102), (30, 910, 2102), (30, 911, 2101), (30, 912, 2101), (30, 913, 2101), (30, 914, 2100), (30, 915, 2100), (30, 916, 2100), (30, 917, 2099), (30, 918, 2099), (30, 919, 2099), (30, 920, 2099), (30, 921, 2098), (30, 922, 2098), (30, 923, 2098), (29, 924, 2099), (29, 925, 2098), (29, 926, 2098), (29, 927, 2098), (29, 928, 2098), (29, 929, 2097), (29, 930, 2097), (29, 931, 2097), (29, 932, 2097), (29, 933, 2096), (29, 934, 2096), (29, 935, 2096), (29, 936, 2096), (29, 937, 2095), (29, 938, 2095), (29, 939, 2095), (29, 940, 2095), (29, 941, 2095), (29, 942, 2094), (29, 943, 2094), (29, 944, 2094), (29, 945, 2094), (29, 946, 2093), (29, 947, 2093), (29, 948, 2093), (29, 949, 2093), (29, 950, 2093), (29, 951, 2092), (29, 952, 2092), (28, 953, 2093), (28, 954, 2093), (28, 955, 2093), (28, 956, 2092), (28, 957, 2092), (28, 958, 2092), (28, 959, 2092), (28, 960, 2092), (28, 961, 2091), (28, 962, 2091), (28, 963, 2091), (28, 964, 2091), (28, 965, 2091), (28, 966, 2090), (28, 967, 2090), (28, 968, 2090), (28, 969, 2090), (28, 970, 2090), (28, 971, 2089), (28, 972, 2089), (28, 973, 2089), (28, 974, 2089), (28, 975, 2089), (28, 976, 2089), (28, 977, 2088), (28, 978, 2088), (28, 979, 2088), (28, 980, 2088), (28, 981, 2088), (28, 982, 2087), (27, 983, 2088), (27, 984, 2088), (27, 985, 2088), (27, 986, 2088), (27, 987, 2088), (27, 988, 2087), (27, 989, 2087), (27, 990, 2087), (27, 991, 2087), (27, 992, 2086), (27, 993, 2086), (27, 994, 2086), (27, 995, 2086), (27, 996, 2085), (27, 997, 2085), (28, 998, 2084), (28, 999, 2083), (28, 1000, 2083), (28, 1001, 2083), (28, 1002, 2082), (28, 1003, 2082), (28, 1004, 2082), (28, 1005, 2081), (28, 1006, 2081), (28, 1007, 2081), (28, 1008, 2080), (28, 1009, 2080), (28, 1010, 2080), (28, 1011, 2079), (28, 1012, 2079), (28, 1013, 2079), (28, 1014, 2078), (28, 1015, 2078), (28, 1016, 2077), (28, 1017, 2077), (28, 1018, 2077), (28, 1019, 2076), (28, 1020, 2076), (28, 1021, 2076), (28, 1022, 2075), (28, 1023, 2075), (28, 1024, 2074), (28, 1025, 2074), (28, 1026, 2074), (28, 1027, 2073), (29, 1028, 2072), (29, 1029, 2072), (29, 1030, 2071), (29, 1031, 2071), (29, 1032, 2070), (29, 1033, 2070), (29, 1034, 2070), (29, 1035, 2069), (29, 1036, 2069), (29, 1037, 2068), (29, 1038, 2068), (29, 1039, 2067), (29, 1040, 2067), (29, 1041, 2067), (29, 1042, 2066), (29, 1043, 2066), (29, 1044, 2065), (29, 1045, 2065), (29, 1046, 2064), (29, 1047, 2064), (29, 1048, 2064), (29, 1049, 2063), (29, 1050, 2063), (29, 1051, 2062), (29, 1052, 2062), (29, 1053, 2061), (29, 1054, 2061), (29, 1055, 2060), (30, 1056, 2059), (30, 1057, 2058), (30, 1058, 2058), (30, 1059, 2057), (30, 1060, 2057), (30, 1061, 2056), (30, 1062, 2056), (30, 1063, 2055), (30, 1064, 2055), (30, 1065, 2054), (30, 1066, 2054), (30, 1067, 2053), (30, 1068, 2052), (30, 1069, 2052), (30, 1070, 2051), (30, 1071, 2050), (30, 1072, 2049), (30, 1073, 2048), (30, 1074, 2048), (30, 1075, 2047), (30, 1076, 2046), (30, 1077, 2045), (30, 1078, 2044), (30, 1079, 2043), (30, 1080, 2042), (30, 1081, 2041), (30, 1082, 2040), (30, 1083, 2039), (29, 1084, 2039), (29, 1085, 2038), (29, 1086, 2037), (29, 1087, 2036), (29, 1088, 2035), (29, 1089, 2034), (29, 1090, 2033), (29, 1091, 2032), (29, 1092, 2031), (29, 1093, 2030), (29, 1094, 2029), (29, 1095, 2028), (29, 1096, 2027), (29, 1097, 2026), (29, 1098, 2025), (29, 1099, 2025), (29, 1100, 2024), (29, 1101, 2023), (29, 1102, 2022), (29, 1103, 2021), (29, 1104, 2021), (29, 1105, 2020), (29, 1106, 2019), (29, 1107, 2018), (29, 1108, 2018), (29, 1109, 2017), (29, 1110, 2016), (29, 1111, 2016), (29, 1112, 2015), (29, 1113, 2014), (29, 1114, 2014), (29, 1115, 2013), (29, 1116, 2012), (29, 1117, 2012), (29, 1118, 2011), (29, 1119, 2011), (29, 1120, 2010), (29, 1121, 2010), (29, 1122, 2009), (29, 1123, 2008), (29, 1124, 2008), (29, 1125, 2007), (29, 1126, 2007), (29, 1127, 2006), (29, 1128, 2006), (29, 1129, 2005), (29, 1130, 2005), (29, 1131, 2004), (29, 1132, 2004), (29, 1133, 2003), (29, 1134, 2003), (29, 1135, 2003), (28, 1136, 2003), (28, 1137, 2003), (28, 1138, 2002), (28, 1139, 2002), (28, 1140, 2001), (28, 1141, 2001), (28, 1142, 2001), (28, 1143, 2000), (28, 1144, 2000), (28, 1145, 2000), (28, 1146, 1999), (28, 1147, 1999), (28, 1148, 1999), (28, 1149, 1998), (29, 1150, 1997), (29, 1151, 1997), (29, 1152, 1996), (29, 1153, 1996), (29, 1154, 1996), (29, 1155, 1995), (29, 1156, 1995), (29, 1157, 1995), (29, 1158, 1994), (29, 1159, 1994), (29, 1160, 1994), (29, 1161, 1993), (29, 1162, 1993), (29, 1163, 1993), (29, 1164, 1992), (29, 1165, 1992), (29, 1166, 1992), (29, 1167, 1991), (29, 1168, 1991), (29, 1169, 1990), (29, 1170, 1990), (29, 1171, 1990), (29, 1172, 1989), (29, 1173, 1989), (29, 1174, 1988), (29, 1175, 1988), (29, 1176, 1988), (29, 1177, 1987), (29, 1178, 1987), (29, 1179, 1986), (29, 1180, 1986), (29, 1181, 1986), (29, 1182, 1985), (29, 1183, 1985), (29, 1184, 1984), (29, 1185, 1984), (29, 1186, 1983), (29, 1187, 1983), (29, 1188, 1983), (29, 1189, 1982), (29, 1190, 1982), (29, 1191, 1981), (29, 1192, 1981), (29, 1193, 1980), (29, 1194, 1980), (29, 1195, 1979), (29, 1196, 1979), (29, 1197, 1978), (29, 1198, 1978), (29, 1199, 1977), (29, 1200, 1977), (29, 1201, 1977), (29, 1202, 1976), (29, 1203, 1976), (29, 1204, 1975), (29, 1205, 1974), (29, 1206, 1974), (29, 1207, 1973), (29, 1208, 1973), (29, 1209, 1972), (29, 1210, 1972), (29, 1211, 1971), (29, 1212, 1971), (29, 1213, 1970), (29, 1214, 1970), (29, 1215, 1969), (29, 1216, 1969), (29, 1217, 1968), (29, 1218, 1967), (29, 1219, 1967), (29, 1220, 1966), (29, 1221, 1965), (29, 1222, 1965), (29, 1223, 1964), (29, 1224, 1963), (29, 1225, 1962), (29, 1226, 1962), (29, 1227, 1961), (29, 1228, 1960), (29, 1229, 1959), (29, 1230, 1958), (29, 1231, 1958), (29, 1232, 1957), (29, 1233, 1956), (29, 1234, 1955), (29, 1235, 1954), (29, 1236, 1953), (29, 1237, 1952), (29, 1238, 1952), (29, 1239, 1951), (29, 1240, 1950), (29, 1241, 1949), (29, 1242, 1949), (29, 1243, 1948), (30, 1244, 1946), (30, 1245, 1945), (30, 1246, 1945), (30, 1247, 1944), (30, 1248, 1943), (30, 1249, 1943), (30, 1250, 1942), (30, 1251, 1941), (30, 1252, 1941), (30, 1253, 1940), (30, 1254, 1940), (30, 1255, 1939), (30, 1256, 1938), (30, 1257, 1938), (30, 1258, 1937), (30, 1259, 1937), (30, 1260, 1936), (30, 1261, 1936), (30, 1262, 1935), (30, 1263, 1935), (30, 1264, 1934), (30, 1265, 1934), (30, 1266, 1933), (30, 1267, 1933), (30, 1268, 1932), (30, 1269, 1932), (30, 1270, 1931), (30, 1271, 1931), (30, 1272, 1930), (30, 1273, 1930), (30, 1274, 1929), (30, 1275, 1929), (30, 1276, 1929), (30, 1277, 1928), (30, 1278, 1928), (30, 1279, 1927), (30, 1280, 1927), (30, 1281, 1927), (30, 1282, 1926), (30, 1283, 1926), (30, 1284, 1925), (30, 1285, 1925), (30, 1286, 1925), (30, 1287, 1924), (30, 1288, 1924), (30, 1289, 1924), (30, 1290, 1923), (30, 1291, 1923), (30, 1292, 1923), (30, 1293, 1922), (30, 1294, 1922), (30, 1295, 1922), (30, 1296, 1922), (30, 1297, 1921), (30, 1298, 1921), (30, 1299, 1921), (30, 1300, 1921), (30, 1301, 1920), (30, 1302, 1920), (30, 1303, 1920), (30, 1304, 1920), (30, 1305, 1920), (30, 1306, 1919), (30, 1307, 1919), (30, 1308, 1919), (30, 1309, 1919), (30, 1310, 1918), (30, 1311, 1918), (30, 1312, 1918), (31, 1313, 1917), (31, 1314, 1916), (31, 1315, 1916), (31, 1316, 1916), (31, 1317, 1916), (31, 1318, 1915), (31, 1319, 1915), (31, 1320, 1915), (31, 1321, 1915), (31, 1322, 1914), (31, 1323, 1914), (31, 1324, 1914), (31, 1325, 1914), (31, 1326, 1913), (31, 1327, 1913), (31, 1328, 1913), (31, 1329, 1913), (31, 1330, 1912), (31, 1331, 1912), (31, 1332, 1912), (31, 1333, 1912), (31, 1334, 1911), (31, 1335, 1911), (31, 1336, 1911), (31, 1337, 1911), (31, 1338, 1910), (31, 1339, 1910), (31, 1340, 1910), (32, 1341, 1909), (32, 1342, 1908), (32, 1343, 1908), (32, 1344, 1908), (32, 1345, 1907), (32, 1346, 1907), (32, 1347, 1907), (32, 1348, 1907), (32, 1349, 1906), (32, 1350, 1906), (32, 1351, 1906), (32, 1352, 1906), (32, 1353, 1905), (32, 1354, 1905), (32, 1355, 1905), (32, 1356, 1904), (32, 1357, 1904), (32, 1358, 1904), (32, 1359, 1904), (32, 1360, 1903), (32, 1361, 1903), (32, 1362, 1903), (32, 1363, 1902), (32, 1364, 1902), (32, 1365, 1902), (32, 1366, 1902), (32, 1367, 1901), (33, 1368, 1900), (33, 1369, 1900), (33, 1370, 1899), (33, 1371, 1899), (33, 1372, 1898), (33, 1373, 1898), (33, 1374, 1897), (33, 1375, 1897), (33, 1376, 1896), (33, 1377, 1896), (33, 1378, 1895), (33, 1379, 1895), (33, 1380, 1894), (33, 1381, 1894), (34, 1382, 1892), (34, 1383, 1892), (34, 1384, 1891), (34, 1385, 1890), (34, 1386, 1890), (34, 1387, 1889), (34, 1388, 1889), (34, 1389, 1888), (34, 1390, 1887), (34, 1391, 1887), (34, 1392, 1886), (34, 1393, 1885), (34, 1394, 1885), (34, 1395, 1884), (35, 1396, 1882), (35, 1397, 1882), (35, 1398, 1881), (35, 1399, 1880), (35, 1400, 1879), (35, 1401, 1879), (35, 1402, 1878), (35, 1403, 1877), (35, 1404, 1876), (35, 1405, 1875), (35, 1406, 1875), (35, 1407, 1874), (35, 1408, 1873), (36, 1409, 1871), (36, 1410, 1870), (36, 1411, 1869), (36, 1412, 1868), (36, 1413, 1867), (36, 1414, 1866), (36, 1415, 1866), (36, 1416, 1865), (36, 1417, 1864), (36, 1418, 1863), (36, 1419, 1862), (36, 1420, 1861), (36, 1421, 1861), (37, 1422, 1859), (37, 1423, 1858), (37, 1424, 1857), (37, 1425, 1857), (37, 1426, 1856), (37, 1427, 1855), (37, 1428, 1855), (37, 1429, 1854), (37, 1430, 1853), (37, 1431, 1853), (37, 1432, 1852), (37, 1433, 1851), (37, 1434, 1851), (38, 1435, 1849), (38, 1436, 1849), (38, 1437, 1848), (38, 1438, 1847), (38, 1439, 1847), (38, 1440, 1846), (38, 1441, 1846), (38, 1442, 1845), (38, 1443, 1845), (38, 1444, 1844), (38, 1445, 1844), (38, 1446, 1844), (38, 1447, 1844), (38, 1448, 1843), (38, 1449, 1843), (38, 1450, 1843), (38, 1451, 1843), (38, 1452, 1842), (38, 1453, 1842), (38, 1454, 1842), (39, 1455, 1841), (39, 1456, 1840), (39, 1457, 1840), (39, 1458, 1840), (39, 1459, 1840), (39, 1460, 1839), (39, 1461, 1839), (39, 1462, 1839), (39, 1463, 1839), (39, 1464, 1839), (39, 1465, 1838), (39, 1466, 1838), (39, 1467, 1838), (39, 1468, 1838), (39, 1469, 1837), (39, 1470, 1837), (39, 1471, 1837), (39, 1472, 1837), (39, 1473, 1837), (39, 1474, 1836), (39, 1475, 1836), (39, 1476, 1836), (39, 1477, 1836), (39, 1478, 1835), (39, 1479, 1835), (39, 1480, 1835), (39, 1481, 1835), (39, 1482, 1835), (39, 1483, 1834), (39, 1484, 1834), (39, 1485, 1834), (39, 1486, 1834), (39, 1487, 1834), (39, 1488, 1833), (39, 1489, 1833), (39, 1490, 1833), (39, 1491, 1833), (39, 1492, 1833), (39, 1493, 1832), (39, 1494, 1832), (39, 1495, 1832), (40, 1496, 1831), (40, 1497, 1831), (40, 1498, 1830), (40, 1499, 1830), (40, 1500, 1830), (40, 1501, 1830), (40, 1502, 1830), (40, 1503, 1830), (40, 1504, 1829), (40, 1505, 1829), (40, 1506, 1829), (40, 1507, 1829), (40, 1508, 1829), (40, 1509, 1828), (40, 1510, 1828), (40, 1511, 1828), (40, 1512, 1828), (40, 1513, 1828), (40, 1514, 1828), (40, 1515, 1827), (40, 1516, 1827), (40, 1517, 1827), (40, 1518, 1827), (40, 1519, 1827), (40, 1520, 1827), (40, 1521, 1826), (40, 1522, 1826), (40, 1523, 1826), (40, 1524, 1826), (40, 1525, 1826), (40, 1526, 1826), (40, 1527, 1826), (40, 1528, 1826), (40, 1529, 1826), (40, 1530, 1825), (40, 1531, 1825), (40, 1532, 1825), (40, 1533, 1825), (40, 1534, 1825), (40, 1535, 1825), (40, 1536, 1825), (41, 1537, 1824), (41, 1538, 1823), (41, 1539, 1823), (41, 1540, 1823), (41, 1541, 1823), (41, 1542, 1823), (41, 1543, 1823), (41, 1544, 1823), (41, 1545, 1823), (41, 1546, 1823), (41, 1547, 1822), (41, 1548, 1822), (41, 1549, 1822), (41, 1550, 1822), (41, 1551, 1822), (41, 1552, 1822), (41, 1553, 1822), (41, 1554, 1822), (41, 1555, 1821), (41, 1556, 1821), (41, 1557, 1821), (41, 1558, 1821), (41, 1559, 1821), (41, 1560, 1821), (41, 1561, 1821), (41, 1562, 1821), (41, 1563, 1821), (41, 1564, 1820), (41, 1565, 1820), (41, 1566, 1820), (41, 1567, 1820), (41, 1568, 1820), (41, 1569, 1820), (41, 1570, 1820), (41, 1571, 1820), (41, 1572, 1819), (41, 1573, 1819), (41, 1574, 1819), (41, 1575, 1819), (41, 1576, 1819), (41, 1577, 1819), (41, 1578, 1819), (41, 1579, 1819), (41, 1580, 1818), (42, 1581, 1817), (42, 1582, 1817), (42, 1583, 1817), (42, 1584, 1817), (42, 1585, 1817), (42, 1586, 1817), (42, 1587, 1817), (42, 1588, 1817), (42, 1589, 1816), (42, 1590, 1816), (42, 1591, 1816), (42, 1592, 1816), (42, 1593, 1816), (42, 1594, 1816), (42, 1595, 1816), (42, 1596, 1816), (42, 1597, 1815), (42, 1598, 1815), (42, 1599, 1815), (42, 1600, 1815), (42, 1601, 1815), (41, 1602, 1816), (41, 1603, 1816), (41, 1604, 1816), (41, 1605, 1816), (41, 1606, 1816), (41, 1607, 1815), (41, 1608, 1815), (41, 1609, 1815), (41, 1610, 1815), (41, 1611, 1815), (41, 1612, 1815), (41, 1613, 1815), (41, 1614, 1815), (41, 1615, 1815), (41, 1616, 1814), (41, 1617, 1814), (41, 1618, 1814), (41, 1619, 1814), (41, 1620, 1814), (41, 1621, 1814), (41, 1622, 1814), (41, 1623, 1814), (41, 1624, 1813), (41, 1625, 1813), (41, 1626, 1813), (40, 1627, 1814), (40, 1628, 1814), (40, 1629, 1814), (40, 1630, 1814), (40, 1631, 1814), (40, 1632, 1814), (40, 1633, 1813), (40, 1634, 1813), (40, 1635, 1813), (40, 1636, 1813), (40, 1637, 1813), (40, 1638, 1813), (40, 1639, 1813), (40, 1640, 1813), (40, 1641, 1812), (40, 1642, 1812), (40, 1643, 1812), (40, 1644, 1812), (40, 1645, 1812), (40, 1646, 1812), (40, 1647, 1812), (40, 1648, 1812), (40, 1649, 1811), (40, 1650, 1811), (40, 1651, 1811), (39, 1652, 1812), (39, 1653, 1812), (39, 1654, 1812), (39, 1655, 1812), (39, 1656, 1812), (39, 1657, 1811), (39, 1658, 1811), (39, 1659, 1811), (39, 1660, 1811), (39, 1661, 1811), (39, 1662, 1811), (39, 1663, 1811), (39, 1664, 1810), (39, 1665, 1810), (39, 1666, 1810), (39, 1667, 1810), (39, 1668, 1810), (39, 1669, 1810), (39, 1670, 1810), (39, 1671, 1809), (39, 1672, 1809), (39, 1673, 1809), (39, 1674, 1809), (40, 1675, 1808), (40, 1676, 1807), (40, 1677, 1807), (40, 1678, 1807), (40, 1679, 1807), (40, 1680, 1806), (40, 1681, 1806), (41, 1682, 1805), (41, 1683, 1805), (41, 1684, 1804), (41, 1685, 1804), (41, 1686, 1804), (41, 1687, 1804), (42, 1688, 1802), (42, 1689, 1802), (42, 1690, 1802), (42, 1691, 1802), (42, 1692, 1801), (42, 1693, 1801), (43, 1694, 1800), (43, 1695, 1799), (43, 1696, 1799), (43, 1697, 1799), (43, 1698, 1799), (43, 1699, 1798), (44, 1700, 1797), (44, 1701, 1797), (44, 1702, 1797), (44, 1703, 1796), (44, 1704, 1796), (44, 1705, 1796), (45, 1706, 1794), (45, 1707, 1794), (45, 1708, 1794), (45, 1709, 1793), (45, 1710, 1793), (45, 1711, 1793), (46, 1712, 1792), (46, 1713, 1791), (46, 1714, 1791), (46, 1715, 1791), (46, 1716, 1790), (47, 1717, 1789), (47, 1718, 1789), (47, 1719, 1788), (47, 1720, 1788), (47, 1721, 1788), (47, 1722, 1787), (48, 1723, 1786), (48, 1724, 1786), (48, 1725, 1785), (48, 1726, 1785), (49, 1727, 1784), (49, 1728, 1783), (49, 1729, 1783), (49, 1730, 1783), (49, 1731, 1782), (50, 1732, 1781), (50, 1733, 1780), (50, 1734, 1780), (50, 1735, 1780), (50, 1736, 1779), (51, 1737, 1778), (51, 1738, 1778), (51, 1739, 1777), (51, 1740, 1777), (51, 1741, 1776), (52, 1742, 1775), (52, 1743, 1775), (52, 1744, 1774), (52, 1745, 1774), (52, 1746, 1773), (52, 1747, 1773), (52, 1748, 1773), (52, 1749, 1772), (52, 1750, 1772), (52, 1751, 1771), (52, 1752, 1771), (52, 1753, 1770), (52, 1754, 1770), (52, 1755, 1769), (52, 1756, 1769), (52, 1757, 1769), (52, 1758, 1768), (52, 1759, 1768), (52, 1760, 1767), (52, 1761, 1767), (52, 1762, 1766), (52, 1763, 1766), (52, 1764, 1766), (52, 1765, 1765), (53, 1766, 1764), (53, 1767, 1763), (53, 1768, 1763), (53, 1769, 1763), (53, 1770, 1762), (53, 1771, 1762), (53, 1772, 1761), (53, 1773, 1761), (53, 1774, 1761), (53, 1775, 1760), (53, 1776, 1760), (53, 1777, 1759), (53, 1778, 1759), (53, 1779, 1759), (53, 1780, 1758), (53, 1781, 1758), (53, 1782, 1758), (53, 1783, 1757), (53, 1784, 1757), (53, 1785, 1757), (53, 1786, 1756), (53, 1787, 1756), (53, 1788, 1756), (53, 1789, 1755), (53, 1790, 1755), (53, 1791, 1754), (53, 1792, 1754), (53, 1793, 1754), (53, 1794, 1753), (53, 1795, 1753), (53, 1796, 1753), (53, 1797, 1753), (53, 1798, 1752), (53, 1799, 1752), (53, 1800, 1752), (53, 1801, 1751), (53, 1802, 1751), (53, 1803, 1751), (53, 1804, 1750), (53, 1805, 1750), (53, 1806, 1750), (53, 1807, 1749), (53, 1808, 1749), (53, 1809, 1749), (53, 1810, 1748), (53, 1811, 1748), (53, 1812, 1748), (53, 1813, 1748), (53, 1814, 1747), (53, 1815, 1747), (53, 1816, 1747), (53, 1817, 1746), (53, 1818, 1746), (53, 1819, 1746), (53, 1820, 1746), (53, 1821, 1745), (54, 1822, 1744), (54, 1823, 1744), (55, 1824, 1742), (55, 1825, 1742), (56, 1826, 1741), (56, 1827, 1740), (57, 1828, 1739), (57, 1829, 1739), (58, 1830, 1737), (58, 1831, 1737), (58, 1832, 1737), (59, 1833, 1735), (59, 1834, 1735), (60, 1835, 1734), (60, 1836, 1733), (61, 1837, 1732), (61, 1838, 1731), (62, 1839, 1730), (62, 1840, 1730), (63, 1841, 1728), (63, 1842, 1728), (64, 1843, 1727), (64, 1844, 1726), (65, 1845, 1725), (65, 1846, 1725), (65, 1847, 1724), (66, 1848, 1723), (66, 1849, 1723), (67, 1850, 1721), (67, 1851, 1721), (68, 1852, 1719), (68, 1853, 1719), (69, 1854, 1718), (69, 1855, 1717), (69, 1856, 1717), (70, 1857, 1716), (70, 1858, 1715), (71, 1859, 1714), (71, 1860, 1713), (72, 1861, 1712), (72, 1862, 1712), (73, 1863, 1710), (73, 1864, 1710), (73, 1865, 1710), (74, 1866, 1708), (74, 1867, 1708), (75, 1868, 1706), (75, 1869, 1706), (76, 1870, 1705), (76, 1871, 1704), (76, 1872, 1704), (77, 1873, 1702), (77, 1874, 1702), (78, 1875, 1701), (78, 1876, 1700), (79, 1877, 1699), (79, 1878, 1698), (79, 1879, 1698), (80, 1880, 1697), (80, 1881, 1696), (81, 1882, 1695), (81, 1883, 1694), (82, 1884, 1693), (82, 1885, 1693), (82, 1886, 1692), (83, 1887, 1691), (83, 1888, 1690), (84, 1889, 1689), (84, 1890, 1688), (84, 1891, 1688), (85, 1892, 1687), (85, 1893, 1686), (86, 1894, 1685), (86, 1895, 1684), (87, 1896, 1683), (87, 1897, 1682), (87, 1898, 1682), (88, 1899, 1680), (88, 1900, 1680), (88, 1901, 1680), (89, 1902, 1678), (89, 1903, 1678), (89, 1904, 1677), (90, 1905, 1676), (90, 1906, 1675), (91, 1907, 1674), (91, 1908, 1673), (91, 1909, 1673), (92, 1910, 1671), (92, 1911, 1670), (93, 1912, 1669), (93, 1913, 1668), (93, 1914, 1668), (94, 1915, 1666), (94, 1916, 1666), (95, 1917, 1664), (95, 1918, 1664), (96, 1919, 1662), (96, 1920, 1661), (96, 1921, 1661), (97, 1922, 1659), (97, 1923, 1659), (98, 1924, 1657), (98, 1925, 1656), (99, 1926, 1655), (99, 1927, 1654), (100, 1928, 1653), (100, 1929, 1652), (101, 1930, 1650), (101, 1931, 1650), (102, 1932, 1648), (102, 1933, 1647), (103, 1934, 1646), (103, 1935, 1645), (104, 1936, 1643), (104, 1937, 1643), (105, 1938, 1641), (106, 1939, 1639), (106, 1940, 1639), (107, 1941, 1637), (107, 1942, 1636), (108, 1943, 1634), (109, 1944, 1632), (109, 1945, 1632), (110, 1946, 1630), (110, 1947, 1629), (111, 1948, 1627), (112, 1949, 1625), (112, 1950, 1624), (113, 1951, 1623), (114, 1952, 1621), (114, 1953, 1620), (115, 1954, 1618), (116, 1955, 1616), (116, 1956, 1616), (117, 1957, 1614), (118, 1958, 1612), (119, 1959, 1610), (119, 1960, 1610), (120, 1961, 1608), (121, 1962, 1606), (122, 1963, 79), (213, 1963, 1513), (123, 1964, 65), (219, 1964, 1507), (123, 1965, 54), (226, 1965, 1499), (124, 1966, 42), (233, 1966, 1491), (125, 1967, 32), (240, 1967, 1484), (126, 1968, 22), (246, 1968, 1477), (127, 1969, 12), (253, 1969, 1469), (128, 1970, 3), (260, 1970, 1461), (267, 1971, 1454), (275, 1972, 1445), (282, 1973, 1437), (289, 1974, 1429), (294, 1975, 1423), (299, 1976, 1417), (304, 1977, 1411), (309, 1978, 1405), (315, 1979, 1398), (321, 1980, 1391), (327, 1981, 1384), (334, 1982, 1376), (340, 1983, 1368), (348, 1984, 1359), (355, 1985, 1351), (363, 1986, 1342), (371, 1987, 1333), (375, 1988, 1327), (379, 1989, 1322), (384, 1990, 1316), (388, 1991, 1311), (392, 1992, 1305), (397, 1993, 1299), (402, 1994, 1293), (406, 1995, 1287), (411, 1996, 1281), (416, 1997, 1274), (421, 1998, 1268), (426, 1999, 1261), (431, 2000, 1255), (436, 2001, 1248), (441, 2002, 1242), (447, 2003, 1234), (452, 2004, 1227), (457, 2005, 1221), (462, 2006, 1214), (467, 2007, 1207), (472, 2008, 1201), (477, 2009, 1194), (483, 2010, 1186), (488, 2011, 1179), (493, 2012, 1172), (498, 2013, 1165), (504, 2014, 1133), (509, 2015, 1080), (515, 2016, 1063), (520, 2017, 1054), (526, 2018, 1045), (531, 2019, 1036), (536, 2020, 1027), (541, 2021, 1018), (546, 2022, 1010), (550, 2023, 1002), (555, 2024, 994), (559, 2025, 987), (564, 2026, 978), (568, 2027, 971), (572, 2028, 964), (576, 2029, 957), (580, 2030, 949), (584, 2031, 942), (588, 2032, 935), (592, 2033, 928), (596, 2034, 921), (599, 2035, 916), (603, 2036, 909), (606, 2037, 903), (610, 2038, 896), (613, 2039, 890), (616, 2040, 884), (618, 2041, 873), (620, 2042, 862), (623, 2043, 850), (625, 2044, 839), (627, 2045, 829), (629, 2046, 818), (631, 2047, 808), (633, 2048, 798), (635, 2049, 788), (637, 2050, 781), (638, 2051, 777), (640, 2052, 772), (642, 2053, 767), (644, 2054, 761), (646, 2055, 756), (647, 2056, 752), (649, 2057, 746), (651, 2058, 740), (654, 2059, 734), (656, 2060, 728), (658, 2061, 722), (660, 2062, 716), (662, 2063, 710), (664, 2064, 704), (667, 2065, 697), (669, 2066, 691), (672, 2067, 683), (674, 2068, 677), (677, 2069, 669), (680, 2070, 662), (682, 2071, 654), (685, 2072, 646), (688, 2073, 637), (691, 2074, 628), (694, 2075, 619), (699, 2076, 608), (704, 2077, 596), (708, 2078, 586), (713, 2079, 575), (717, 2080, 564), (722, 2081, 553), (726, 2082, 542), (730, 2083, 532), (735, 2084, 521), (739, 2085, 511), (743, 2086, 502), (747, 2087, 493), (752, 2088, 483), (756, 2089, 475), (760, 2090, 466), (764, 2091, 457), (768, 2092, 449), (772, 2093, 440), (776, 2094, 432), (779, 2095, 425), (782, 2096, 418), (785, 2097, 411), (788, 2098, 404), (792, 2099, 396), (795, 2100, 389), (798, 2101, 383), (801, 2102, 376), (804, 2103, 371), (807, 2104, 366), (810, 2105, 360), (813, 2106, 355), (816, 2107, 350), (819, 2108, 345), (822, 2109, 340), (825, 2110, 335), (828, 2111, 330), (831, 2112, 325), (834, 2113, 320), (836, 2114, 316), (839, 2115, 311), (842, 2116, 306), (845, 2117, 301), (848, 2118, 296), (850, 2119, 293), (853, 2120, 288), (856, 2121, 283), (859, 2122, 279), (862, 2123, 274), (864, 2124, 270), (867, 2125, 265), (871, 2126, 259), (874, 2127, 254), (877, 2128, 249), (880, 2129, 244), (883, 2130, 239), (887, 2131, 233), (890, 2132, 228), (894, 2133, 222), (897, 2134, 217), (901, 2135, 210), (904, 2136, 205), (908, 2137, 198), (912, 2138, 192), (916, 2139, 185), (920, 2140, 179), (924, 2141, 170), (928, 2142, 158), (932, 2143, 146), (938, 2144, 132), (949, 2145, 113), (962, 2146, 91), (974, 2147, 71), (987, 2148, 49), (1000, 2149, 27), (1014, 2150, 4)], ['949,2145,784,2096,694,2075,609,2037,370,1986,212,1962,128,1970,96,1921,53,1821,52,1742,39,1674,39,1455,29,1243,29,952,21,671,38,462,93,309,194,216,278,183,370,133,523,124,1426,124,1577,129,1664,141,1810,190,1904,206,2015,299,2081,385,2150,539,2169,621,2172,706,2165,839,2128,917,2112,995,2081,1069,2036,1123,1997,1216,1958,1274,1932,1369,1881,1444,1867,1509,1847,1675,1774,1885,1744,1940,1662,2013,1578,2015,1499,2040,1423,2048,1170,2104,1098,2140'])], 'temp/1743157274_1341668_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3684414 proportion of common points : 0.9978655551628459 #&_# TEST FAILED #&_# : 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 : 0.2664158344268799 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 Fri Mar 28 11:21:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0020990371704101562 nb_pixel_total : 3781 time to create 1 rle with old method : 0.009196996688842773 time for calcul the mask position with numpy : 0.0022001266479492188 nb_pixel_total : 83667 time to create 1 rle with old method : 0.21527767181396484 time for calcul the mask position with numpy : 0.0017123222351074219 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0034515857696533203 time for calcul the mask position with numpy : 0.0074901580810546875 nb_pixel_total : 13976 time to create 1 rle with old method : 0.03379631042480469 time for calcul the mask position with numpy : 0.0018527507781982422 nb_pixel_total : 6603 time to create 1 rle with old method : 0.01636648178100586 time for calcul the mask position with numpy : 0.001634359359741211 nb_pixel_total : 5926 time to create 1 rle with old method : 0.01474618911743164 time for calcul the mask position with numpy : 0.0015828609466552734 nb_pixel_total : 3928 time to create 1 rle with old method : 0.009836912155151367 time for calcul the mask position with numpy : 0.0015974044799804688 nb_pixel_total : 2351 time to create 1 rle with old method : 0.0061492919921875 time for calcul the mask position with numpy : 0.002067089080810547 nb_pixel_total : 39143 time to create 1 rle with old method : 0.09606695175170898 time for calcul the mask position with numpy : 0.0017304420471191406 nb_pixel_total : 10846 time to create 1 rle with old method : 0.027199268341064453 time for calcul the mask position with numpy : 0.001664876937866211 nb_pixel_total : 14743 time to create 1 rle with old method : 0.03633928298950195 time for calcul the mask position with numpy : 0.0016145706176757812 nb_pixel_total : 5630 time to create 1 rle with old method : 0.014363765716552734 time for calcul the mask position with numpy : 0.0015282630920410156 nb_pixel_total : 2379 time to create 1 rle with old method : 0.006051301956176758 time for calcul the mask position with numpy : 0.0015170574188232422 nb_pixel_total : 2939 time to create 1 rle with old method : 0.0073587894439697266 time for calcul the mask position with numpy : 0.001880645751953125 nb_pixel_total : 29431 time to create 1 rle with old method : 0.07525968551635742 time for calcul the mask position with numpy : 0.0016469955444335938 nb_pixel_total : 7502 time to create 1 rle with old method : 0.018326520919799805 time for calcul the mask position with numpy : 0.0015130043029785156 nb_pixel_total : 2335 time to create 1 rle with old method : 0.006452798843383789 time for calcul the mask position with numpy : 0.0015690326690673828 nb_pixel_total : 4275 time to create 1 rle with old method : 0.010519742965698242 time for calcul the mask position with numpy : 0.0017426013946533203 nb_pixel_total : 27277 time to create 1 rle with old method : 0.06690073013305664 time for calcul the mask position with numpy : 0.0015292167663574219 nb_pixel_total : 1653 time to create 1 rle with old method : 0.004061698913574219 time for calcul the mask position with numpy : 0.0014750957489013672 nb_pixel_total : 3098 time to create 1 rle with old method : 0.007639169692993164 time for calcul the mask position with numpy : 0.0018222332000732422 nb_pixel_total : 1200 time to create 1 rle with old method : 0.002934694290161133 time for calcul the mask position with numpy : 0.0014986991882324219 nb_pixel_total : 2079 time to create 1 rle with old method : 0.005251169204711914 time for calcul the mask position with numpy : 0.0016684532165527344 nb_pixel_total : 16457 time to create 1 rle with old method : 0.04455208778381348 time for calcul the mask position with numpy : 0.0015895366668701172 nb_pixel_total : 4280 time to create 1 rle with old method : 0.012549400329589844 time for calcul the mask position with numpy : 0.0018925666809082031 nb_pixel_total : 5509 time to create 1 rle with old method : 0.01314854621887207 time for calcul the mask position with numpy : 0.0015952587127685547 nb_pixel_total : 16238 time to create 1 rle with old method : 0.03634810447692871 time for calcul the mask position with numpy : 0.0015316009521484375 nb_pixel_total : 1506 time to create 1 rle with old method : 0.003561735153198242 time for calcul the mask position with numpy : 0.0014357566833496094 nb_pixel_total : 5438 time to create 1 rle with old method : 0.012567520141601562 time for calcul the mask position with numpy : 0.0015838146209716797 nb_pixel_total : 11912 time to create 1 rle with old method : 0.027372121810913086 time for calcul the mask position with numpy : 0.0018091201782226562 nb_pixel_total : 8642 time to create 1 rle with old method : 0.01987433433532715 time for calcul the mask position with numpy : 0.0015347003936767578 nb_pixel_total : 3942 time to create 1 rle with old method : 0.009424209594726562 time for calcul the mask position with numpy : 0.0015404224395751953 nb_pixel_total : 9877 time to create 1 rle with old method : 0.02345585823059082 time for calcul the mask position with numpy : 0.0015277862548828125 nb_pixel_total : 3536 time to create 1 rle with old method : 0.008620262145996094 time for calcul the mask position with numpy : 0.0014874935150146484 nb_pixel_total : 4134 time to create 1 rle with old