python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 20 -a ' -a 3318 ' -s datou_3318 -M 0 -S 0 -U 95,95,120 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/caffe_cuda8_python3/python', '/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', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 1619204 load datou : 3318 # 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 ! 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 ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? [(photo_id, hashtag_id, hashtag_type, x0, x1, y0, y1, score, seg_temp, polygons), ...] was removed should we ? chemin de la photo was removed should we ? [ (photo_id_loc, hashtag_id, hashtag_type, x0, x1, y0, y1, score, None), ...] was removed should we ? chemin de la photo was removed should we ? id de la photo (peut être local ou global) was removed should we ? chemin de la photo was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? donnée sous forme de texte was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? id de la photo (peut être local ou global) was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? None was removed should we ? donnée sous forme de nombre was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? donnée sous forme de texte was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) load thcls load THCL from format json or kwargs add thcl : 2847 in CacheModelConfig load pdts add pdt : 5275 in CacheModelConfig Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 3318, datou_cur_ids : ['2732571'] with mtr_portfolio_ids : ['22142991'] and first list_photo_ids : [] new path : /proc/1619204/ Inside batchDatouExec : verbose : 0 # 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 ! 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 ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 15 ; length of list_pids : 15 ; length of list_args : 15 time to download the photos : 2.7972848415374756 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 : 0 number of steps : 10 step1:mask_detect Tue Apr 8 14:10:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10219 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-08 14:10:35.233829: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-08 14:10:35.259146: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-08 14:10:35.260727: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f1074000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-08 14:10:35.260772: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-08 14:10:35.263332: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-08 14:10:35.421246: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2d922950 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-08 14:10:35.421303: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-08 14:10:35.422129: 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 2025-04-08 14:10:35.422511: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-08 14:10:35.424927: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-08 14:10:35.427531: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-08 14:10:35.428103: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-08 14:10:35.431611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-08 14:10:35.433517: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-08 14:10:35.439145: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-08 14:10:35.440820: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-08 14:10:35.440938: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-08 14:10:35.441730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-08 14:10:35.441748: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-08 14:10:35.441757: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-08 14:10:35.443158: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9319 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. 2025-04-08 14:10:35.741982: 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 2025-04-08 14:10:35.742083: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-08 14:10:35.742108: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-08 14:10:35.742130: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-08 14:10:35.742150: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-08 14:10:35.742171: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-08 14:10:35.742191: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-08 14:10:35.742212: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-08 14:10:35.744091: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-08 14:10:35.745527: 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 2025-04-08 14:10:35.745637: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-08 14:10:35.745669: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-08 14:10:35.745696: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-08 14:10:35.745720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-08 14:10:35.745745: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-08 14:10:35.745771: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-08 14:10:35.745798: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-08 14:10:35.747237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-08 14:10:35.747284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-08 14:10:35.747293: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-08 14:10:35.747301: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-08 14:10:35.748656: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9319 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 To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] 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 learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 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 : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-08 14:10:47.511032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-08 14:10:47.681929: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-08 14:10:49.056297: 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 2025-04-08 14:10:49.056905: 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 2025-04-08 14:10:49.057485: 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 2025-04-08 14:10:49.058027: 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 2025-04-08 14:10:49.058568: 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 2025-04-08 14:10:49.059135: 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 2025-04-08 14:10:49.059688: 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 2025-04-08 14:10:49.059713: 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. 2025-04-08 14:10:49.060279: 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 2025-04-08 14:10:49.060296: 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. 2025-04-08 14:10:49.067387: 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 2025-04-08 14:10:49.067413: 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. 2025-04-08 14:10:49.067977: 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 2025-04-08 14:10:49.067993: 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. 2025-04-08 14:10:49.074561: 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 2025-04-08 14:10:49.074602: 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. 2025-04-08 14:10:49.075182: 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 2025-04-08 14:10:49.075200: 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. 2025-04-08 14:10:49.106166: 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 2025-04-08 14:10:49.106213: 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. 2025-04-08 14:10:49.106777: 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 2025-04-08 14:10:49.106793: 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. 2025-04-08 14:10:49.112622: 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 2025-04-08 14:10:49.112643: 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. 2025-04-08 14:10:49.113204: 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 2025-04-08 14:10:49.113220: 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. 2025-04-08 14:10:49.147107: 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 2025-04-08 14:10:49.147643: 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 2025-04-08 14:10:49.149453: 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 2025-04-08 14:10:49.149987: 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 2025-04-08 14:10:49.193911: 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 2025-04-08 14:10:49.194499: 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 2025-04-08 14:10:49.196741: 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 2025-04-08 14:10:49.197334: 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 2025-04-08 14:10:49.228090: 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 2025-04-08 14:10:49.228701: 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 2025-04-08 14:10:49.230308: 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 2025-04-08 14:10:49.230993: 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 2025-04-08 14:10:49.236689: 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 2025-04-08 14:10:49.237263: 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 2025-04-08 14:10:49.238971: 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 2025-04-08 14:10:49.239541: 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 2025-04-08 14:10:49.245406: 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 2025-04-08 14:10:49.245984: 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 2025-04-08 14:10:49.247554: 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 2025-04-08 14:10:49.248123: 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 2025-04-08 14:10:49.274809: 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 2025-04-08 14:10:49.275406: 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 2025-04-08 14:10:49.275979: 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 2025-04-08 14:10:49.276557: 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 2025-04-08 14:10:49.280096: 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 2025-04-08 14:10:49.280680: 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 2025-04-08 14:10:49.296146: 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 2025-04-08 14:10:49.296741: 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 2025-04-08 14:10:49.297311: 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 2025-04-08 14:10:49.297875: 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 2025-04-08 14:10:49.310413: 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 2025-04-08 14:10:49.311031: 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 2025-04-08 14:10:49.311602: 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 2025-04-08 14:10:49.312167: 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 2025-04-08 14:10:49.316512: 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 2025-04-08 14:10:49.317085: 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 2025-04-08 14:10:49.321669: 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 2025-04-08 14:10:49.322241: 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 2025-04-08 14:10:49.334191: 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 2025-04-08 14:10:49.334769: 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 2025-04-08 14:10:49.338850: 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 2025-04-08 14:10:49.339442: 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 2025-04-08 14:10:49.340011: 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 2025-04-08 14:10:49.340576: 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 2025-04-08 14:10:49.341341: 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 2025-04-08 14:10:49.341914: 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 2025-04-08 14:10:49.352511: 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 2025-04-08 14:10:49.353084: 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 2025-04-08 14:10:49.353663: 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 2025-04-08 14:10:49.354226: 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 2025-04-08 14:10:49.354789: 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 2025-04-08 14:10:49.355361: 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 2025-04-08 14:10:49.355926: 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 2025-04-08 14:10:49.356487: 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 2025-04-08 14:10:49.365880: 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 2025-04-08 14:10:49.366451: 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 2025-04-08 14:10:49.372747: 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 2025-04-08 14:10:49.373320: 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 2025-04-08 14:10:49.407779: 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 2025-04-08 14:10:49.407840: 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. 