method : 0.009973287582397461 time for calcul the mask position with numpy : 0.0015556812286376953 nb_pixel_total : 2804 time to create 1 rle with old method : 0.006840229034423828 time for calcul the mask position with numpy : 0.0015223026275634766 nb_pixel_total : 1629 time to create 1 rle with old method : 0.004032611846923828 time for calcul the mask position with numpy : 0.0014939308166503906 nb_pixel_total : 2499 time to create 1 rle with old method : 0.005934715270996094 time for calcul the mask position with numpy : 0.001520395278930664 nb_pixel_total : 2773 time to create 1 rle with old method : 0.0066301822662353516 time for calcul the mask position with numpy : 0.0014674663543701172 nb_pixel_total : 1315 time to create 1 rle with old method : 0.003303050994873047 time for calcul the mask position with numpy : 0.0014903545379638672 nb_pixel_total : 10537 time to create 1 rle with old method : 0.024842500686645508 time for calcul the mask position with numpy : 0.0015037059783935547 nb_pixel_total : 3329 time to create 1 rle with old method : 0.00799107551574707 time for calcul the mask position with numpy : 0.0015268325805664062 nb_pixel_total : 1029 time to create 1 rle with old method : 0.0025322437286376953 time for calcul the mask position with numpy : 0.0014870166778564453 nb_pixel_total : 8427 time to create 1 rle with old method : 0.019804716110229492 time for calcul the mask position with numpy : 0.0015802383422851562 nb_pixel_total : 12921 time to create 1 rle with old method : 0.030378103256225586 time for calcul the mask position with numpy : 0.001505136489868164 nb_pixel_total : 342 time to create 1 rle with old method : 0.0008723735809326172 time for calcul the mask position with numpy : 0.0014200210571289062 nb_pixel_total : 1249 time to create 1 rle with old method : 0.002950429916381836 time for calcul the mask position with numpy : 0.0014545917510986328 nb_pixel_total : 12966 time to create 1 rle with old method : 0.029580354690551758 time for calcul the mask position with numpy : 0.0013947486877441406 nb_pixel_total : 4176 time to create 1 rle with old method : 0.00974726676940918 time for calcul the mask position with numpy : 0.001363992691040039 nb_pixel_total : 850 time to create 1 rle with old method : 0.002199888229370117 time for calcul the mask position with numpy : 0.0013303756713867188 nb_pixel_total : 876 time to create 1 rle with old method : 0.002218008041381836 time for calcul the mask position with numpy : 0.0013418197631835938 nb_pixel_total : 890 time to create 1 rle with old method : 0.0021915435791015625 time for calcul the mask position with numpy : 0.0013628005981445312 nb_pixel_total : 595 time to create 1 rle with old method : 0.0013608932495117188 time for calcul the mask position with numpy : 0.0014383792877197266 nb_pixel_total : 2341 time to create 1 rle with old method : 0.005243539810180664 time for calcul the mask position with numpy : 0.0013580322265625 nb_pixel_total : 2420 time to create 1 rle with old method : 0.005711793899536133 time for calcul the mask position with numpy : 0.0014259815216064453 nb_pixel_total : 885 time to create 1 rle with old method : 0.0021593570709228516 time for calcul the mask position with numpy : 0.0013668537139892578 nb_pixel_total : 1602 time to create 1 rle with old method : 0.003711700439453125 time for calcul the mask position with numpy : 0.001428842544555664 nb_pixel_total : 577 time to create 1 rle with old method : 0.0014238357543945312 time for calcul the mask position with numpy : 0.0013332366943359375 nb_pixel_total : 337 time to create 1 rle with old method : 0.0008573532104492188 time for calcul the mask position with numpy : 0.0014300346374511719 nb_pixel_total : 693 time to create 1 rle with old method : 0.0016477108001708984 time for calcul the mask position with numpy : 0.0014233589172363281 nb_pixel_total : 583 time to create 1 rle with old method : 0.0013790130615234375 time for calcul the mask position with numpy : 0.0014383792877197266 nb_pixel_total : 1686 time to create 1 rle with old method : 0.0040433406829833984 time for calcul the mask position with numpy : 0.0014767646789550781 nb_pixel_total : 874 time to create 1 rle with old method : 0.0020904541015625 time for calcul the mask position with numpy : 0.00146484375 nb_pixel_total : 1075 time to create 1 rle with old method : 0.002599954605102539 time for calcul the mask position with numpy : 0.0014309883117675781 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0025489330291748047 time for calcul the mask position with numpy : 0.0014984607696533203 nb_pixel_total : 16682 time to create 1 rle with old method : 0.03868889808654785 time for calcul the mask position with numpy : 0.0013775825500488281 nb_pixel_total : 8600 time to create 1 rle with old method : 0.019847869873046875 time for calcul the mask position with numpy : 0.0014104843139648438 nb_pixel_total : 3170 time to create 1 rle with old method : 0.009014129638671875 time for calcul the mask position with numpy : 0.0016088485717773438 nb_pixel_total : 39231 time to create 1 rle with old method : 0.09114265441894531 time for calcul the mask position with numpy : 0.0016007423400878906 nb_pixel_total : 1732 time to create 1 rle with old method : 0.0040738582611083984 time for calcul the mask position with numpy : 0.0014929771423339844 nb_pixel_total : 967 time to create 1 rle with old method : 0.0023355484008789062 time for calcul the mask position with numpy : 0.001483917236328125 nb_pixel_total : 2772 time to create 1 rle with old method : 0.006719112396240234 time for calcul the mask position with numpy : 0.0017924308776855469 nb_pixel_total : 9676 time to create 1 rle with old method : 0.02263355255126953 time for calcul the mask position with numpy : 0.0013821125030517578 nb_pixel_total : 1500 time to create 1 rle with old method : 0.003551006317138672 time for calcul the mask position with numpy : 0.0014486312866210938 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006561279296875 time for calcul the mask position with numpy : 0.0013399124145507812 nb_pixel_total : 1340 time to create 1 rle with old method : 0.003228425979614258 time for calcul the mask position with numpy : 0.001316070556640625 nb_pixel_total : 713 time to create 1 rle with old method : 0.0017595291137695312 time for calcul the mask position with numpy : 0.0014390945434570312 nb_pixel_total : 7443 time to create 1 rle with old method : 0.01725602149963379 time for calcul the mask position with numpy : 0.0015866756439208984 nb_pixel_total : 18503 time to create 1 rle with old method : 0.04309439659118652 time for calcul the mask position with numpy : 0.0014667510986328125 nb_pixel_total : 621 time to create 1 rle with old method : 0.0015316009521484375 time for calcul the mask position with numpy : 0.0014231204986572266 nb_pixel_total : 276 time to create 1 rle with old method : 0.0007426738739013672 time for calcul the mask position with numpy : 0.001399993896484375 nb_pixel_total : 248 time to create 1 rle with old method : 0.0006573200225830078 time for calcul the mask position with numpy : 0.0014317035675048828 nb_pixel_total : 975 time to create 1 rle with old method : 0.002341747283935547 time for calcul the mask position with numpy : 0.0013587474822998047 nb_pixel_total : 9079 time to create 1 rle with old method : 0.021793603897094727 time for calcul the mask position with numpy : 0.0015799999237060547 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006155967712402344 time for calcul the mask position with numpy : 0.0014760494232177734 nb_pixel_total : 735 time to create 1 rle with old method : 0.0019123554229736328 time for calcul the mask position with numpy : 0.0014462471008300781 nb_pixel_total : 1632 time to create 1 rle with old method : 0.003980398178100586 time for calcul the mask position with numpy : 0.001444101333618164 nb_pixel_total : 1417 time to create 1 rle with old method : 0.003471851348876953 time for calcul the mask position with numpy : 0.0014688968658447266 nb_pixel_total : 7498 time to create 1 rle with old method : 0.017244815826416016 time for calcul the mask position with numpy : 0.0013926029205322266 nb_pixel_total : 5013 time to create 1 rle with old method : 0.011796951293945312 time for calcul the mask position with numpy : 0.0014042854309082031 nb_pixel_total : 2258 time to create 1 rle with old method : 0.005453586578369141 time for calcul the mask position with numpy : 0.0013363361358642578 nb_pixel_total : 595 time to create 1 rle with old method : 0.0014429092407226562 time for calcul the mask position with numpy : 0.0014293193817138672 nb_pixel_total : 1124 time to create 1 rle with old method : 0.00267791748046875 time for calcul the mask position with numpy : 0.0016977787017822266 nb_pixel_total : 39070 time to create 1 rle with old method : 0.08890080451965332 time for calcul the mask position with numpy : 0.0013861656188964844 nb_pixel_total : 890 time to create 1 rle with old method : 0.002050161361694336 time for calcul the mask position with numpy : 0.0013964176177978516 nb_pixel_total : 297 time to create 1 rle with old method : 0.0008382797241210938 time for calcul the mask position with numpy : 0.0014297962188720703 nb_pixel_total : 942 time to create 1 rle with old method : 0.002276897430419922 time for calcul the mask position with numpy : 0.0014328956604003906 nb_pixel_total : 2198 time to create 1 rle with old method : 0.005412101745605469 time for calcul the mask position with numpy : 0.0014526844024658203 nb_pixel_total : 818 time to create 1 rle with old method : 0.002056121826171875 time for calcul the mask position with numpy : 0.0013318061828613281 nb_pixel_total : 948 time to create 1 rle with old method : 0.0023806095123291016 time for calcul the mask position with numpy : 0.0014486312866210938 nb_pixel_total : 483 time to create 1 rle with old method : 0.0012128353118896484 time for calcul the mask position with numpy : 0.001432657241821289 nb_pixel_total : 1211 time to create 1 rle with old method : 0.0030050277709960938 time for calcul the mask position with numpy : 0.0014655590057373047 nb_pixel_total : 16543 time to create 1 rle with old method : 0.03734755516052246 batch 1 Loaded 103 chid ids of type : 4677 Number RLEs to save : 9918 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 : 0.15919089317321777 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.373746633529663 time spend to save output : 0.15953969955444336 total time spend for step 1 : 11.533286333084106 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1743157305_1341668_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 103 ############################### TEST frcnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : frcnn list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.15153813362121582 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 Fri Mar 28 11:21:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1743157316_1341668_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg image_size (600, 800, 3) [[[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] ... [[ 14 16 16] [ 13 15 15] [ 11 13 13] ... [198 206 205] [198 206 205] [198 206 205]] [[ 16 18 18] [ 14 16 16] [ 11 13 13] ... [206 214 213] [206 214 213] [206 214 213]] [[ 13 15 15] [ 12 14 14] [ 9 11 11] ... [210 218 217] [210 218 217] [210 218 217]]] Detection took 0.110s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 2.6192142963409424 time spend to save output : 0.00013494491577148438 total time spend for step 1 : 2.619349241256714 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False Inside saveFrcnn : final : True verbose : False threshold to save the result : 0.1 Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.011770009994506836 [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.012564659118652344 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.06383158, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05221642, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012269998, None)], 'temp/1743157316_1341668_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.13875126838684082 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Fri Mar 28 11:21:59 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 355 time to import caffe and check if the image exist : 0.009870052337646484 time to convert the images to numpy array : 0.0016872882843017578 total time to convert the images to numpy array : 0.011835575103759766 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__port_506135,astra_1027_gao__port_506215,2_1027_gao__port_506151,doblo_1027_gao__port_506251,807_1027_gao__port_506152,206_1027_gao__port_506126,a7_1027_gao__port_506373,renegade_1027_gao__port_506346', 'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'c_elysee_1027_gao__port_506302,mokka_1027_gao__port_506374,captur_1027_gao__port_506399,sorento_1027_gao__port_506192,navara_1027_gao__port_506205,xc90_1027_gao__port_506350,saxo_1027_gao__port_506052,trafic_1027_gao__port_506295,punto_evo_1027_gao__port_506066,5_1027_gao__port_506117,250_1027_gao__port_506065,d_max_1027_gao__port_506125,panamera_1027_gao__port_506387,alhambra_1027_gao__port_506381,x6_1027_gao__port_506349,vitara_1027_gao__port_506328,fiesta_1027_gao__port_506377,qashqai_1027_gao__port_506286,147_1027_gao__port_506124,c5_1027_gao__port_506172,q5_1027_gao__port_506206,giulia_1027_gao__port_506178,karl_1027_gao__port_506371,mehari_1027_gao__port_506076,911_1027_gao__port_506114,508_1027_gao__port_506329,idea_1027_gao__port_506122,megane_1027_gao__port_506220,ghibli_1027_gao__port_506174,touareg_1027_gao__port_506224,i10_1027_gao__port_506232,jumper_1027_gao__port_506234,classe_clk_1027_gao__port_506173,kuga_1027_gao__port_506181,ct_1027_gao__port_506323,leon_1027_gao__port_506326,ds5_1027_gao__port_506376,cordoba_1027_gao__port_506048,classe_cla_1027_gao__port_506400,jumpy_1027_gao__port_506179,avensis_1027_gao__port_506311,juke_1027_gao__port_506325,4008_1027_gao__port_506402,190_series_1027_gao__port_506051,serie_3_1027_gao__port_506294,q7_1027_gao__port_506318,glc_1027_gao__port_506303,grand_vitara_1027_gao__port_506175,s40_1027_gao__port_506099,toledo_1027_gao__port_506061,5008_1027_gao__port_506337,continental_1027_gao__port_506250,coupe_1027_gao__port_506082,iq_1027_gao__port_506166,407_1027_gao__port_506133,touran_1027_gao__port_506308,300c_1027_gao__port_506078,classe_gl_1027_gao__port_506340,vivaro_1027_gao__port_506310,sl_1027_gao__port_506100,elise_1027_gao__port_506121,1007_1027_gao__port_506070,i40_1027_gao__port_506218,bipper_tepee_1027_gao__port_506227,focus_1027_gao__port_506272,primera_1027_gao__port_506147,r4_1027_gao__port_506160,a8_1027_gao__port_506265,boxer_1027_gao__port_506202,s5_1027_gao__port_506222,r21_1027_gao__port_506093,c3_1027_gao__port_506257,santa_fe_1027_gao__port_506208,m4_1027_gao__port_506344,safrane_1027_gao__port_506077,classe_gle_1027_gao__port_506395,0_1027_gao__port_506094,ix35_1027_gao__port_506219,carens_1027_gao__port_506298,classe_a_1027_gao__port_506339,ix20_1027_gao__port_506343,note_1027_gao__port_506365,a5_1027_gao__port_506200,sx4_1027_gao__port_506348,sandero_1027_gao__port_506198,3008_1027_gao__port_506385,q50_1027_gao__port_506239,latitude_1027_gao__port_506236,v40_1027_gao__port_506391,xsara_1027_gao__port_506087,grand_c_max_1027_gao__port_506342,swift_1027_gao__port_506149,serie_1_1027_gao__port_506184,xc70_1027_gao__port_506393,master_1027_gao__port_506203,clio_1027_gao__port_506280,duster_1027_gao__port_506216,traveller_1027_gao__port_506403,tipo_1027_gao__port_506355,rav_4_1027_gao__port_506332,coccinelle_1027_gao__port_506259,spacetourer_1027_gao__port_506401,xe_1027_gao__port_506357,ds3_1027_gao__port_506324,mx_5_1027_gao__port_506098,land_cruiser_1027_gao__port_506315,classe_b_1027_gao__port_506335,806_1027_gao__port_506088,rx_8_1027_gao__port_506046,spark_1027_gao__port_506185,6_1027_gao__port_506171,bravo_1027_gao__port_506080,nx_1027_gao__port_506345,sharan_1027_gao__port_506347,x_type_1027_gao__port_506067,jimny_1027_gao__port_506233,wrangler_1027_gao__port_506225,c_crosser_1027_gao__port_506312,v70_1027_gao__port_506278,classe_e_1027_gao__port_506300,classe_v_1027_gao__port_506258,m3_1027_gao__port_506182,abarth_500_1027_gao__port_506226,serie_6_1027_gao__port_506262,modus_1027_gao__port_506146,3_1027_gao__port_506113,405_1027_gao__port_506108,allroad_1027_gao__port_506297,auris_1027_gao__port_506322,galaxy_1027_gao__port_506143,giulietta_1027_gao__port_506363,106_1027_gao__port_506073,classe_m_1027_gao__port_506154,espace_1027_gao__port_506313,panda_1027_gao__port_506189,rcz_1027_gao__port_506197,4007_1027_gao__port_506162,classe_cl_1027_gao__port_506249,leaf_1027_gao__port_506139,octavia_1027_gao__port_506237,ds4_1027_gao__port_506336,freelander_1027_gao__port_506084,evasion_1027_gao__port_506109,punto_1027_gao__port_506106,2cv_1027_gao__port_506045,x4_1027_gao__port_506392,antara_1027_gao__port_506247,murano_1027_gao__port_506316,alto_1027_gao__port_506201,meriva_1027_gao__port_506353,orlando_1027_gao__port_506305,new_beetle_1027_gao__port_506050,306_1027_gao__port_506145,tiguan_1027_gao__port_506362,s_type_1027_gao__port_506101,c1_1027_gao__port_506128,vectra_1027_gao__port_506044,outlander_1027_gao__port_506317,307_1027_gao__port_506074,a6_s6_1027_gao__port_506134,nemo_combi_1027_gao__port_506196,berlingo_1027_gao__port_506194,partner_1027_gao__port_506285,cayenne_1027_gao__port_506177,quattroporte_1027_gao__port_506240,c_max_1027_gao__port_506282,fabia_1027_gao__port_506396,cx_3_1027_gao__port_506281,x_trail_1027_gao__port_506264,scirocco_1027_gao__port_506276,matiz_1027_gao__port_506144,tigra_1027_gao__port_506069,escort_1027_gao__port_506091,c2_1027_gao__port_506081,mini_1027_gao__port_506168,i30_1027_gao__port_506291,picanto_1027_gao__port_506238,mito_1027_gao__port_506072,impreza_1027_gao__port_506085,kangoo_1027_gao__port_506235,a4_1027_gao__port_506193,cayman_1027_gao__port_506268,sportage_1027_gao__port_506148,up_1027_gao__port_506356,optima_1027_gao__port_506386,defender_1027_gao__port_506229,serie_2_1027_gao__port_506256,edge_1027_gao__port_506187,r19_1027_gao__port_506110,jetta_1027_gao__port_506304,eos_1027_gao__port_506115,accord_1027_gao__port_506214,yaris_1027_gao__port_506334,classe_cls_1027_gao__port_506289,polo_1027_gao__port_506361,serie_4_1027_gao__port_506366,mini_cabriolet_1027_gao__port_506204,prius_1027_gao__port_506190,lodgy_1027_gao__port_506188,serie_7_1027_gao__port_506307,c15_1027_gao__port_506055,kadjar_1027_gao__port_506389,insignia_1027_gao__port_506364,308_1027_gao__port_506279,roomster_1027_gao__port_506241,80_1027_gao__port_506057,309_1027_gao__port_506063,tucson_1027_gao__port_506320,x3_1027_gao__port_506212,xf_1027_gao__port_506263,2008_1027_gao__port_506394,passat_1027_gao__port_506306,compass_1027_gao__port_506260,twingo_1027_gao__port_506309,micra_1027_gao__port_506221,golf_1027_gao__port_506155,soul_1027_gao__port_506176,rapid_1027_gao__port_506398,forester_1027_gao__port_506360,slk_1027_gao__port_506210,forfour_1027_gao__port_506341,serie_5_1027_gao__port_506209,xj_1027_gao__port_506170,pajero_1027_gao__port_506097,agila_1027_gao__port_506119,a6_1027_gao__port_506163,fox_1027_gao__port_506092,boxster_1027_gao__port_506267,altea_1027_gao__port_506246,samurai_1027_gao__port_506047,trax_1027_gao__port_506296,getz_1027_gao__port_506058,cherokee_1027_gao__port_506269,koleos_1027_gao__port_506378,z_series_1027_gao__port_506123,ecosport_1027_gao__port_506271,space_star_1027_gao__port_506277,rs3_sportback_1027_gao__port_506207,civic_1027_gao__port_506141,talisman_1027_gao__port_506390,f_pace_1027_gao__port_506314,classe_c_1027_gao__port_506299,tt_1027_gao__port_506075,pathfinder_1027_gao__port_506183,156_1027_gao__port_506157,cx_5_1027_gao__port_506228,scenic_1027_gao__port_506255,yeti_1027_gao__port_506358,mustang_1027_gao__port_506053,stilo_1027_gao__port_506060,ateca_1027_gao__port_506382,fiorino_1027_gao__port_506217,classe_glk_1027_gao__port_506290,fortwo_1027_gao__port_506230,cruze_1027_gao__port_506186,107_1027_gao__port_506213,aygo_1027_gao__port_506248,rx_1027_gao__port_506354,500_1027_gao__port_506245,bora_1027_gao__port_506104,transit_1027_gao__port_506111,pt_cruiser_1027_gao__port_506054,patrol_1027_gao__port_506068,r8_1027_gao__port_506156,xm_1027_gao__port_506102,s60_1027_gao__port_506191,aveo_1027_gao__port_506158,captiva_1027_gao__port_506159,ax_1027_gao__port_506153,rexton_1027_gao__port_506107,camaro_1027_gao__port_506056,ypsilon_1027_gao__port_506131,delta_1027_gao__port_506165,c4_1027_gao__port_506370,zx_1027_gao__port_506161,verso_1027_gao__port_506242,superb_1027_gao__port_506327,r5_1027_gao__port_506253,caddy_1027_gao__port_506330,x5_1027_gao__port_506243,f_type_1027_gao__port_506231,fusion_1027_gao__port_506096,dokker_1027_gao__port_506331,205_1027_gao__port_506062,macan_1027_gao__port_506195,tourneo_1027_gao__port_506369,108_1027_gao__port_506384,9_3_1027_gao__port_506071,mondeo_1027_gao__port_506116,cr_v_1027_gao__port_506164,c30_1027_gao__port_506090,pulsar_1027_gao__port_506397,ibiza_1027_gao__port_506273,a1_1027_gao__port_506338,matrix_1027_gao__port_506140,carnival_1027_gao__port_506136,xantia_1027_gao__port_506086,terrano_1027_gao__port_506083,q3_1027_gao__port_506275,hr_v_1027_gao__port_506283,expert_1027_gao__port_506142,multivan_1027_gao__port_506383,venga_1027_gao__port_506380,scudo_1027_gao__port_506129,laguna_1027_gao__port_506368,vel_satis_1027_gao__port_506130,b_max_1027_gao__port_506367,ignis_1027_gao__port_506292,159_1027_gao__port_506064,grande_punto_1027_gao__port_506138,logan_1027_gao__port_506167,s_max_1027_gao__port_506223,caravelle_1027_gao__port_506351,adam_1027_gao__port_506079,406_1027_gao__port_506132,q30_1027_gao__port_506293,almera_1027_gao__port_506089,corsa_1027_gao__port_506095,corolla_1027_gao__port_506120,xc60_1027_gao__port_506388,viano_1027_gao__port_506211,pro_cee_d_1027_gao__port_506274,a3_1027_gao__port_506321,v50_1027_gao__port_506150,voyager_1027_gao__port_506169,corvette_1027_gao__port_506049,rio_1027_gao__port_506379,jazz_1027_gao__port_506252,200_1027_gao__port_506112,tts_1027_gao__port_506199,zafira_1027_gao__port_506287,asx_1027_gao__port_506266,607_1027_gao__port_506118,207_1027_gao__port_506103,classe_s_1027_gao__port_506301,c6_1027_gao__port_506105,express_1027_gao__port_506137,classe_gla_1027_gao__port_506352,v60_1027_gao__port_506333,ka_1027_gao__port_506180,range_rover_1027_gao__port_506254,discovery_1027_gao__port_506375,classe_r_1027_gao__port_506270,transporter_1027_gao__port_506319,cee_d_1027_gao__port_506288,zoe_1027_gao__port_506244,i20_1027_gao__port_506284,gtv_1027_gao__port_506059,s4_avant_1027_gao__port_506261,x1_1027_gao__port_506372,autres_1027_gao__port_506127,208_1027_gao__port_506359,c8_1027_gao__port_506135,astra_1027_gao__port_506215,2_1027_gao__port_506151,doblo_1027_gao__port_506251,807_1027_gao__port_506152,206_1027_gao__port_506126,a7_1027_gao__port_506373,renegade_1027_gao__port_506346', 'svm_portfolios_learning': '506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6496 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : car_360_1027 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6496 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 0.009189844131469727 time used to do the prediction : 0.06013679504394531 save descriptor for thcl : 355 time to traite the descriptors : 0.06499433517456055 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 1.1345241069793701 Inside saveOutput : final : False verbose : False time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 1.33514404296875e-05 save missing photos in datou_result : time spend for datou_step_exec : 6.245959043502808 time spend to save output : 1.5310418605804443 total time spend for step 1 : 7.777000904083252 step2:argmax Fri Mar 28 11:22:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 355 Inside saveOutput : final : True verbose : False photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.017710606, 332, '355'), 'temp/1743157319_1341668_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.009806394577026367 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.01300048828125 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.013333797454833984 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 5.9604644775390625e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0004031658172607422 time spend to save output : 0.03652620315551758 total time spend for step 2 : 0.03692936897277832 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.017710606, 332, '355'), 'temp/1743157319_1341668_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12835 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12836 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.3234241008758545 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Fri Mar 28 11:22:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory 2025-03-28 11:22:11.443612: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-03-28 11:22:11.444419: 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 2025-03-28 11:22:11.444547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:22:11.444603: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:22:11.447058: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:22:11.447165: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:22:11.449425: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:22:11.450545: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:22:11.455094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:22:11.456378: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:22:11.456852: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-03-28 11:22:11.487152: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-03-28 11:22:11.489275: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0bf4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-03-28 11:22:11.489331: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-03-28 11:22:11.493264: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x29ced5c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-03-28 11:22:11.493321: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-03-28 11:22:11.494624: 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 2025-03-28 11:22:11.494771: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:22:11.494805: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-03-28 11:22:11.494929: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-03-28 11:22:11.494998: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-03-28 11:22:11.495049: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-03-28 11:22:11.495101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-03-28 11:22:11.495153: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-03-28 11:22:11.496845: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-03-28 11:22:11.496942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-03-28 11:22:11.497026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-03-28 11:22:11.497044: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-03-28 11:22:11.497059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-03-28 11:22:11.498941: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) l 3637 free memory gpu now : 6496 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 12.015971899032593 time used to load_weights : 0.1558833122253418 0it [00:00, ?it/s] 3it [00:00, 1089.34it/s]2025-03-28 11:22:26.316433: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1743157327_1341668_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1743157327_1341668_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg temp/1743157327_1341668_1171252764_29d5179a892cc50aadc9d67245534b59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.34968900680542 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 time to traite the descriptors : 0.02995467185974121 storage_type for insertDescriptorsMulti : 3 To insert : 1171252487 To insert : 1171252784 To insert : 1171252764 time to insert the descriptors : 1.0015075206756592 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252487, 1171252784, 1171252764] Looping around the photos to save general results len do output : 3 /1171252487Didn't retrieve data . /1171252784Didn't retrieve data . /1171252764Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.012423992156982422 save_final save missing photos in datou_result : time spend for datou_step_exec : 22.45177984237671 time spend to save output : 0.01295328140258789 total time spend for step 1 : 22.464733123779297 step2:argmax Fri Mar 28 11:22:30 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 Argmax ! calculate argmax for thcl : 3609 Inside saveOutput : final : True verbose : False photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.926258, 4674, '3609'), 'temp/1743157327_1341668_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.967761, 4674, '3609'), 'temp/1743157327_1341668_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.98536855, 4674, '3609'), 'temp/1743157327_1341668_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.01240086555480957 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.014019489288330078 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.013030529022216797 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 6.4373016357421875e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00018358230590820312 time spend to save output : 0.04473686218261719 total time spend for step 2 : 0.04492044448852539 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252487': [(1171252487, 'jrm', 0.926258, 4674, '3609'), 'temp/1743157327_1341668_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'], '1171252784': [(1171252784, 'jrm', 0.967761, 4674, '3609'), 'temp/1743157327_1341668_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'], '1171252764': [(1171252764, 'jrm', 0.98536855, 4674, '3609'), 'temp/1743157327_1341668_1171252764_29d5179a892cc50aadc9d67245534b59.