2025-04-08 14:10:49.408835: 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 2025-04-08 14:10:49.409823: 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 2025-04-08 14:10:49.417161: 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 2025-04-08 14:10:49.418120: 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 2025-04-08 14:10:49.419072: 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 2025-04-08 14:10:49.420050: 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 2025-04-08 14:10:49.428828: 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 2025-04-08 14:10:49.429883: 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 2025-04-08 14:10:49.450755: 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 2025-04-08 14:10:49.451842: 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 2025-04-08 14:10:49.453034: 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 2025-04-08 14:10:49.453998: 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 2025-04-08 14:10:49.459396: 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 2025-04-08 14:10:49.460596: 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 2025-04-08 14:10:49.461697: 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 2025-04-08 14:10:49.462743: 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 2025-04-08 14:10:49.465173: 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 2025-04-08 14:10:49.474810: 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 2025-04-08 14:10:49.475483: 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 2025-04-08 14:10:49.486920: 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 2025-04-08 14:10:49.488094: 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 2025-04-08 14:10:49.489203: 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 2025-04-08 14:10:49.490241: 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 2025-04-08 14:10:49.504388: 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 2025-04-08 14:10:49.507190: 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/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 15 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 93 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 45 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 62 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 62 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 46 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 34 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 37 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 31 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 44 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 48 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 10.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 21 Detection mask done ! Trying to reset tf kernel 1620014 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 9179 tf kernel not reseted sub process len(results) : 15 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 15 len(list_Values) 0 process is alive 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 : 10151 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.000934600830078125 nb_pixel_total : 20839 time to create 1 rle with old method : 0.02998948097229004 length of segment : 165 time for calcul the mask position with numpy : 0.0008630752563476562 nb_pixel_total : 18791 time to create 1 rle with old method : 0.02219557762145996 length of segment : 208 time for calcul the mask position with numpy : 0.0004115104675292969 nb_pixel_total : 9720 time to create 1 rle with old method : 0.012394905090332031 length of segment : 120 time for calcul the mask position with numpy : 0.004183053970336914 nb_pixel_total : 14992 time to create 1 rle with old method : 0.025331497192382812 length of segment : 146 time for calcul the mask position with numpy : 0.0008585453033447266 nb_pixel_total : 27273 time to create 1 rle with old method : 0.032627105712890625 length of segment : 178 time for calcul the mask position with numpy : 0.0007929801940917969 nb_pixel_total : 19499 time to create 1 rle with old method : 0.022863149642944336 length of segment : 241 time for calcul the mask position with numpy : 0.009258508682250977 nb_pixel_total : 89617 time to create 1 rle with old method : 0.11131453514099121 length of segment : 366 time for calcul the mask position with numpy : 0.0012805461883544922 nb_pixel_total : 45055 time to create 1 rle with old method : 0.05243229866027832 length of segment : 381 time for calcul the mask position with numpy : 0.0062103271484375 nb_pixel_total : 18261 time to create 1 rle with old method : 0.023827075958251953 length of segment : 147 time for calcul the mask position with numpy : 0.0005660057067871094 nb_pixel_total : 17270 time to create 1 rle with old method : 0.020703554153442383 length of segment : 221 time for calcul the mask position with numpy : 0.002967357635498047 nb_pixel_total : 92371 time to create 1 rle with old method : 0.12308645248413086 length of segment : 628 time for calcul the mask position with numpy : 0.0016896724700927734 nb_pixel_total : 44716 time to create 1 rle with old method : 0.05488395690917969 length of segment : 342 time for calcul the mask position with numpy : 0.026034116744995117 nb_pixel_total : 190519 time to create 1 rle with new method : 0.0284121036529541 length of segment : 625 time for calcul the mask position with numpy : 0.0034275054931640625 nb_pixel_total : 14252 time to create 1 rle with old method : 0.01912212371826172 length of segment : 181 time for calcul the mask position with numpy : 0.003955364227294922 nb_pixel_total : 35297 time to create 1 rle with old method : 0.044450998306274414 length of segment : 221 time for calcul the mask position with numpy : 0.00159454345703125 nb_pixel_total : 42589 time to create 1 rle with old method : 0.04975128173828125 length of segment : 258 time for calcul the mask position with numpy : 0.0004775524139404297 nb_pixel_total : 17253 time to create 1 rle with old method : 0.01948833465576172 length of segment : 167 time for calcul the mask position with numpy : 0.0002639293670654297 nb_pixel_total : 8419 time to create 1 rle with old method : 0.010145187377929688 length of segment : 94 time for calcul the mask position with numpy : 0.0059452056884765625 nb_pixel_total : 58535 time to create 1 rle with old method : 0.06915760040283203 length of segment : 382 time for calcul the mask position with numpy : 0.0002231597900390625 nb_pixel_total : 4556 time to create 1 rle with old method : 0.005794048309326172 length of segment : 77 time for calcul the mask position with numpy : 0.0003714561462402344 nb_pixel_total : 14064 time to create 1 rle with old method : 0.016928672790527344 length of segment : 180 time for calcul the mask position with numpy : 0.0016489028930664062 nb_pixel_total : 46599 time to create 1 rle with old method : 0.07416272163391113 length of segment : 294 time for calcul the mask position with numpy : 0.001020669937133789 nb_pixel_total : 29291 time to create 1 rle with old method : 0.03511810302734375 length of segment : 360 time for calcul the mask position with numpy : 0.0042607784271240234 nb_pixel_total : 37822 time to create 1 rle with old method : 0.04664111137390137 length of segment : 288 time for calcul the mask position with numpy : 0.0005509853363037109 nb_pixel_total : 15820 time to create 1 rle with old method : 0.019128084182739258 length of segment : 148 time for calcul the mask position with numpy : 0.0007894039154052734 nb_pixel_total : 9518 time to create 1 rle with old method : 0.011578083038330078 length of segment : 122 time for calcul the mask position with numpy : 0.00048089027404785156 nb_pixel_total : 11568 time to create 1 rle with old method : 0.014414787292480469 length of segment : 115 time for calcul the mask position with numpy : 0.0022377967834472656 nb_pixel_total : 25396 time to create 1 rle with old method : 0.030714750289916992 length of segment : 175 time for calcul the mask position with numpy : 0.002126455307006836 nb_pixel_total : 7644 time to create 1 rle with old method : 0.008987903594970703 length of segment : 191 time for calcul the mask position with numpy : 0.0005834102630615234 nb_pixel_total : 12762 time to create 1 rle with old method : 0.015894174575805664 length of segment : 134 time for calcul the mask position with numpy : 0.0010800361633300781 nb_pixel_total : 28353 time to create 1 rle with old method : 0.0473787784576416 length of segment : 269 time for calcul the mask position with numpy : 0.0018460750579833984 nb_pixel_total : 15494 time to create 1 rle with old method : 0.018691539764404297 length of segment : 194 time for calcul the mask position with numpy : 0.0003032684326171875 nb_pixel_total : 9659 time to create 1 rle with old method : 0.011763334274291992 length of segment : 130 time for calcul the mask position with numpy : 0.0009551048278808594 nb_pixel_total : 3616 time to create 1 rle with old method : 0.004476308822631836 length of segment : 65 time for calcul the mask position with numpy : 0.0006413459777832031 nb_pixel_total : 14950 time to create 1 rle with old method : 0.01797008514404297 length of segment : 155 time for calcul the mask position with numpy : 0.0008080005645751953 nb_pixel_total : 24412 time to create 1 rle with old method : 0.029179096221923828 length of segment : 205 time for calcul the mask position with numpy : 0.0004837512969970703 nb_pixel_total : 15332 time to create 1 rle with old method : 0.02345442771911621 length of segment : 184 time for calcul the mask position with numpy : 0.002151966094970703 nb_pixel_total : 28404 time to create 1 rle with old method : 0.03390860557556152 length of segment : 217 time for calcul the mask position with numpy : 0.0005526542663574219 nb_pixel_total : 8910 time to create 1 rle with old method : 0.010959863662719727 length of segment : 120 time for calcul the mask position with numpy : 0.004362583160400391 nb_pixel_total : 71412 time to create 1 rle with old method : 0.08478593826293945 length of segment : 337 time for calcul the mask position with numpy : 0.0046236515045166016 nb_pixel_total : 148187 time to create 1 rle with old method : 0.17713594436645508 length of segment : 500 time for calcul the mask position with numpy : 0.0007791519165039062 nb_pixel_total : 25022 time to create 1 rle with old method : 0.039133548736572266 length of segment : 164 time for calcul the mask position with numpy : 0.0189056396484375 nb_pixel_total : 531663 time to create 1 rle with new method : 0.042920827865600586 length of segment : 647 time for calcul the mask position with numpy : 0.0045948028564453125 nb_pixel_total : 36176 time to create 1 rle with old method : 0.04897570610046387 length of segment : 459 time for calcul the mask position with numpy : 0.0006909370422363281 nb_pixel_total : 19720 time to create 1 rle with old method : 0.03149294853210449 length of segment : 163 time for calcul the mask position with numpy : 0.0013549327850341797 nb_pixel_total : 51015 time to create 1 rle with old method : 0.06426024436950684 length of segment : 386 time for calcul the mask position with numpy : 0.017854928970336914 nb_pixel_total : 175075 time to create 1 rle with new method : 0.01895928382873535 length of segment : 600 time for calcul the mask position with numpy : 0.02349066734313965 nb_pixel_total : 124829 time to create 1 rle with old method : 0.14903521537780762 length of segment : 601 time for calcul the mask position with numpy : 0.00017690658569335938 nb_pixel_total : 4838 time to create 1 rle with old method : 0.006005525588989258 length of segment : 59 time for calcul the mask position with numpy : 0.0012340545654296875 nb_pixel_total : 5998 time to create 1 rle with old method : 0.007384777069091797 length of segment : 84 time for calcul the mask position with numpy : 0.0008080005645751953 nb_pixel_total : 27300 time to create 1 rle with old method : 0.03306722640991211 length of segment : 214 time for calcul the mask position with numpy : 0.001440286636352539 nb_pixel_total : 3325 time to create 1 rle with old method : 0.004636049270629883 length of segment : 216 time for calcul the mask position with numpy : 0.09830713272094727 nb_pixel_total : 50165 time to create 1 rle with old method : 0.07356095314025879 length of segment : 320 time for calcul the mask position with numpy : 0.0005843639373779297 nb_pixel_total : 22037 time to create 1 rle with old method : 0.026571989059448242 length of segment : 162 time for calcul the mask position with numpy : 0.006166934967041016 nb_pixel_total : 16237 time to create 1 rle with old method : 0.022212743759155273 length of segment : 124 time for calcul the mask position with numpy : 0.0018630027770996094 nb_pixel_total : 11351 time to create 1 rle with old method : 0.03559398651123047 length of segment : 113 time for calcul the mask position with numpy : 0.01329946517944336 nb_pixel_total : 12399 time to create 1 rle with old method : 0.0401158332824707 length of segment : 161 time for calcul the mask position with numpy : 0.015148401260375977 nb_pixel_total : 19332 time to create 1 rle with old method : 0.031503915786743164 length of segment : 202 time for calcul the mask position with numpy : 0.006968259811401367 nb_pixel_total : 296723 time to create 1 rle with new method : 0.017408370971679688 length of segment : 473 time for calcul the mask position with numpy : 0.00024628639221191406 nb_pixel_total : 7412 time to create 1 rle with old method : 0.009038209915161133 length of segment : 95 time for calcul the mask position with numpy : 0.020552635192871094 nb_pixel_total : 26331 time to create 1 rle with old method : 0.035236358642578125 length of segment : 254 time for calcul the mask position with numpy : 0.0008661746978759766 nb_pixel_total : 25932 time to create 1 rle with old method : 0.03516077995300293 length of segment : 183 time for calcul the mask position with numpy : 0.002341032028198242 nb_pixel_total : 9622 time to create 1 rle with old method : 0.013065814971923828 length of segment : 123 time for calcul the mask position with numpy : 0.0008504390716552734 nb_pixel_total : 15276 time to create 1 rle with old method : 0.018694400787353516 length of segment : 245 time for calcul the mask position with numpy : 0.0003921985626220703 nb_pixel_total : 14342 time to create 1 rle with old method : 0.017170190811157227 length of segment : 131 time for calcul the mask position with numpy : 0.002912759780883789 nb_pixel_total : 15340 time to create 1 rle with old method : 0.0185544490814209 length of segment : 113 time for calcul the mask position with numpy : 0.006656169891357422 nb_pixel_total : 13400 time to create 1 rle with old method : 0.01791238784790039 length of segment : 177 time for calcul the mask position with numpy : 0.004426479339599609 nb_pixel_total : 18777 time to create 1 rle with old method : 0.02507805824279785 length of segment : 118 time for calcul the mask position with numpy : 0.013770818710327148 nb_pixel_total : 76882 time to create 1 rle with old method : 0.09963202476501465 length of segment : 403 time for calcul the mask position with numpy : 0.0009646415710449219 nb_pixel_total : 14357 time to create 1 rle with old method : 0.02049565315246582 length of segment : 161 time for calcul the mask position with numpy : 0.0039141178131103516 nb_pixel_total : 6064 time to create 1 rle with old method : 0.010625123977661133 length of segment : 99 time for calcul the mask position with numpy : 0.0004532337188720703 nb_pixel_total : 8344 time to create 1 rle with old method : 0.01443624496459961 length of segment : 93 time for calcul the mask position with numpy : 0.008704423904418945 nb_pixel_total : 67286 time to create 1 rle with old method : 0.08483481407165527 length of segment : 346 time for calcul the mask position with numpy : 0.007255077362060547 nb_pixel_total : 36185 time to create 1 rle with old method : 0.04493832588195801 length of segment : 267 time for calcul the mask position with numpy : 0.007738351821899414 nb_pixel_total : 9765 time to create 1 rle with old method : 0.013714313507080078 length of segment : 164 time for calcul the mask position with numpy : 0.01938319206237793 nb_pixel_total : 27773 time to create 1 rle with old method : 0.03703951835632324 length of segment : 206 time for calcul the mask position with numpy : 0.0032987594604492188 nb_pixel_total : 6484 time to create 1 rle with old method : 0.007993221282958984 length of segment : 69 time for calcul the mask position with numpy : 0.0060977935791015625 nb_pixel_total : 15452 time to create 1 rle with old method : 0.020620346069335938 length of segment : 241 time for calcul the mask position with numpy : 0.010795354843139648 nb_pixel_total : 29945 time to create 1 rle with old method : 0.04004549980163574 length of segment : 258 time for calcul the mask position with numpy : 0.00032830238342285156 nb_pixel_total : 15668 time to create 1 rle with old method : 0.01859116554260254 length of segment : 184 time for calcul the mask position with numpy : 0.00044918060302734375 nb_pixel_total : 15697 time to create 1 rle with old method : 0.018450260162353516 length of segment : 213 time for calcul the mask position with numpy : 0.00015735626220703125 nb_pixel_total : 4462 time to create 1 rle with old method : 0.005556344985961914 length of segment : 76 time for calcul the mask position with numpy : 0.006392240524291992 nb_pixel_total : 9372 time to create 1 rle with old method : 0.012766122817993164 length of segment : 121 time for calcul the mask position with numpy : 0.0009634494781494141 nb_pixel_total : 29432 time to create 1 rle with old method : 0.03485107421875 length of segment : 299 time for calcul the mask position with numpy : 0.00048661231994628906 nb_pixel_total : 16871 time to create 1 rle with old method : 0.019723176956176758 length of segment : 144 time for calcul the mask position with numpy : 0.00419926643371582 nb_pixel_total : 14847 time to create 1 rle with old method : 0.019935131072998047 length of segment : 135 time for calcul the mask position with numpy : 0.000255584716796875 nb_pixel_total : 3928 time to create 1 rle with old method : 0.005924701690673828 length of segment : 69 time for calcul the mask position with numpy : 0.0005195140838623047 nb_pixel_total : 24559 time to create 1 rle with old method : 0.028249740600585938 length of segment : 138 time for calcul the mask position with numpy : 0.006750822067260742 nb_pixel_total : 33623 time to create 1 rle with old method : 0.055889129638671875 length of segment : 323 time for calcul the mask position with numpy : 0.016063451766967773 nb_pixel_total : 33048 time to create 1 rle with old method : 0.03900289535522461 length of segment : 249 time for calcul the mask position with numpy : 0.0002944469451904297 nb_pixel_total : 6630 time to create 1 rle with old method : 0.008050918579101562 length of segment : 93 time for calcul the mask position with numpy : 0.0008058547973632812 nb_pixel_total : 27318 time to create 1 rle with old method : 0.0325167179107666 length of segment : 285 time for calcul the mask position with numpy : 0.000701904296875 nb_pixel_total : 12171 time to create 1 rle with old method : 0.016366243362426758 length of segment : 133 time for calcul the mask position with numpy : 0.0020456314086914062 nb_pixel_total : 46901 time to create 1 rle with old method : 0.057824134826660156 length of segment : 332 time for calcul the mask position with numpy : 0.0009741783142089844 nb_pixel_total : 26553 time to create 1 rle with old method : 0.029918193817138672 length of segment : 379 time for calcul the mask position with numpy : 0.025222301483154297 nb_pixel_total : 78064 time to create 1 rle with old method : 0.09181404113769531 length of segment : 588 time for calcul the mask position with numpy : 0.0002849102020263672 nb_pixel_total : 9181 time to create 1 rle with old method : 0.011220932006835938 length of segment : 84 time for calcul the mask position with numpy : 0.001416921615600586 nb_pixel_total : 5277 time to create 1 rle with old method : 0.006419658660888672 length of segment : 73 time for calcul the mask position with numpy : 0.0006406307220458984 nb_pixel_total : 13823 time to create 1 rle with old method : 0.01657891273498535 length of segment : 295 time for calcul the mask position with numpy : 0.003116130828857422 nb_pixel_total : 27925 time to create 1 rle with old method : 0.03270888328552246 length of segment : 284 time for calcul the mask position with numpy : 0.010562419891357422 nb_pixel_total : 34300 time to create 1 rle with old method : 0.044429779052734375 length of segment : 293 time for calcul the mask position with numpy : 0.0006153583526611328 nb_pixel_total : 15872 time to create 1 rle with old method : 0.01897716522216797 length of segment : 147 time for calcul the mask position with numpy : 0.005949735641479492 nb_pixel_total : 118212 time to create 1 rle with old method : 0.1347064971923828 length of segment : 446 time for calcul the mask position with numpy : 0.0003838539123535156 nb_pixel_total : 16220 time to create 1 rle with old method : 0.018802404403686523 length of segment : 150 time for calcul the mask position with numpy : 0.006844997406005859 nb_pixel_total : 32651 time to create 1 rle with old method : 0.04058980941772461 length of segment : 232 time for calcul the mask position with numpy : 0.0002675056457519531 nb_pixel_total : 8322 time to create 1 rle with old method : 0.00988626480102539 length of segment : 119 time for calcul the mask position with numpy : 0.0003521442413330078 nb_pixel_total : 8561 time to create 1 rle with old method : 0.010607719421386719 length of segment : 128 time for calcul the mask position with numpy : 0.0006334781646728516 nb_pixel_total : 30593 time to create 1 rle with old method : 0.03595304489135742 length of segment : 189 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 719 time to create 1 rle with old method : 0.0009949207305908203 length of segment : 46 time for calcul the mask position with numpy : 0.00021409988403320312 nb_pixel_total : 8393 time to create 1 rle with old method : 0.009835481643676758 length of segment : 103 time for calcul the mask position with numpy : 0.0006163120269775391 nb_pixel_total : 28841 time to create 1 rle with old method : 0.03277897834777832 length of segment : 150 time for calcul the mask position with numpy : 0.005177974700927734 nb_pixel_total : 64569 time to create 1 rle with old method : 0.07279372215270996 length of segment : 572 time for calcul the mask position with numpy : 0.00022149085998535156 nb_pixel_total : 7946 time to create 1 rle with old method : 0.009643316268920898 length of segment : 111 time for calcul the mask position with numpy : 0.0011832714080810547 nb_pixel_total : 1320 time to create 1 rle with old method : 0.001645803451538086 length of segment : 58 time for calcul the mask position with numpy : 0.0007605552673339844 nb_pixel_total : 23981 time to create 1 rle with old method : 0.02791118621826172 length of segment : 224 time for calcul the mask position with numpy : 0.0006425380706787109 nb_pixel_total : 11246 time to create 1 rle with old method : 0.013392210006713867 length of segment : 131 time for calcul the mask position with numpy : 0.0004534721374511719 nb_pixel_total : 9933 time to create 1 rle with old method : 0.011956453323364258 length of segment : 101 time for calcul the mask position with numpy : 0.00019073486328125 nb_pixel_total : 6699 time to create 1 rle with old method : 0.008423089981079102 length of segment : 81 time for calcul the mask position with numpy : 0.0010488033294677734 nb_pixel_total : 25454 time to create 1 rle with old method : 0.03578329086303711 length of segment : 184 time for calcul the mask position with numpy : 0.00047779083251953125 nb_pixel_total : 10318 time to create 1 rle with old method : 0.012204885482788086 length of segment : 119 time for calcul the mask position with numpy : 0.0004131793975830078 nb_pixel_total : 8047 time to create 1 rle with old method : 0.009889364242553711 length of segment : 79 time for calcul the mask position with numpy : 0.009471893310546875 nb_pixel_total : 221047 time to create 1 rle with new method : 0.028705596923828125 length of segment : 739 time for calcul the mask position with numpy : 0.0009982585906982422 nb_pixel_total : 24725 time to create 1 rle with old method : 0.02836322784423828 length of segment : 259 time for calcul the mask position with numpy : 0.001119375228881836 nb_pixel_total : 15110 time to create 1 rle with old method : 0.01753520965576172 length of segment : 188 time for calcul the mask position with numpy : 0.001611471176147461 nb_pixel_total : 19630 time to create 1 rle with old method : 0.022748708724975586 length of segment : 175 time for calcul the mask position with numpy : 0.0006449222564697266 nb_pixel_total : 28313 time to create 1 rle with old method : 0.03284931182861328 length of segment : 224 time for calcul the mask position with numpy : 0.0036516189575195312 nb_pixel_total : 111510 time to create 1 rle with old method : 0.12790465354919434 length of segment : 750 time for calcul the mask position with numpy : 0.0008344650268554688 nb_pixel_total : 34439 time to create 1 rle with old method : 0.03948712348937988 length of segment : 182 time for calcul the mask position with numpy : 0.0006661415100097656 nb_pixel_total : 31049 time to create 1 rle with old method : 0.03520798683166504 length of segment : 294 time for calcul the mask position with numpy : 0.00080108642578125 nb_pixel_total : 16902 time to create 1 rle with old method : 0.019776105880737305 length of segment : 103 time for calcul the mask position with numpy : 0.000743865966796875 