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.21927237510681152 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Fri Mar 28 11:22:30 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 TFHub with tf2 ! we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory inside check gpu memory l 3637 free memory gpu now : 2944 max_wait_temp : 6 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_1[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_2[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 11.23012900352478 time used to load_weights : 0.17888879776000977 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.420372486114502 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 time to traite the descriptors : 0.045738935470581055 storage_type for insertDescriptorsMulti : 3 To insert : 1171275372 To insert : 1171275314 To insert : 1171291875 time to insert the descriptors : 1.3363831043243408 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171275372, 1171275314, 1171291875] Looping around the photos to save general results len do output : 3 /1171275372Didn't retrieve data . /1171275314Didn't retrieve data . /1171291875Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.014552116394042969 save_final save missing photos in datou_result : time spend for datou_step_exec : 23.792731046676636 time spend to save output : 0.015019893646240234 total time spend for step 1 : 23.807750940322876 step2:argmax Fri Mar 28 11:22:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 Inside saveOutput : final : True verbose : False photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.96744335, 4723, '3655'), 'temp/1743157350_1341668_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96516097, 4723, '3655'), 'temp/1743157350_1341668_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.97066116, 4723, '3655'), 'temp/1743157350_1341668_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.012375593185424805 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.02477407455444336 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.012711286544799805 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 3.814697265625e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00019788742065429688 time spend to save output : 0.05832481384277344 total time spend for step 2 : 0.058522701263427734 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171275372': [(1171275372, 'tapis_vide', 0.96744335, 4723, '3655'), 'temp/1743157350_1341668_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96516097, 4723, '3655'), 'temp/1743157350_1341668_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171291875': [(1171291875, 'tapis_vide', 0.97066116, 4723, '3655'), 'temp/1743157350_1341668_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 51 thcl : 358 photo_hashtag_type : 337 ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.17833900451660156 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rotate Fri Mar 28 11:22:55 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! Needs to change image size ! Needs to change image size ! About to upload 3 photos upload in portfolio : 551782 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743157375_1341668 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.0579748153686523 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 1.2863943576812744 time spend to save output : 4.601478576660156e-05 total time spend for step 1 : 1.286440372467041 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 3 /1348234970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348234971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348234972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('230', None, None, None, None, None, None, None, None) ('230', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.013328313827514648 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1348234970: ['917849322', 'temp/1743157374_1341668_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1348234971: ['917849322', 'temp/1743157374_1341668_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1348234972: ['917849322', 'temp/1743157374_1341668_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.18293237686157227 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:thcl Fri Mar 28 11:22:56 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 500 time to import caffe and check if the image exist : 0.0003390312194824219 time to convert the images to numpy array : 1.027728796005249 total time to convert the images to numpy array : 1.028587818145752 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2944 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : orientation_carte_grise_all_2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 2944 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 1.8159101009368896 time used to do the prediction : 0.11233115196228027 save descriptor for thcl : 500 time to traite the descriptors : 0.06626582145690918 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.7182488441467285 time spend for datou_step_exec : 9.042904138565063 time spend to save output : 6.29425048828125e-05 total time spend for step 1 : 9.042967081069946 step2:argmax Fri Mar 28 11:23:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step Argmax ! calculate argmax for thcl : 500 time spend for datou_step_exec : 0.0002422332763671875 time spend to save output : 3.409385681152344e-05 total time spend for step 2 : 0.00027632713317871094 step3:rotate Fri Mar 28 11:23:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_rotate ! We are in a datou with depends ! angle_condi : {'carteGrisesVerticales__port_549774': 0, 'cartegrise_90deg__port_550987': 270, 'portfolio_270deg__port_550988': 90, 'cartesGrisesEnvers__port_549765': 180} rotate photos for hashtag carteGrisesVerticales__port_549774 of 0 degres 1 photos founded : [917849322] batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! About to upload 1 photos upload in portfolio : 551782 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743157386_1341668 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.7311515808105469 Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres 0 photos founded : [] rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres 0 photos founded : [] rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres 0 photos founded : [] time spend for datou_step_exec : 0.8291225433349609 time spend to save output : 3.218650817871094e-05 total time spend for step 3 : 0.8291547298431396 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 1 /1348235135Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('233', None, None, None, None, None, None, None, None) ('233', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.012743711471557617 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1348235135: ['917849322', 'temp/1743157376_1341668_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : crop, tile, rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.17406988143920898 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:crop Fri Mar 28 11:23:06 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Crop ! param_json : {'hashtag_id_ellipse': 2087736828, 'photo_hashtag_type_from_ellipse': 520, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'crop_detect_varroa', 'photo_hashtag_type': 407, 'feed_id_new_photos_not_used': 549103, 'host': 'www.fotonower.com', 'margin': 8, 'upload_type': 'python'} margin_type : margin margin_value : [8, 8, 8, 8] Loading chi in step crop with photo_hashtag_type : 407 Loading chi in step crop for list_pids : 1 ! batch 1 Loaded 4 chid ids of type : 407 +WARNING : Unexpected points, we should remove this data for chi_id : 8165075, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165076, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165077, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165078, for now we just ignore these empty polygon points WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 4 Here we crop with rles About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=crop_detect_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 21840337 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743157389_1341668 we have uploaded 4 photos in the portfolio 21840337 time of upload the photos Elapsed time : 3.2491250038146973 Now we prepare data that will be used for ellipse search ! About to compute ellipse and record with type : 520 score : 5120 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2311 | arg_min : -30.0 min_score : 1968 | arg_min : 17.8125 min_score : 1614 | arg_min : 31.875 min_score : 1105 | arg_min : 28.5 min_score : 1105 arg_min : 1.9500000000000002 min_score : 1105 arg_min : 25.0 min_score : 1088 arg_min : 24.9375 min_score : 979 arg_min : 31.875 min_score : 979 arg_min : 28.5 min_score : 979 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! score : 5362 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2281 | arg_min : -10.0 min_score : 2127 | arg_min : 25.0 min_score : 2127 | arg_min : 30.9375 min_score : 714 | arg_min : 25.0 min_score : 714 arg_min : 1.9500000000000002 min_score : 714 arg_min : -5.0 min_score : 668 arg_min : 23.4375 min_score : 655 arg_min : 29.53125 min_score : 631 arg_min : 25.0 min_score : 631 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! score : 4603 strategy_opt : 5| | arg_min : 1.85 min_score : 2981 | arg_min : -50.0 min_score : 1356 | arg_min : 30.28125 min_score : 1079 | arg_min : 23.625 min_score : 995 | arg_min : 27.0 min_score : 995 arg_min : 1.6500000000000001 min_score : 961 arg_min : -70.0 min_score : 852 arg_min : 28.6875 min_score : 847 arg_min : 23.625 min_score : 847 arg_min : 27.0 min_score : 847 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! score : 7970 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 1576 | arg_min : 40.0 min_score : 632 | arg_min : 20.15625 min_score : 561 | arg_min : 26.0 min_score : 561 | arg_min : 26.0 min_score : 561 arg_min : 1.8 min_score : 520 arg_min : 40.0 min_score : 520 arg_min : 18.8125 min_score : 494 arg_min : 26.0 min_score : 494 arg_min : 26.0 min_score : 494 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 21840351 Result OK ! uploaded one batch 0 Elapsed time : 19.586241006851196 time spend for datou_step_exec : 26.31213402748108 time spend to save output : 2.384185791015625e-05 total time spend for step 1 : 26.31215786933899 step2:tile Fri Mar 28 11:23:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'photo_tile_type': 17, 'whiten': True, 'remove_crop_border': True, 'minimal_size_crop_border': 900, 'stride': 240, 'crop_hashtag_type_tiled': 521, 'ETA': 86400, 'new_width': 480, 'new_height': 480, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_taggage_varroa', 'crop_hashtag_type': 520, 'host': 'www.fotonower.com', 'arg_aux_upload': {'type_upload': 'python'}} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {520: 521} TO DEPRECATE VR 14-6-18 map_filenames : {937852786: 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 Loaded 4 chid ids of type : 520 ++++https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 21840378 with name tile_taggage_varroa feed_id_new_photos : 21840378 filename : temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg photo_id : 937852786 height_image_input : 480 width_image_input : 480 new_width : 480 new_height : 480 stride : 240 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 4 list_bib_to_crops : 1 [(0, 480, 0, 480, 0)] new_crops_tiles : 1 crop_transformed : 4 batch 1 Loaded 1 chid ids of type : 17 treat the image : temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.009691238403320312 on upload les photos avec python init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743157420_1341668 we have uploaded 1 photos in the portfolio 21840378 Importing ! upload mediasElapsed time : 3.504682779312134 , 0Saving 4 CHIs. batch 1 Loaded 4 chid ids of type : 521 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 3.574143409729004 time spend for datou_step_exec : 10.334291696548462 time spend to save output : 3.910064697265625e-05 total time spend for step 2 : 10.334330797195435 step3:rotate Fri Mar 28 11:23:43 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a datou with depends ! rotate photos of 0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345 degres batch 1 Loaded 4 chid ids of type : 521 ++++++++ map_chi of length : 1 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 21840445 Needs to change image size ! time for calcul the mask position with numpy : 0.0006380081176757812 nb_pixel_total : 1389 time to create 1 rle with old method : 0.004571437835693359 .time for calcul the mask position with numpy : 0.00044655799865722656 nb_pixel_total : 1157 time to create 1 rle with old method : 0.004151582717895508 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004341602325439453 nb_pixel_total : 694 time to create 1 rle with old method : 0.0022077560424804688 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036025047302246094 nb_pixel_total : 1162 time to create 1 rle with old method : 0.003404378890991211 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003802776336669922 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006222724914550781 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003604888916015625 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0028107166290283203 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003948211669921875 nb_pixel_total : 143 time to create 1 rle with old method : 0.00048351287841796875 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036072731018066406 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0035009384155273438 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00039839744567871094 nb_pixel_total : 414 time to create 1 rle with old method : 0.0010941028594970703 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003719329833984375 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002881288528442383 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00038313865661621094 nb_pixel_total : 1204 time to create 1 rle with old method : 0.002857685089111328 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002809762954711914 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034332275390625 nb_pixel_total : 264 time to create 1 rle with old method : 0.0008580684661865234 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003859996795654297 nb_pixel_total : 1389 time to create 1 rle with old method : 0.003321409225463867 .time for calcul the mask position with numpy : 0.0003654956817626953 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027666091918945312 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003936290740966797 nb_pixel_total : 694 time to create 1 rle with old method : 0.0017483234405517578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035381317138671875 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002761363983154297 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003552436828613281 nb_pixel_total : 221 time to create 1 rle with old method : 0.000637054443359375 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003573894500732422 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002785205841064453 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00035262107849121094 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004563331604003906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038433074951171875 nb_pixel_total : 1160 time to create 1 rle with old method : 0.0028247833251953125 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00037550926208496094 nb_pixel_total : 414 time to create 1 rle with old method : 0.0010867118835449219 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036263465881347656 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002786874771118164 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003383159637451172 nb_pixel_total : 1 time to create 1 rle with old method : 2.4557113647460938e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 1204 time to create 1 rle with old method : 0.002897977828979492 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038242340087890625 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0027861595153808594 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003440380096435547 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007801055908203125 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00037741661071777344 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0033118724822998047 .time for calcul the mask position with numpy : 0.00037860870361328125 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027313232421875 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003707408905029297 nb_pixel_total : 727 time to create 1 rle with old method : 0.0018277168273925781 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003616809844970703 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002852916717529297 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.000377655029296875 nb_pixel_total : 250 time to create 1 rle with old method : 0.0008797645568847656 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.000392913818359375 nb_pixel_total : 1155 time to create 1 rle with old method : 0.00347137451171875 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003714561462402344 nb_pixel_total : 169 time to create 1 rle with old method : 0.0006203651428222656 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037026405334472656 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0029990673065185547 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00039887428283691406 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011968612670898438 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037097930908203125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0034499168395996094 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034618377685546875 nb_pixel_total : 1 time to create 1 rle with old method : 2.5987625122070312e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.00040793418884277344 nb_pixel_total : 1237 time to create 1 rle with old method : 0.002992868423461914 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035762786865234375 nb_pixel_total : 1158 time to create 1 rle with old method : 0.0027952194213867188 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00035309791564941406 nb_pixel_total : 234 time to create 1 rle with old method : 0.000667572021484375 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004286766052246094 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0033371448516845703 .time for calcul the mask position with numpy : 0.00036263465881347656 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0027883052825927734 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004086494445800781 nb_pixel_total : 727 time to create 1 rle with old method : 0.0018224716186523438 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003609657287597656 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0027709007263183594 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003948211669921875 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006890296936035156 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003571510314941406 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002710103988647461 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003910064697265625 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005123615264892578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003566741943359375 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0027008056640625 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00042176246643066406 nb_pixel_total : 450 time to create 1 rle with old method : 0.0011713504791259766 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037097930908203125 nb_pixel_total : 1159 time to create 1 rle with old method : 0.002753734588623047 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00042247772216796875 nb_pixel_total : 1237 time to create 1 rle with old method : 0.002987384796142578 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003502368927001953 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0189361572265625 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003445148468017578 nb_pixel_total : 234 time to create 1 rle with old method : 0.0007233619689941406 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 21840445 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743157426_1341668 we have uploaded 24 photos in the portfolio 21840445 time of upload the photos Elapsed time : 10.592864990234375 Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 batch 1 Loaded 28 chid ids of type : 529 Number RLEs to save : 1197 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 14.77161717414856 time spend to save output : 8.797645568847656e-05 total time spend for step 3 : 14.771705150604248 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1348235285'] Looping around the photos to save general results len do output : 24 /1348235297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235305Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235307Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235309Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235311Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235314Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235315Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235318Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235320Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235321Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '1348235285', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 time used for this insertion : 9.797547578811646 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1348235297: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1348235298: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1348235299: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1348235300: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1348235301: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1348235302: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1348235304: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1348235305: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1348235306: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1348235307: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1348235308: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1348235309: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1348235310: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1348235311: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1348235312: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1348235313: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1348235314: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1348235315: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1348235316: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1348235318: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1348235319: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1348235320: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1348235321: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1348235322: ['937852786', 'temp/1743157386_1341668_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} list chi : [[, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], []] ############################### TEST flip ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : flip list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.16666579246520996 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:flip Fri Mar 28 11:24:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_flip ! We are in a linear step without datou_depend ! batch 1 Loaded 6 chid ids of type : 741 +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points + map_chi_objs of length : 1 photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 Horizontal flip of photo 911785586 About to upload 2 photos upload in portfolio : 1090565 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743157451_1341668 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.8737695217132568 Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 batch 1 Loaded 12 chid ids of type : 741 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 3.1193792819976807 time spend to save output : 3.5762786865234375e-05 total time spend for step 1 : 3.119415044784546 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] Looping around the photos to save general results len do output : 2 /1348235331 /1348235333 before output type Managing all output in save final without adding information in the mtr_datou_result ('571', None, None, None, None, None, None, None, None) ('571', None, '911785586', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.012053489685058594 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1348235331': ['911785586', 'temp/1743157448_1341668_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1348235333': ['911785586', 'temp/1743157448_1341668_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', [, , , , , ]]} ############################### TEST crop_rles ################################ # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : TEST CROP RLES Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : crop list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.3014082908630371 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:crop Fri Mar 28 11:24:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Crop ! param_json : {'photo_hashtag_type': 755, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 0, 'host': 'www.fotonower.com', 'crop_type': 'rle', 'margin_relative': 0.1, 'min_score': 0.3, 'upload,type': 'python'} margin_type : margin_relative margin_value : [0.1, 0.1, 0.1, 0.1] Loading chi in step crop with photo_hashtag_type : 755 Loading chi in step crop for list_pids : 1 ! batch 1 Loaded 8 chid ids of type : 755 ++++++++WARNING : margin is only used for type bib ! we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles map_result returned by crop_photo_return_map_crop : length : 8 Here we crop with rles About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 21840446 Result OK ! uploaded one batch 0 Elapsed time : 22.483323574066162 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 22.54099178314209 time spend to save output : 1.811981201171875e-05 total time spend for step 1 : 22.5410099029541 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] Looping around the photos to save general results len do output : 8 /1348235336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1348235354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('686', None, None, None, None, None, None, None, None) ('686', None, '950103132', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 time used for this insertion : 0.012486457824707031 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1348235336': ['950103132', 'temp/1743157451_1341668_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1348235339': ['950103132', 'temp/1743157451_1341668_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1348235340': ['950103132', 'temp/1743157451_1341668_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1348235342': ['950103132', 'temp/1743157451_1341668_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1348235345': ['950103132', 'temp/1743157451_1341668_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1348235347': ['950103132', 'temp/1743157451_1341668_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1348235350': ['950103132', 'temp/1743157451_1341668_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1348235354': ['950103132', 'temp/1743157451_1341668_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : angular_coeff list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.18199634552001953 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:angular_coeff Fri Mar 28 11:24:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter param_json : {'input_type': 846, 'output_type': -1, 'orientation_type': 872, 'ref_crop_type': 846, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'crops_coeffs': {'CAR_EXTERIEUR_angle_avant_droit.*': {'aile-avant': [[15, 0.0], [240, 0.0], [285, 1.0], [345, 1.0]], 'capot': [[45, 1.0], [60, 0.5], [270, 0.0], [315, 1.0], [360, 1.0]]}}} angular_coefficients_to_crops batch 1 Loaded 19 chid ids of type : 846 treating photo 932296368 time spend for datou_step_exec : 0.06596040725708008 time spend to save output : 3.528594970703125e-05 total time spend for step 1 : 0.06599569320678711 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {932296368: ([(932296368, 2106233860, 846, 1066, 1277, 93, 340, 0.31964028378983567, 0, []), (932296368, 2106233860, 846, 434, 690, 218, 498, 0.7170410105787726, 0, []), (932296368, 503548896, 846, 902, 1111, 466, 576, 0.31724966, 769189715, []), (932296368, 599722655, 846, 523, 1100, 152, 337, 0.98039776, 0, []), (932296368, 492601069, 846, 143, 1190, 90, 695, 0.9696157, 769189717, []), (932296368, 492601069, 846, 0, 408, 246, 719, 0.9431181, 769189718, []), (932296368, 2096875722, 846, 567, 964, 162, 215, 0.55490255, 769189721, []), (932296368, 2096875709, 846, 437, 939, 24, 198, 0.9983077, 769189723, []), (932296368, 2096875709, 846, 1004, 1263, 28, 144, 0.9485744, 769189724, []), (932296368, 624624117, 846, 595, 1122, 331, 640, 0.99100167, 769189725, []), (932296368, 492624020, 846, 585, 874, 308, 393, 0.78697366, 769189727, []), (932296368, 2096875719, 846, 943, 1100, 428, 547, 0.96733797, 769189729, []), (932296368, 492654799, 846, 253, 467, 35, 441, 0.99621326, 769189730, []), (932296368, 492689227, 846, 1118, 1264, 270, 438, 0.9901647, 769189732, []), (932296368, 492689227, 846, 486, 671, 378, 690, 0.98789483, 769189733, []), (932296368, 492689227, 846, 161, 255, 229, 409, 0.70801014, 769189734, []), (932296368, 492925064, 846, 261, 421, 27, 193, 0.92215157, 769189737, []), (932296368, 492925064, 846, 873, 1045, 46, 156, 0.7535122, 769189738, []), (932296368, 492925064, 846, 1090, 1279, 20, 107, 0.45259848, 769189739, [])],)} test angular coeff is a success ! ############################### TEST detection_filter_by_crop ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_crop list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1577625274658203 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_crop Fri Mar 28 11:24:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter param_json : {'input_type': 631, 'output_type': -1, 'condition_type': 445, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'min_surface_ratio': 0.7} conditional_crop_copy batch 1 Loaded 3 chid ids of type : 445 +++batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++batch 1 Loaded 3 chid ids of type : 445 +++ treating photo 946711423 time spend for datou_step_exec : 0.13046813011169434 time spend to save output : 9.250640869140625e-05 total time spend for step 1 : 0.13056063652038574 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, ['491,350,489,350,488,349,487,350,483,350,480,348,480,341,482,339,482,337,485,334,487,334,491,330,494,330,495,328,498,326,501,326,503,324,507,325,509,323,514,321,516,319,518,321,520,321,521,319,522,319,524,321,527,321,530,317,530,315,531,314,535,313,540,309,543,310,544,311,542,313,542,314,544,316,541,318,541,322,536,322,535,323,533,323,532,322,528,322,527,321,524,321,522,323,518,322,516,324,517,327,516,328,512,327,510,329,512,332,513,332,515,330,516,331,516,333,514,332,511,333,511,336,514,337,516,336,516,339,515,339,513,338,511,340,512,341,512,342,510,343,507,343,502,347,500,347,497,349,492,349', '514,325,515,324,513,322,512,322,511,325,512,326', '522,327,521,327,521,326,522,325']), (946711423, 599722655, 631, 176, 535, 138, 264, 0.9818268, 1947740373, ['453,253,413,253,412,252,387,252,386,250,386,248,383,246,379,245,376,243,361,243,361,240,362,239,359,238,358,237,356,237,355,236,352,236,351,235,333,235,332,234,329,234,329,233,331,231,331,229,329,228,328,224,330,222,330,221,324,218,308,219,307,218,302,218,298,216,288,217,287,218,285,218,283,220,283,221,287,224,295,225,295,225,294,226,289,226,288,227,283,227,282,228,273,228,272,229,271,228,259,228,258,227,254,227,253,226,247,225,247,225,251,221,248,218,243,216,247,213,248,213,249,212,248,211,246,211,245,210,241,210,240,209,237,209,236,208,231,207,230,206,228,202,224,201,223,200,221,200,220,199,214,198,213,195,211,193,208,193,203,189,203,184,201,181,201,176,198,171,199,170,199,158,203,154,205,153,205,151,206,149,209,149,210,148,225,148,226,147,283,147,284,148,287,148,288,147,305,147,306,148,312,148,313,147,354,147,355,146,428,146,429,147,433,147,434,148,437,148,438,149,451,149,457,156,459,162,462,165,464,166,471,166,472,165,477,165,480,167,480,171,486,175,488,175,489,176,502,176,503,178,503,180,509,185,509,189,512,193,512,199,513,200,513,203,514,204,514,210,513,211,514,217,512,221,513,222,513,225,510,229,510,235,507,237,504,238,502,243,490,243,489,244,485,244,484,245,480,245,479,246,463,246,462,247,460,247,458,249,457,252,454,252', '528,212,528,207,526,206,524,203,526,203,527,202,528,202', '299,215,302,212,299,211,298,210,291,210,290,211,281,212,286,215,290,215,291,216', '375,242,376,240,375,238,363,239,368,242,371,242,372,243']), (946711423, 492844413, 631, 89, 163, 93, 144, 0.9772748, 1947740375, ['159,142,153,141,151,139,148,138,145,135,141,133,139,133,138,132,131,132,130,131,125,131,124,130,121,130,120,129,116,129,115,128,112,128,108,126,106,126,100,123,98,121,94,113,94,104,97,101,103,98,105,98,106,97,110,97,111,96,116,96,117,95,132,95,133,96,139,97,141,99,144,100,149,105,150,107,154,108,155,113,157,115,158,115,160,118,160,120,161,121,161,133,160,134,160,140']), (946711423, 2096875709, 631, 185, 431, 39, 136, 0.97171515, 1947740377, ['331,134,287,134,286,133,284,133,283,134,272,134,271,133,264,133,263,134,258,134,257,133,254,133,253,132,236,132,235,131,225,131,224,132,223,131,213,131,212,130,208,130,207,129,204,129,203,128,199,127,193,121,192,117,189,113,189,110,188,109,187,93,186,92,187,91,187,89,186,88,186,65,185,64,186,63,186,61,185,60,185,48,186,47,186,42,187,40,232,40,233,41,248,41,249,42,281,43,282,44,290,44,291,45,300,45,301,46,308,46,309,47,314,47,315,48,322,49,328,53,334,54,336,56,339,57,344,62,349,64,351,66,353,67,356,67,358,69,359,72,363,76,367,78,369,80,379,91,380,93,383,94,390,100,393,101,395,103,396,106,399,109,402,110,406,115,408,115,410,117,410,120,412,123,411,127,409,129,399,129,398,130,395,130,394,131,378,131,377,132,368,132,367,131,346,131,345,132,342,132,341,133,332,133']), (946711423, 2096875722, 631, 198, 395, 118, 142, 0.9699756, 1947740378, ['328,137,251,137,250,136,249,137,241,137,240,136,219,136,218,135,213,135,212,134,206,133,205,132,201,131,200,130,200,122,201,121,205,121,206,122,222,122,226,124,239,124,240,125,369,125,370,124,371,125,389,125,391,127,391,133,390,134,386,134,385,135,380,135,379,134,375,134,374,135,341,135,340,136,329,136']), (946711423, 499500794, 631, 93, 107, 127, 146, 0.9574813, 1947740379, ['101,143,98,143,95,139,95,131,97,129,100,129,101,133,102,134,102,136,103,137,103,140']), (946711423, 492925064, 631, 71, 125, 36, 95, 0.95296955, 1947740380, ['104,92,96,92,93,90,91,90,86,86,83,85,83,84,81,82,80,82,75,77,75,75,74,74,74,66,75,65,75,62,77,60,77,58,80,55,80,54,83,51,83,50,88,45,94,44,95,43,99,43,100,42,113,42,117,45,117,47,116,48,116,51,115,52,114,59,113,60,112,65,111,66,111,69,110,70,110,75,109,76,109,83,108,84,108,86,109,87,108,89']), (946711423, 492925064, 631, 101, 167, 38, 127, 0.9508439, 1947740381, ['154,117,152,115,152,112,150,110,148,106,148,104,145,101,143,100,138,100,137,99,135,99,133,95,131,95,126,93,126,91,128,88,128,83,129,82,129,70,127,68,127,66,128,65,125,61,127,59,127,56,129,52,129,49,130,47,135,42,144,42,148,45,151,49,151,60,152,61,152,75,153,76,153,80,155,83,155,87,156,88,156,105,155,106,156,107,156,110,154,112,156,116', '109,100,108,100,107,99,109,97']), (946711423, 492624020, 631, 249, 400, 219, 316, 0.8792459, 1947740382, ['395,313,390,313,386,311,384,312,381,312,376,309,358,308,357,307,354,307,353,306,350,306,349,307,345,305,343,303,341,304,337,304,334,302,325,302,324,301,315,300,313,298,313,297,310,295,304,295,300,293,295,293,291,288,289,287,283,287,281,285,281,283,278,280,274,280,272,279,272,276,270,273,270,270,269,268,266,265,265,265,264,264,264,262,261,260,260,258,260,256,261,255,259,252,259,248,258,246,255,244,256,241,252,239,251,238,251,226,265,226,266,227,268,227,272,232,276,232,277,233,279,233,281,234,283,237,285,238,290,239,293,241,296,241,304,246,312,247,316,251,318,252,320,252,326,255,328,255,337,260,342,260,343,261,345,261,349,264,351,264,355,266,357,268,364,271,366,273,370,275,374,275,376,276,377,279,379,281,383,282,384,283,386,283,387,286,390,289,390,290,394,294,396,294,398,296,398,308,397,309,397,311']), (946711423, 503548896, 631, 302, 540, 339, 403, 0.7406652, 1947740386, ['442,401,372,401,372,397,370,395,369,392,366,390,367,389,366,386,357,386,354,384,350,384,349,383,320,383,319,382,320,378,318,376,318,374,314,370,309,370,308,369,306,369,305,363,305,357,306,356,306,353,307,353,308,354,313,354,314,355,315,354,320,354,321,353,331,353,332,354,335,354,336,355,339,355,340,356,379,356,380,357,406,357,407,356,409,356,410,357,474,357,475,356,482,356,484,357,485,356,488,356,493,353,501,354,502,353,506,353,507,352,517,352,518,351,522,351,525,347,527,346,530,347,530,349,533,351,530,355,528,355,527,356,515,356,509,359,508,361,505,362,503,365,497,368,494,372,490,373,489,374,492,376,495,376,493,377,488,378,490,380,495,380,497,381,497,381,487,382,485,385,476,387,469,392,466,392,465,393,460,393,456,396,453,397,451,399,443,400', '519,353,518,352,517,353,518,354']), (946711423, 2106233860, 631, 53, 85, 75, 182, 0.73015845, 1947740387, ['70,147,68,145,65,139,65,137,62,132,61,128,57,126,56,124,56,121,54,119,54,110,56,108,56,103,59,100,60,101,61,100,61,96,63,93,63,89,66,84,65,83,65,80,66,78,68,78,68,79,70,80,70,83,74,87,74,90,75,91,75,100,77,102,77,105,75,106,75,125,76,126,77,125,77,125,77,128,76,129,77,131,77,136,75,139,75,143,78,145,76,146,71,146', '61,107,60,106,59,107,60,108', '77,134,76,131,75,134,76,135']), (946711423, 2096875717, 631, 477, 510, 220, 243, 0.69028217, 1947740388, ['501,241,493,241,489,239,488,237,487,237,480,232,479,230,479,226,484,222,487,222,488,223,492,224,496,228,497,228,497,229,502,234,502,235,504,236,504,240,502,240']), (946711423, 2096875712, 631, 309, 326, 382, 404, 0.6633776, 1947740390, ['309,383,309,382,311,382', '325,385,324,383,319,383,318,382,325,382', '320,400,311,400,309,398,309,385,310,386,311,385,315,385,316,384,318,384,319,385,322,385,325,387,325,398,323,398']), (946711423, 2096875719, 631, 427, 553, 258, 315, 0.6446218, 1947740391, ['531,284,526,284,525,283,525,281,523,279,522,280,519,281,521,282,521,283,519,284,518,283,519,281,515,279,516,277,515,276,513,276,512,277,513,279,511,280,507,279,505,276,504,279,497,279,496,278,495,279,485,279,484,280,480,280,479,281,481,283,482,283,482,283,469,283,468,284,438,284,436,283,440,279,446,279,447,278,456,278,458,276,457,275,457,275,467,275,468,274,490,274,491,273,494,273,496,271,496,269,499,268,501,266,503,265,506,265,510,263,514,263,516,261,520,259,544,259,544,259,543,260,545,262,548,262,550,263,550,276,549,277,547,277,540,282,538,282,537,283,532,283']), (946711423, 2106233861, 631, 144, 267, 181, 307, 0.63958377, 1947740392, ['212,251,209,251,208,250,203,251,201,250,201,249,195,243,189,242,188,241,185,241,184,240,182,236,180,236,179,235,173,235,172,234,170,235,164,235,163,234,163,232,162,231,163,217,166,217,168,218,170,215,171,210,172,209,173,209,176,212,178,210,178,208,181,203,186,203,188,201,193,201,194,202,195,201,201,201,202,202,204,202,205,201,209,201,210,202,212,202,215,200,217,200,220,202,221,201,227,201,231,205,231,206,234,209,235,209,235,210,238,213,238,224,234,228,235,232,234,233,228,234,225,237,224,241,222,242,216,242,209,246,211,248,212,248,213,250', '221,228,220,227,219,228,220,229', '224,238,224,237,221,235,217,237,219,239']), (946711423, 2096875712, 631, 285, 433, 343, 377, 0.61493844, 1947740393, ['431,376,286,376,285,375,285,368,286,367,286,362,287,361,287,359,291,359,297,362,306,363,307,364,312,364,313,365,322,366,323,367,331,366,332,368,334,368,335,369,338,368,337,366,336,366,337,365,336,364,327,364,325,361,319,361,317,357,317,356,318,355,325,355,326,353,331,353,333,351,332,350,330,350,328,348,326,348,325,347,319,347,315,345,306,345,305,344,297,344,299,344,300,343,431,343,432,344,432,353,431,354,431,358,430,359,430,365,427,365,425,363,424,363,422,364,421,366,418,366,413,369,404,370,409,371,409,371,399,371,398,372,395,373,419,374,420,373,426,372,428,370,428,367,429,367,430,368,429,369,430,370,430,373,431,374', '381,373,378,372,377,371,356,371,356,371,359,370,347,369,345,367,343,367,342,368,343,369,341,370,354,371,354,371,352,372,353,373,359,373,360,374']), (946711423, 2106233860, 631, 146, 287, 140, 311, 0.54784286, 1947740394, ['234,254,227,254,221,251,219,248,215,253,212,253,210,252,206,247,203,247,198,243,197,243,194,239,189,238,186,236,182,236,181,235,167,235,164,233,164,228,159,227,158,226,158,219,159,218,159,213,162,207,162,205,169,192,169,186,170,185,172,185,177,179,175,175,173,173,177,171,181,171,182,170,184,170,187,167,187,164,188,163,188,161,199,161,202,164,205,165,207,167,209,167,212,165,215,165,216,168,218,170,219,170,221,168,221,164,220,163,220,161,222,161,223,160,230,160,231,159,242,159,244,158,247,161,248,161,247,162,246,168,248,172,248,174,253,176,254,180,253,182,249,182,247,185,249,188,253,188,254,189,254,194,249,194,247,196,247,198,249,200,252,200,253,199,255,202,255,205,254,206,254,208,250,207,249,206,246,209,246,210,249,214,252,212,254,212,254,214,255,215,255,217,254,218,254,221,252,221,249,219,247,221,247,225,249,228,250,228,252,226,253,224,253,224,253,229,252,229,251,228,249,228,247,230,247,233,246,234,246,237,245,238,245,240,243,244,243,247,239,251,237,251', '230,167,229,166,227,167,228,168']), (946711423, 495920967, 631, 202, 524, 112, 333, 0.45109355, 1947740396, ['483,289,483,286,482,285,482,283,480,279,480,274,476,270,472,268,465,268,464,269,459,269,458,268,454,268,453,267,437,267,436,268,428,268,427,269,418,269,417,270,414,270,410,266,410,265,416,262,418,262,421,260,423,260,425,259,426,257,424,255,422,255,419,253,417,253,416,252,412,251,410,250,410,249,412,249,413,248,415,248,416,247,422,246,428,243,429,242,428,241,424,240,423,239,420,239,419,238,390,238,389,237,386,237,385,236,369,236,368,235,363,234,363,233,364,232,364,230,366,226,365,225,357,220,344,220,341,218,339,218,339,218,342,212,342,210,336,207,327,207,326,206,319,206,318,205,314,205,313,204,297,204,291,207,288,210,288,212,291,217,290,220,288,222,284,224,282,224,278,227,273,228,271,230,270,235,265,239,262,236,261,232,263,228,266,226,261,224,256,219,256,210,249,206,242,205,237,202,234,195,226,186,227,184,227,180,228,179,225,175,225,174,222,171,225,165,227,163,229,158,230,157,232,156,235,156,236,155,239,155,240,154,245,154,246,155,254,155,255,156,258,156,259,157,268,157,269,156,272,156,273,155,280,155,281,156,298,156,300,155,301,156,307,156,308,157,311,157,318,152,322,151,323,150,333,150,338,146,339,146,342,143,343,143,346,140,357,140,362,136,366,134,368,134,369,133,373,133,374,132,377,132,378,131,388,131,389,130,410,130,411,131,417,131,428,140,432,142,434,142,435,143,443,145,446,147,448,147,451,154,453,156,457,158,462,159,463,160,466,160,467,161,472,162,474,163,474,164,481,171,489,175,491,175,492,176,494,176,495,177,499,178,500,179,502,184,507,189,517,194,518,195,514,201,514,203,518,207,519,209,518,214,515,218,517,227,515,229,515,231,514,232,515,236,518,239,518,252,519,253,519,263,518,264,518,267,517,269,514,272,512,273,512,274,506,280,500,277,498,273,496,272,493,272,491,274,491,278,490,279,490,281', '312,179,311,178,308,179,309,180', '268,269,264,269,259,266,259,262,261,258,261,250,265,245,269,250,270,257,274,260,278,265,275,267,269,268', '414,281,401,281,414,281']), (946711423, 2096875722, 631, 433, 558, 248, 286, 0.44133398, 1947740397, ['492,272,474,272,473,271,468,271,465,269,460,269,460,268,465,266,467,266,468,265,470,265,471,264,475,264,476,263,479,263,480,262,486,262,487,261,491,261,492,260,495,260,496,259,502,259,506,257,510,257,514,255,517,255,518,254,530,253,531,252,535,252,536,251,538,251,539,252,543,252,544,253,547,253,549,251,553,251,555,253,555,267,552,270,550,270,550,269,548,267,547,267,547,267,548,266,547,265,545,266,540,266,539,264,530,264,529,263,524,263,519,266,513,266,510,268,507,268,506,269,499,270,498,271,493,271', '438,279,435,279,435,273,436,272,448,271,449,272,448,274,443,274,440,277,440,278']), (946711423, 492654799, 631, 399, 569, 68, 251, 0.41876298, 1947740399, []), (946711423, 492624020, 631, 420, 552, 244, 293, 0.35962066, 1947740400, ['474,289,453,289,452,288,439,288,437,286,431,286,427,284,423,284,422,283,422,275,427,275,428,273,430,272,435,272,436,271,438,271,442,269,447,269,450,267,454,267,460,264,464,264,467,262,483,261,484,260,488,260,489,259,494,259,495,258,502,258,503,257,505,257,509,255,512,255,516,252,520,252,521,251,526,250,530,248,534,248,535,247,546,247,547,248,549,248,549,250,550,251,550,266,551,267,551,275,550,276,550,278,549,279,549,281,537,282,535,284,528,284,527,285,504,285,503,286,495,286,492,288,488,287,487,288,475,288']), (946711423, 503548896, 631, 301, 540, 339, 403, 0.740756, 3140491551, ['442,401,371,401,371,397,366,390,365,386,356,386,353,384,348,383,319,383,319,378,314,370,310,370,305,368,304,357,305,353,330,353,339,356,378,356,379,357,474,357,475,356,488,356,493,353,501,354,507,352,517,352,522,351,527,346,530,347,533,351,530,355,527,356,515,356,505,362,503,365,497,368,494,372,489,374,492,376,488,378,490,380,495,380,487,382,485,385,476,387,469,392,461,393,456,395,451,399,447,399', '519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] origin we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.004929780960083008 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Fri Mar 28 11:24:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter with classification results param_json : {'input_type': 631, 'output_type': 816, 'condition_type': 872, 'crops_ok': {'CAR_DOCUMENT.*': {}, 'CAR_INTERIEUR.*': {}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'Retroviseur': 2, 'Roue': 2, 'Capot': 1, 'Pare-brise': 1, 'vitre': 10, 'phare': 2, 'Feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'Plaque-immatriculation': 1, 'Essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1}}, 'separation': {'CAR_EXTERIEUR_avant.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-gauche', 'a-droite-de', 'phare-droit']}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-droite', 'a-gauche-de', 'phare-gauche'], 'porte': ['porte-avant', 'a-droite-de', 'porte-arriere']}}} conditional_crop_by_classif_copy batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ treating photo 946711423 batch 1 Loaded 0 chid ids of type : 0 batch 1 Loaded 23 chid ids of type : 816 Number RLEs to save : 1600 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 2.8082985877990723 time spend to save output : 9.703636169433594e-05 total time spend for step 1 : 2.8083956241607666 caffe_path_current : About to save ! 0 After save, about to update current ! test detection filter by classif is a success ! ############################### TEST blur_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : blur_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1200857162475586 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:blur_detection Fri Mar 28 11:24:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blur_detection methode: ratio et variance treat image : temp/1743157478_1341668_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.2823069095611572 time spend to save output : 7.224082946777344e-05 total time spend for step 1 : 0.282379150390625 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {930729675: [(930729675, 12.961859636534896, 492688767)]} {930729675: [(930729675, 12.961859636534896, 492688767)]} ############################### TEST detect_point_224x224 ################################ test_detect_point_224x224 Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 4589 thcl is not linked in the step_by_step architecture ! WARNING : step 4590 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BBBBBFFBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBBFBFBFBFBFBFBFBFBFBFBFFFBFBFBFBFBFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 1.667975902557373 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Fri Mar 28 11:24:40 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 Thcl ! we are using the classfication for only one thcl 1528 time to import caffe and check if the image exist : 0.0021851062774658203 time to convert the images to numpy array : 0.0042972564697265625 time to import caffe and check if the image exist : 0.004866838455200195 time to convert the images to numpy array : 0.0486447811126709 time to import caffe and check if the image exist : 0.012218475341796875 time to convert the images to numpy array : 0.04738974571228027 time to import caffe and check if the image exist : 0.007586479187011719 time to convert the images to numpy array : 0.051792144775390625 time to import caffe and check if the image exist : 0.015920639038085938 time to convert the images to numpy array : 0.04609990119934082 time to import caffe and check if the image exist : 0.008425712585449219 time to convert the images to numpy array : 0.05495643615722656 time to import caffe and check if the image exist : 0.008884668350219727 time to convert the images to numpy array : 0.05504655838012695 time to import caffe and check if the image exist : 0.01500701904296875 time to convert the images to numpy array : 0.05093789100646973 time to import caffe and check if the image exist : 0.014624357223510742 time to convert the images to numpy array : 0.051143646240234375 time to import caffe and check if the image exist : 0.012689828872680664 time to convert the images to numpy array : 0.05502915382385254 total time to convert the images to numpy array : 0.0709526538848877 list photo_ids error: [] list photo_ids correct : [987515238, 987515246, 987515247, 987515248, 987515249, 987515250, 987515188, 987515189, 987515190, 987515192, 987515193, 987515195, 987515196, 987515198, 987515200, 987515185, 987515186, 987515187, 987515207, 987515208, 987515209, 987515211, 987515201, 987515202, 987515204, 987515205, 987515175, 987515176, 987515177, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515178, 987515179, 987515180, 987515181, 987515182, 987515183, 987515184, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515220, 987515222, 987515223, 987515224, 987515226, 987515227, 987515228, 987515230] number of photos to traite : 64 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3165 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3165 max_wait_temp : 1 max_wait : 0 dict_keys(['res5b', 'prob']) time used to do the prepocess of the images : 0.05292391777038574 time used to do the prediction : 0.2518742084503174 save descriptor for thcl : 1528 time to traite the descriptors : 4.3497631549835205 storage_type for insertDescriptorsMulti : 1 To insert : 987515238 To insert : 987515246 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515185 To insert : 987515186 To insert : 987515187 To insert : 987515207 To insert : 987515208 To insert : 987515209 To insert : 987515211 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515239 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515178 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515184 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515220 To insert : 987515222 To insert : 987515223 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 time to insert the descriptors : 17.90051579475403 time spend for datou_step_exec : 26.314446449279785 time spend to save output : 0.00011515617370605469 total time spend for step 1 : 26.31456160545349 step2:argmax Fri Mar 28 11:25:06 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 time spend for datou_step_exec : 0.0013415813446044922 time spend to save output : 1.4781951904296875e-05 total time spend for step 2 : 0.001356363296508789 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515238': [('987515238', 'Carton', 0.9995802, 1927, '1528'), 'temp/1743157478_1341668_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515246': [('987515246', 'Carton', 0.99923277, 1927, '1528'), 'temp/1743157478_1341668_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966896, 1927, '1528'), 'temp/1743157478_1341668_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.98128474, 1927, '1528'), 'temp/1743157478_1341668_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.9813064, 1927, '1528'), 'temp/1743157478_1341668_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9807928, 1927, '1528'), 'temp/1743157478_1341668_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515188': [('987515188', 'Carton', 0.9956585, 1927, '1528'), 'temp/1743157478_1341668_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.9977876, 1927, '1528'), 'temp/1743157478_1341668_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9763623, 1927, '1528'), 'temp/1743157478_1341668_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999114, 1927, '1528'), 'temp/1743157478_1341668_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993994, 1927, '1528'), 'temp/1743157478_1341668_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.9846726, 1927, '1528'), 'temp/1743157478_1341668_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.9846742, 1927, '1528'), 'temp/1743157478_1341668_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.96608937, 1927, '1528'), 'temp/1743157478_1341668_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.9859616, 1927, '1528'), 'temp/1743157478_1341668_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7978192, 1927, '1528'), 'temp/1743157478_1341668_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847002, 1927, '1528'), 'temp/1743157478_1341668_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.9810821, 1927, '1528'), 'temp/1743157478_1341668_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.8738981, 1927, '1528'), 'temp/1743157478_1341668_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.99172264, 1927, '1528'), 'temp/1743157478_1341668_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.9677539, 1927, '1528'), 'temp/1743157478_1341668_987515209_02dfe1ae39f51994652f4a8538844aea.jpg'], '987515211': [('987515211', 'Carton', 0.9734379, 1927, '1528'), 'temp/1743157478_1341668_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515201': [('987515201', 'Carton', 0.9954632, 1927, '1528'), 'temp/1743157478_1341668_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.9911209, 1927, '1528'), 'temp/1743157478_1341668_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950735, 1927, '1528'), 'temp/1743157478_1341668_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.9908551, 1927, '1528'), 'temp/1743157478_1341668_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.9998141, 1927, '1528'), 'temp/1743157478_1341668_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998141, 1927, '1528'), 'temp/1743157478_1341668_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.97710097, 