nb_pixel_total : 25858 time to create 1 rle with old method : 0.03413963317871094 length of segment : 233 time for calcul the mask position with numpy : 0.00046515464782714844 nb_pixel_total : 9835 time to create 1 rle with old method : 0.011729001998901367 length of segment : 108 time for calcul the mask position with numpy : 0.0005300045013427734 nb_pixel_total : 26784 time to create 1 rle with old method : 0.03243112564086914 length of segment : 90 time for calcul the mask position with numpy : 0.0017125606536865234 nb_pixel_total : 34502 time to create 1 rle with old method : 0.041080474853515625 length of segment : 336 time for calcul the mask position with numpy : 0.0002722740173339844 nb_pixel_total : 9250 time to create 1 rle with old method : 0.011375188827514648 length of segment : 94 time for calcul the mask position with numpy : 0.0007228851318359375 nb_pixel_total : 12616 time to create 1 rle with old method : 0.015153884887695312 length of segment : 133 time for calcul the mask position with numpy : 0.001522064208984375 nb_pixel_total : 20659 time to create 1 rle with old method : 0.024237632751464844 length of segment : 229 time for calcul the mask position with numpy : 0.0006082057952880859 nb_pixel_total : 9409 time to create 1 rle with old method : 0.011411428451538086 length of segment : 129 time for calcul the mask position with numpy : 0.0014786720275878906 nb_pixel_total : 11333 time to create 1 rle with old method : 0.014792919158935547 length of segment : 122 time for calcul the mask position with numpy : 0.00433349609375 nb_pixel_total : 14689 time to create 1 rle with old method : 0.018774032592773438 length of segment : 189 time for calcul the mask position with numpy : 0.007915258407592773 nb_pixel_total : 14426 time to create 1 rle with old method : 0.020964860916137695 length of segment : 119 time for calcul the mask position with numpy : 0.0004572868347167969 nb_pixel_total : 9450 time to create 1 rle with old method : 0.011092662811279297 length of segment : 127 time for calcul the mask position with numpy : 0.0040171146392822266 nb_pixel_total : 46984 time to create 1 rle with old method : 0.053992509841918945 length of segment : 320 time for calcul the mask position with numpy : 0.0010271072387695312 nb_pixel_total : 17739 time to create 1 rle with old method : 0.023163557052612305 length of segment : 201 time for calcul the mask position with numpy : 0.010771989822387695 nb_pixel_total : 21315 time to create 1 rle with old method : 0.029099702835083008 length of segment : 290 time for calcul the mask position with numpy : 0.002762317657470703 nb_pixel_total : 51344 time to create 1 rle with old method : 0.05904507637023926 length of segment : 240 time for calcul the mask position with numpy : 0.000701904296875 nb_pixel_total : 11243 time to create 1 rle with old method : 0.013033628463745117 length of segment : 114 time for calcul the mask position with numpy : 0.003307819366455078 nb_pixel_total : 50927 time to create 1 rle with old method : 0.06274247169494629 length of segment : 410 time for calcul the mask position with numpy : 0.0007219314575195312 nb_pixel_total : 20341 time to create 1 rle with old method : 0.0243222713470459 length of segment : 132 time for calcul the mask position with numpy : 0.004500865936279297 nb_pixel_total : 70534 time to create 1 rle with old method : 0.08192300796508789 length of segment : 377 time for calcul the mask position with numpy : 0.010121345520019531 nb_pixel_total : 13637 time to create 1 rle with old method : 0.022855520248413086 length of segment : 146 time for calcul the mask position with numpy : 0.00015211105346679688 nb_pixel_total : 5242 time to create 1 rle with old method : 0.006360054016113281 length of segment : 79 time for calcul the mask position with numpy : 0.0002460479736328125 nb_pixel_total : 8804 time to create 1 rle with old method : 0.010814189910888672 length of segment : 121 time for calcul the mask position with numpy : 0.0006985664367675781 nb_pixel_total : 14130 time to create 1 rle with old method : 0.017374515533447266 length of segment : 126 time for calcul the mask position with numpy : 0.00048661231994628906 nb_pixel_total : 8016 time to create 1 rle with old method : 0.00984334945678711 length of segment : 113 time for calcul the mask position with numpy : 0.0028214454650878906 nb_pixel_total : 22762 time to create 1 rle with old method : 0.026347875595092773 length of segment : 177 time for calcul the mask position with numpy : 0.0009484291076660156 nb_pixel_total : 17926 time to create 1 rle with old method : 0.021302223205566406 length of segment : 168 time for calcul the mask position with numpy : 0.0006456375122070312 nb_pixel_total : 10246 time to create 1 rle with old method : 0.012308359146118164 length of segment : 121 time for calcul the mask position with numpy : 0.0013153553009033203 nb_pixel_total : 17754 time to create 1 rle with old method : 0.021513700485229492 length of segment : 155 time for calcul the mask position with numpy : 0.004713773727416992 nb_pixel_total : 30648 time to create 1 rle with old method : 0.04030179977416992 length of segment : 231 time for calcul the mask position with numpy : 0.002424478530883789 nb_pixel_total : 3618 time to create 1 rle with old method : 0.004387617111206055 length of segment : 97 time for calcul the mask position with numpy : 0.005802631378173828 nb_pixel_total : 42130 time to create 1 rle with old method : 0.05153012275695801 length of segment : 422 time for calcul the mask position with numpy : 0.0004458427429199219 nb_pixel_total : 9717 time to create 1 rle with old method : 0.011517763137817383 length of segment : 101 time for calcul the mask position with numpy : 0.012597799301147461 nb_pixel_total : 63497 time to create 1 rle with old method : 0.08292222023010254 length of segment : 460 time for calcul the mask position with numpy : 0.0010662078857421875 nb_pixel_total : 20221 time to create 1 rle with old method : 0.02310967445373535 length of segment : 223 time for calcul the mask position with numpy : 0.0003676414489746094 nb_pixel_total : 17240 time to create 1 rle with old method : 0.01984858512878418 length of segment : 167 time for calcul the mask position with numpy : 0.00412750244140625 nb_pixel_total : 6302 time to create 1 rle with old method : 0.009942770004272461 length of segment : 89 time for calcul the mask position with numpy : 0.0023696422576904297 nb_pixel_total : 44508 time to create 1 rle with old method : 0.051503658294677734 length of segment : 330 time for calcul the mask position with numpy : 0.013849258422851562 nb_pixel_total : 33757 time to create 1 rle with old method : 0.04200410842895508 length of segment : 261 time for calcul the mask position with numpy : 0.017808198928833008 nb_pixel_total : 47926 time to create 1 rle with old method : 0.06049394607543945 length of segment : 343 time for calcul the mask position with numpy : 0.001756906509399414 nb_pixel_total : 23742 time to create 1 rle with old method : 0.028163433074951172 length of segment : 200 time for calcul the mask position with numpy : 0.0034296512603759766 nb_pixel_total : 56296 time to create 1 rle with old method : 0.06545639038085938 length of segment : 524 time for calcul the mask position with numpy : 0.0019054412841796875 nb_pixel_total : 18825 time to create 1 rle with old method : 0.02244710922241211 length of segment : 400 time for calcul the mask position with numpy : 0.004376888275146484 nb_pixel_total : 21333 time to create 1 rle with old method : 0.029682636260986328 length of segment : 163 time for calcul the mask position with numpy : 0.0005412101745605469 nb_pixel_total : 9820 time to create 1 rle with old method : 0.012013435363769531 length of segment : 123 time for calcul the mask position with numpy : 0.0026450157165527344 nb_pixel_total : 51129 time to create 1 rle with old method : 0.06121563911437988 length of segment : 316 time for calcul the mask position with numpy : 0.001245260238647461 nb_pixel_total : 19920 time to create 1 rle with old method : 0.02355360984802246 length of segment : 190 time for calcul the mask position with numpy : 0.0007333755493164062 nb_pixel_total : 13000 time to create 1 rle with old method : 0.015807628631591797 length of segment : 103 time for calcul the mask position with numpy : 0.004683732986450195 nb_pixel_total : 55447 time to create 1 rle with old method : 0.06823396682739258 length of segment : 327 time for calcul the mask position with numpy : 0.002306699752807617 nb_pixel_total : 38355 time to create 1 rle with old method : 0.04747462272644043 length of segment : 434 time for calcul the mask position with numpy : 0.008267879486083984 nb_pixel_total : 153094 time to create 1 rle with new method : 0.008962154388427734 length of segment : 357 time for calcul the mask position with numpy : 0.009272575378417969 nb_pixel_total : 58728 time to create 1 rle with old method : 0.07047462463378906 length of segment : 328 time for calcul the mask position with numpy : 0.000820159912109375 nb_pixel_total : 13346 time to create 1 rle with old method : 0.01579880714416504 length of segment : 526 time for calcul the mask position with numpy : 0.0012784004211425781 nb_pixel_total : 19642 time to create 1 rle with old method : 0.022726058959960938 length of segment : 202 time for calcul the mask position with numpy : 0.0005452632904052734 nb_pixel_total : 19223 time to create 1 rle with old method : 0.021836280822753906 length of segment : 197 time for calcul the mask position with numpy : 0.005348682403564453 nb_pixel_total : 119872 time to create 1 rle with old method : 0.13852286338806152 length of segment : 437 time for calcul the mask position with numpy : 0.003744840621948242 nb_pixel_total : 17957 time to create 1 rle with old method : 0.02252936363220215 length of segment : 170 time for calcul the mask position with numpy : 0.0008242130279541016 nb_pixel_total : 19553 time to create 1 rle with old method : 0.023077011108398438 length of segment : 211 time for calcul the mask position with numpy : 0.0006120204925537109 nb_pixel_total : 18773 time to create 1 rle with old method : 0.0218353271484375 length of segment : 179 time for calcul the mask position with numpy : 0.0008695125579833984 nb_pixel_total : 18645 time to create 1 rle with old method : 0.02163410186767578 length of segment : 183 time for calcul the mask position with numpy : 0.0023255348205566406 nb_pixel_total : 43965 time to create 1 rle with old method : 0.05060267448425293 length of segment : 279 time for calcul the mask position with numpy : 0.0004589557647705078 nb_pixel_total : 5320 time to create 1 rle with old method : 0.0065424442291259766 length of segment : 82 time for calcul the mask position with numpy : 0.0008971691131591797 nb_pixel_total : 10613 time to create 1 rle with old method : 0.012227535247802734 length of segment : 92 time for calcul the mask position with numpy : 0.002513885498046875 nb_pixel_total : 34929 time to create 1 rle with old method : 0.04006695747375488 length of segment : 184 time for calcul the mask position with numpy : 0.003939151763916016 nb_pixel_total : 72526 time to create 1 rle with old method : 0.08203840255737305 length of segment : 343 time for calcul the mask position with numpy : 0.0004909038543701172 nb_pixel_total : 18419 time to create 1 rle with old method : 0.021880149841308594 length of segment : 213 time for calcul the mask position with numpy : 0.00029158592224121094 nb_pixel_total : 12350 time to create 1 rle with old method : 0.014547109603881836 length of segment : 156 time for calcul the mask position with numpy : 0.0008134841918945312 nb_pixel_total : 11608 time to create 1 rle with old method : 0.013479232788085938 length of segment : 170 time for calcul the mask position with numpy : 0.0009968280792236328 nb_pixel_total : 24062 time to create 1 rle with old method : 0.027564287185668945 length of segment : 210 time for calcul the mask position with numpy : 0.0004303455352783203 nb_pixel_total : 10834 time to create 1 rle with old method : 0.013095617294311523 length of segment : 140 time for calcul the mask position with numpy : 0.0008249282836914062 nb_pixel_total : 17304 time to create 1 rle with old method : 0.021015644073486328 length of segment : 127 time for calcul the mask position with numpy : 0.0024809837341308594 nb_pixel_total : 60348 time to create 1 rle with old method : 0.07044124603271484 length of segment : 303 time for calcul the mask position with numpy : 0.0008950233459472656 nb_pixel_total : 23184 time to create 1 rle with old method : 0.027157068252563477 length of segment : 174 time for calcul the mask position with numpy : 0.0005457401275634766 nb_pixel_total : 25062 time to create 1 rle with old method : 0.029270648956298828 length of segment : 133 time for calcul the mask position with numpy : 0.00875544548034668 nb_pixel_total : 449053 time to