1927, '1528'), 'temp/1743157478_1341668_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515239': [('987515239', 'Carton', 0.99978346, 1927, '1528'), 'temp/1743157478_1341668_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.9995209, 1927, '1528'), 'temp/1743157478_1341668_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.982126, 1927, '1528'), 'temp/1743157478_1341668_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9359199, 1927, '1528'), 'temp/1743157478_1341668_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.87435496, 1927, '1528'), 'temp/1743157478_1341668_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.8175035, 1927, '1528'), 'temp/1743157478_1341668_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.86611205, 1927, '1528'), 'temp/1743157478_1341668_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515231': [('987515231', 'Carton', 0.9994216, 1927, '1528'), 'temp/1743157478_1341668_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.9992435, 1927, '1528'), 'temp/1743157478_1341668_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.983529, 1927, '1528'), 'temp/1743157478_1341668_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94486254, 1927, '1528'), 'temp/1743157478_1341668_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.8917866, 1927, '1528'), 'temp/1743157478_1341668_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.53597766, 1927, '1528'), 'temp/1743157478_1341668_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7698991, 1927, '1528'), 'temp/1743157478_1341668_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515178': [('987515178', 'Carton', 0.85767967, 1927, '1528'), 'temp/1743157478_1341668_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9267085, 1927, '1528'), 'temp/1743157478_1341668_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.98997843, 1927, '1528'), 'temp/1743157478_1341668_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977818, 1927, '1528'), 'temp/1743157478_1341668_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.9924269, 1927, '1528'), 'temp/1743157478_1341668_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999213, 1927, '1528'), 'temp/1743157478_1341668_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.9997323, 1927, '1528'), 'temp/1743157478_1341668_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515212': [('987515212', 'Carton', 0.98692375, 1927, '1528'), 'temp/1743157478_1341668_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.98691535, 1927, '1528'), 'temp/1743157478_1341668_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.9939161, 1927, '1528'), 'temp/1743157478_1341668_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.9774596, 1927, '1528'), 'temp/1743157478_1341668_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.52883285, 1927, '1528'), 'temp/1743157478_1341668_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.9993699, 1927, '1528'), 'temp/1743157478_1341668_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515220': [('987515220', 'Carton', 0.9963791, 1927, '1528'), 'temp/1743157478_1341668_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.9974717, 1927, '1528'), 'temp/1743157478_1341668_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920855, 1927, '1528'), 'temp/1743157478_1341668_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515224': [('987515224', 'Carton', 0.90848714, 1927, '1528'), 'temp/1743157478_1341668_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.986986, 1927, '1528'), 'temp/1743157478_1341668_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.900393, 1927, '1528'), 'temp/1743157478_1341668_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.5220865, 1927, '1528'), 'temp/1743157478_1341668_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.9994072, 1927, '1528'), 'temp/1743157478_1341668_987515230_846ad925884264181565c81d152a2e94.jpg']} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detect_points list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.09136962890625 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detect_points Fri Mar 28 11:25:06 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : 1 To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 1 Inside predict_points step exec : nb paths : 1 treate image : temp/1743157506_1341668_987515173_91fa471b1a04f95b356afdbaf021f623.jpg error in segmantation_predict of script classifier_new.py cannot identify image file 'temp/1743157506_1341668_987515173_91fa471b1a04f95b356afdbaf021f623.jpg' there is something wrong with the photo : temp/1743157506_1341668_987515173_91fa471b1a04f95b356afdbaf021f623.jpg time spend for datou_step_exec : 1.3259670734405518 time spend to save output : 6.413459777832031e-05 total time spend for step 1 : 1.32603120803833 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {987515173: []} (304, 208) ERROR detect_point_224x224 FAILED ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Step 4442 tile have less inputs used (1) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 4441 detect_points is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 4443 count_percent_refus is not consistent : 4 used against 3 in the step definition ! Step 4444 send_mail_dechet have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 1 of step 4440 have datatype=1 whereas input 0 of step 4443 have datatype=2 WARNING : type of output 1 of step 4441 doesn't seem to be define in the database( WARNING : type of input 4 of step 4443 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] origin Catched exception ! Connect or reconnect ! We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.22983026504516602 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 7 step1:init_dechet Fri Mar 28 11:25:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec debut step init detect dechets input : temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [987321136] Looping around the photos to save general results len do output : 1 /987321136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.01239919662475586 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.0002434253692626953 time spend to save output : 0.012720584869384766 total time spend for step 1 : 0.012964010238647461 step2:tile Fri Mar 28 11:25:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_correct_upm', 'ETA': 86400, 'new_width': 1500, 'new_height': 20000, 'host': 'www.fotonower.com', 'protocol': 'https', 'photo_tile_type': 1522, 'option_bande': 'True'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {406: 410} map_filenames : {987321136: 'temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 Loaded 0 chid ids of type : 0 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_correct_upm&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 21840466 with name tile_correct_upm feed_id_new_photos : 21840466 filename : temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea.jpg photo_id : 987321136 height_image_input : 439 width_image_input : 562 new_width : 1500 new_height : 20000 stride : 0 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 0 list_bib_to_crops : 1 [(0, 562, 0, 439, 0)] new_crops_tiles : 1 crop_transformed : 0 batch 1 Loaded 1 chid ids of type : 1522 treat the image : temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.011593818664550781 About to upload 1 photos upload in portfolio : 21840466 Result OK ! uploaded one batch 0 Elapsed time : 5.398451805114746 upload mediasElapsed time : 5.410135984420776 , 0Saving 0 CHIs. end of tileElapsed time : 5.422665596008301 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1348235443'] Looping around the photos to save general results len do output : 1 /1348235443Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1348235443', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.06945276260375977 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.138165473937988 time spend to save output : 0.06966733932495117 total time spend for step 2 : 12.20783281326294 step3:detect_points Fri Mar 28 11:25:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : False To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 2 Inside predict_points step exec : nb paths : 1 treate image : temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea_0.jpg size of numpy array img : 2960752 scale method : caffe/skimage size of numpy array img_scale : 2655880 (416, 532, 3) nb_h 7 nb_w 11 size of sub images : (224, 224, 3) size of caffe_input : 46362776 (77, 3, 224, 224) time to do the preprocess : 0.04870963096618652 time to do a prediction : 15.716437101364136 dict_keys(['prob']) shape of output (77, 10, 1, 1) shape of the out_put heatmap (10, 7, 11) number of sub_photos vertical and horizon 7 11 size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) Inside saveOutput : final : False verbose : False Inside savePoints : final : False verbose : False threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 final : False save missing photos in datou_result : time spend for datou_step_exec : 17.0209743976593 time spend to save output : 0.06046938896179199 total time spend for step 3 : 17.081443786621094 step4:count_percent_refus Fri Mar 28 11:25:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure debut step count percent refus (987321136, 0.9481481481481482) ('temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) list_photo : [987321136] list_photo_correc : [1348235443] debut step count percent refus Treating photo_id : 987321136 Calcul du count_res count res : ((492774966, 3), (2107752386, 7)) Hashtag_id : 492774966 Hashtag_id : 2107752386 We have 2 classes in this image Inside saveOutput : final : False verbose : False begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.050489187240600586 save missing photos in datou_result : time spend for datou_step_exec : 0.021646976470947266 time spend to save output : 0.05075788497924805 total time spend for step 4 : 0.07240486145019531 step5:brightness Fri Mar 28 11:25:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea.jpg Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.00800776481628418 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.007880687713623047 save missing photos in datou_result : time spend for datou_step_exec : 0.11708807945251465 time spend to save output : 0.020043611526489258 total time spend for step 5 : 0.1371316909790039 step6:blur_detection Fri Mar 28 11:25:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1743157508_1341668_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.008749246597290039 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.008780956268310547 save missing photos in datou_result : time spend for datou_step_exec : 0.16874170303344727 time spend to save output : 0.022226572036743164 total time spend for step 6 : 0.19096827507019043 step7:send_mail_dechet Fri Mar 28 11:25:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail dechet senders@fotonower.com retour de l'envoi du mail : None Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1348235443'] Looping around the photos to save general results len do output : 1 /987321136. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1348235443', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.015529394149780273 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.41286563873291016 time spend to save output : 0.015891551971435547 total time spend for step 7 : 0.4287571907043457 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 30.0, 61.64383561643836, {'carton': 3, 'Papier_Magazine': 7}, {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 0.6164383561643836)} ############################### TEST image_temperature_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_temperature_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.5541598796844482 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_temperature_detection Fri Mar 28 11:25:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanche_jaune_detection treat image : temp/1743157538_1341668_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 time spend for datou_step_exec : 0.18486356735229492 time spend to save output : 6.079673767089844e-05 total time spend for step 1 : 0.18492436408996582 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {984484223: [(984484223, 1.004309911525615, 492630606)]} {984484223: [(984484223, 1.004309911525615, 492630606)]} ############################### TEST broca ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.015355587005615234 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:split_time_score Fri Mar 28 11:25:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec split portfolio by speed calcul order for each photo with time calcul time for a portfolio 2021-12-01 10:11:30 2021-12-01 10:11:32 2021-12-01 10:11:30 2021-12-01 10:11:34 2021-12-01 10:11:32 2021-12-01 10:11:40 2021-12-01 10:11:34 2021-12-01 10:12:17 2021-12-01 10:11:40 2021-12-01 10:12:24 2021-12-01 10:12:17 2021-12-01 10:12:27 2021-12-01 10:12:24 2021-12-01 10:12:29 2021-12-01 10:12:27 2021-12-01 10:12:56 2021-12-01 10:12:29 2021-12-01 10:13:04 2021-12-01 10:12:56 2021-12-01 10:13:13 2021-12-01 10:13:04 2021-12-01 10:13:04 distance 1.4513659170185111 2021-12-01 10:13:13 2021-12-01 10:13:22 2021-12-01 10:13:13 2021-12-01 10:13:30 2021-12-01 10:13:22 2021-12-01 10:16:14 2021-12-01 10:13:30 2021-12-01 10:13:30 distance 8.382409567451603 2021-12-01 10:16:14 2021-12-01 10:16:18 2021-12-01 10:16:14 2021-12-01 10:16:47 2021-12-01 10:16:18 2021-12-01 10:16:53 2021-12-01 10:16:47 2021-12-01 10:16:47 distance 8.03396608896571 2021-12-01 10:16:53 2021-12-01 10:16:57 2021-12-01 10:16:53 dict_time_useful: {0: [1098136690, 1098136784, 48.864288393888884, 2.19199505125, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94]], 1: [1098136974, 1098137007, 48.86291258986111, 2.19361357125, [datetime.datetime(2021, 12, 1, 10, 16, 14), datetime.datetime(2021, 12, 1, 10, 16, 47), 33]]} get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "CS"; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "OM"; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 time spend for datou_step_exec : 0.16472458839416504 time spend to save output : 0.0001327991485595703 total time spend for step 1 : 0.1648573875427246 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(21840496, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]} résultat du premier test BROCA : True True ############################### TEST crop_conditional ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1335 frcnn is not linked in the step_by_step architecture ! WARNING : step 1336 crop_condition is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] origin We have 1 , BBBFBFBFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 time to download the photos : 0.3061025142669678 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:frcnn Fri Mar 28 11:25:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! Inside try reload ! To loadFromThcl() model_param file didn't exist model_name : learn_piece_voiture_0808_v2 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] WARNING: Logging before InitGoogleLogging() is written to STDERR F0328 11:25:41.614884 1341668 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1950: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1957: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2141: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2148: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None Name Stmts Miss Cover Missing -------------------------------------------------------------------------------------------------------------------------------------- /home/admin/.local/lib/python3.8/site-packages/PIL/BmpImagePlugin.py 218 181 17% 52, 56, 76-264, 276-284, 291-355, 366, 384, 388-449 /home/admin/.local/lib/python3.8/site-packages/PIL/ExifTags.py 340 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/GifImagePlugin.py 585 527 10% 55, 71-74, 77-80, 84-108, 112-120, 124-139, 142-155, 158-410, 413-430, 433-456, 459, 480-491, 506-543, 547-564, 568-574, 578-649, 653, 658-670, 674-680, 684-746, 756-793, 812-844, 849-854, 865-872, 882, 886-905, 914-967, 971-983, 1002-1015, 1036-1048 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpGradientFile.py 68 53 22% 32-43, 47, 51, 55, 59, 70-98, 105-137 /home/admin/.local/lib/python3.8/site-packages/PIL/GimpPaletteFile.py 26 20 23% 28-53, 56 /home/admin/.local/lib/python3.8/site-packages/PIL/Image.py 1693 1334 21% 44-45, 64-79, 106-128, 135-136, 150, 250-255, 275, 287, 302, 313, 333-334, 339-340, 345-346, 351-352, 357-358, 375-387, 396-414, 420, 429, 434-436, 445-446, 454-455, 458, 461-463, 468, 471, 474-476, 481-483, 487-488, 526-527, 548-553, 559, 562-569, 583-600, 603-606, 610, 615-634, 637, 648, 662, 678-684, 689-709, 712, 715-723, 748-775, 788-793, 812-829, 848-861, 865-871, 883, 933-1112, 1150-1191, 1201-1202, 1219-1230, 1244-1250, 1273, 1276-1279, 1289-1306, 1316, 1330-1331, 1347-1357, 1377-1380, 1392-1398, 1401-1429, 1437-1465, 1468-1471, 1474-1514, 1523-1524, 1539-1546, 1554-1569, 1581-1584, 1594-1596, 1619-1627, 1645-1653, 1695-1734, 1750-1785, 1811-1834, 1847-1890, 1906-1908, 1929-1943, 1967-1986, 1999-2071, 2077-2083, 2124-2193, 2208-2226, 2262-2343, 2380-2381, 2385-2389, 2392, 2406-2412, 2415, 2417, 2427, 2433-2441, 2463-2464, 2486, 2502-2507, 2520-2529, 2540, 2582-2626, 2685-2718, 2723-2797, 2810-2811, 2819-2820, 2824-2829, 2833-2838, 2873, 2885-2886, 2888-2889, 2891-2892, 2917, 2922-2924, 2929-2932, 2960-2971, 3009-3028, 3078-3112, 3117-3122, 3127-3132, 3163-3176, 3212-3298, 3315-3317, 3338-3340, 3355-3357, 3373, 3388-3400, 3485-3486, 3514, 3522-3524, 3541, 3552, 3561, 3570, 3589-3606, 3651-3655, 3658-3663, 3668, 3671-3682, 3685-3693, 3702-3722, 3725-3744, 3747-3761, 3764-3782, 3785-3882, 3885-3888, 3891-3896, 3899-3902, 3905-3908, 3911, 3914-3916, 3919-3922, 3925-3928 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageChops.py 77 55 29% 27, 36, 48-49, 62-64, 77-79, 92-94, 109-111, 123-125, 135-137, 147-149, 159-161, 174-176, 189-191, 202-204, 215-217, 233-235, 248-250, 263-265, 275, 285, 300-303 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageColor.py 55 50 9% 35-120, 136-150 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageDraw.py 542 471 13% 64, 69-73, 75, 83, 88, 111-116, 120-123, 126-130, 133, 135, 141-143, 147-152, 156-160, 164-168, 172-228, 232-237, 241-245, 249-251, 259-281, 287-288, 292-296, 302-423, 426-428, 431-433, 437-439, 467-564, 582-646, 659-677, 695-712, 724-749, 766-791, 807-890, 913-914, 928-938, 960-994, 1038-1115, 1124-1127 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageFile.py 375 292 22% 65-72, 89-134, 137-140, 143-144, 151-153, 158-290, 294-298, 302, 313-325, 337-338, 341-350, 354-355, 377, 388-454, 457, 460, 472-490, 507, 518-519, 527, 532-533, 538-541, 546-547, 565-584, 589-592, 595, 600-604, 613, 621, 630, 643-667, 682, 693, 705-716, 731, 742, 752-757, 768-773 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageFont.py 280 227 19% 47-54, 59-62, 88-110, 114-135, 151-152, 172, 188-189, 198-199, 213-252, 255, 258-259, 262-263, 270, 278, 349, 408-413, 477-481, 541-553, 571-572, 652, 752-778, 792-797, 810-815, 822-834, 841-848, 855-859, 876-877, 887-893, 896-899, 904-909, 912-915, 927-929, 992-1039, 1051-1060, 1070-1202 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageMode.py 21 15 29% 26-30, 33, 39-90 /home/admin/.local/lib/python3.8/site-packages/PIL/ImagePalette.py 162 135 17% 38-46, 50, 54-55, 59-67, 71, 74-82, 91-93, 100-106, 116-167, 174-189, 197-201, 209-215, 219-222, 226-228, 232-237, 241-242, 246-247, 253-272 /home/admin/.local/lib/python3.8/site-packages/PIL/ImageSequence.py 31 25 19% 32-36, 39-43, 46, 49-54, 66-76 /home/admin/.local/lib/python3.8/site-packages/PIL/JpegImagePlugin.py 438 350 20% 57-58, 66-179, 185-190, 201-240, 251-264, 339, 351-399, 407-415, 418-451, 456-477, 480, 483, 493-498, 502-504, 514-580, 616-617, 628-631, 636-637, 641-643, 654-656, 658-661, 663-664, 667, 669, 672, 674, 676, 680, 682-685, 690-722, 725-728, 736-753, 766, 768-769, 795, 798, 811-816, 822-839 /home/admin/.local/lib/python3.8/site-packages/PIL/JpegPresets.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/PaletteFile.py 24 19 21% 25-48, 51 /home/admin/.local/lib/python3.8/site-packages/PIL/PngImagePlugin.py 871 759 13% 137-140, 146-151, 155, 164-165, 169-185, 188, 191, 194, 197, 202-203, 211-223, 228, 234-248, 267-270, 280, 292-295, 308-323, 334-350, 359-373, 376-382, 385, 392-394, 398-421, 425-441, 445-453, 457, 461-464, 468-484, 488-490, 496-499, 508-515, 519-532, 536-551, 555-585, 589-625, 628-630, 634-651, 654-680, 683-695, 703, 715-779, 784-794, 799-811, 814-826, 829-919, 922, 927-931, 936-964, 968-1015, 1018-1022, 1025-1028, 1037, 1069-1074, 1081-1082, 1085, 1092-1094, 1097-1098, 1102-1223, 1227, 1233-1421, 1431-1453 /home/admin/.local/lib/python3.8/site-packages/PIL/PpmImagePlugin.py 219 188 14% 46, 58-65, 68-91, 94-141, 152, 155-157, 160-191, 198-216, 219-264, 267-276, 283-302, 310-329 /home/admin/.local/lib/python3.8/site-packages/PIL/TiffTags.py 46 5 89% 33, 49-53 /home/admin/.local/lib/python3.8/site-packages/PIL/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/PIL/_binary.py 28 13 54% 22, 26, 37, 47, 57, 67, 77, 81, 85, 90, 94, 98, 102 /home/admin/.local/lib/python3.8/site-packages/PIL/_deprecate.py 25 21 16% 41-67 /home/admin/.local/lib/python3.8/site-packages/PIL/_util.py 11 3 73% 11, 16, 19 /home/admin/.local/lib/python3.8/site-packages/PIL/_version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/cached_property.py 93 61 34% 14-15, 30-37, 40-47, 57-59, 62-74, 85-91, 94-95, 98-115, 118, 121, 124-128, 143-144, 147-148 /home/admin/.local/lib/python3.8/site-packages/cffi/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/cffi/api.py 544 481 12% 8-11, 45-99, 112, 115-117, 120-135, 144-153, 160, 164-178, 182-192, 199-211, 217-221, 227-229, 238-240, 265-267, 284-291, 298-300, 318, 335, 361-365, 382, 392-403, 411-419, 431, 434-443, 454-473, 476, 478, 483, 486-487, 495-508, 511-515, 526-538, 541, 544, 547, 556-577, 580-585, 589-635, 638-647, 652-658, 661-684, 687-695, 699-707, 720-725, 735-751, 754-777, 780, 788-801, 805-828, 831-950, 955-965 /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, 219-225, 228-234, 238, 254-255, 259-260, 287-300, 303-315, 320, 323, 333-334, 337-342, 345-348, 359, 362-365, 368-370, 382-383, 459-480, 485-486, 489-710, 714-725, 729-736, 739-741, 763-777, 793-806, 809-819, 822-828, 831-844, 848-850, 853-855, 862, 865, 868, 871, 954-994, 1010-1018, 1021-1024, 1033, 1081-1109, 1112-1150, 1153, 1159-1164, 1169-1175, 1178-1220, 1223-1228, 1231-1234, 1237-1241, 1245, 1248, 1266-1279, 1285, 1305-1312, 1315-1328, 1331-1453, 1456, 1466-1467, 1473, 1553-1577, 1580-1583, 1586-1588, 1593-1674, 1702-1714, 1737-1760, 1799-1806, 1814, 1819-1828, 1834-1847 /home/admin/.local/lib/python3.8/site-packages/dateutil/tz/win.py 153 149 3% 14-370 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/curve.py 20 2 90% 29, 32 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/ecdsa.py 35 24 31% 13-27, 31-41 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/math.py 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, 231-232, 239 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/integer.py 5 1 80% 16 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_common.py 101 56 45% 35-46, 56, 68-72, 77, 82, 87, 95-104, 112-114, 129-141, 145, 156-158, 167, 176, 184-185, 194-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_compat.py 58 36 38% 13, 20-23, 28-29, 42, 46, 49-75, 101-103, 107, 117-126 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/abc.py 65 23 65% 26, 39, 47, 52, 79-80, 86-87, 109-124, 130, 161, 164, 167, 170 /home/admin/.local/lib/python3.8/site-packages/matplotlib/__init__.py 517 270 48% 165-178, 186-198, 223, 240-243, 276-277, 356-460, 465-480, 505, 511, 514-515, 521-537, 608-609, 617, 702-706, 708-709, 711-714, 717-718, 721-722, 724-725, 731-734, 737-740, 745-748, 758-764, 767, 775, 788-789, 803, 808-814, 821-828, 863-865, 872, 875-878, 889-902, 927-945, 977, 1033-1052, 1074-1077, 1090-1092, 1115-1119, 1168-1177, 1233-1240, 1252, 1263, 1270, 1288-1293, 1308-1316, 1320-1325, 1348, 1366, 1368, 1445-1472 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_afm.py 242 190 21% 54, 61-65, 69, 73-74, 78, 82-85, 105-168, 206-237, 252-269, 306-323, 339-355, 362-364, 367-369, 376-394, 398-424, 428, 432-434, 440-442, 446, 450-452, 458-459, 466, 470, 474, 478-481, 485-493, 498, 502, 506, 510, 514, 518, 525, 532 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/__init__.py 126 49 61% 47, 54, 58, 83, 89-93, 123-131, 158-168, 187, 191-192, 225, 256, 270, 281, 331-336, 341, 357-359, 378-390 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/deprecation.py 173 56 68% 27-45, 92-96, 142-143, 156-159, 162-164, 167-169, 193, 199-200, 291-297, 310, 370-373, 381-410, 448-454, 486-503 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_blocking_input.py 8 7 12% 21-30 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm.py 141 12 91% 59-64, 145-152 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm_listed.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_color_data.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_constrained_layout.py 373 352 6% 102-149, 162-194, 202-240, 247-260, 264-297, 303-335, 347-440, 447-479, 507-576, 583-596, 615-624, 632-665, 689-751, 761-768, 772-783 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_docstring.py 39 4 90% 35, 53, 59-60 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_enums.py 57 36 37% 24, 89-111, 161-177 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_fontconfig_pattern.py 46 7 85% 89-91, 97, 101-105, 114-118 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_layoutgrid.py 208 174 16% 40-103, 106-118, 126-128, 132-137, 144-162, 166, 173-206, 213-245, 266-267, 287-288, 303-304, 322-323, 339-347, 352, 359-367, 374-391, 398-411, 418-429, 436-448, 455-466, 473-484, 490, 497, 502-547 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext.py 1244 988 21% 57-66, 100-102, 105-111, 116-147, 170-171, 180, 218-219, 227-228, 234, 240, 247, 255, 264, 274-282, 285-295, 298-300, 304-323, 334-343, 349, 353-358, 380-387, 392-405, 464, 489-519, 524, 527-586, 589-592, 599-617, 621-632, 699-704, 709-753, 757-773, 912-919, 926, 929, 932, 939, 949-952, 955-959, 962, 969, 976, 993-1002, 1005, 1008-1015, 1018, 1027-1034, 1037, 1042-1047, 1057-1061, 1064-1065, 1068, 1077-1083, 1086, 1093-1104, 1108-1113, 1120-1123, 1133-1148, 1187-1226, 1233-1234, 1258-1305, 1320-1321, 1324, 1331-1334, 1341-1342, 1368-1375, 1378-1381, 1391, 1401, 1419-1420, 1423, 1426-1428, 1441-1467, 1480-1495, 1508-1637, 1646-1649, 1664-1668, 1671, 1675, 1679-1681, 1685, 1701-1711, 1800-1955, 1964-1977, 1981, 1985, 1989, 1992, 1995, 1998-2000, 2003-2009, 2019-2028, 2046-2048, 2051, 2054-2096, 2099, 2127-2141, 2148-2150, 2153-2185, 2188-2192, 2195-2196, 2199, 2204-2205, 2208-2209, 2212-2216, 2219-2221, 2224-2226, 2229, 2232-2391, 2394-2429, 2432, 2435, 2441, 2446, 2451, 2456-2483, 2488-2525, 2528-2544, 2547-2566, 2569 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext_data.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_pylab_helpers.py 67 40 40% 39-42, 55-67, 72-75, 80-83, 88, 93, 98, 103, 108-116, 121-122, 130-132 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_text_helpers.py 23 17 26% 16-34, 58-74 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_bbox.py 47 44 6% 18-70, 80-84 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_layout.py 133 125 6% 48-157, 170-191, 226-301 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_version.py 11 2 82% 5-6 /home/admin/.local/lib/python3.8/site-packages/matplotlib/artist.py 664 445 33% 33-39, 56-82, 95-99, 104-105, 113, 145, 181-214, 217-221, 241-259, 268-269, 278-281, 290-293, 298, 302-309, 317, 321-330, 350, 367-376, 405, 415, 428, 436, 446-449, 453-458, 462, 483-485, 504-508, 518, 531-555, 590, 602, 606, 616, 620, 630, 638-641, 668-669, 689, 713-717, 727-728, 731, 735, 746-759, 774-776, 803-838, 845, 849, 853, 864, 879-881, 892, 896, 900, 908-910, 923-927, 931-937, 941, 959-964, 968, 985-986, 1003-1005, 1016-1023, 1035-1046, 1056-1058, 1076-1078, 1091, 1095, 1106-1111, 1115, 1126-1130, 1157, 1161-1174, 1178, 1187-1203, 1213, 1223, 1231, 1238-1243, 1271-1286, 1317, 1337-1373, 1380, 1397-1403, 1414-1417, 1435-1437, 1441, 1485, 1493, 1515-1519, 1596-1600, 1614, 1616-1617, 1635-1666, 1683-1700, 1704-1715, 1746-1751, 1816-1838 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/__init__.py 9 1 89% 10 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py 2254 2048 9% 98-102, 150-181, 193-195, 312-320, 323, 381-398, 461-511, 547-550, 585-591, 617-623, 681-692, 699-706, 761-776, 829-844, 849-851, 904-926, 966-974, 1022-1031, 1072-1111, 1152-1191, 1304-1437, 1687-1695, 1772-1776, 1821-1829, 1872-1876, 1919-1923, 1996, 2073-2105, 2174-2176, 2190-2228, 2340-2525, 2640-2643, 2697-2810, 2856-2878, 2972-3063, 3180-3301, 3310-3333, 3475-3704, 3903-4014, 4116-4301, 4359-4452, 4572-4708, 4867-5099, 5130-5138, 5142-5144, 5148-5152, 5160-5164, 5172-5176, 5224-5230, 5321-5421, 5425, 5439, 5653-5676, 5685-5792, 5941-6026, 6213-6253, 6367-6435, 6448-6451, 6464-6467, 6487, 6685-6956, 7001-7033, 7128-7140, 7225-7251, 7331-7353, 7419-7439, 7497-7508, 7566-7577, 7630-7641, 7743-7797, 7878-7936, 7974-7987, 8079-8090, 8182-8259, 8281-8284 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_base.py 1737 1446 17% 63, 74, 87, 109-110, 116, 143-208, 223-225, 229, 232-233, 236-239, 242-311, 316-318, 327-336, 343-345, 348-352, 356-404, 451-544, 567, 571, 629-719, 737, 749-756, 760-767, 770-784, 788, 792-793, 797, 816, 820-825, 829-839, 844-852, 857-858, 873-879, 894-914, 934-943, 965-966, 991-992, 1014-1023, 1045-1046, 1071-1072, 1078, 1098-1104, 1129-1132, 1141-1148, 1158-1160, 1170-1171, 1177, 1181-1187, 1204, 1221, 1232-1241, 1251-1260, 1270-1386, 1392-1395, 1401-1404, 1437-1439, 1445, 1448, 1452-1454, 1457, 1462-1466, 1469-1473, 1477, 1483, 1488, 1492, 1496, 1500, 1504, 1508, 1518-1520, 1527-1532, 1597-1606, 1614, 1664-1686, 1700, 1732-1748, 1764, 1789-1801, 1814, 1849-1860, 1870-1874, 1906-2005, 2065-2128, 2132, 2136, 2140, 2151, 2162, 2179-2188, 2192, 2202, 2219-2225, 2237-2243, 2249-2276, 2282-2289, 2292-2293, 2299-2310, 2316-2321, 2327-2369, 2375-2382, 2394-2422, 2428-2433, 2439-2444, 2450-2457, 2472-2483, 2503-2508, 2539-2567, 2575, 2584, 2596-2597, 2614, 2618, 2637-2641, 2659-2663, 2717-2738, 2757-2758, 2762, 2786-2807, 2848-2933, 2942-2996, 3002-3068, 3074, 3080-3084, 3088, 3094, 3104-3105, 3119, 3145-3153, 3192-3196, 3243-3270, 3306-3312, 3378-3392, 3400-3401, 3409-3410, 3418-3419, 3448-3470, 3482, 3496-3500, 3521-3530, 3554, 3566-3573, 3642-3652, 3669-3670, 3699-3721, 3733, 3747-3751, 3772-3781, 3805, 3874-3884, 3907, 3917, 3922, 3934-3944, 3948-3949, 3957, 3963, 3969, 3979, 3985, 3995, 4011-4013, 4027-4029, 4040-4109, 4147-4154, 4171, 4187, 4197-4249, 4268-4271, 4275, 4287-4290, 4297, 4308-4328, 4374-4415, 4420-4436, 4458-4466, 4488-4495, 4499, 4503, 4513-4514, 4518-4535, 4539-4556, 4597-4613 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_secondary_axes.py 113 92 19% 22-55, 68-74, 92-115, 120-121, 125-128, 147-159, 170-173, 180-202, 209-221, 228, 238-247 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axis.py 1252 1002 20% 86-189, 194, 197-206, 214-218, 221, 225-230, 233-235, 239-241, 245, 254-257, 267-268, 272, 291, 295-303, 313-314, 326-327, 337-340, 343, 349, 352-396, 400, 403, 406, 417-433, 439, 442, 446-453, 457-463, 467, 478-494, 500, 503, 507-514, 518-524, 528, 544-547, 551, 555-558, 562, 566-569, 584-601, 652, 666-699, 703, 707, 711, 715, 719, 723, 727, 731, 740, 743, 762-768, 771, 775, 778-787, 816-830, 834, 838, 849, 852, 856-857, 862-863, 880-908, 917-928, 943-973, 1022-1027, 1045-1092, 1095-1098, 1102, 1117, 1121, 1135, 1145-1146, 1156-1158, 1192-1247, 1250-1252, 1257-1267, 1274-1310, 1314-1316, 1331-1370, 1373-1378, 1384-1403, 1407-1408, 1413, 1417, 1421, 1425-1429, 1433-1437, 1457-1468, 1472-1477, 1481-1486, 1490-1492, 1496, 1501-1514, 1533, 1549-1553, 1558-1563, 1572-1575, 1579-1585, 1589, 1593, 1597, 1601, 1605, 1622-1631, 1648-1657, 1680-1699, 1707-1720, 1727-1754, 1757, 1761-1774, 1788-1803, 1807, 1822-1828, 1854, 1868, 1871-1894, 1904-1910, 1920-1926, 1938-1940, 1949, 2007-2057, 2063-2084, 2120-2126, 2136-2152, 2159, 2166, 2180-2183, 2188, 2204-2225, 2231, 2241, 2244, 2255, 2259-2269, 2283-2284, 2293-2309, 2313-2327, 2337-2341, 2348-2379, 2388-2405, 2413-2428, 2443-2468, 2474-2480, 2486-2492, 2498, 2508, 2514-2521, 2524-2533, 2542-2543, 2552-2569, 2573-2587, 2597-2602, 2609-2639, 2648-2655, 2665-2670, 2674-2689, 2704-2725, 2731-2738, 2744-2751, 2757, 2767, 2773-2780, 2783-2791 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backend_bases.py 1287 973 24% 97-115, 131-134, 142-148, 173-177, 195, 215-218, 250-266, 278-285, 305, 323, 340-352, 364-369, 396-447, 455, 487, 497, 504, 527, 566, 588-606, 629-633, 641-663, 671, 675, 679-681, 685, 706, 747-754, 761-778, 782-799, 812, 816, 820, 826, 834-841, 851, 858, 862, 866, 870, 874, 878, 889, 900-906, 911, 922, 926, 930-931, 953-961, 974-981, 992, 996, 1000, 1004, 1015, 1019, 1023, 1027-1030, 1034, 1038, 1042, 1062, 1079, 1123-1126, 1130, 1142-1144, 1148, 1151, 1154, 1159, 1165-1167, 1172, 1176-1177, 1187-1188, 1200-1211, 1225-1236, 1257-1259, 1263, 1289-1290, 1309-1310, 1340-1367, 1432-1444, 1447, 1494-1499, 1535-1536, 1542-1545, 1551-1571, 1585-1603, 1610, 1622, 1672, 1676-1695, 1708-1729, 1742, 1746-1750, 1757, 1761-1762, 1776-1779, 1786-1788, 1796-1799, 1807-1812, 1825-1829, 1837-1841, 1855-1859, 1874-1881, 1896-1900, 1923-1926, 1950-1954, 1970-1972, 1990-1992, 2009-2016, 2025-2027, 2036-2037, 2080-2082, 2095, 2124-2133, 2156, 2162, 2172-2176, 2201-2239, 2295-2377, 2388, 2395-2400, 2411-2413, 2476, 2490, 2519, 2546-2554, 2563, 2586-2724, 2734-2743, 2814-2848, 2858, 2889-2905, 2915-2921, 2926, 2929, 2939, 2958, 2962, 3020-3036, 3059-3061, 3071-3073, 3083-3085, 3091-3102, 3115-3124, 3128-3146, 3149-3150, 3153-3162, 3170-3180, 3186-3199, 3204-3208, 3212-3221, 3224-3235, 3241-3259, 3265-3280, 3284-3321, 3325-3332, 3339-3350, 3353-3373, 3377, 3381-3382, 3406-3410, 3420, 3435-3445, 3449-3460, 3471, 3501, 3514, 3529, 3540, 3571-3574, 3579, 3583-3591, 3602-3620, 3626-3644, 3655 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backend_managers.py 152 120 21% 7-10, 16-17, 27-29, 48-60, 65-67, 72, 76, 88-96, 126, 138, 142-147, 152, 169-170, 173-174, 187-196, 208-224, 249-281, 297-324, 341-355, 358-364, 369, 390-398 /home/admin/.local/lib/python3.8/site-packages/matplotlib/backend_tools.py 501 318 37% 54-57, 62-66, 107-109, 124, 128, 139, 156, 165, 197-198, 202-206, 214, 229, 234, 237-252, 263-272, 275-279, 283-287, 291-292, 297-299, 302-311, 321-322, 325-329, 334-339, 346-351, 359, 367, 377, 387, 397-402, 412-417, 427, 434-436, 439-440, 443-444, 454, 464, 482-485, 490-497, 501-505, 515-536, 542-550, 567, 576-580, 584-585, 589-590, 594-595, 604-607, 655-663, 667-672, 677-681, 684-688, 692-716, 729-730, 733-740, 747-774, 777-778, 781-782, 787-796, 802-837, 850-851, 854-858, 861-878, 882-896, 899-903, 917, 921-922, 925, 930-932, 935-939, 951-952, 992-993, 1011-1013 /home/admin/.local/lib/python3.8/site-packages/matplotlib/bezier.py 222 186 16% 18-22, 42-62, 72-81, 91-92, 100-110, 151-178, 192-198, 214-215, 222, 227, 232, 237, 264-273, 291-305, 330-337, 348-401, 413-418, 424-429, 451-459, 474-533, 541-543, 554-594 /home/admin/.local/lib/python3.8/site-packages/matplotlib/category.py 85 50 41% 48-58, 80-85, 104-108, 112-113, 127, 131, 135, 147, 151, 155-156, 161-165, 178-181, 188-196, 211-223 /home/admin/.local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py 901 724 20% 64-98, 102-105, 114, 117, 120, 123, 130-133, 198-204, 207, 220-229, 233-243, 251-253, 258-273, 281-299, 308-321, 336-346, 371-373, 376-380, 386-395, 404-418, 435, 443, 451-456, 488-508, 513-514, 519, 536-557, 584-588, 603-604, 607-609, 620-621, 625-628, 631, 634, 638-639, 643-645, 653-655, 663-666, 670, 674-675, 686-698, 709-715, 719-729, 748-798, 839, 843-847, 853-865, 869-870, 874-877, 885-888, 892-894, 901, 942-945, 981-1023, 1057-1088, 1171-1289, 1303-1321, 1331-1334, 1343, 1351-1360, 1376-1420, 1476-1518, 1549-1556, 1587-1592, 1623-1630, 1661-1672, 1682, 1695-1725, 1732, 1763-1789, 1808-1828, 1841, 1895-1897, 1945-1949, 1976-2000, 2009-2045, 2050, 2053, 2056, 2059, 2062-2063, 2066, 2077-2088, 2095-2108, 2119-2126, 2131, 2145-2166, 2174, 2185-2188, 2197, 2205-2215, 2229-2243, 2265-2290, 2295-2297, 2302-2314, 2338-2341 /home/admin/.local/lib/python3.8/site-packages/matplotlib/cm.py 213 150 30% 81-82, 88, 91, 102, 129-149, 178-181, 200-210, 252-264, 285-293, 341-343, 364-371, 396-403, 415-425, 458-495, 509-518, 527, 531, 537, 555-563, 573, 583-587, 591, 595-620, 636, 643-647, 654-658, 665-666, 718-724 /home/admin/.local/lib/python3.8/site-packages/matplotlib/collections.py 835 666 20% 156-202, 205, 208, 211, 215-221, 232, 251-300, 305, 310-341, 345-419, 431, 434, 443-471, 484-485, 494, 529-531, 535, 545-553, 558, 562, 574-582, 610-623, 635, 638, 649, 652, 676-690, 700-703, 707, 722-723, 727, 730-734, 748-751, 754, 757-760, 764, 767-783, 798-801, 815-817, 822, 825, 841-859, 868-897, 901, 906-925, 943, 957-967, 971-972, 994-997, 1000-1001, 1004, 1069-1144, 1170-1173, 1189-1215, 1221-1226, 1238-1244, 1264-1271, 1315-1320, 1323, 1326, 1330-1337, 1408-1412, 1415-1421, 1434-1448, 1451, 1454, 1457, 1460, 1473, 1478, 1541-1549, 1555-1556, 1560-1571, 1575-1580, 1585, 1591, 1598-1603, 1613-1618, 1622, 1626-1635, 1639, 1643-1652, 1656, 1659, 1663, 1680-1683, 1711-1718, 1723-1760, 1764-1765, 1807-1821, 1824-1826, 1836-1846, 1849-1851, 1854, 1864-1866, 1870-1889, 1928-1940, 1943-1945, 1948-1949, 1973-1986, 1989, 1999, 2009-2020, 2028-2058, 2062-2107, 2110-2113 /home/admin/.local/lib/python3.8/site-packages/matplotlib/colorbar.py 698 608 13% 120, 125-127, 133, 136-138, 141-143, 151-152, 155-186, 190, 301-440, 446, 450-451, 456, 460-461, 466, 470-471, 476, 480-481, 485-489, 506-519, 527, 534-579, 584-601, 604-625, 628-644, 653-738, 765-818, 825-828, 840-876, 894-901, 912-915, 950, 956-957, 961-962, 985-989, 998, 1026, 1035-1063, 1071-1116, 1125-1148, 1152-1160, 1164-1165, 1173-1199, 1206-1219, 1228-1233, 1240-1271, 1280-1296, 1300-1301, 1305-1306, 1310-1312, 1316-1318, 1323, 1328, 1334-1339, 1343-1348, 1355-1371, 1375, 1381, 1409-1488, 1524-1594 /home/admin/.local/lib/python3.8/site-packages/matplotlib/colors.py 1035 681 34% 66-67, 70-71, 93, 137-140, 143, 146, 149, 172-184, 194-197, 204-210, 234, 241-243, 252-262, 291-293, 302-303, 320, 344, 350-354, 358-362, 370, 377-378, 381, 395, 432, 435-455, 461, 464, 471, 474, 482-487, 490, 496, 515-518, 608-644, 706-760, 767, 771-779, 783-785, 789-791, 795-797, 801-803, 807-809, 813-815, 822-827, 835-837, 840-848, 852, 856-858, 863-872, 891, 895-907, 911-923, 1009-1020, 1024-1025, 1047, 1068-1073, 1078, 1147-1148, 1151, 1155-1161, 1165-1168, 1172-1178, 1235-1239, 1243, 1247-1250, 1254, 1258-1261, 1265, 1269-1271, 1278, 1301-1313, 1333-1359, 1362-1371, 1375-1380, 1384-1388, 1392, 1428-1434, 1439, 1443-1445, 1451-1455, 1461-1473, 1476-1483, 1524-1527, 1533-1534, 1539-1541, 1545, 1549-1553, 1557, 1561-1565, 1569, 1573-1577, 1581-1583, 1587-1592, 1664-1677, 1683-1690, 1697-1715, 1718-1732, 1736-1739, 1754, 1758, 1831, 1835, 1863, 1867, 1876-1877, 1880-1906, 1909-1918, 1970-1991, 2001-2036, 2046, 2055, 2058, 2076-2110, 2127-2192, 2199-2202, 2240-2245, 2252-2254, 2299-2311, 2339-2357, 2420-2433, 2483-2507, 2549-2579, 2598, 2616-2618, 2644-2675 /home/admin/.local/lib/python3.8/site-packages/matplotlib/container.py 44 25 43% 14, 18, 21-23, 26-31, 34, 71-75, 104-107, 138-142 /home/admin/.local/lib/python3.8/site-packages/matplotlib/contour.py 700 623 11% 43-45, 49-74, 175-233, 238, 244, 249, 253, 258-259, 264-266, 274-277, 281-290, 296-324, 346-414, 418-432, 436-441, 465-505, 509-511, 515-557, 560-561, 570, 595-606, 723-882, 889-894, 897-902, 926-962, 970-996, 1000-1015, 1021-1030, 1043-1046, 1050-1075, 1091-1118, 1124-1143, 1157-1180, 1205-1223, 1228-1246, 1249-1270, 1274, 1281-1282, 1326-1361, 1364-1366, 1384-1436, 1439-1461, 1468-1504, 1519-1545 /home/admin/.local/lib/python3.8/site-packages/matplotlib/dates.py 654 512 22% 222-234, 273, 300-302, 316-318, 332-342, 356-381, 404-415, 440-467, 486-491, 509-514, 543-544, 567, 590-608, 612-621, 645-647, 651-652, 655, 735-785, 789-791, 794-870, 873, 876, 958-965, 976, 979-996, 1016-1019, 1023-1025, 1028-1056, 1060-1063, 1068-1101, 1104-1114, 1117, 1136, 1147, 1151-1155, 1159-1162, 1169, 1175, 1182-1193, 1200-1201, 1205-1210, 1213-1217, 1222-1241, 1245-1246, 1250-1266, 1269, 1337-1372, 1377-1379, 1382, 1387-1396, 1399-1402, 1406-1502, 1531-1534, 1539-1552, 1574-1579, 1604-1606, 1627-1634, 1654-1659, 1679-1684, 1704-1708, 1743-1745, 1748-1749, 1753-1758, 1761-1771, 1775, 1779, 1790-1823, 1835-1836, 1845-1853, 1864, 1872-1884, 1892-1897, 1901-1910, 1923-1927, 1930, 1933, 1936 /home/admin/.local/lib/python3.8/site-packages/matplotlib/dviread.py 535 387 28% 78, 83-89, 94, 104, 121-122, 135, 143, 151-153, 160, 167, 175, 225-227, 264-268, 272, 278, 296-297, 301-302, 309-345, 371-391, 398-404, 408-409, 413-414, 418-419, 423, 426-438, 445, 448-449, 453, 457-461, 465-466, 470, 474, 478, 482-484, 488-490, 494, 498-500, 504-506, 510, 514, 518-519, 526, 529-538, 542-556, 560, 566, 570, 611-621, 625, 629, 632, 636-640, 644-661, 689-695, 698, 705-749, 752-757, 760-763, 766-772, 779, 806-825, 882-894, 897-905, 940-1005, 1024-1030, 1037-1039, 1042, 1048-1053, 1079-1110, 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, 812-815, 825-828, 831-832, 835-836, 839-840, 845-849, 852-853, 856-857, 860-861, 866-867, 870-871, 874-875, 878-879, 887-890, 898-901, 911-922, 932-943 /home/admin/.local/lib/python3.8/site-packages/matplotlib/mathtext.py 114 67 41% 55-57, 61-63, 70, 76, 83, 90, 100-105, 108, 114-116, 119-125, 130-139, 142-144, 147-148, 161-163, 166-167, 170, 173, 225-226, 230-252, 278-287 /home/admin/.local/lib/python3.8/site-packages/matplotlib/mlab.py 275 235 15% 69, 80, 108-127, 152-157, 179, 198-213, 246-250, 255-288, 298-446, 455-472, 584-587, 638-651, 772-790, 829-840, 888-925, 929, 932, 959-985 /home/admin/.local/lib/python3.8/site-packages/matplotlib/offsetbox.py 659 494 25% 56-60, 66-67, 72-73, 122-154, 187-205, 218-225, 235-237, 242-245, 271-278, 295-296, 312, 325-326, 336-337, 341, 345, 362, 367-368, 387-388, 393-394, 398-405, 412-418, 458-465, 476-495, 508-524, 552-564, 568-569, 573-583, 586-589, 593-594, 616-623, 631, 635-636, 642, 658-661, 665, 669-670, 676-683, 688-706, 735-744, 748-749, 753, 764-765, 771, 787-790, 794, 797-817, 821-823, 841-846, 850-852, 859, 877-880, 884, 888-898, 902-905, 971-990, 1001-1004, 1008, 1012, 1016-1018, 1022-1029, 1040-1054, 1059-1065, 1068-1070, 1074-1086, 1096-1098, 1131-1140, 1161-1178, 1181-1183, 1186, 1189-1190, 1193, 1197, 1200, 1203-1208, 1212-1214, 1229, 1311-1341, 1345, 1349-1350, 1354, 1358-1359, 1362-1367, 1371-1374, 1377-1380, 1388-1392, 1396, 1400-1403, 1408-1411, 1419-1458, 1462-1475, 1508-1514, 1531-1541, 1544-1556, 1559-1563, 1566-1570, 1574-1575, 1578, 1581, 1584, 1589-1590, 1593-1597, 1600-1601, 1604-1609, 1614-1615, 1618-1619, 1622-1623 /home/admin/.local/lib/python3.8/site-packages/matplotlib/patches.py 1704 1259 26% 66-100, 109-114, 117-126, 136-156, 203-204, 232-233, 239-254, 260, 264, 271, 282, 286, 290, 294, 298, 302, 312-315, 318-330, 340-341, 344-348, 358-359, 374-375, 379-381, 392-397, 424-432, 442-445, 449, 468-470, 474, 488-490, 494, 525-527, 531, 544-580, 585-591, 601, 604, 608-610, 615, 637-644, 650-652, 655, 658, 661-662, 685-687, 714-728, 732, 736-740, 747-755, 766, 770-776, 781, 785, 789, 796, 801, 805, 809, 813, 817-818, 822-823, 831-832, 842-843, 847-848, 852-853, 866-874, 878-879, 888-889, 916-922, 925, 928, 940-941, 952-953, 956, 959, 999-1004, 1007-1040, 1044-1045, 1058-1067, 1074-1078, 1093-1095, 1099, 1103, 1114-1118, 1129, 1147-1162, 1172-1175, 1190-1195, 1199-1222, 1225-1227, 1230-1232, 1235-1237, 1240-1242, 1245-1247, 1250-1252, 1260, 1297-1298, 1306, 1309, 1320, 1364-1376, 1401-1416, 1419-1475, 1490-1491, 1508, 1516-1519, 1542-1555, 1566-1570, 1578, 1581-1582, 1592-1593, 1597, 1609-1610, 1616, 1628-1629, 1633, 1645-1646, 1650, 1661, 1695-1701, 1704-1709, 1720-1722, 1726, 1740-1746, 1750, 1762-1764, 1768, 1780-1782, 1792-1794, 1808-1816, 1820, 1825, 1833-1844, 1847-1849, 1857-1859, 1873-1874, 1884-1885, 1889, 1902-1906, 1945-1955, 2003-2098, 2102-2108, 2113-2138, 2150-2161, 2170-2174, 2207-2219, 2224, 2253-2256, 2310, 2313-2319, 2333, 2336-2340, 2358, 2361-2369, 2382, 2386-2397, 2408-2411, 2425, 2429-2441, 2463-2464, 2469-2506, 2521-2522, 2527-2555, 2570-2571, 2576-2611, 2614-2616, 2623-2631, 2690, 2697, 2708-2718, 2726-2737, 2756, 2759-2775, 2797-2798, 2801-2815, 2841-2844, 2847-2879, 2911-2916, 2919-2975, 3003-3006, 3009-3047, 3055-3059, 3137-3142, 3156, 3165-3181, 3216-3255, 3267-3297, 3302-3323, 3327-3408, 3417, 3466, 3485, 3506, 3524, 3547, 3569, 3588-3590, 3594-3648, 3667-3669, 3673-3736, 3756-3758, 3762-3780, 3796-3797, 3842-3849, 3884-3889, 3893, 3903-3904, 3908, 3918-3919, 3923, 3928-3934, 3940, 3944, 3948, 3952, 3962-3963, 3973-3974, 3984-3985, 3995-3996, 4014-4022, 4026, 4045-4049, 4124-4153, 4165-4169, 4179-4180, 4190-4191, 4226-4231, 4235, 4269-4274, 4278, 4288-4289, 4299, 4309-4310, 4314, 4321-4324, 4328-4349, 4352-4367, 4377, 4462-4483, 4487-4540, 4555-4556, 4564, 4568-4582, 4587-4607, 4610-4614 /home/admin/.local/lib/python3.8/site-packages/matplotlib/path.py 381 270 29% 135, 140, 155-158, 177-189, 216, 220-223, 235, 239-242, 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, 950, 969-991, 997, 1017, 1022-1025, 1032, 1118-1126, 1133-1135, 1142-1143, 1149, 1290-1352, 1501-1506, 1613-1621, 1664-1683, 1696-1699, 1712-1715, 1726-1734, 1753-1756, 1791-1795, 1828-1832, 1878-1895, 1941-1958, 2020-2029, 2088-2097, 2105-2108, 2116-2118, 2130-2138, 2155-2159, 2165, 2184-2190, 2195, 2200, 2242-2252, 2267-2274, 2284, 2295, 2303, 2309, 2315, 2324, 2335, 2343, 2349, 2355, 2361, 2370, 2380, 2389, 2395, 2401, 2407, 2413, 2419, 2425, 2431, 2439, 2447, 2457, 2467, 2483, 2500, 2508, 2517, 2527-2531, 2537-2541, 2550, 2564, 2578, 2588, 2598, 2608, 2616, 2627-2635, 2645, 2658, 2669-2674, 2682, 2695-2704, 2710, 2716, 2722, 2730, 2739, 2745, 2751, 2759-2764, 2773-2778, 2786, 2799, 2812, 2822, 2833, 2843-2847, 2853, 2862-2868, 2874, 2880, 2890-2897, 2905-2909, 2917, 2929, 2940, 2953-2963, 2974, 2985, 2991, 2999, 3008-3010, 3016-3018, 3026-3030, 3036, 3045, 3058, 3069, 3078, 3084, 3091, 3099, 3107, 3113, 3124, 3135, 3146, 3157, 3168, 3179, 3190, 3201, 3212, 3223, 3234, 3245, 3256, 3267, 3278, 3289, 3300, 3311, 3322 /home/admin/.local/lib/python3.8/site-packages/matplotlib/quiver.py 390 338 13% 291-314, 318, 322-345, 348, 357-362, 365, 373-374, 377-385, 407-437, 441-443, 477-506, 516-527, 530-536, 540-544, 549-571, 575-577, 592-596, 599-605, 608-663, 670-723, 897-941, 967-973, 1024-1117, 1122-1162, 1173-1180 /home/admin/.local/lib/python3.8/site-packages/matplotlib/rcsetup.py 414 127 69% 68-69, 75-82, 99, 110, 127, 135-136, 159, 169-170, 185, 188-189, 218, 230-234, 238, 260, 282, 288, 290, 292, 294, 296-300, 304, 344-347, 354, 366-367, 381-384, 395-398, 411, 415, 427-428, 438, 457-483, 506-524, 534, 537-541, 549-552, 560, 568, 583-589, 683, 686, 689-692, 695-698, 705, 716-718, 738-739, 746, 750, 758, 761, 783, 786-792 /home/admin/.local/lib/python3.8/site-packages/matplotlib/scale.py 274 155 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, 327, 340-342, 346, 350-361, 369-512, 531-552, 559, 568-582, 585-589, 593-594, 598-599, 603-604, 608, 626, 633-652, 659-675, 681-685, 692-736, 742-806, 810, 814, 824, 834, 844, 854, 864, 874, 884, 891, 897-899, 905, 909, 916, 940-963, 977-983, 995-998, 1010-1012, 1026-1028, 1040-1042, 1065-1066, 1080-1081, 1095-1096, 1113-1114, 1126, 1148, 1164-1165, 1181-1182, 1192-1193, 1203-1204, 1214-1215, 1227-1236, 1246-1247, 1259-1263, 1277-1281, 1296-1305, 1318-1319, 1329-1333, 1337, 1349, 1353, 1371, 1395-1397, 1407-1408, 1412, 1415-1419, 1436-1454, 1463-1467, 1470-1478, 1482-1562, 1569, 1594, 1602, 1606-1607, 1611-1618, 1639-1654, 1673, 1848-1885, 1888-1895, 1899, 1903-1909, 1918, 1922, 1930, 1938, 1945-1947, 1954-2016, 2021-2035, 2041-2058, 2062-2064 /home/admin/.local/lib/python3.8/site-packages/matplotlib/textpath.py 192 152 21% 34-37, 40, 46, 49-70, 112-134, 142-164, 173-215, 221-223, 230-280, 287-298, 354-369, 373-374, 378, 385-386, 393, 402-408 /home/admin/.local/lib/python3.8/site-packages/matplotlib/ticker.py 1228 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, 1927-1930, 1934-1940, 1944-1947, 1951-1954, 2006-2008, 2012-2023, 2029, 2050-2072, 2081-2132, 2135-2136, 2139-2153, 2156-2166, 2171-2176, 2181-2189, 2198, 2209, 2220-2225, 2234-2239, 2244, 2248, 2284-2292, 2296-2303, 2308, 2315, 2321-2334, 2340-2341, 2344-2429, 2433-2441, 2444-2462, 2489-2502, 2506-2509, 2514-2515, 2518-2604, 2608-2620, 2659-2664, 2669-2676, 2679-2685, 2690-2732, 2752-2753, 2757-2759, 2763, 2767, 2771-2844, 2847-2880, 2894-2900, 2916, 2920-2957, 2960 /home/admin/.local/lib/python3.8/site-packages/matplotlib/transforms.py 1162 794 32% 119-124, 127-129, 133, 137-141, 146-155, 162-165, 182-191, 204-210, 219, 238-244, 247, 251, 261, 271, 281, 291, 301, 311, 316, 321, 326, 331, 336, 341, 350, 359, 364-365, 370-371, 376-377, 382-383, 388, 391, 397-398, 404-405, 411, 421-431, 437-438, 444-445, 451, 462-472, 478-481, 511-519, 530-531, 544-555, 563-566, 574-577, 589-593, 604, 612-617, 621-622, 626, 635-636, 643-647, 652-658, 666-670, 761-772, 774-782, 786-788, 793, 798, 807, 826-829, 832, 837, 840, 854, 874-890, 908-909, 928-929, 951-955, 960-961, 965-966, 970-971, 975-976, 980-981, 985-986, 990-991, 995-996, 1000-1004, 1015, 1026, 1037, 1044-1045, 1053-1055, 1061-1063, 1067, 1071, 1076, 1094-1105, 1111-1137, 1140-1145, 1149, 1153, 1183-1192, 1198-1204, 1207-1212, 1219-1222, 1226-1228, 1235-1238, 1242-1244, 1251-1254, 1258-1260, 1267-1270, 1274-1276, 1350, 1368, 1382, 1394-1401, 1414-1419, 1451-1467, 1473, 1493-1508, 1536, 1561, 1570, 1574, 1578, 1592-1594, 1603, 1613, 1623-1624, 1650-1673, 1685, 1709-1711, 1714, 1720, 1729-1757, 1773-1774, 1778, 1781-1783, 1787, 1791, 1796, 1800, 1804, 1809, 1813, 1837, 1841-1842, 1848-1849, 1852-1856, 1859-1870, 1874-1881, 1899-1904, 1909, 1926, 1939-1942, 1954-1955, 1962-1964, 1975, 1982-1984, 1994-2007, 2017, 2027, 2038-2039, 2049-2052, 2065-2075, 2088-2101, 2114, 2126, 2132, 2136, 2140, 2144, 2148, 2152, 2156, 2160, 2164, 2171-2176, 2179, 2207-2211, 2215, 2220, 2228, 2232-2256, 2260, 2264-2275, 2300-2313, 2317-2328, 2339-2342, 2364-2373, 2377-2382, 2391-2396, 2400-2404, 2407-2410, 2423, 2427-2432, 2436-2441, 2446-2449, 2454, 2474-2486, 2490, 2493-2496, 2502-2508, 2529-2535, 2550-2558, 2564-2578, 2594-2601, 2607-2617, 2627-2637, 2648-2655, 2661-2673, 2682-2687, 2693-2699, 2720-2721, 2726-2729, 2751-2757, 2762-2770, 2780-2781, 2789-2790, 2796-2797, 2800, 2817-2818, 2821-2827, 2858-2885, 2904-2907, 2932-2936, 2955-2956, 2979-2988 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triangulation.py 98 80 18% 43-91, 101, 113-115, 122-131, 137-140, 154-168, 172-191, 199-203, 216-218, 228-247 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tricontour.py 54 38 30% 29, 35-51, 54-79, 245-246, 271-272 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trifinder.py 26 15 42% 20-21, 38-42, 55-63, 79, 86, 93 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triinterpolate.py 535 450 16% 34-56, 157-207, 228, 258-261, 265, 270, 275-283, 381-418, 421, 426, 431-446, 466-476, 497-515, 539-543, 561-571, 689-706, 727-762, 783-787, 803-828, 846-879, 896-909, 935-978, 996-1004, 1007, 1013-1017, 1043-1058, 1065-1068, 1084-1105, 1112-1127, 1135-1153, 1163-1164, 1172-1210, 1224-1227, 1234-1235, 1243-1248, 1254-1259, 1265-1269, 1272, 1277-1280, 1312-1350, 1406-1426, 1440-1472, 1479, 1486, 1494-1514, 1531-1544, 1556-1574 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tripcolor.py 62 56 10% 61-154 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triplot.py 28 23 18% 38-86 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trirefine.py 93 81 13% 43-44, 62, 94-131, 157-169, 191-307 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tritools.py 77 65 16% 29-30, 44-47, 79-115, 165-190, 220-238, 260-263 /home/admin/.local/lib/python3.8/site-packages/matplotlib/units.py 61 37 39% 62-69, 100-105, 117, 122, 132, 150-156, 167-191 /home/admin/.local/lib/python3.8/site-packages/matplotlib/widgets.py 1888 1586 16% 39, 43-45, 49-51, 55, 59, 63, 76, 80, 91, 107, 133-135, 144-145, 149-150, 194-215, 218-221, 224-228, 231-241, 249, 253, 265-301, 305-315, 326, 330-331, 430-503, 507-527, 531-552, 556-561, 571-586, 603, 703-803, 814-824, 828-835, 839-846, 850, 854-865, 869-906, 910-920, 930, 940, 950-969, 986, 990-991, 1053-1107, 1111-1118, 1121-1143, 1156-1159, 1173-1176, 1190-1196, 1214-1246, 1256-1260, 1268, 1277, 1281, 1287-1305, 1311-1335, 1383-1420, 1424, 1435-1458, 1461-1465, 1468-1501, 1504-1511, 1515-1532, 1536-1548, 1551-1563, 1566, 1569-1575, 1583, 1592, 1596, 1659-1721, 1725-1731, 1734-1753, 1766-1769, 1783-1791, 1797, 1801-1808, 1816-1841, 1849, 1853, 1859-1870, 1888-1919, 1922-1926, 1929-1942, 1976-1992, 1996-1999, 2003-2024, 2027-2035, 2080-2107, 2116-2117, 2124-2127, 2131-2141, 2144-2155, 2158-2172, 2179-2207, 2212-2214, 2224, 2232-2252, 2256-2262, 2266-2286, 2291-2308, 2312-2316, 2326-2332, 2336-2347, 2354-2362, 2369-2373, 2380-2381, 2388-2405, 2412-2419, 2426-2428, 2432, 2436, 2440-2441, 2445-2446, 2449-2450, 2455-2456, 2463-2468, 2476-2485, 2488-2492, 2511-2512, 2531-2532, 2640-2687, 2691-2714, 2718-2722, 2729-2732, 2736-2743, 2747-2749, 2753-2781, 2786, 2791-2802, 2806-2835, 2839-2850, 2856-2895, 2898-2905, 2910-2931, 2935, 2941-2942, 2949-2955, 2960-2967, 2993-3006, 3010, 3015-3016, 3021, 3033-3035, 3039-3040, 3044-3045, 3049-3050, 3067-3078, 3103-3109, 3113, 3117, 3121, 3125-3128, 3131, 3134, 3138-3144, 3272-3331, 3335, 3339, 3346-3370, 3374-3418, 3430-3565, 3569, 3572-3577, 3580-3581, 3593-3598, 3606-3613, 3618-3619, 3627-3630, 3635-3643, 3651, 3657-3660, 3663-3678, 3683-3706, 3710, 3720-3725, 3747, 3750-3760, 3764-3767, 3813-3823, 3826-3827, 3830-3835, 3838-3843, 3933-3967, 3970, 3973-3987, 3990-3992, 3996-4000, 4009-4028, 4032, 4036-4052, 4057-4064, 4069-4088, 4096-4100, 4105-4138, 4144-4148, 4154-4166, 4170-4182, 4187, 4197-4202, 4232-4244, 4247-4255, 4258-4275 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/art3d.py 488 390 20% 28-31, 36-39, 62-73, 97-98, 102, 116-119, 129-130, 144-146, 150-159, 164, 179-180, 208-209, 224-229, 248-254, 265, 269-273, 289-290, 296-300, 306-314, 320-329, 337-344, 354-355, 361-362, 368-377, 382-384, 404-405, 421-422, 426, 429-434, 455-456, 472-473, 476-481, 486-489, 494-496, 501-506, 529-531, 534, 546-547, 551-552, 570-580, 583-592, 595-602, 605, 611-613, 636-640, 643-645, 649-650, 668-695, 698-700, 703-705, 708, 720-721, 724-754, 758-768, 771-778, 781, 787-789, 809-816, 871-896, 914-916, 920-928, 944-947, 953-955, 960-966, 970-971, 977-1040, 1044-1045, 1049-1050, 1054-1065, 1070-1073, 1078-1081, 1097-1101, 1111-1118, 1127-1132, 1141-1146, 1173-1189, 1198-1227 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axes3d.py 1305 1135 13% 122-180, 183-184, 187-188, 195, 200-207, 211-213, 217, 231, 234-235, 246, 249-253, 257, 260-275, 325-360, 372-381, 407-419, 422-436, 440-492, 495-500, 506, 527-531, 541-564, 577-602, 607-616, 628-678, 682-685, 696-706, 714, 718, 722, 767, 818-835, 853-865, 869, 875-936, 951-957, 961, 967, 973, 983-992, 996-1001, 1004-1009, 1012-1017, 1021, 1026-1030, 1038-1043, 1052-1083, 1093-1144, 1150-1180, 1189-1202, 1212-1250, 1273-1288, 1306-1319, 1325-1327, 1333-1334, 1340, 1350-1351, 1364-1367, 1386-1395, 1403-1404, 1412-1416, 1425-1434, 1446-1448, 1471-1492, 1566-1689, 1729-1807, 1862-1904, 1911-1951, 1955-1962, 1965, 1974-1990, 2021-2028, 2064-2083, 2088-2093, 2118-2125, 2157-2176, 2191-2209, 2261-2283, 2313-2340, 2404-2501, 2505-2508, 2553-2649, 2724-2860, 2969-3184, 3188-3200, 3268-3317, 3324-3335 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/axis3d.py 301 250 17% 22, 30-31, 37, 43-50, 71, 74, 77-154, 161-179, 183, 186-191, 194-199, 204, 207-210, 223-228, 235-236, 239-242, 245-283, 290-302, 315-322, 332-346, 350-528, 534-576 /home/admin/.local/lib/python3.8/site-packages/mpl_toolkits/mplot3d/proj3d.py 101 81 20% 20-29, 39-48, 58-68, 95-107, 125-130, 148-150, 154-162, 167-173, 177-181, 185-192, 199-206, 210, 217-218, 230-231, 235, 239-240, 244-249 /home/admin/.local/lib/python3.8/site-packages/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, 1041, 1095-1096, 1100, 1135, 1140, 1171, 1205, 1235, 1266-1271, 1307, 1312, 1349, 1354, 1398-1399, 1433, 1438, 1467, 1472, 1503, 1548-1549, 1586-1587, 1626-1627, 1631, 1662-1667, 1702-1703, 1707, 1734-1737, 1768-1770, 1800-1802, 1950-1979, 1983-1984, 1987-1996, 2011, 2021, 2031, 2041, 2051, 2061, 2072, 2083, 2094, 2105, 2117, 2120, 2140, 2153, 2164, 2176, 2187, 2198, 2210, 2222, 2234, 2245, 2258, 2271, 2283, 2296, 2309, 2321, 2334, 2346, 2358, 2370, 2382, 2392, 2404, 2417, 2429, 2441, 2451, 2463, 2475, 2487, 2499, 2511, 2523, 2535, 2548, 2562, 2574, 2586, 2598, 2610, 2675-2743, 2794 /home/admin/.local/lib/python3.8/site-packages/numpy/core/einsumfunc.py 408 385 6% 49-54, 79-82, 128-142, 176-213, 248-270, 291-310, 348-410, 457-520, 550-693, 703, 818-986, 992-993, 1353-1431 /home/admin/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py 363 201 45% 39-48, 52-66, 82, 90, 190, 194, 298, 302-304, 429, 433, 479, 483, 537-543, 547, 594, 598, 660, 664, 749-756, 760, 839, 843, 992-999, 1003, 1114, 1118, 1195, 1199, 1276, 1280, 1350, 1354, 1416-1434, 1438, 1501-1508, 1512, 1638-1642, 1647, 1707-1711, 1715, 1819-1822, 1826, 1921, 1925, 1967-1971, 1975, 2039, 2043, 2115, 2248-2257, 2364, 2450, 2455, 2532, 2536, 2620-2630, 2635, 2754, 2760, 2879, 2884, 2919-2925, 2930, 3051, 3056, 3120, 3161-3162, 3166, 3204-3213, 3217, 3314, 3319, 3427-3440, 3446, 3568-3581, 3587, 3708-3723, 3739, 3751, 3763, 3777, 3789 /home/admin/.local/lib/python3.8/site-packages/numpy/core/function_base.py 117 56 52% 122, 144-148, 153-159, 167, 170, 173, 180, 275-278, 283, 389-440, 453, 456, 463, 527-529 /home/admin/.local/lib/python3.8/site-packages/numpy/core/getlimits.py 199 84 58% 260-281, 287-288, 383-413, 416-437, 440-451, 454-457, 517-518, 523, 528-536, 545, 553-560, 563 /home/admin/.local/lib/python3.8/site-packages/numpy/core/machar.py 188 178 5% 113-114, 117-325, 328-338, 342 /home/admin/.local/lib/python3.8/site-packages/numpy/core/memmap.py 91 73 20% 211-286, 289-298, 315-316, 319-331, 334-337 /home/admin/.local/lib/python3.8/site-packages/numpy/core/multiarray.py 104 23 78% 145, 244-245, 338, 492-495, 610, 661, 822, 880, 957, 1018, 1068, 1148, 1206, 1290, 1365, 1406, 1460, 1554, 1622, 1690 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numeric.py 495 346 30% 73, 138-142, 146, 202, 215, 280-282, 286, 337-345, 354, 416-418, 422, 485-496, 564, 568, 614-618, 622, 663, 667, 741, 745, 837-844, 848, 934-936, 940, 1072-1133, 1137, 1211-1238, 1242, 1317-1332, 1379-1391, 1395, 1447-1466, 1471, 1475, 1592-1673, 1764-1779, 1783, 1842-1846, 1855, 2011-2051, 2093-2108, 2116-2123, 2127, 2164, 2176, 2249-2250, 2254, 2337-2378, 2382, 2439-2453, 2457, 2496-2505 /home/admin/.local/lib/python3.8/site-packages/numpy/core/numerictypes.py 150 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 /home/admin/.local/lib/python3.8/site-packages/numpy/core/records.py 356 310 13% 81, 149-151, 156-172, 178-208, 211-221, 234-236, 239-241, 244-265, 269-279, 283-290, 295-299, 423-434, 437-440, 446-470, 480-507, 510-524, 528-557, 560-574, 578-586, 637-680, 725-763, 821-838, 841-846, 893-945, 1031-1092 /home/admin/.local/lib/python3.8/site-packages/numpy/core/shape_base.py 192 146 24% 20, 63-74, 78, 119-132, 136, 189-204, 209-214, 219, 276-282, 334-345, 349-354, 416-433, 448-449, 483-525, 531, 535, 576-594, 624-647, 659-666, 673-677, 830-849, 861-870, 874-889, 893-900 /home/admin/.local/lib/python3.8/site-packages/numpy/core/umath.