create 1 rle with new method : 0.5018877983093262 length of segment : 803 time for calcul the mask position with numpy : 0.0002071857452392578 nb_pixel_total : 8601 time to create 1 rle with old method : 0.009921550750732422 length of segment : 87 time for calcul the mask position with numpy : 0.0006747245788574219 nb_pixel_total : 23282 time to create 1 rle with old method : 0.02691960334777832 length of segment : 199 time for calcul the mask position with numpy : 0.001957416534423828 nb_pixel_total : 36509 time to create 1 rle with old method : 0.04240131378173828 length of segment : 266 time for calcul the mask position with numpy : 0.0009374618530273438 nb_pixel_total : 15523 time to create 1 rle with old method : 0.01779341697692871 length of segment : 171 time for calcul the mask position with numpy : 0.0006518363952636719 nb_pixel_total : 16396 time to create 1 rle with old method : 0.019018888473510742 length of segment : 148 time for calcul the mask position with numpy : 0.0023336410522460938 nb_pixel_total : 91470 time to create 1 rle with old method : 0.10561966896057129 length of segment : 260 time for calcul the mask position with numpy : 0.001085042953491211 nb_pixel_total : 15896 time to create 1 rle with old method : 0.01911473274230957 length of segment : 195 time for calcul the mask position with numpy : 0.001287221908569336 nb_pixel_total : 30383 time to create 1 rle with old method : 0.0361332893371582 length of segment : 179 time for calcul the mask position with numpy : 0.010007143020629883 nb_pixel_total : 51071 time to create 1 rle with old method : 0.05984210968017578 length of segment : 327 time for calcul the mask position with numpy : 0.0013964176177978516 nb_pixel_total : 21285 time to create 1 rle with old method : 0.025395870208740234 length of segment : 164 time for calcul the mask position with numpy : 0.010657548904418945 nb_pixel_total : 157135 time to create 1 rle with new method : 0.016320228576660156 length of segment : 689 time for calcul the mask position with numpy : 0.0004131793975830078 nb_pixel_total : 8745 time to create 1 rle with old method : 0.01040196418762207 length of segment : 98 time for calcul the mask position with numpy : 0.0011277198791503906 nb_pixel_total : 25287 time to create 1 rle with old method : 0.02982020378112793 length of segment : 260 time for calcul the mask position with numpy : 0.0005135536193847656 nb_pixel_total : 14171 time to create 1 rle with old method : 0.016844511032104492 length of segment : 184 time for calcul the mask position with numpy : 0.006415128707885742 nb_pixel_total : 174805 time to create 1 rle with new method : 0.013370513916015625 length of segment : 464 time for calcul the mask position with numpy : 0.0008692741394042969 nb_pixel_total : 36465 time to create 1 rle with old method : 0.04301095008850098 length of segment : 211 time for calcul the mask position with numpy : 0.0004677772521972656 nb_pixel_total : 9548 time to create 1 rle with old method : 0.011202812194824219 length of segment : 96 time for calcul the mask position with numpy : 0.0011181831359863281 nb_pixel_total : 23295 time to create 1 rle with old method : 0.027707815170288086 length of segment : 305 time for calcul the mask position with numpy : 0.001989603042602539 nb_pixel_total : 38641 time to create 1 rle with old method : 0.045638084411621094 length of segment : 278 time for calcul the mask position with numpy : 0.00038695335388183594 nb_pixel_total : 7790 time to create 1 rle with old method : 0.009455204010009766 length of segment : 97 time for calcul the mask position with numpy : 0.0036606788635253906 nb_pixel_total : 65300 time to create 1 rle with old method : 0.07642650604248047 length of segment : 283 time for calcul the mask position with numpy : 0.0007762908935546875 nb_pixel_total : 17529 time to create 1 rle with old method : 0.02041482925415039 length of segment : 170 time for calcul the mask position with numpy : 0.0005440711975097656 nb_pixel_total : 12510 time to create 1 rle with old method : 0.014549732208251953 length of segment : 114 time for calcul the mask position with numpy : 0.0006015300750732422 nb_pixel_total : 20498 time to create 1 rle with old method : 0.024804353713989258 length of segment : 135 time for calcul the mask position with numpy : 0.005137443542480469 nb_pixel_total : 123536 time to create 1 rle with old method : 0.13989615440368652 length of segment : 372 time for calcul the mask position with numpy : 0.0003190040588378906 nb_pixel_total : 10274 time to create 1 rle with old method : 0.01703786849975586 length of segment : 140 time for calcul the mask position with numpy : 0.005692720413208008 nb_pixel_total : 88573 time to create 1 rle with old method : 0.10885477066040039 length of segment : 705 time for calcul the mask position with numpy : 0.0047435760498046875 nb_pixel_total : 163640 time to create 1 rle with new method : 0.005953788757324219 length of segment : 501 time for calcul the mask position with numpy : 0.0010747909545898438 nb_pixel_total : 26042 time to create 1 rle with old method : 0.030963897705078125 length of segment : 206 time for calcul the mask position with numpy : 0.011783361434936523 nb_pixel_total : 229185 time to create 1 rle with new method : 0.029204368591308594 length of segment : 1215 time for calcul the mask position with numpy : 0.0033082962036132812 nb_pixel_total : 122204 time to create 1 rle with old method : 0.14208173751831055 length of segment : 861 time for calcul the mask position with numpy : 0.0008378028869628906 nb_pixel_total : 15461 time to create 1 rle with old method : 0.01840829849243164 length of segment : 146 time for calcul the mask position with numpy : 0.0029952526092529297 nb_pixel_total : 59837 time to create 1 rle with old method : 0.06910252571105957 length of segment : 475 time for calcul the mask position with numpy : 0.001092672348022461 nb_pixel_total : 28295 time to create 1 rle with old method : 0.033156394958496094 length of segment : 290 time for calcul the mask position with numpy : 0.0011925697326660156 nb_pixel_total : 32816 time to create 1 rle with old method : 0.03764200210571289 length of segment : 146 time for calcul the mask position with numpy : 0.0016942024230957031 nb_pixel_total : 76140 time to create 1 rle with old method : 0.08644509315490723 length of segment : 192 time for calcul the mask position with numpy : 0.010707616806030273 nb_pixel_total : 293718 time to create 1 rle with new method : 0.028368473052978516 length of segment : 918 time for calcul the mask position with numpy : 0.009036540985107422 nb_pixel_total : 9673 time to create 1 rle with old method : 0.013541698455810547 length of segment : 197 time for calcul the mask position with numpy : 0.0012271404266357422 nb_pixel_total : 34685 time to create 1 rle with old method : 0.04054403305053711 length of segment : 167 time for calcul the mask position with numpy : 0.0030808448791503906 nb_pixel_total : 85972 time to create 1 rle with old method : 0.09915900230407715 length of segment : 367 time for calcul the mask position with numpy : 0.0020427703857421875 nb_pixel_total : 44339 time to create 1 rle with old method : 0.05325126647949219 length of segment : 285 time for calcul the mask position with numpy : 0.002460956573486328 nb_pixel_total : 82079 time to create 1 rle with old method : 0.09627890586853027 length of segment : 259 time for calcul the mask position with numpy : 0.0016148090362548828 nb_pixel_total : 33902 time to create 1 rle with old method : 0.042082786560058594 length of segment : 326 time for calcul the mask position with numpy : 0.0029239654541015625 nb_pixel_total : 56790 time to create 1 rle with old method : 0.06613779067993164 length of segment : 274 time for calcul the mask position with numpy : 0.004193305969238281 nb_pixel_total : 106116 time to create 1 rle with old method : 0.12162995338439941 length of segment : 603 time for calcul the mask position with numpy : 0.0014190673828125 nb_pixel_total : 47011 time to create 1 rle with old method : 0.05366349220275879 length of segment : 305 time for calcul the mask position with numpy : 0.0035314559936523438 nb_pixel_total : 100529 time to create 1 rle with old method : 0.11776113510131836 length of segment : 302 time for calcul the mask position with numpy : 0.00048732757568359375 nb_pixel_total : 11043 time to create 1 rle with old method : 0.013542652130126953 length of segment : 90 time for calcul the mask position with numpy : 0.0005133152008056641 nb_pixel_total : 7877 time to create 1 rle with old method : 0.009731054306030273 length of segment : 152 time for calcul the mask position with numpy : 0.006915092468261719 nb_pixel_total : 137884 time to create 1 rle with old method : 0.15839433670043945 length of segment : 1148 time for calcul the mask position with numpy : 0.0011200904846191406 nb_pixel_total : 24672 time to create 1 rle with old method : 0.02930903434753418 length of segment : 197 time for calcul the mask position with numpy : 0.0005512237548828125 nb_pixel_total : 18782 time to create 1 rle with old method : 0.021607160568237305 length of segment : 162 time for calcul the mask position with numpy : 0.002923250198364258 nb_pixel_total : 118912 time to create 1 rle with old method : 0.13483643531799316 length of segment : 510 time for calcul the mask position with numpy : 0.0011854171752929688 nb_pixel_total : 51079 time to create 1 rle with old method : 0.06369686126708984 length of segment : 143 time for calcul the mask position with numpy : 0.009781599044799805 nb_pixel_total : 135288 time to create 1 rle with old method : 0.18024373054504395 length of segment : 708 time for calcul the mask position with numpy : 0.0005238056182861328 nb_pixel_total : 22146 time to create 1 rle with old method : 0.02606678009033203 length of segment : 184 time for calcul the mask position with numpy : 0.002652883529663086 nb_pixel_total : 58848 time to create 1 rle with old method : 0.06918644905090332 length of segment : 310 time for calcul the mask position with numpy : 0.0010645389556884766 nb_pixel_total : 26096 time to create 1 rle with old method : 0.031707763671875 length of segment : 112 time for calcul the mask position with numpy : 0.015195846557617188 nb_pixel_total : 491971 time to create 1 rle with new method : 0.7658424377441406 length of segment : 970 time for calcul the mask position with numpy : 0.008809328079223633 nb_pixel_total : 267788 time to create 1 rle with new method : 0.019138097763061523 length of segment : 996 time for calcul the mask position with numpy : 0.0035517215728759766 nb_pixel_total : 130884 time to create 1 rle with old method : 0.15216326713562012 length of segment : 354 time for calcul the mask position with numpy : 0.006424665451049805 nb_pixel_total : 147967 time to create 1 rle with old method : 0.182464599609375 length of segment : 696 time for calcul the mask position with numpy : 0.0007994174957275391 nb_pixel_total : 25239 time to create 1 rle with old method : 0.0296628475189209 length of segment : 261 time for calcul the mask position with numpy : 0.0076181888580322266 nb_pixel_total : 267286 time to create 1 rle with new method : 0.018304109573364258 length of segment : 872 time for calcul the mask position with numpy : 0.0007252693176269531 nb_pixel_total : 31244 time to create 1 rle with old method : 0.03677487373352051 length of segment : 157 time for calcul the mask position with numpy : 0.0005700588226318359 nb_pixel_total : 24085 time to create 1 rle with old method : 0.02900099754333496 length of segment : 171 time for calcul the mask position with numpy : 0.004694700241088867 nb_pixel_total : 134415 time to create 1 rle with old method : 0.17020797729492188 length of segment : 408 time for calcul the mask position with numpy : 0.007954835891723633 nb_pixel_total : 255599 time to create 1 rle with new method : 0.014511346817016602 length of segment : 583 time for calcul the mask position with numpy : 0.002162933349609375 nb_pixel_total : 26227 time to create 1 rle with old method : 0.031054258346557617 length of segment : 372 time for calcul the mask position with numpy : 0.0005614757537841797 nb_pixel_total : 15773 time to create 1 rle with old method : 0.01900935173034668 length of segment : 118 time for calcul the mask position with numpy : 0.0012121200561523438 nb_pixel_total : 30885 time to create 1 rle with old method : 0.036459922790527344 length of segment : 411 time for calcul the mask position with numpy : 0.00019669532775878906 nb_pixel_total : 3173 time to create 1 rle with old method : 0.0039806365966796875 length of segment : 57 time for calcul the mask position with numpy : 0.0012421607971191406 nb_pixel_total : 29612 time to create 1 rle with old method : 0.03724956512451172 length of segment : 213 time for calcul the mask position with numpy : 0.0009951591491699219 nb_pixel_total : 19179 time to create 1 rle with old method : 0.03103494644165039 length of segment : 140 time for calcul the mask position with numpy : 0.0014986991882324219 nb_pixel_total : 43788 time to create 1 rle with old method : 0.051758766174316406 length of segment : 309 time for calcul the mask position with numpy : 0.0014078617095947266 nb_pixel_total : 60742 time to create 1 rle with old method : 0.07110190391540527 length of segment : 175 time for calcul the mask position with numpy : 0.007817745208740234 nb_pixel_total : 108091 time to create 1 rle with old method : 0.12600135803222656 length of segment : 394 time for calcul the mask position with numpy : 0.002691984176635742 nb_pixel_total : 40876 time to create 1 rle with old method : 0.048545122146606445 length of segment : 300 time for calcul the mask position with numpy : 0.0017771720886230469 nb_pixel_total : 20428 time to create 1 rle with old method : 0.02484297752380371 length of segment : 191 time for calcul the mask position with numpy : 0.0010459423065185547 nb_pixel_total : 17643 time to create 1 rle with old method : 0.021730661392211914 length of segment : 144 time for calcul the mask position with numpy : 0.0007917881011962891 nb_pixel_total : 13102 time to create 1 rle with old method : 0.01601243019104004 length of segment : 102 time for calcul the mask position with numpy : 0.0018763542175292969 nb_pixel_total : 25504 time to create 1 rle with old method : 0.030601978302001953 length of segment : 262 time for calcul the mask position with numpy : 0.0007376670837402344 nb_pixel_total : 8786 time to create 1 rle with old method : 0.010522603988647461 length of segment : 183 time for calcul the mask position with numpy : 0.0018868446350097656 nb_pixel_total : 25891 time to create 1 rle with old method : 0.030340909957885742 length of segment : 222 time for calcul the mask position with numpy : 0.001787424087524414 nb_pixel_total : 20868 time to create 1 rle with old method : 0.025090694427490234 length of segment : 186 time for calcul the mask position with numpy : 0.003335237503051758 nb_pixel_total : 41675 time to create 1 rle with old method : 0.049051523208618164 length of segment : 332 time for calcul the mask position with numpy : 0.0032444000244140625 nb_pixel_total : 38219 time to create 1 rle with old method : 0.046082258224487305 length of segment : 268 time for calcul the mask position with numpy : 0.0022907257080078125 nb_pixel_total : 30677 time to create 1 rle with old method : 0.03670620918273926 length of segment : 209 time for calcul the mask position with numpy : 0.012660503387451172 nb_pixel_total : 131362 time to create 1 rle with old method : 0.1549358367919922 length of segment : 492 time for calcul the mask position with numpy : 0.0003495216369628906 nb_pixel_total : 9235 time to create 1 rle with old method : 0.011241912841796875 length of segment : 108 time for calcul the mask position with numpy : 0.0017390251159667969 nb_pixel_total : 61411 time to create 1 rle with old method : 0.07207131385803223 length of segment : 361 time for calcul the mask position with numpy : 0.006639719009399414 nb_pixel_total : 36608 time to create 1 rle with old method : 0.06060957908630371 length of segment : 466 time for calcul the mask position with numpy : 0.0025413036346435547 nb_pixel_total : 33124 time to create 1 rle with old method : 0.03877449035644531 length of segment : 389 time for calcul the mask position with numpy : 0.0026471614837646484 nb_pixel_total : 40292 time to create 1 rle with old method : 0.047684669494628906 length of segment : 263 time for calcul the mask position with numpy : 0.0013496875762939453 nb_pixel_total : 19419 time to create 1 rle with old method : 0.023368120193481445 length of segment : 211 time for calcul the mask position with numpy : 0.0008745193481445312 nb_pixel_total : 11755 time to create 1 rle with old method : 0.014542102813720703 length of segment : 130 time for calcul the mask position with numpy : 0.0009284019470214844 nb_pixel_total : 8573 time to create 1 rle with old method : 0.010622262954711914 length of segment : 169 time for calcul the mask position with numpy : 0.0006492137908935547 nb_pixel_total : 7333 time to create 1 rle with old method : 0.008982419967651367 length of segment : 92 time for calcul the mask position with numpy : 0.002500772476196289 nb_pixel_total : 35999 time to create 1 rle with old method : 0.0431513786315918 length of segment : 178 time for calcul the mask position with numpy : 0.0014100074768066406 nb_pixel_total : 15030 time to create 1 rle with old method : 0.018228769302368164 length of segment : 171 time for calcul the mask position with numpy : 0.0054225921630859375 nb_pixel_total : 80153 time to create 1 rle with old method : 0.09917354583740234 length of segment : 355 time for calcul the mask position with numpy : 0.0014390945434570312 nb_pixel_total : 22377 time to create 1 rle with old method : 0.03179287910461426 length of segment : 196 time for calcul the mask position with numpy : 0.0042476654052734375 nb_pixel_total : 58422 time to create 1 rle with old method : 0.06858301162719727 length of segment : 323 time for calcul the mask position with numpy : 0.00394129753112793 nb_pixel_total : 38535 time to create 1 rle with old method : 0.04582476615905762 length of segment : 463 time for calcul the mask position with numpy : 0.0011861324310302734 nb_pixel_total : 20832 time to create 1 rle with old method : 0.024695396423339844 length of segment : 247 time for calcul the mask position with numpy : 0.0034019947052001953 nb_pixel_total : 43471 time to create 1 rle with old method : 0.05350470542907715 length of segment : 323 time for calcul the mask position with numpy : 0.0005908012390136719 nb_pixel_total : 4871 time to create 1 rle with old method : 0.00841212272644043 length of segment : 70 time for calcul the mask position with numpy : 0.0062351226806640625 nb_pixel_total : 77877 time to create 1 rle with old method : 0.09364151954650879 length of segment : 401 time for calcul the mask position with numpy : 0.0029060840606689453 nb_pixel_total : 28069 time to create 1 rle with old method : 0.033748626708984375 length of segment : 334 time for calcul the mask position with numpy : 0.0007312297821044922 nb_pixel_total : 12449 time to create 1 rle with old method : 0.015069961547851562 length of segment : 104 time for calcul the mask position with numpy : 0.0018165111541748047 nb_pixel_total : 25661 time to create 1 rle with old method : 0.03061509132385254 length of segment : 208 time for calcul the mask position with numpy : 0.00031757354736328125 nb_pixel_total : 4140 time to create 1 rle with old method : 0.005129575729370117 length of segment : 101 time for calcul the mask position with numpy : 0.0006341934204101562 nb_pixel_total : 7763 time to create 1 rle with old method : 0.009331226348876953 length of segment : 159 time for calcul the mask position with numpy : 0.00162506103515625 nb_pixel_total : 19392 time to create 1 rle with old method : 0.02300119400024414 length of segment : 200 time for calcul the mask position with numpy : 0.003238677978515625 nb_pixel_total : 45096 time to create 1 rle with old method : 0.05661463737487793 length of segment : 295 time for calcul the mask position with numpy : 0.0002884864807128906 nb_pixel_total : 5482 time to create 1 rle with old method : 0.006752729415893555 length of segment : 70 time for calcul the mask position with numpy : 0.001283407211303711 nb_pixel_total : 22283 time to create 1 rle with old method : 0.026471614837646484 length of segment : 156 time for calcul the mask position with numpy : 0.0036182403564453125 nb_pixel_total : 63284 time to create 1 rle with old method : 0.07537269592285156 length of segment : 245 time for calcul the mask position with numpy : 0.005492210388183594 nb_pixel_total : 88851 time to create 1 rle with old method : 0.10371136665344238 length of segment : 459 time for calcul the mask position with numpy : 0.0008823871612548828 nb_pixel_total : 6194 time to create 1 rle with old method : 0.00742650032043457 length of segment : 251 time for calcul the mask position with numpy : 0.000179290771484375 nb_pixel_total : 6207 time to create 1 rle with old method : 0.0075261592864990234 length of segment : 130 time for calcul the mask position with numpy : 0.001031637191772461 nb_pixel_total : 10972 time to create 1 rle with old method : 0.013483047485351562 length of segment : 132 time for calcul the mask position with numpy : 0.00042891502380371094 nb_pixel_total : 5715 time to create 1 rle with old method : 0.006955623626708984 length of segment : 74 time for calcul the mask position with numpy : 0.0003082752227783203 nb_pixel_total : 8518 time to create 1 rle with old method : 0.010544538497924805 length of segment : 170 time for calcul the mask position with numpy : 0.0006701946258544922 nb_pixel_total : 15633 time to create 1 rle with old method : 0.018838167190551758 length of segment : 147 time for calcul the mask position with numpy : 0.0012662410736083984 nb_pixel_total : 18085 time to create 1 rle with old method : 0.02106451988220215 length of segment : 146 time for calcul the mask position with numpy : 0.002168893814086914 nb_pixel_total : 33717 time to create 1 rle with old method : 0.039772748947143555 length of segment : 316 time for calcul the mask position with numpy : 0.0011746883392333984 nb_pixel_total : 23974 time to create 1 rle with old method : 0.028663158416748047 length of segment : 237 time for calcul the mask position with numpy : 0.00014138221740722656 nb_pixel_total : 3914 time to create 1 rle with old method : 0.0048809051513671875 length of segment : 47 time for calcul the mask position with numpy : 0.00012946128845214844 nb_pixel_total : 2785 time to create 1 rle with old method : 0.0036840438842773438 length of segment : 98 time for calcul the mask position with numpy : 0.001241922378540039 nb_pixel_total : 19231 time to create 1 rle with old method : 0.023454904556274414 length of segment : 169 time for calcul the mask position with numpy : 0.0034170150756835938 nb_pixel_total : 29785 time to create 1 rle with old method : 0.03595399856567383 length of segment : 388 time for calcul the mask position with numpy : 0.0006887912750244141 nb_pixel_total : 9178 time to create 1 rle with old method : 0.011845827102661133 length of segment : 75 time for calcul the mask position with numpy : 0.00029206275939941406 nb_pixel_total : 10283 time to create 1 rle with old method : 0.012508869171142578 length of segment : 102 time for calcul the mask position with numpy : 0.0011775493621826172 nb_pixel_total : 15166 time to create 1 rle with old method : 0.018494606018066406 length of segment : 129 time for calcul the mask position with numpy : 0.0002627372741699219 nb_pixel_total : 5653 time to create 1 rle with old method : 0.007123231887817383 length of segment : 74 time for calcul the mask position with numpy : 0.0009243488311767578 nb_pixel_total : 10277 time to create 1 rle with old method : 0.01237034797668457 length of segment : 258 time for calcul the mask position with numpy : 0.0017020702362060547 nb_pixel_total : 26418 time to create 1 rle with old method : 0.031571149826049805 length of segment : 196 time for calcul the mask position with numpy : 0.0022182464599609375 nb_pixel_total : 23179 time to create 1 rle with old method : 0.02934432029724121 length of segment : 255 time for calcul the mask position with numpy : 0.0019366741180419922 nb_pixel_total : 25807 time to create 1 rle with old method : 0.030709505081176758 length of segment : 220 time for calcul the mask position with numpy : 0.001987457275390625 nb_pixel_total : 32521 time to create 1 rle with old method : 0.04040193557739258 length of segment : 194 time for calcul the mask position with numpy : 0.0010991096496582031 nb_pixel_total : 13276 time to create 1 rle with old method : 0.016174793243408203 length of segment : 141 time for calcul the mask position with numpy : 0.0021805763244628906 nb_pixel_total : 23252 time to create 1 rle with old method : 0.027947664260864258 length of segment : 237 time for calcul the mask position with numpy : 0.0033583641052246094 nb_pixel_total : 39795 time to create 1 rle with old method : 0.04691600799560547 length of segment : 336 time for calcul the mask position with numpy : 0.0008955001831054688 nb_pixel_total : 9583 time to create 1 rle with old method : 0.011680126190185547 length of segment : 120 time for calcul the mask position with numpy : 0.0015413761138916016 nb_pixel_total : 21130 time to create 1 rle with old method : 0.02518630027770996 length of segment : 254 time for calcul the mask position with numpy : 0.0014574527740478516 nb_pixel_total : 13877 time to create 1 rle with old method : 0.01690983772277832 length of segment : 199 time for calcul the mask position with numpy : 0.0010440349578857422 nb_pixel_total : 17631 time to create 1 rle with old method : 0.021178245544433594 