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ctypeslib.py 213 175 18% 63-64, 67-82, 118-154, 158-161, 166-171, 177-192, 208, 217-220, 280-341, 347-351, 373-387, 391-393, 398-443, 451-456, 497, 509-518, 524-539 /home/admin/.local/lib/python3.8/site-packages/numpy/fft/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/fft/_pocketfft.py 164 120 27% 50-75, 79-88, 93-102, 111-114, 119, 211-216, 312-317, 405-410, 509-514, 607-612, 674-679, 683-698, 702-708, 712, 815, 918, 1014, 1107, 1200-1205, 1257, 1362-1367, 1424 /home/admin/.local/lib/python3.8/site-packages/numpy/fft/helper.py 46 33 28% 16, 64-73, 111-120, 160-169, 216-221 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/__init__.py 39 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_datasource.py 177 139 21% 59-66, 104-128, 146-147, 150-151, 192-193, 248-254, 258-261, 267-268, 274-278, 288-291, 295-300, 306-315, 325-342, 357-373, 399-415, 421-429, 463-485, 521-533, 578-579, 582, 586-591, 595, 618, 652, 683, 700-704 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_iotools.py 352 300 15% 30-35, 42-46, 53-57, 81-84, 120-131, 168, 172-197, 201-206, 209-216, 219-224, 227, 288-310, 339-380, 383, 413-419, 505, 524, 529, 539-541, 568-582, 587-596, 601-669, 672-675, 678-700, 703, 707-723, 746-751, 754-763, 796-820, 861-898 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_version.py 75 61 19% 56-76, 80-97, 101-112, 115-134, 137, 140, 143, 146, 149, 152, 155 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraypad.py 218 200 8% 29-30, 55, 81-83, 109-126, 146-151, 175-183, 208-227, 257-293, 321-378, 401-451, 482-518, 522, 736-876 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py 197 166 16% 34, 82-122, 127-130, 135, 270-317, 325-359, 364, 430-463, 467, 500-510, 514, 584-631, 635, 732-733, 738, 775, 779, 819-824 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arrayterator.py 71 58 18% 85-90, 93, 101-125, 132-134, 161-162, 172, 177-219 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/format.py 266 234 12% 192-194, 212-216, 230-235, 238-245, 270-281, 304-336, 352-364, 371-389, 396-413, 430-440, 452, 468, 499, 532, 552-567, 576-625, 663-696, 731-789, 842-890, 902-918 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/function_base.py 1153 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 /home/admin/.local/lib/python3.8/site-packages/pygit2/__init__.py 92 43 53% 123-162, 203-225 /home/admin/.local/lib/python3.8/site-packages/pygit2/_build.py 18 12 33% 46-53, 58-67 /home/admin/.local/lib/python3.8/site-packages/pygit2/blame.py 69 36 48% 33-36, 44-47, 52, 58, 63, 68, 72, 77, 82, 86, 91-95, 102-105, 108, 111, 114-118, 130-137, 140 /home/admin/.local/lib/python3.8/site-packages/pygit2/callbacks.py 209 162 22% 84-86, 89-93, 111-115, 148, 170, 221-238, 243-263, 268-288, 293-309, 328-341, 352-367, 372-378, 383-390, 395-402, 407-412, 417-423, 428-435, 440-448, 458-505 /home/admin/.local/lib/python3.8/site-packages/pygit2/config.py 186 125 33% 35-38, 44-45, 48, 51, 54-58, 61, 64, 69-70, 78-87, 91-95, 98-101, 104-109, 112-118, 121-128, 136-138, 141-151, 154-157, 166-170, 178-185, 191-196, 202-206, 216-221, 231-236, 241-243, 251-255, 263-267, 271-275, 283-289, 295, 301, 307, 321-336, 339-340, 345, 349, 353, 358, 363, 368 /home/admin/.local/lib/python3.8/site-packages/pygit2/credentials.py 56 18 68% 52, 56, 60, 63, 74-75, 79, 83, 86, 113-116, 120, 124, 127, 132, 138 /home/admin/.local/lib/python3.8/site-packages/pygit2/errors.py 26 20 23% 34-65, 70 /home/admin/.local/lib/python3.8/site-packages/pygit2/ffi.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/pygit2/index.py 226 171 24% 44-50, 54-59, 63, 66, 69, 72-77, 80-94, 97, 110-111, 115-116, 119-120, 129-144, 157-167, 172-173, 178-180, 188-190, 202-212, 232-249, 272-295, 322-331, 338-343, 348, 353, 356, 359-360, 365-369, 377-384, 388-396, 402, 405-417, 420-421, 424, 430-434, 437, 440, 443-457, 460 /home/admin/.local/lib/python3.8/site-packages/pygit2/packbuilder.py 40 23 42% 37-43, 47, 50, 53, 57-59, 62-64, 67-69, 72, 75-77, 81 /home/admin/.local/lib/python3.8/site-packages/pygit2/refspec.py 37 17 54% 37-38, 43, 48, 53, 58, 63, 69, 74, 77-84, 90, 96 /home/admin/.local/lib/python3.8/site-packages/pygit2/remote.py 155 112 28% 41-60, 69-71, 74, 80, 86, 92, 97-101, 106-107, 122-130, 138-164, 169-171, 177, 181-182, 188-192, 198-202, 220-224, 241-249, 252-265, 268-275, 284-296, 306-319, 326-327, 332-333, 338-339, 345-346, 352-353 /home/admin/.local/lib/python3.8/site-packages/pygit2/repository.py 552 422 24% 77, 85, 105-117, 121, 139-163, 169-174, 184-198, 204-205, 208-211, 214, 217, 223-224, 237-241, 250-254, 281-291, 308-316, 324-346, 353-354, 361-362, 369-373, 410-429, 444-453, 459-479, 543-568, 575, 615-636, 644-648, 690-698, 704-729, 743-762, 818-838, 894-919, 983-1039, 1074-1091, 1095-1104, 1128-1129, 1141, 1148-1149, 1187-1224, 1249-1262, 1288-1303, 1310-1316, 1326-1327, 1347-1363, 1372-1375, 1383-1393, 1396-1399, 1402-1404, 1407, 1410, 1413-1416, 1423-1424, 1427, 1439-1442, 1445-1446, 1449, 1452, 1455, 1459, 1462, 1491, 1493, 1497, 1501-1506 /home/admin/.local/lib/python3.8/site-packages/pygit2/settings.py 74 25 66% 40, 43, 65-71, 81, 86, 90, 98, 102, 110, 120, 128, 138, 148, 153, 158, 163, 168, 173, 178 /home/admin/.local/lib/python3.8/site-packages/pygit2/submodule.py 37 19 49% 35-40, 43, 47-51, 56-57, 62-63, 68-69, 74-75, 80-81 /home/admin/.local/lib/python3.8/site-packages/pygit2/utils.py 60 46 23% 33-36, 40-49, 53-62, 66-70, 85-102, 105, 108, 119-121, 124, 127-132 /home/admin/.local/lib/python3.8/site-packages/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, 671-675, 683, 692-696, 704, 708, 717-721, 731-755, 764, 768, 777-781, 789, 797, 806-809, 817, 821, 829-833, 841, 849, 853, 861-865, 872, 880, 889, 897, 906, 914, 923, 931, 940, 948, 957, 966-986, 997-1013 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail_settings.py 93 58 38% 38-69, 77, 86, 94, 103, 111, 120, 128, 137, 145, 154, 162, 171, 179, 188, 196, 206, 215-243 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mime_type.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking.py 27 16 41% 16-23, 31, 40, 50, 65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking_substitution_tag.py 13 6 54% 12-15, 26, 39, 49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/personalization.py 130 83 36% 8-16, 19-29, 32-42, 51, 55, 62-78, 86, 90, 98, 106, 110, 118, 129, 133, 141, 145, 152, 160, 164, 171-174, 182, 186, 193, 203, 207, 216, 220-223, 232-252 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/plain_text_content.py 25 14 44% 15-19, 27, 35, 44-45, 54-60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/reply_to.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/sandbox_mode.py 16 9 44% 12-15, 23, 32, 41-44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/section.py 25 14 44% 12-18, 26, 35, 43, 52, 61-64 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/send_at.py 24 12 50% 22-28, 36, 45, 53, 63, 70, 79 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_check.py 44 27 39% 18-27, 35, 44, 54, 68-71, 80, 91-94, 103-112 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_threshold.py 13 6 54% 15-18, 29, 44, 53 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_url.py 13 6 54% 12-15, 24, 35, 44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subject.py 23 11 52% 13-18, 26, 35, 43, 53, 60, 69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_html.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_substitution_tag.py 13 6 54% 18-21, 32, 48, 58 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_text.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_tracking.py 49 30 39% 21-33, 41, 50, 59, 71, 80, 92, 103, 120, 129-142 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/substitution.py 34 19 44% 17-26, 34, 43, 51, 60, 68, 78, 87-90 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/template_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/to_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/tracking_settings.py 49 30 39% 30-45, 53, 63, 71, 81, 89, 98, 106, 115, 124-134 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_campaign.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_content.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_medium.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_source.py 13 6 54% 11-14, 23, 34, 43 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_term.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/validators.py 27 21 22% 18-28, 42-55, 66-69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/stats.py 166 108 35% 12-22, 29, 38-53, 61, 70, 78, 87, 95, 104, 112, 121, 129, 138, 146, 155, 163, 172, 187-194, 202-220, 228, 236-238, 253-260, 268-286, 294, 302-304, 317-319, 327, 336, 344, 357-359, 367, 376, 384 /home/admin/.local/lib/python3.8/site-packages/sendgrid/sendgrid.py 7 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 1126 24% 44, 57, 69, 87, 95-97, 100-104, 117-135, 149-153, 166-170, 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, 1055, 1083, 1090, 1123-1124, 1127-1129, 1135-1138, 1141-1146, 1150-1153, 1158, 1164, 1179, 1191-1195, 1204-1208, 1211-1218, 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, 2132-2140, 2144-2212, 2257-2281, 2297-2298, 2301, 2303, 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/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 158 118 25% 33-50, 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 558 474 15% 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, 927-972, 975-986, 989-1010 /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py 511 453 11% 31-50, 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, 611-624, 627-631, 634-638, 642-662, 666-712, 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 1191 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, 1620-1625, 1634-1653, 1656-1659, 1662-1672, 1675-1678, 1682-1735, 1739-1742, 1747-1785, 1790-1791, 1794-1806, 1809-1813, 1819-1822, 1827-1871, 1878-1894, 1903-1919, 1924-1941, 1945-1957, 1966-1969, 1975-1985, 1988-1999, 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, 2343-2345, 2355, 2365-2366, 2371, 2375-2381, 2391, 2422, 2426, 2428, 2430, 2432, 2434, 2436, 2438, 2440, 2442, 2444, 2446, 2448, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2471, 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, 2616-2667, 2682, 2688, 2701-2703, 2718-2720, 2726, 2731, 2736, 2742-2744, 2746, 2751, 2758-2775, 2782-2789, 2799-2808, 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 1218 5% 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, 1095, 1098, 1102, 1118-1213, 1223-1253, 1257-1279, 1295-1332, 1336-1357, 1362-1387, 1393-1457, 1472-1500, 1503-1519, 1523-1534, 1538-1619, 1623-1638, 1658-1727, 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/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 484 480 1% 7-29, 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 977 8% 28-121, 127-305, 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, 2677-2865 /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 298 285 4% 6-55, 62-134, 143-155, 162-209, 213-219, 224-231, 236-315, 319-324, 327-333, 337-398, 402-411, 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 1826 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, 2981-2998, 3010-3056, 3066-3121, 3131-3186, 3196-3217, 3227-3270, 3280-3328, 3338-3371, 3381-3436, 3446-3492, 3503-3542, 3553-3592, 3674-3676, 3681, 3700-3701, 3706 /home/admin/workarea/git/Velours/python/tests/python_tests.py 221 61 72% 41, 94-95, 99, 102, 105, 107, 112, 122, 124, 128, 130, 132, 134, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 172, 188, 195-202, 208, 222-225, 245, 248-254, 259, 271-272, 275-278, 288-289, 370, 377 /usr/lib/python3/dist-packages/babel/__init__.py 3 0 100% /usr/lib/python3/dist-packages/babel/_compat.py 51 26 49% 34-56, 63-67, 77-80 /usr/lib/python3/dist-packages/babel/core.py 329 213 35% 27, 70-77, 102-105, 157-168, 192-193, 216-219, 261-331, 334-337, 343, 346, 349-355, 358, 363-365, 378-393, 418-421, 432-435, 446-449, 468, 482, 494, 506, 515, 531, 542, 554, 566, 580, 592, 604, 615-618, 626, 632, 641, 650, 659, 673, 687, 704, 720, 731, 740, 749, 759, 773, 787, 801, 814, 836, 851, 867, 884, 896, 907, 918, 932, 962-977, 1026-1040, 1083-1115, 1131-1133 /usr/lib/python3/dist-packages/babel/localedata.py 112 86 23% 34-39, 49-55, 66, 96-123, 138-156, 167, 170, 181-189, 198-201, 204, 207, 210-221, 224, 227, 230 /usr/lib/python3/dist-packages/babel/plural.py 280 181 35% 43-73, 112-119, 122-123, 137-139, 149-150, 158, 161, 164-166, 184-189, 211-229, 242-252, 272, 292, 306-315, 334-349, 353, 358-359, 363, 367, 371, 375, 413-421, 425-432, 435-438, 441-444, 447-461, 464-471, 474-478, 481-484, 487-495, 498, 520-521, 538, 550-553, 567-583, 598-603, 620, 623-629 /usr/lib/python3/dist-packages/certifi/__init__.py 2 0 100% /usr/lib/python3/dist-packages/certifi/core.py 5 0 100% /usr/lib/python3/dist-packages/chardet/__init__.py 11 7 36% 31-39 /usr/lib/python3/dist-packages/chardet/big5freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/big5prober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/chardistribution.py 117 83 29% 49-59, 65-68, 72-82, 88-98, 103, 110, 115-118, 125-129, 134-137, 144-148, 153-156, 163-167, 172-175, 182-189, 194-197, 204-214, 219-222, 229-233 /usr/lib/python3/dist-packages/chardet/charsetgroupprober.py 72 61 15% 34-37, 40-47, 51-55, 59-63, 66-83, 86-106 /usr/lib/python3/dist-packages/chardet/charsetprober.py 55 36 35% 40-42, 45, 49, 52, 56, 59, 63-64, 81-101, 115-145 /usr/lib/python3/dist-packages/chardet/codingstatemachine.py 28 18 36% 56-61, 64, 69-78, 81, 84, 88 /usr/lib/python3/dist-packages/chardet/compat.py 10 4 60% 26-29 /usr/lib/python3/dist-packages/chardet/cp949prober.py 16 6 62% 36-41, 45, 49 /usr/lib/python3/dist-packages/chardet/enums.py 35 1 97% 62 /usr/lib/python3/dist-packages/chardet/escprober.py 58 45 22% 43-56, 59-67, 71, 75, 78-81, 84-101 /usr/lib/python3/dist-packages/chardet/escsm.py 17 0 100% /usr/lib/python3/dist-packages/chardet/eucjpprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/euckrfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euckrprober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/euctwfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euctwprober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/gb2312freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/gb2312prober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/hebrewprober.py 77 48 38% 155-162, 165-171, 175-176, 179, 193, 223-253, 259-280, 284, 289-292 /usr/lib/python3/dist-packages/chardet/jisfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/jpcntx.py 81 61 25% 124-129, 132-141, 144-168, 171, 175-178, 181, 185-186, 190, 193-210, 214-231 /usr/lib/python3/dist-packages/chardet/langbulgarianmodel.py 5 0 100% /usr/lib/python3/dist-packages/chardet/langcyrillicmodel.py 13 0 100% /usr/lib/python3/dist-packages/chardet/langgreekmodel.py 5 0 100% /usr/lib/python3/dist-packages/chardet/langhebrewmodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/langthaimodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/langturkishmodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/latin1prober.py 52 29 44% 98-101, 104-106, 110, 114, 117-128, 131-145 /usr/lib/python3/dist-packages/chardet/mbcharsetprober.py 44 33 25% 40-43, 46-51, 55, 59, 62-88, 91 /usr/lib/python3/dist-packages/chardet/mbcsgroupprober.py 14 3 79% 43-54 /usr/lib/python3/dist-packages/chardet/mbcssm.py 41 0 100% /usr/lib/python3/dist-packages/chardet/sbcharsetprober.py 75 60 20% 40-51, 54-61, 65-68, 72-75, 78-122, 125-132 /usr/lib/python3/dist-packages/chardet/sbcsgroupprober.py 19 8 58% 45-73 /usr/lib/python3/dist-packages/chardet/sjisprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/universaldetector.py 124 104 16% 82-92, 100-109, 125-218, 229-286 /usr/lib/python3/dist-packages/chardet/utf8prober.py 43 29 33% 39-42, 45-47, 51, 55, 58-74, 77-82 /usr/lib/python3/dist-packages/chardet/version.py 2 0 100% /usr/lib/python3/dist-packages/dbus/__init__.py 17 2 88% 67, 95 /usr/lib/python3/dist-packages/dbus/_compat.py 3 0 100% /usr/lib/python3/dist-packages/dbus/_dbus.py 63 24 62% 46, 95-100, 112-115, 123, 136, 147, 159, 164-173, 195, 231 /usr/lib/python3/dist-packages/dbus/_expat_introspect_parser.py 38 29 24% 34-37, 40-45, 48-56, 59-65, 84-87 /usr/lib/python3/dist-packages/dbus/bus.py 133 72 46% 64-86, 95-100, 137-164, 168-171, 180, 183-186, 225, 229-236, 238, 254-255, 302-303, 319-320, 330-333, 343-346, 359, 375, 385, 399, 416, 430, 445 /usr/lib/python3/dist-packages/dbus/connection.py 327 269 18% 45, 52, 63, 73-124, 128, 132, 136, 141-157, 160, 164, 168-180, 183-238, 241-244, 292, 314-328, 402-429, 432-458, 466-517, 521, 528-551, 566-613, 624, 627, 633, 635, 644-649, 654-660, 669 /usr/lib/python3/dist-packages/dbus/exceptions.py 59 25 58% 52, 60-68, 72-76, 79-90, 100, 107, 114, 121, 129, 136 /usr/lib/python3/dist-packages/dbus/lowlevel.py 3 0 100% /usr/lib/python3/dist-packages/dbus/mainloop/__init__.py 9 0 100% /usr/lib/python3/dist-packages/dbus/mainloop/glib.py 8 3 62% 41-43 /usr/lib/python3/dist-packages/dbus/proxies.py 200 134 33% 32-33, 57-61, 64-72, 75, 87-103, 106-141, 150-163, 211-215, 220-225, 229, 236, 253-268, 365, 374-377, 388-390, 393-405, 408-420, 423-430, 433-442, 445-448, 473-486, 489, 511-515, 542-545, 549-552, 562-564, 567 /usr/lib/python3/dist-packages/dbus/types.py 6 2 67% 15-16 /usr/lib/python3/dist-packages/debtcollector/__init__.py 6 2 67% 44-47 /usr/lib/python3/dist-packages/debtcollector/_utils.py 93 64 31% 54-61, 66-69, 80, 94-101, 111-124, 129-134, 142-180 /usr/lib/python3/dist-packages/debtcollector/removals.py 170 112 34% 25, 30-34, 82-93, 96-107, 110-115, 118-123, 126-133, 136-142, 192-242, 258-261, 271-296, 316-333 /usr/lib/python3/dist-packages/debtcollector/renames.py 16 5 69% 38-43 /usr/lib/python3/dist-packages/entrypoints.py 148 68 54% 37, 40, 47-48, 53-54, 57, 73, 82-83, 100, 103, 111, 127-156, 160-181, 197, 208-214, 221-225, 242-243 /usr/lib/python3/dist-packages/idna/__init__.py 2 0 100% /usr/lib/python3/dist-packages/idna/core.py 282 244 13% 37-41, 44, 47, 50, 55-57, 62-64, 70-124, 129-131, 136-140, 145-146, 151-194, 199-235, 240-267, 272-292, 297-313, 318-339, 346-372, 377-400 /usr/lib/python3/dist-packages/idna/idnadata.py 4 0 100% /usr/lib/python3/dist-packages/idna/intranges.py 29 24 17% 18-29, 32, 35, 40-53 /usr/lib/python3/dist-packages/idna/package_data.py 1 0 100% /usr/lib/python3/dist-packages/iso8601/__init__.py 1 0 100% /usr/lib/python3/dist-packages/iso8601/iso8601.py 79 64 19% 22, 76-134, 144-151, 158-172, 191-214 /usr/lib/python3/dist-packages/keyring/__init__.py 3 0 100% /usr/lib/python3/dist-packages/keyring/backend.py 84 20 76% 85-87, 90-91, 99, 108, 119, 133-140, 151, 157, 165, 168, 196-198 /usr/lib/python3/dist-packages/keyring/backends/OS_X.py 46 25 46% 30, 33-43, 47-58, 62-69 /usr/lib/python3/dist-packages/keyring/backends/SecretService.py 62 40 35% 10, 13-15, 31-39, 46-59, 64-72, 77-84, 89-94 /usr/lib/python3/dist-packages/keyring/backends/Windows.py 95 59 38% 16-18, 22-24, 56, 60, 64-71, 74-84, 87-94, 97-103, 106-114, 117-126, 129-138, 151, 155, 159, 163-165 /usr/lib/python3/dist-packages/keyring/backends/_OS_X_API.py 151 136 10% 24-345 /usr/lib/python3/dist-packages/keyring/backends/__init__.py 0 0 100% /usr/lib/python3/dist-packages/keyring/backends/chainer.py 36 18 50% 47-50, 53-57, 60-64, 67-70 /usr/lib/python3/dist-packages/keyring/backends/fail.py 8 2 75% 19-25 /usr/lib/python3/dist-packages/keyring/backends/kwallet.py 95 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 19% 132-177, 181-196, 199-248, 263, 278-282, 298-303, 323, 341-345, 349, 366, 383, 386, 389, 392, 395, 398, 401, 412-447, 463-515, 537-552, 558, 564 /usr/lib/python3/dist-packages/keystoneauth1/discover.py 534 450 16% 48, 58, 99-162, 202-252, 269-340, 352-355, 371-391, 410-417, 421-435, 445-470, 480-495, 519-524, 534-535, 554-576, 589-657, 670-675, 693-703, 719-720, 745-787, 804-806, 823-832, 837, 842, 850, 858, 866, 874, 879, 910-930, 934-956, 960-965, 970, 999-1001, 1048-1062, 1073-1086, 1097-1106, 1112-1190, 1194-1249, 1261-1370, 1380, 1419-1461, 1493-1498, 1533 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/__init__.py 10 0 100% /usr/lib/python3/dist-packages/keystoneauth1/exceptions/auth.py 15 8 47% 25-32 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/auth_plugins.py 23 10 57% 43-45, 59-62, 89-93 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/base.py 6 2 67% 23-24 /usr/lib/python3/dist-packages/keystoneauth1/exceptions/catalog.py 8 0 100% /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, 1158-1165, 1182-1183, 1205, 1220-1225, 1241-1242, 1257-1258, 1284-1285, 1325-1343, 1353-1354, 1371-1372, 1389-1390, 1397, 1401, 1416-1451 /usr/lib/python3/dist-packages/keystoneclient/__init__.py 15 0 100% /usr/lib/python3/dist-packages/keystoneclient/_discover.py 137 109 20% 36-70, 76-106, 125-132, 142, 161-183, 199-241, 255-262, 274-275, 307-312, 329 /usr/lib/python3/dist-packages/keystoneclient/access.py 435 225 48% 52-85, 88-89, 94, 103-110, 121, 128, 138, 142, 146-149, 157, 165, 177, 185, 196, 207, 215, 223, 231, 239, 247, 252, 268, 276, 284, 292, 302, 310, 318, 328, 333, 344, 355, 372, 388, 396, 404, 412, 420, 428, 440, 449, 456-458, 465-470, 473, 477-480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524-544, 553-561, 565, 569, 573, 577, 581, 586, 590-610, 614-615, 619-620, 629-638, 647-656, 660, 664, 668, 672-675, 679-682, 689-696, 700-705, 708, 712, 716, 720, 724, 728-733, 737-742, 746, 750, 754, 758-760, 764-766, 770-772, 776-778, 782-784, 788-790, 799-803, 807, 811, 815, 819, 823, 827, 836-845, 854-864, 868, 872, 876-879, 883-886 /usr/lib/python3/dist-packages/keystoneclient/auth/__init__.py 4 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/base.py 82 45 45% 46-48, 63-67, 86-93, 126, 159-164, 186, 199, 215, 230, 245, 257, 267, 285-297, 315-318, 328-329, 344-347, 366-374 /usr/lib/python3/dist-packages/keystoneclient/auth/cli.py 29 21 28% 43-65, 87-95 /usr/lib/python3/dist-packages/keystoneclient/auth/conf.py 28 14 50% 41, 57, 83-93, 123-132 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/__init__.py 12 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/identity/base.py 123 80 35% 29, 49-66, 74-78, 86-90, 98-102, 110-114, 122-126, 134-138, 146-150, 158-162, 206, 215-228, 250-254, 270-274, 312-360, 363, 366, 395-414, 418-420 /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/__init__.py 4 0 100% /usr/lib/python3/dist-packages/keystoneclient/auth/identity/generic/base.py 74 44 41% 30, 63-73, 78, 83, 112, 118, 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% 30-31, 53, 57-65 /usr/lib/python3/dist-packages/keystoneclient/base.py 274 195 29% 38-49, 58-61, 66, 72-86, 103-104, 116-119, 122-124, 138-156, 167-168, 177-178, 192-195, 208-220, 232-237, 246-247, 251-265, 282-296, 304-323, 364-378, 382-383, 390, 396, 399-409, 418, 423-459, 463, 469-471, 479, 485-508, 528-531, 535-539, 544-548, 551-564, 568-576, 585-591, 595-600, 604, 607, 610, 613, 616 /usr/lib/python3/dist-packages/keystoneclient/baseclient.py 20 10 50% 19-24, 27-28, 31, 34, 37, 40, 43, 46 /usr/lib/python3/dist-packages/keystoneclient/exceptions.py 92 17 82% 75-78, 85-87, 113-115, 366-368, 375-377, 428-431, 438-439 /usr/lib/python3/dist-packages/keystoneclient/httpclient.py 331 245 26% 45-48, 80, 96, 127-143, 254-403, 406, 410-417, 420-423, 426, 429, 438, 442-443, 448-451, 455, 466-471, 482-487, 540-601, 611, 623-639, 643-650, 653-659, 669-685, 689, 696, 720, 723, 739, 742-743, 752-757, 774, 791, 808, 825, 842, 859, 870-893, 897-920 /usr/lib/python3/dist-packages/keystoneclient/i18n.py 4 0 100% /usr/lib/python3/dist-packages/keystoneclient/service_catalog.py 152 108 29% 51-56, 59-67, 80-85, 88, 138-178, 187-208, 255-294, 315-316, 323, 326-329, 332, 335, 338-346, 351-362, 372-374, 381, 384-387, 390-393, 396, 399-412, 417-427 /usr/lib/python3/dist-packages/keystoneclient/session.py 348 276 21% 48-58, 62, 66-82, 139-164, 169-178, 182-222, 225-255, 337-445, 460-519, 527, 535, 543, 551, 559, 567, 589-593, 597-607, 618-632, 635-641, 658-660, 686, 701-703, 737-757, 767-769, 785-787, 803-805, 834-837, 882-885, 901-909, 917-943, 961-967, 979-1018 /usr/lib/python3/dist-packages/keystoneclient/utils.py 56 41 27% 27-46, 50-52, 60-71, 80-91, 111-118, 122-123 /usr/lib/python3/dist-packages/keystoneclient/v2_0/__init__.py 2 0 100% /usr/lib/python3/dist-packages/keystoneclient/v2_0/certificates.py 9 5 44% 19, 28-29, 38-40 /usr/lib/python3/dist-packages/keystoneclient/v2_0/client.py 53 33 38% 150-176, 193-219 /usr/lib/python3/dist-packages/keystoneclient/v2_0/ec2.py 17 7 59% 22, 25, 36-38, 46, 54, 59 /usr/lib/python3/dist-packages/keystoneclient/v2_0/endpoints.py 13 5 62% 24, 34, 39-44, 48 /usr/lib/python3/dist-packages/keystoneclient/v2_0/extensions.py 8 2 75% 21, 31 /usr/lib/python3/dist-packages/keystoneclient/v2_0/roles.py 42 29 31% 25, 28, 37, 41-42, 46, 50, 53-59, 67-75, 83-91 /usr/lib/python3/dist-packages/keystoneclient/v2_0/services.py 15 6 60% 25, 35, 39, 43-46, 50 /usr/lib/python3/dist-packages/keystoneclient/v2_0/tenants.py 76 54 29% 37, 40, 44-58, 61, 66, 71, 80-82, 85, 89-98, 110-130, 135-149, 153, 157, 161, 167 /usr/lib/python3/dist-packages/keystoneclient/v2_0/tokens.py 58 36 38% 24, 28, 32, 36, 44-69, 72, 75, 85, 94-96, 108-115, 124-125 /usr/lib/python3/dist-packages/keystoneclient/v2_0/users.py 51 32 37% 27, 30, 33, 42-43, 46, 55-57, 61-63, 68-70, 75-78, 87-91, 97-102, 106, 113-126, 130 /usr/lib/python3/dist-packages/keystoneclient/v3/__init__.py 2 0 100% /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, 121-122, 125, 129 /usr/lib/python3/dist-packages/keystoneclient/v3/domains.py 19 7 63% 54, 70, 85-87, 105, 122 /usr/lib/python3/dist-packages/keystoneclient/v3/ec2.py 15 6 60% 30, 51, 68-69, 81, 97 /usr/lib/python3/dist-packages/keystoneclient/v3/endpoint_groups.py 20 6 70% 54, 72, 86, 99, 117, 135 /usr/lib/python3/dist-packages/keystoneclient/v3/endpoints.py 28 12 57% 50-53, 75-76, 94, 119-120, 149-150, 169 /usr/lib/python3/dist-packages/keystoneclient/v3/groups.py 27 15 44% 31-44, 68, 87-91, 106, 122, 138 /usr/lib/python3/dist-packages/keystoneclient/v3/limits.py 21 9 57% 61-73, 93, 115, 133, 150 /usr/lib/python3/dist-packages/keystoneclient/v3/policies.py 24 12 50% 31-42, 64, 79, 92, 108, 124 /usr/lib/python3/dist-packages/keystoneclient/v3/projects.py 106 76 28% 41-55, 58, 61, 64, 67, 70, 73, 105-108, 136-157, 161-164, 168-171, 201-222, 225-227, 246, 264, 274, 283-285, 298-302, 311, 323-326, 337-345 /usr/lib/python3/dist-packages/keystoneclient/v3/regions.py 20 5 75% 61, 75, 87, 112, 129 /usr/lib/python3/dist-packages/keystoneclient/v3/registered_limits.py 22 10 55% 61-72, 99, 120, 140, 157-158 /usr/lib/python3/dist-packages/keystoneclient/v3/role_assignments.py 69 48 30% 40-42, 45-47, 50-52, 55-57, 60-62, 95-124, 127, 131, 135, 139, 143, 147 /usr/lib/python3/dist-packages/keystoneclient/v3/roles.py 149 101 32% 61-92, 95-113, 116-121, 137-141, 156, 190-203, 217, 235, 275-284, 327-336, 377-386, 395, 401, 407, 413, 419, 430-433, 456-458, 481-482, 501-503, 521-523, 543-544, 559, 562, 566, 570 /usr/lib/python3/dist-packages/keystoneclient/v3/services.py 23 11 52% 57-58, 75, 89-90, 111-112, 130-134 /usr/lib/python3/dist-packages/keystoneclient/v3/tokens.py 37 28 24% 18-21, 28, 37-39, 54-58, 78-94, 116-121 /usr/lib/python3/dist-packages/keystoneclient/v3/users.py 57 34 40% 43-45, 82-92, 126-132, 148, 187-197, 213-226, 240-243, 259-262, 278-281, 295 /usr/lib/python3/dist-packages/netaddr/__init__.py 19 1 95% 16 /usr/lib/python3/dist-packages/netaddr/compat.py 60 37 38% 39, 50-53, 56-59, 62-113 /usr/lib/python3/dist-packages/netaddr/contrib/__init__.py 1 0 100% /usr/lib/python3/dist-packages/netaddr/contrib/subnet_splitter.py 17 11 35% 23, 27-38, 42, 46 /usr/lib/python3/dist-packages/netaddr/core.py 73 40 45% 61-74, 89, 112-113, 122-124, 136, 145-149, 158-161, 169-170, 184-196, 199-200, 203, 206 /usr/lib/python3/dist-packages/netaddr/eui/__init__.py 361 276 24% 24, 28, 32, 37-39, 44, 52, 72-101, 104-109, 112-117, 121, 125, 129-152, 157, 169, 173-174, 181, 202-216, 230-268, 271-276, 279-284, 288, 292, 296-308, 312, 316-318, 327, 357-390, 394, 401-413, 416, 419-450, 456, 459-468, 477-480, 485-488, 492, 500-501, 506, 515-525, 529-548, 552, 559-564, 571-576, 583-588, 595-600, 607-612, 619-624, 633, 638, 643, 652, 663-671, 685-687, 699-700, 710, 718-722, 726, 730 /usr/lib/python3/dist-packages/netaddr/ip/__init__.py 822 596 27% 33-38, 47, 54, 60, 69-72, 81-84, 93-96, 105-108, 117-120, 129-132, 136, 140-143, 151-154, 162-174, 181-184, 191-199, 206, 213, 222-223, 228, 262-266, 275, 278, 285-293, 305, 316-319, 323, 330-339, 348-371, 377-378, 384-385, 396-400, 411-415, 426-429, 442-445, 456-459, 468, 472, 480, 485-487, 492, 500, 508, 513, 521, 530, 535, 544-557, 570-586, 596-600, 609, 618, 627, 636, 645, 651, 657, 661, 676-678, 685, 693-697, 705-734, 743-752, 760, 768-776, 782, 787-788, 796-798, 804-816, 820-823, 827-828, 906-908, 911-913, 915-917, 919-921, 924, 934-935, 938, 946, 953-968, 972-977, 989, 994, 999-1002, 1010, 1018-1019, 1024-1025, 1030, 1035-1036, 1041, 1049, 1064-1072, 1085-1093, 1102-1123, 1129, 1135-1138, 1146-1161, 1174-1193, 1202-1205, 1214-1217, 1229-1240, 1255-1281, 1297-1318, 1322-1323, 1327, 1361, 1365, 1371-1375, 1378-1397, 1402, 1407, 1413, 1419-1420, 1427, 1431, 1435, 1446-1448, 1477-1531, 1549-1576, 1589-1591, 1606-1650, 1663-1684, 1701-1730, 1746-1767, 1782-1796, 1811-1823, 1838-1852 /usr/lib/python3/dist-packages/netaddr/ip/glob.py 137 117 15% 26-67, 79-97, 109-127, 141-201, 213, 225-231, 283-285, 289, 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, 232-234, 238-240, 244-246, 250-252, 256-258, 263-265, 269, 273 /usr/lib/python3/dist-packages/netaddr/strategy/ipv4.py 103 51 50% 16, 91-107, 121, 141-148, 158-161, 171, 182, 186, 196-199, 212-214, 218, 222, 226-228, 232, 236, 240, 261-278 /usr/lib/python3/dist-packages/netaddr/strategy/ipv6.py 106 47 56% 18, 24-25, 119-126, 141-142, 154-172, 182-187, 197-198, 221, 225-229, 233, 237, 241, 245-247, 251, 255, 259 /usr/lib/python3/dist-packages/oauthlib/__init__.py 10 2 80% 27, 34 /usr/lib/python3/dist-packages/oauthlib/common.py 212 144 32% 22-24, 27-28, 59, 64-70, 74-80, 84-89, 96-101, 108-113, 129-165, 176-194, 209, 221, 232-233, 237-251, 255-257, 266, 271-275, 280-285, 297-303, 308-328, 338-340, 343, 346-348, 351-352, 355, 358-359, 362-364, 385-430, 433-436, 439-447, 452, 456-458, 463-468 /usr/lib/python3/dist-packages/oauthlib/oauth1/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/__init__.py 125 91 27% 51, 86-101, 104-110, 122-151, 156-186, 199-223, 256-327 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/__init__.py 8 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/access_token.py 59 48 19% 44-54, 104-119, 131-217 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/authorization.py 38 25 34% 50-57, 111-139, 154-163 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/base.py 87 75 14% 23-24, 32-66, 70-106, 110-112, 117-177, 182-216 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/pre_configured.py 8 4 50% 11-14 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/request_token.py 56 45 20% 42-49, 99-110, 122-211 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/resource.py 43 35 19% 70-165 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/signature_only.py 34 26 24% 35-84 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/errors.py 36 15 58% 39-46, 49, 53-58, 62 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/parameters.py 36 23 36% 48-91, 105-112, 124, 136-139 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/request_validator.py 108 48 56% 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 162-163, 170-171, 178-179, 186-187, 194-195, 200, 207-208, 232, 248, 264, 300, 333, 366, 383, 398, 416, 440, 467, 504, 539, 574, 625, 659, 678, 713, 745, 764, 788, 812, 833, 854 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/signature.py 149 117 21% 84-106, 131-205, 279-340, 423-438, 442, 468-495, 499, 525-552, 559-562, 580-586, 590-592, 616-627, 631, 651-661, 681-691, 695-697, 716-729, 739-743 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/utils.py 40 22 45% 31-32, 40-44, 55-60, 64-66, 72, 78, 83-90 /usr/lib/python3/dist-packages/os_service_types/__init__.py 9 3 67% 39-41 /usr/lib/python3/dist-packages/os_service_types/data/__init__.py 8 0 100% /usr/lib/python3/dist-packages/os_service_types/exc.py 10 2 80% 24-25 /usr/lib/python3/dist-packages/os_service_types/service_types.py 111 69 38% 28-29, 59, 64-71, 75-78, 83, 88, 93, 98, 103, 108, 114, 120, 129-133, 141-144, 152, 160-161, 169, 191-202, 210-211, 221-234, 242-245, 254-258, 269-270, 281-285 /usr/lib/python3/dist-packages/oslo_config/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_config/cfg.py 1258 889 29% 38-39, 77, 80, 87, 94-97, 104-105, 108-109, 116, 119, 126, 129, 136-137, 140-141, 149, 156, 159, 167, 170, 178, 181, 188-189, 192, 211, 237-248, 262-265, 269-278, 309, 339, 355-357, 375-388, 392-395, 400-402, 414-423, 541, 545, 548, 559, 574, 584-585, 589-592, 603-604, 612-613, 623-632, 635, 638, 648-676, 688-697, 713-729, 738-741, 753-763, 776-779, 797-806, 809, 869-870, 873, 876, 879, 930-934, 938-949, 966-968, 972-973, 977-988, 994-1006, 1026, 1047, 1086, 1104, 1134-1136, 1152, 1173, 1199-1200, 1229, 1233-1238, 1253, 1291-1295, 1299-1312, 1346-1351, 1354, 1358-1360, 1395-1406, 1409, 1414-1416, 1461-1469, 1478, 1482, 1497-1502, 1509-1510, 1513-1517, 1521, 1524, 1529-1530, 1533, 1548-1552, 1555, 1558-1559, 1562-1566, 1570-1581, 1584, 1587, 1596-1617, 1640-1647, 1672-1697, 1707-1710, 1717, 1724, 1737-1747, 1758-1788, 1802-1807, 1831-1858, 1861-1863, 1878-1879, 1882-1886, 1894-1910, 1913-1914, 1917-1918, 1921-1922, 1979-1999, 2003, 2031-2034, 2039-2049, 2059, 2066-2071, 2120-2141, 2145-2148, 2151-2187, 2196-2201, 2205, 2209, 2213-2215, 2219, 2223-2224, 2236-2244, 2247-2252, 2257-2269, 2289-2293, 2300, 2304, 2307, 2316-2317, 2332-2335, 2340-2341, 2351-2354, 2364-2380, 2385-2386, 2404-2405, 2422-2423, 2438-2441, 2459-2462, 2468-2471, 2484-2485, 2497-2498, 2502-2506, 2510-2511, 2515-2517, 2521-2523, 2542-2554, 2567-2591, 2603-2605, 2617-2619, 2622-2633, 2646-2735, 2749-2768, 2783-2786, 2806-2815, 2818-2830, 2839-2863, 2871-2879, 2893-2897, 2906-2937, 2940-2955, 2958-2965, 2975-2987, 2996, 3009-3032, 3040-3054, 3065-3081, 3089-3094, 3112-3114, 3129-3130, 3134, 3138, 3142, 3146-3147, 3151, 3167-3169, 3173-3185, 3202-3204, 3212-3229 /usr/lib/python3/dist-packages/oslo_config/iniparser.py 75 57 24% 18-20, 23, 31-32, 35-40, 43-56, 59-97, 101, 105, 109, 112, 116, 119, 123, 127 /usr/lib/python3/dist-packages/oslo_config/sources/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oslo_config/sources/_environment.py 22 11 50% 70, 73, 81-82, 85-92 /usr/lib/python3/dist-packages/oslo_config/types.py 419 300 28% 44-53, 56-61, 65, 113, 120-123, 129, 133-139, 142-167, 173-180, 183, 194, 201, 209-215, 218, 238, 241-250, 253, 256, 259, 282-304, 307-324, 327-338, 341, 352, 388, 411, 437-446, 479, 487-519, 522, 525, 531-540, 562-565, 568-586, 589, 596, 618-626, 629-680, 683, 686, 692-694, 713-722, 725-729, 732, 735, 738-739, 742-743, 746-748, 751, 766, 785-799, 802, 805, 808, 829-831, 841-848, 851, 854, 857, 880-882, 885-906, 910, 914, 918, 921, 924-932, 935 /usr/lib/python3/dist-packages/oslo_i18n/__init__.py 4 0 100% /usr/lib/python3/dist-packages/oslo_i18n/_factory.py 75 32 57% 83, 99-121, 136-152, 169, 182, 185, 190, 195, 200, 205 /usr/lib/python3/dist-packages/oslo_i18n/_gettextutils.py 41 20 51% 48-50, 61-101 /usr/lib/python3/dist-packages/oslo_i18n/_lazy.py 4 1 75% 38 /usr/lib/python3/dist-packages/oslo_i18n/_locale.py 2 0 100% /usr/lib/python3/dist-packages/oslo_i18n/_message.py 95 71 25% 59-69, 94-104, 115-132, 137-179, 194-215, 221-227, 238-251, 254-259, 262-264, 267 /usr/lib/python3/dist-packages/oslo_i18n/_translate.py 17 13 24% 39-49, 67-73 /usr/lib/python3/dist-packages/oslo_log/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_serialization/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_serialization/jsonutils.py 82 53 35% 85-181, 201, 217, 235-236, 248, 260, 268-270 /usr/lib/python3/dist-packages/oslo_utils/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_utils/_i18n.py 4 0 100% /usr/lib/python3/dist-packages/oslo_utils/encodeutils.py 60 53 12% 38-63, 84-104, 114-119, 135-188 /usr/lib/python3/dist-packages/oslo_utils/importutils.py 40 24 40% 29-34, 44, 60-65, 92-97, 117-122 /usr/lib/python3/dist-packages/oslo_utils/reflection.py 107 83 22% 44-47, 55-58, 63, 78-96, 107-111, 121-153, 158-163, 168-186, 191, 196, 208-214, 219-220 /usr/lib/python3/dist-packages/oslo_utils/strutils.py 183 135 26% 127, 146-165, 177-178, 214-247, 265-272, 333-359, 416-441, 453-456, 470-488, 503-520, 545-569, 579-586 /usr/lib/python3/dist-packages/oslo_utils/timeutils.py 230 163 29% 35, 56-64, 69-74, 95-97, 102, 107-110, 120-125, 135-140, 151-165, 176-183, 201, 217, 226-231, 240, 249, 258-267, 283-297, 306-307, 318-319, 333-334, 339, 344, 347-349, 379-396, 421-428, 435-441, 446, 450-459, 465-468, 473, 477-486, 490-491, 495-498, 508-516, 520-525, 529, 533, 537-541, 546-553 /usr/lib/python3/dist-packages/pbr/__init__.py 0 0 100% /usr/lib/python3/dist-packages/pbr/version.py 221 127 43% 30-31, 60, 64-66, 69, 83-88, 100-102, 105, 108, 111, 114, 117, 146-147, 154, 160, 162-172, 183-188, 194-195, 200-202, 205, 210-211, 213-223, 228-232, 245, 258-273, 291-314, 326-343, 350, 360, 367, 387-405, 423, 427, 444-449, 457, 480-483 /usr/lib/python3/dist-packages/pkg_resources/__init__.py 1579 855 46% 48-50, 54-55, 65-67, 76-77, 93, 97-98, 131-132, 144-148, 152-155, 159, 163-164, 168, 172, 194-198, 256, 271, 275, 278, 285-288, 301, 312, 316, 320-322, 325, 328, 359-365, 369-381, 385, 398-408, 425-461, 466-470, 484, 490, 495, 500, 583-588, 597-610, 629, 644, 654, 663-667, 679, 699, 702, 713, 743-806, 844-890, 901-906, 915, 919-920, 924, 927, 933-937, 953-957, 982-985, 994-999, 1003, 1013-1018, 1028-1029, 1034-1038, 1054-1066, 1077-1078, 1082-1084, 1088-1096, 1100-1103, 1135, 1139, 1145, 1151, 1157, 1163, 1170-1193, 1208-1218, 1230-1244, 1261-1264, 1285-1290, 1312, 1344, 1352, 1360-1366, 1377-1381, 1392-1393, 1396, 1399, 1402, 1405, 1412, 1419, 1423, 1426-1430, 1436, 1439, 1442, 1445-1447, 1450-1470, 1473, 1478, 1483, 1491, 1554-1562, 1569-1571, 1583-1584, 1589-1598, 1608, 1611, 1614, 1639, 1642, 1665-1673, 1688-1695, 1705-1706, 1711-1716, 1723-1726, 1732, 1735-1745, 1749-1754, 1758-1809, 1815-1825, 1828-1834, 1837-1851, 1854-1855, 1858, 1861, 1864, 1867, 1889, 1896, 1907-1909, 1946-1952, 1979-1999, 2043-2048, 2098, 2101, 2110-2122, 2130, 2144-2148, 2156-2162, 2206-2220, 2228-2254, 2271, 2274-2275, 2298, 2306-2313, 2324, 2373-2377, 2389-2391, 2413-2419, 2422-2427, 2430, 2436-2445, 2451-2455, 2458-2468, 2490-2497, 2501-2506, 2511-2519, 2524-2538, 2542-2547, 2602, 2612, 2615, 2618, 2621, 2624, 2627-2630, 2633, 2649-2652, 2655-2678, 2684-2693, 2701-2705, 2714-2727, 2730-2734, 2738-2748, 2754-2765, 2791-2798, 2801-2804, 2807-2812, 2821, 2831, 2835, 2842-2847, 2851-2854, 2858-2866, 2870, 2894, 2903, 2911-2938, 2941-2957, 2963-2968, 2972-2976, 2980, 3013-3018, 3022-3026, 3030-3049, 3060-3069, 3074, 3088, 3091-3095, 3104-3105, 3122, 3128, 3133, 3143, 3146, 3160, 3174-3175, 3180-3188, 3199-3214, 3218-3225 /usr/lib/python3/dist-packages/pkg_resources/_vendor/__init__.py 0 0 100% /usr/lib/python3/dist-packages/pkg_resources/_vendor/appdirs.py 257 218 15% 29-39, 77-97, 131-163, 195-203, 236-254, 290-311, 345-353, 388-404, 411-415, 419, 424, 429, 434, 439, 444, 449, 460-476, 480-503, 507-530, 533-556, 559-571, 577-608 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/__about__.py 10 0 100% /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/__init__.py 3 0 100% /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/_compat.py 12 1 92% 17 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/_structures.py 41 17 59% 10, 13, 16, 19, 22, 25, 28, 31, 42, 45, 48, 51, 54, 57, 60, 63, 66 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/markers.py 130 73 44% 47, 50, 53, 56, 62, 68, 74, 142-145, 149-168, 184-197, 204-211, 215-238, 242-246, 250-257, 275-280, 283, 286, 297-301 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/requirements.py 72 17 76% 91-92, 98-102, 110-124, 127 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/specifiers.py 306 190 38% 83-93, 96-102, 109, 112, 115-123, 126-134, 137, 140-142, 146, 150, 154, 158, 161, 165-180, 183-211, 243-245, 248, 251, 254, 257, 260, 263, 269-271, 397-410, 416-446, 450, 454, 458, 464-483, 489-514, 517, 523-541, 545, 552-559, 563-583, 600-603, 613-619, 622, 628-648, 651-658, 661-668, 671, 680-691, 695, 698, 709, 718, 733-774 /usr/lib/python3/dist-packages/pkg_resources/_vendor/packaging/version.py 160 36 78% 45, 51, 60, 63, 67, 79, 82, 86, 90, 94, 98, 102, 146, 150, 234, 241, 256, 268, 272-281, 285-287, 291, 295, 303, 312, 314, 316, 318, 324-326, 363 /usr/lib/python3/dist-packages/pkg_resources/_vendor/pyparsing.py 2533 1341 47% 96-97, 103-106, 110-114, 151-181, 189-193, 212-213, 226, 234-241, 244, 247, 252-257, 259, 309, 312, 320, 322, 381, 385, 392, 394, 398, 418, 425-426, 440-442, 448-452, 455, 463-466, 469, 472, 485-504, 545-561, 580-583, 599-603, 617, 632-635, 641-642, 650-656, 659-661, 680-685, 688, 691, 697, 699, 718, 739-753, 770-825, 828-832, 856-869, 889-914, 937, 941, 948-958, 961, 964, 978-979, 991, 996-1001, 1004, 1007, 1010, 1014, 1053-1058, 1075-1090, 1097-1098, 1121, 1142, 1201, 1226-1227, 1237-1248, 1319-1320, 1335-1336, 1339-1349, 1353, 1359, 1375-1393, 1413-1425, 1436-1437, 1445, 1448-1453, 1457-1475, 1480-1533, 1543-1563, 1600-1606, 1640, 1646-1654, 1688-1727, 1746-1770, 1790-1797, 1812-1819, 1836-1838, 1848-1850, 1857-1863, 1869-1875, 1902, 1904-1909, 1914-1916, 1919, 1921, 1923, 1932-1935, 1940, 1946, 1953, 1955-1957, 1964-1970, 1977, 1979-1981, 1988-1994, 2000-2006, 2012-2018, 2076-2077, 2092-2100, 2106-2110, 2147-2151, 2157, 2165, 2171, 2179-2191, 2194-2199, 2202, 2205, 2208, 2211, 2226-2230, 2319-2361, 2388-2392, 2395, 2418-2421, 2459-2477, 2480-2491, 2494-2496, 2502, 2516-2520, 2523-2525, 2537, 2540-2543, 2571-2577, 2580-2603, 2658, 2671, 2676, 2693, 2701, 2704-2705, 2726, 2732, 2734-2735, 2740, 2785, 2794-2806, 2822-2823, 2869-2870, 2875-2878, 2891-2892, 2903, 2909, 2911-2912, 2918-2921, 2929-2961, 2997, 3002, 3019-3030, 3040, 3073, 3078-3079, 3082-3094, 3109-3110, 3113-3119, 3122-3127, 3156-3158, 3170-3178, 3191-3192, 3205, 3208-3211, 3222-3224, 3227-3231, 3242-3245, 3248-3253, 3263, 3266, 3271, 3274-3277, 3281, 3284-3286, 3298-3307, 3310-3317, 3358-3361, 3386-3388, 3404-3405, 3407-3415, 3423-3425, 3428-3432, 3436, 3463, 3477-3483, 3486-3494, 3500, 3504-3506, 3510, 3518-3520, 3545, 3558-3561, 3569, 3572-3574, 3578, 3586-3588, 3646-3649, 3652-3698, 3701-3707, 3710-3712, 3725, 3741, 3751-3760, 3769-3773, 3776-3779, 3810-3811, 3814-3815, 3837-3839, 3843, 3856, 3864, 3869, 3875, 3877, 3916, 3947, 3956, 3959, 4008-4012, 4019, 4082-4092, 4095-4139, 4165, 4177, 4180-4181, 4191-4195, 4199, 4205-4212, 4218-4220, 4224, 4262-4266, 4274, 4340-4361, 4387, 4395-4396, 4398-4402, 4404, 4427-4445, 4465, 4487-4498, 4501-4507, 4522-4535, 4551-4563, 4596-4644, 4679, 4712-4713, 4723, 4745-4746, 4784-4785, 4792-4795, 4809, 4823, 4858, 4863-4864, 4878-4879, 4885-4886, 4930, 4982-4994, 5029-5030, 5097-5146, 5215-5245, 5325-5359, 5369, 5607-5612, 5629-5634, 5659, 5675-5740 /usr/lib/python3/dist-packages/pkg_resources/_vendor/six.py 444 209 53% 49-72, 98-99, 112, 118-121, 131-133, 145, 154-157, 192-193, 203, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843 /usr/lib/python3/dist-packages/pkg_resources/extern/__init__.py 36 5 86% 21, 32, 54-57 /usr/lib/python3/dist-packages/pkg_resources/py2_warn.py 6 0 100% /usr/lib/python3/dist-packages/pkg_resources/py31compat.py 12 5 58% 9-13 /usr/lib/python3/dist-packages/rfc3986/__init__.py 16 0 100% /usr/lib/python3/dist-packages/rfc3986/_mixin.py 112 86 23% 28-51, 54, 59-63, 68-72, 77-81, 91, 113-123, 139-147, 165-168, 182-185, 199-202, 216-219, 229, 247-299, 308-319, 341-353 /usr/lib/python3/dist-packages/rfc3986/abnf_regexp.py 63 0 100% /usr/lib/python3/dist-packages/rfc3986/api.py 15 6 60% 38, 52, 77, 92-93, 106 /usr/lib/python3/dist-packages/rfc3986/compat.py 23 10 57% 20-21, 25-26, 45-47, 52-54 /usr/lib/python3/dist-packages/rfc3986/exceptions.py 45 24 47% 18, 28, 37, 52-59, 71-81, 89-95, 103-110 /usr/lib/python3/dist-packages/rfc3986/iri.py 50 34 32% 49-57, 61-73, 76, 86-89, 111-142 /usr/lib/python3/dist-packages/rfc3986/misc.py 31 5 84% 116-121 /usr/lib/python3/dist-packages/rfc3986/normalizers.py 79 63 20% 24, 29-37, 42, 47, 52-67, 72-76, 81-83, 88-90, 101-105, 114-139, 144-167 /usr/lib/python3/dist-packages/rfc3986/parseresult.py 166 124 25% 34-46, 50, 55, 60, 65, 81-92, 98-112, 134-139, 152, 159-172, 176-181, 193-198, 208-220, 227-244, 267-273, 287, 294-314, 327-337, 342-364, 368-385 /usr/lib/python3/dist-packages/rfc3986/uri.py 30 17 43% 88-96, 102-115, 128, 144-147 /usr/lib/python3/dist-packages/rfc3986/validators.py 129 99 23% 60-73, 87-89, 103-105, 119-123, 135-136, 148-149, 165-174, 190-199, 220-240, 245-251, 256-258, 265-271, 284-289, 306-309, 324-329, 344, 359, 374, 389, 396, 411-430, 435-450 /usr/lib/python3/dist-packages/secretstorage/__init__.py 21 18 14% 17-53 /usr/lib/python3/dist-packages/secretstorage/collection.py 104 99 5% 22-201 /usr/lib/python3/dist-packages/secretstorage/defines.py 11 0 100% /usr/lib/python3/dist-packages/secretstorage/dhcrypto.py 28 22 21% 18-59 /usr/lib/python3/dist-packages/secretstorage/exceptions.py 5 0 100% /usr/lib/python3/dist-packages/secretstorage/item.py 73 68 7% 17-145 /usr/lib/python3/dist-packages/secretstorage/util.py 112 107 4% 15-180 /usr/lib/python3/dist-packages/simplejson/__init__.py 80 57 29% 120-122, 126-130, 248-279, 372-385, 457, 514-535, 539-562, 577 /usr/lib/python3/dist-packages/simplejson/compat.py 29 16 45% 5-18, 24, 26 /usr/lib/python3/dist-packages/simplejson/decoder.py 225 178 21% 14-15, 26-27, 60-133, 145-234, 237-270, 368-374, 387-400 /usr/lib/python3/dist-packages/simplejson/encoder.py 394 341 13% 13-14, 42-62, 69-103, 244, 247, 249, 251, 272, 284-302, 314-380, 400-404, 407-417, 441-722 /usr/lib/python3/dist-packages/simplejson/errors.py 29 23 21% 7-12, 16-23, 41-50, 53 /usr/lib/python3/dist-packages/simplejson/raw_json.py 3 1 67% 9 /usr/lib/python3/dist-packages/simplejson/scanner.py 64 53 17% 9-10, 21-83 /usr/lib/python3/dist-packages/six.py 491 239 51% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 308, 488, 496, 501-507, 519-525, 530-532, 538-540, 545, 550, 554-568, 583, 586, 589, 592, 600-616, 628, 631, 645-647, 653-673, 679, 683, 687, 691, 698-721, 737-738, 743-795, 797-804, 814-834, 845-861, 870-873, 893-898, 912-918, 932-937, 948-955, 976-977 /usr/lib/python3/dist-packages/stevedore/__init__.py 9 0 100% /usr/lib/python3/dist-packages/stevedore/driver.py 29 17 41% 51-53, 66, 100-105, 108-118, 139-141, 147-148 /usr/lib/python3/dist-packages/stevedore/enabled.py 13 7 46% 64-65, 77-84 /usr/lib/python3/dist-packages/stevedore/exception.py 3 0 100% /usr/lib/python3/dist-packages/stevedore/extension.py 104 71 32% 46-49, 58, 99-107, 141-146, 150-152, 155-156, 160-165, 176-179, 183, 187-214, 220-230, 237, 259-265, 269, 290, 294-301, 309, 317, 326, 331 /usr/lib/python3/dist-packages/stevedore/hook.py 11 6 45% 59, 74-78, 87-89 /usr/lib/python3/dist-packages/stevedore/named.py 34 24 29% 74-89, 123-129, 134-140, 143-146, 154-156 /usr/lib/python3/dist-packages/swiftclient/__init__.py 7 2 71% 31-32 /usr/lib/python3/dist-packages/swiftclient/client.py 959 847 12% 53-63, 71-72, 75-76, 86-89, 128-135, 146-156, 160-190, 194-222, 230-232, 236-245, 250-260, 275-276, 279, 282, 285-288, 291, 294, 320-328, 331-368, 401-441, 445-450, 454, 458-472, 481, 485-521, 524-526, 531-532, 536-567, 573-574, 584-661, 683-745, 749-753, 765-768, 794-839, 857-875, 896-921, 952-1008, 1027-1048, 1068-1092, 1111-1133, 1155-1180, 1210-1242, 1262-1285, 1329-1400, 1420-1439, 1465-1503, 1529-1557, 1568-1578, 1641-1672, 1675-1679, 1682-1691, 1694-1702, 1712, 1721-1727, 1730-1791, 1795, 1804, 1812, 1818, 1827, 1836, 1842, 1848, 1855, 1861-1878, 1885-1906, 1914, 1920, 1927, 1933-1944, 1947-1950 /usr/lib/python3/dist-packages/swiftclient/exceptions.py 51 45 12% 25-36, 40-43, 48-81 /usr/lib/python3/dist-packages/swiftclient/utils.py 229 178 22% 41, 51-68, 100-197, 201-205, 209-216, 220-239, 248-253, 258, 261, 264, 285-287, 290, 298-307, 310, 313, 332-339, 342, 345, 348, 351-363, 367-369, 373-378, 382-387, 391-392, 396-397, 401-405, 410-416, 419, 424-427 /usr/lib/python3/dist-packages/swiftclient/version.py 6 3 50% 24-28 /usr/lib/python3/dist-packages/urllib3/__init__.py 33 8 76% 56-62, 86 /usr/lib/python3/dist-packages/urllib3/_collections.py 187 137 27% 5-6, 9-16, 47-51, 55-58, 61-73, 76-80, 83-84, 87, 92-99, 102-103, 141-149, 152-153, 156-157, 160, 163, 166-170, 175, 178-179, 184, 188-189, 198-206, 209-212, 223-228, 235-256, 261-268, 275-286, 297, 300-305, 308-310, 314-317, 321-323, 326, 334-354 /usr/lib/python3/dist-packages/urllib3/connection.py 173 116 33% 17-21, 27-30, 105-115, 134, 144, 151-175, 178-184, 187-188, 192-199, 206-234, 256-266, 297-310, 314-402, 409-420, 428 /usr/lib/python3/dist-packages/urllib3/connectionpool.py 318 257 19% 75-80, 83, 86, 89-91, 97, 182-215, 221-236, 250-275, 291-303, 309, 313, 317-325, 330-348, 369-451, 454, 460-472, 479-493, 601-854, 904-927, 935-947, 954-955, 961-991, 997-1004, 1035-1040, 1048-1058 /usr/lib/python3/dist-packages/urllib3/contrib/__init__.py 0 0 100% /usr/lib/python3/dist-packages/urllib3/contrib/_appengine_environ.py 11 1 91% 36 /usr/lib/python3/dist-packages/urllib3/contrib/socks.py 75 66 12% 55-210 /usr/lib/python3/dist-packages/urllib3/exceptions.py 96 21 78% 21-22, 26, 33-34, 38, 79-83, 90-92, 147-150, 222, 225, 241-242, 249-250 /usr/lib/python3/dist-packages/urllib3/fields.py 90 70 22% 18-20, 38-61, 82-91, 113-118, 150-156, 176-192, 205, 218-227, 233-246, 263-273 /usr/lib/python3/dist-packages/urllib3/filepost.py 43 30 30% 19-22, 33-42, 57-60, 74-98 /usr/lib/python3/dist-packages/urllib3/packages/__init__.py 8 2 75% 10-11 /usr/lib/python3/dist-packages/urllib3/packages/ssl_match_hostname/__init__.py 11 6 45% 7, 10-16 /usr/lib/python3/dist-packages/urllib3/poolmanager.py 172 132 23% 89-114, 160-167, 170, 173-175, 187-202, 211, 224-234, 243-247, 257-271, 284-285, 297-307, 318-372, 411-431, 434-439, 448-456, 460-469, 473 /usr/lib/python3/dist-packages/urllib3/request.py 39 28 28% 42, 54, 70-79, 88-97, 144-171 /usr/lib/python3/dist-packages/urllib3/response.py 399 322 19% 34-36, 39, 42-61, 73-74, 77, 80-98, 103-118, 131, 134, 137-139, 143-152, 190, 214-258, 268-271, 274-278, 283-287, 291, 294, 302, 308-354, 362-373, 377, 383-399, 406-410, 421-467, 490-541, 559-567, 578-599, 603, 606, 610, 614-621, 625-634, 637-642, 648-653, 657, 661-666, 675, 680-689, 692-711, 727-781, 789-792, 795-809 /usr/lib/python3/dist-packages/urllib3/util/__init__.py 10 0 100% /usr/lib/python3/dist-packages/urllib3/util/connection.py 66 45 32% 17-26, 51-86, 90-94, 102-105, 118, 130-131 /usr/lib/python3/dist-packages/urllib3/util/queue.py 14 5 64% 7, 12, 15, 18, 21 /usr/lib/python3/dist-packages/urllib3/util/request.py 50 25 50% 13, 63, 65, 71, 74, 77, 80, 85, 95-105, 119-133 /usr/lib/python3/dist-packages/urllib3/util/response.py 35 29 17% 15-35, 54-71, 83-86 /usr/lib/python3/dist-packages/urllib3/util/retry.py 150 102 32% 186-187, 202-218, 223-232, 240-249, 253-265, 270-275, 278-283, 286-289, 300-305, 311, 317, 323-326, 335-341, 350-355, 376-442, 445 /usr/lib/python3/dist-packages/urllib3/util/ssl_.py 148 112 24% 31-34, 43-44, 50-56, 61-63, 104-149, 162-174, 192-201, 208-217, 256-293, 327-383, 393-396, 401-407 /usr/lib/python3/dist-packages/urllib3/util/timeout.py 63 42 33% 96-99, 102, 120-153, 169, 183, 191-194, 204-208, 220-226, 245-258 /usr/lib/python3/dist-packages/urllib3/util/url.py 205 152 26% 101-105, 112, 117-122, 127-129, 150-169, 172, 193-207, 214-241, 246-271, 275-299, 303-317, 322-327, 352-416, 431-432 /usr/lib/python3/dist-packages/urllib3/util/wait.py 76 58 24% 8-9, 43-68, 72-87, 91-107, 111, 118-124, 133-139, 146, 153 /usr/local/lib/python3.8/dist-packages/MySQLdb/__init__.py 46 13 72% 21, 36-37, 44-46, 63, 66, 69, 72, 75-76, 79 /usr/local/lib/python3.8/dist-packages/MySQLdb/_exceptions.py 12 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/compat.py 12 5 58% 4-8 /usr/local/lib/python3.8/dist-packages/MySQLdb/connections.py 146 34 77% 42, 138, 140, 143, 150, 161, 197, 204, 245, 249, 260, 262, 265, 269, 282, 286-293, 304-308, 314-317, 324-328 /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/CLIENT.py 18 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FIELD_TYPE.py 29 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FLAG.py 16 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/converters.py 35 13 63% 47-48, 52, 56, 63-68, 79, 82, 85 /usr/local/lib/python3.8/dist-packages/MySQLdb/cursors.py 261 96 63% 83-90, 93, 96-97, 107, 109, 114-120, 127, 135, 142-144, 160, 171, 187, 194-206, 227, 239-240, 259-260, 296-310, 328, 358-363, 368-372, 378, 391-400, 403-405, 417, 421-426, 431-434, 438-441, 444, 447-450 /usr/local/lib/python3.8/dist-packages/MySQLdb/release.py 3 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/times.py 76 49 36% 21, 25, 29, 34-37, 43-47, 53, 60-64, 75-76, 79-99, 102-113, 116-123, 127, 131 /usr/local/lib/python3.8/dist-packages/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 130375 96721 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 : 0 127.25user 49.31system 5:32.62elapsed 53%CPU (0avgtext+0avgdata 6539392maxresident)k 6088072inputs+69896outputs (6603major+7140942minor)pagefaults 0swaps