length of segment : 172 time for calcul the mask position with numpy : 0.0029022693634033203 nb_pixel_total : 40244 time to create 1 rle with old method : 0.04872536659240723 length of segment : 248 time for calcul the mask position with numpy : 0.0005879402160644531 nb_pixel_total : 5744 time to create 1 rle with old method : 0.009973764419555664 length of segment : 73 time for calcul the mask position with numpy : 0.007503032684326172 nb_pixel_total : 78331 time to create 1 rle with old method : 0.12082886695861816 length of segment : 375 time for calcul the mask position with numpy : 0.0017437934875488281 nb_pixel_total : 24657 time to create 1 rle with old method : 0.0347137451171875 length of segment : 197 time for calcul the mask position with numpy : 0.005570173263549805 nb_pixel_total : 59938 time to create 1 rle with old method : 0.07786107063293457 length of segment : 342 time for calcul the mask position with numpy : 0.003342151641845703 nb_pixel_total : 34725 time to create 1 rle with old method : 0.0410456657409668 length of segment : 279 time for calcul the mask position with numpy : 0.0026276111602783203 nb_pixel_total : 52482 time to create 1 rle with old method : 0.0622406005859375 length of segment : 208 time for calcul the mask position with numpy : 0.001964569091796875 nb_pixel_total : 27021 time to create 1 rle with old method : 0.032323598861694336 length of segment : 359 time for calcul the mask position with numpy : 0.0009372234344482422 nb_pixel_total : 13889 time to create 1 rle with old method : 0.017041921615600586 length of segment : 190 time for calcul the mask position with numpy : 0.001390695571899414 nb_pixel_total : 21881 time to create 1 rle with old method : 0.026968717575073242 length of segment : 128 time for calcul the mask position with numpy : 0.003808736801147461 nb_pixel_total : 45166 time to create 1 rle with old method : 0.05330204963684082 length of segment : 451 time for calcul the mask position with numpy : 0.001233816146850586 nb_pixel_total : 17670 time to create 1 rle with old method : 0.021338701248168945 length of segment : 152 time for calcul the mask position with numpy : 0.0004696846008300781 nb_pixel_total : 12171 time to create 1 rle with old method : 0.014122247695922852 length of segment : 146 time for calcul the mask position with numpy : 0.00941777229309082 nb_pixel_total : 77507 time to create 1 rle with old method : 0.09236288070678711 length of segment : 803 time for calcul the mask position with numpy : 0.0017940998077392578 nb_pixel_total : 24675 time to create 1 rle with old method : 0.029412031173706055 length of segment : 176 time for calcul the mask position with numpy : 0.0005626678466796875 nb_pixel_total : 10718 time to create 1 rle with old method : 0.013164281845092773 length of segment : 174 time for calcul the mask position with numpy : 0.00027942657470703125 nb_pixel_total : 5457 time to create 1 rle with old method : 0.006863594055175781 length of segment : 60 time for calcul the mask position with numpy : 0.0024437904357910156 nb_pixel_total : 27195 time to create 1 rle with old method : 0.032768964767456055 length of segment : 249 time for calcul the mask position with numpy : 0.001729726791381836 nb_pixel_total : 30718 time to create 1 rle with old method : 0.036133527755737305 length of segment : 223 time for calcul the mask position with numpy : 0.0006802082061767578 nb_pixel_total : 19739 time to create 1 rle with old method : 0.023849010467529297 length of segment : 159 time for calcul the mask position with numpy : 0.0016980171203613281 nb_pixel_total : 19540 time to create 1 rle with old method : 0.02375316619873047 length of segment : 204 time for calcul the mask position with numpy : 0.0003514289855957031 nb_pixel_total : 10345 time to create 1 rle with old method : 0.012455463409423828 length of segment : 82 time for calcul the mask position with numpy : 0.002178192138671875 nb_pixel_total : 44846 time to create 1 rle with old method : 0.05312299728393555 length of segment : 397 time for calcul the mask position with numpy : 0.0040509700775146484 nb_pixel_total : 40611 time to create 1 rle with old method : 0.04813265800476074 length of segment : 350 time for calcul the mask position with numpy : 0.0011494159698486328 nb_pixel_total : 17793 time to create 1 rle with old method : 0.021437644958496094 length of segment : 207 time for calcul the mask position with numpy : 0.000278472900390625 nb_pixel_total : 5117 time to create 1 rle with old method : 0.006351470947265625 length of segment : 65 time for calcul the mask position with numpy : 0.0004627704620361328 nb_pixel_total : 6710 time to create 1 rle with old method : 0.008199214935302734 length of segment : 169 time for calcul the mask position with numpy : 0.0011925697326660156 nb_pixel_total : 15109 time to create 1 rle with old method : 0.018458843231201172 length of segment : 111 time for calcul the mask position with numpy : 0.005360603332519531 nb_pixel_total : 61203 time to create 1 rle with old method : 0.07110214233398438 length of segment : 439 time for calcul the mask position with numpy : 0.0008161067962646484 nb_pixel_total : 13993 time to create 1 rle with old method : 0.016952991485595703 length of segment : 137 time for calcul the mask position with numpy : 0.0021758079528808594 nb_pixel_total : 28053 time to create 1 rle with old method : 0.033438682556152344 length of segment : 675 time for calcul the mask position with numpy : 0.0015833377838134766 nb_pixel_total : 21286 time to create 1 rle with old method : 0.025408029556274414 length of segment : 147 time for calcul the mask position with numpy : 0.0007321834564208984 nb_pixel_total : 8145 time to create 1 rle with old method : 0.009820699691772461 length of segment : 97 time for calcul the mask position with numpy : 0.002627134323120117 nb_pixel_total : 51524 time to create 1 rle with old method : 0.061000823974609375 length of segment : 213 time for calcul the mask position with numpy : 0.0006878376007080078 nb_pixel_total : 15292 time to create 1 rle with old method : 0.018535137176513672 length of segment : 150 time for calcul the mask position with numpy : 0.001422882080078125 nb_pixel_total : 11160 time to create 1 rle with old method : 0.01481175422668457 length of segment : 170 time for calcul the mask position with numpy : 0.0010077953338623047 nb_pixel_total : 12023 time to create 1 rle with old method : 0.01472616195678711 length of segment : 125 time for calcul the mask position with numpy : 0.0007305145263671875 nb_pixel_total : 11356 time to create 1 rle with old method : 0.01436471939086914 length of segment : 122 time for calcul the mask position with numpy : 0.0002703666687011719 nb_pixel_total : 3448 time to create 1 rle with old method : 0.0044078826904296875 length of segment : 96 time for calcul the mask position with numpy : 0.0010023117065429688 nb_pixel_total : 11230 time to create 1 rle with old method : 0.013575315475463867 length of segment : 160 time for calcul the mask position with numpy : 0.001300811767578125 nb_pixel_total : 44037 time to create 1 rle with old method : 0.05468487739562988 length of segment : 238 time for calcul the mask position with numpy : 0.004025936126708984 nb_pixel_total : 71862 time to create 1 rle with old method : 0.09784102439880371 length of segment : 504 time for calcul the mask position with numpy : 0.0026674270629882812 nb_pixel_total : 24110 time to create 1 rle with old method : 0.027257919311523438 length of segment : 319 time for calcul the mask position with numpy : 0.0014138221740722656 nb_pixel_total : 35190 time to create 1 rle with old method : 0.040338993072509766 length of segment : 274 time for calcul the mask position with numpy : 0.0006918907165527344 nb_pixel_total : 15950 time to create 1 rle with old method : 0.019426345825195312 length of segment : 153 time for calcul the mask position with numpy : 0.0012388229370117188 nb_pixel_total : 17750 time to create 1 rle with old method : 0.023455142974853516 length of segment : 194 time for calcul the mask position with numpy : 0.0039017200469970703 nb_pixel_total : 40416 time to create 1 rle with old method : 0.057828426361083984 length of segment : 373 time for calcul the mask position with numpy : 0.0012531280517578125 nb_pixel_total : 18588 time to create 1 rle with old method : 0.022193431854248047 length of segment : 148 time for calcul the mask position with numpy : 0.003040313720703125 nb_pixel_total : 42124 time to create 1 rle with old method : 0.04974794387817383 length of segment : 374 time for calcul the mask position with numpy : 0.0011603832244873047 nb_pixel_total : 11355 time to create 1 rle with old method : 0.013889312744140625 length of segment : 152 time for calcul the mask position with numpy : 0.001043081283569336 nb_pixel_total : 14723 time to create 1 rle with old method : 0.018095731735229492 length of segment : 118 time for calcul the mask position with numpy : 0.0016293525695800781 nb_pixel_total : 23440 time to create 1 rle with old method : 0.028115034103393555 length of segment : 165 time for calcul the mask position with numpy : 0.0010404586791992188 nb_pixel_total : 10538 time to create 1 rle with old method : 0.012705564498901367 length of segment : 195 time for calcul the mask position with numpy : 0.001177072525024414 nb_pixel_total : 15616 time to create 1 rle with old method : 0.019013643264770508 length of segment : 153 time for calcul the mask position with numpy : 0.0006132125854492188 nb_pixel_total : 16157 time to create 1 rle with old method : 0.019690990447998047 length of segment : 187 time for calcul the mask position with numpy : 0.0005526542663574219 nb_pixel_total : 10977 time to create 1 rle with old method : 0.013424396514892578 length of segment : 131 time for calcul the mask position with numpy : 0.0010724067687988281 nb_pixel_total : 16903 time to create 1 rle with old method : 0.02010631561279297 length of segment : 310 time for calcul the mask position with numpy : 0.0013208389282226562 nb_pixel_total : 33604 time to create 1 rle with old method : 0.04221630096435547 length of segment : 215 time for calcul the mask position with numpy : 0.0004715919494628906 nb_pixel_total : 8081 time to create 1 rle with old method : 0.009774446487426758 length of segment : 104 time for calcul the mask position with numpy : 0.0007123947143554688 nb_pixel_total : 15722 time to create 1 rle with old method : 0.01881265640258789 length of segment : 126 time for calcul the mask position with numpy : 0.001733541488647461 nb_pixel_total : 43212 time to create 1 rle with old method : 0.0514984130859375 length of segment : 208 time for calcul the mask position with numpy : 0.011013507843017578 nb_pixel_total : 313588 time to create 1 rle with new method : 0.017003297805786133 length of segment : 512 time for calcul the mask position with numpy : 0.011413812637329102 nb_pixel_total : 288130 time to create 1 rle with new method : 0.1059885025024414 length of segment : 631 time for calcul the mask position with numpy : 0.0005166530609130859 nb_pixel_total : 11290 time to create 1 rle with old method : 0.013775825500488281 length of segment : 106 time for calcul the mask position with numpy : 0.0004239082336425781 nb_pixel_total : 6588 time to create 1 rle with old method : 0.008087635040283203 length of segment : 117 time for calcul the mask position with numpy : 0.006831169128417969 nb_pixel_total : 189910 time to create 1 rle with new method : 0.012332677841186523 length of segment : 523 time for calcul the mask position with numpy : 0.001325845718383789 nb_pixel_total : 28986 time to create 1 rle with old method : 0.034450531005859375 length of segment : 202 time for calcul the mask position with numpy : 0.0004892349243164062 nb_pixel_total : 6585 time to create 1 rle with old method : 0.008111953735351562 length of segment : 142 time for calcul the mask position with numpy : 0.002080202102661133 nb_pixel_total : 32172 time to create 1 rle with old method : 0.03884100914001465 length of segment : 250 time for calcul the mask position with numpy : 0.002485513687133789 nb_pixel_total : 66812 time to create 1 rle with old method : 0.07949161529541016 length of segment : 386 time for calcul the mask position with numpy : 0.0004553794860839844 nb_pixel_total : 13071 time to create 1 rle with old method : 0.01605510711669922 length of segment : 107 time for calcul the mask position with numpy : 0.0008549690246582031 nb_pixel_total : 16074 time to create 1 rle with old method : 0.019562244415283203 length of segment : 159 time for calcul the mask position with numpy : 0.001233816146850586 nb_pixel_total : 26910 time to create 1 rle with old method : 0.03191041946411133 length of segment : 291 time for calcul the mask position with numpy : 0.0019197463989257812 nb_pixel_total : 63497 time to create 1 rle with old method : 0.08329916000366211 length of segment : 280 time for calcul the mask position with numpy : 0.003721952438354492 nb_pixel_total : 82214 time to create 1 rle with old method : 0.10134148597717285 length of segment : 373 time for calcul the mask position with numpy : 0.0019071102142333984 nb_pixel_total : 40282 time to create 1 rle with old method : 0.04752540588378906 length of segment : 242 time for calcul the mask position with numpy : 0.0007414817810058594 nb_pixel_total : 31315 time to create 1 rle with old method : 0.03869175910949707 length of segment : 249 time for calcul the mask position with numpy : 0.002454519271850586 nb_pixel_total : 62201 time to create 1 rle with old method : 0.07175731658935547 length of segment : 370 time for calcul the mask position with numpy : 0.00035572052001953125 nb_pixel_total : 20902 time to create 1 rle with old method : 0.024286985397338867 length of segment : 126 time for calcul the mask position with numpy : 0.0059604644775390625 nb_pixel_total : 202752 time to create 1 rle with new method : 0.008682966232299805 length of segment : 566 time for calcul the mask position with numpy : 0.0042705535888671875 nb_pixel_total : 112383 time to create 1 rle with old method : 0.12877631187438965 length of segment : 613 time for calcul the mask position with numpy : 0.0009131431579589844 nb_pixel_total : 21534 time to create 1 rle with old method : 0.024911165237426758 length of segment : 171 time for calcul the mask position with numpy : 0.0036733150482177734 nb_pixel_total : 110908 time to create 1 rle with old method : 0.12595343589782715 length of segment : 441 time for calcul the mask position with numpy : 0.0009424686431884766 nb_pixel_total : 16926 time to create 1 rle with old method : 0.0197296142578125 length of segment : 167 time for calcul the mask position with numpy : 0.004900932312011719 nb_pixel_total : 103107 time to create 1 rle with old method : 0.11704778671264648 length of segment : 466 time for calcul the mask position with numpy : 0.0008268356323242188 nb_pixel_total : 18449 time to create 1 rle with old method : 0.02142620086669922 length of segment : 144 time for calcul the mask position with numpy : 0.0010256767272949219 nb_pixel_total : 33861 time to create 1 rle with old method : 0.039659976959228516 length of segment : 213 time for calcul the mask position with numpy : 0.0007991790771484375 nb_pixel_total : 16687 time to create 1 rle with old method : 0.019847631454467773 length of segment : 153 time for calcul the mask position with numpy : 0.001863718032836914 nb_pixel_total : 36111 time to create 1 rle with old method : 0.042433977127075195 length of segment : 235 time for calcul the mask position with numpy : 0.0006041526794433594 nb_pixel_total : 16991 time to create 1 rle with old method : 0.019702434539794922 length of segment : 156 time for calcul the mask position with numpy : 0.0004916191101074219 nb_pixel_total : 14146 time to create 1 rle with old method : 0.016582727432250977 length of segment : 99 time for calcul the mask position with numpy : 0.0006957054138183594 nb_pixel_total : 27143 time to create 1 rle with old method : 0.032021284103393555 length of segment : 228 time for calcul the mask position with numpy : 0.0006287097930908203 nb_pixel_total : 17186 time to create 1 rle with old method : 0.02049565315246582 length of segment : 233 time for calcul the mask position with numpy : 0.0005245208740234375 nb_pixel_total : 8443 time to create 1 rle with old method : 0.014312267303466797 length of segment : 116 time for calcul the mask position with numpy : 0.0020515918731689453 nb_pixel_total : 34764 time to create 1 rle with old method : 0.04082441329956055 length of segment : 243 time for calcul the mask position with numpy : 0.0004036426544189453 nb_pixel_total : 7785 time to create 1 rle with old method : 0.00963902473449707 length of segment : 86 time for calcul the mask position with numpy : 0.00021147727966308594 nb_pixel_total : 5098 time to create 1 rle with old method : 0.00641322135925293 length of segment : 75 time for calcul the mask position with numpy : 0.00042366981506347656 nb_pixel_total : 6340 time to create 1 rle with old method : 0.007563591003417969 length of segment : 148 time for calcul the mask position with numpy : 0.001607656478881836 nb_pixel_total : 29206 time to create 1 rle with old method : 0.038043975830078125 length of segment : 240 time for calcul the mask position with numpy : 0.000492095947265625 nb_pixel_total : 9710 time to create 1 rle with old method : 0.01179814338684082 length of segment : 111 time for calcul the mask position with numpy : 0.0031087398529052734 nb_pixel_total : 52007 time to create 1 rle with old method : 0.06425166130065918 length of segment : 388 time for calcul the mask position with numpy : 0.0015094280242919922 nb_pixel_total : 52583 time to create 1 rle with old method : 0.06717896461486816 length of segment : 378 time for calcul the mask position with numpy : 0.005246162414550781 nb_pixel_total : 125388 time to create 1 rle with old method : 0.14816904067993164 length of segment : 444 time for calcul the mask position with numpy : 0.004275083541870117 nb_pixel_total : 61448 time to create 1 rle with old method : 0.09207367897033691 length of segment : 284 time for calcul the mask position with numpy : 0.0006389617919921875 nb_pixel_total : 5037 time to create 1 rle with old method : 0.006312131881713867 length of segment : 135 time for calcul the mask position with numpy : 0.0005970001220703125 nb_pixel_total : 17715 time to create 1 rle with old method : 0.023232460021972656 length of segment : 112 time for calcul the mask position with numpy : 0.0014348030090332031 nb_pixel_total : 39847 time to create 1 rle with old method : 0.0628516674041748 length of segment : 248 time for calcul the mask position with numpy : 0.0004782676696777344 nb_pixel_total : 12055 time to create 1 rle with old method : 0.0146484375 length of segment : 145 time for calcul the mask position with numpy : 0.0003631114959716797 nb_pixel_total : 8092 time to create 1 rle with old method : 0.010264873504638672 length of segment : 76 time for calcul the mask position with numpy : 0.0014750957489013672 nb_pixel_total : 42870 time to create 1 rle with old method : 0.05082225799560547 length of segment : 307 time for calcul the mask position with numpy : 0.00025653839111328125 nb_pixel_total : 3855 time to create 1 rle with old method : 0.004700660705566406 length of segment : 74 time for calcul the mask position with numpy : 0.003002643585205078 nb_pixel_total : 103821 time to create 1 rle with old method : 0.1220235824584961 length of segment : 625 time for calcul the mask position with numpy : 0.001062631607055664 nb_pixel_total : 35080 time to create 1 rle with old method : 0.0418391227722168 length of segment : 329 time for calcul the mask position with numpy : 0.0012309551239013672 nb_pixel_total : 46809 time to create 1 rle with old method : 0.05615830421447754 length of segment : 247 time for calcul the mask position with numpy : 0.0007884502410888672 nb_pixel_total : 51292 time to create 1 rle with old method : 0.06096935272216797 length of segment : 265 time for calcul the mask position with numpy : 0.001800537109375 nb_pixel_total : 76944 time to create 1 rle with old method : 0.0900719165802002 length of segment : 350 time for calcul the mask position with numpy : 0.0004305839538574219 nb_pixel_total : 11320 time to create 1 rle with old method : 0.013315916061401367 length of segment : 149 time spent for convertir_results : 36.65896821022034 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 470 chid ids of type : 3594 Number RLEs to save : 116086 save missing photos in datou_result : time spend for datou_step_exec : 206.57180786132812 time spend to save output : 12.970408916473389 total time spend for step 1 : 219.5422167778015 step2:crop_condition Tue Apr 8 14:14:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 15 ! batch 1 Loaded 470 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 358 About to insert : list_path_to_insert length 358 new photo from crops ! About to upload 358 photos upload in portfolio : 3736932 init cache_photo without model_param we have 358 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744114517_1619204 we have uploaded 358 photos in the portfolio 3736932 time of upload the photos Elapsed time : 121.49758172035217 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 63 About to insert : list_path_to_insert length 63 new photo from crops ! About to upload 63 photos upload in portfolio : 3736932 init cache_photo without model_param we have 63 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744114654_1619204 we have uploaded 63 photos in the portfolio 3736932 time of upload the photos Elapsed time : 18.936317682266235 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744114675_1619204 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.1324145793914795 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 33 About to insert : list_path_to_insert length 33 new photo from crops ! About to upload 33 photos upload in portfolio : 3736932 init cache_photo without model_param we have 33 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744114694_1619204 we have uploaded 33 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.994938850402832 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744114707_1619204 we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.6221063137054443 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744114711_1619204 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.0143296718597412 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744114716_1619204 we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.6425974369049072 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350595105, 1350595100, 1350595098, 1350595092, 1350595000, 1350594997, 1350594994, 1350594917, 1350594839, 1350594822, 1350594566, 1350594240, 1350594232, 1350594226, 1350594216] Looping around the photos to save general results len do output : 470 /1350624492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624508Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624532Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624595Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350624672Didn't retrieve data .Didn't retrieve 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/1350625098Didn'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 ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595105', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595100', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595098', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595092', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595000', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594997', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594994', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594917', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594839', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594822', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594566', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594240', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594232', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594226', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594216', None, None, None, None, None, '2732571') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1425 time used for this insertion : 1.1547131538391113 save_final save missing photos in datou_result : time spend for datou_step_exec : 266.59965085983276 time spend to save output : 1.1680388450622559 total time spend for step 2 : 267.767689704895 step3:rle_unique_nms_with_priority Tue Apr 8 14:18:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 470 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 46 nb_hashtags : 5 time to prepare the origin masks : 4.396207809448242 time for calcul the mask position with numpy : 0.4039614200592041 nb_pixel_total : 5039630 time to create 1 rle with new method : 0.7469241619110107 time for calcul the mask position with numpy : 0.0295867919921875 nb_pixel_total : 9720 time to create 1 rle with old method : 0.011713027954101562 time for calcul the mask position with numpy : 0.0342409610748291 nb_pixel_total : 531663 time to create 1 rle with new method : 0.6407530307769775 time for calcul the mask position with numpy : 0.038320064544677734 nb_pixel_total : 8419 time to create 1 rle with old method : 0.009934186935424805 time for calcul the mask position with numpy : 0.02932906150817871 nb_pixel_total : 44716 time to create 1 rle with old method : 0.05187869071960449 time for calcul the mask position with numpy : 0.029107093811035156 nb_pixel_total : 18791 time to create 1 rle with old method : 0.021834850311279297 time for calcul the mask position with numpy : 0.029392719268798828 nb_pixel_total : 46599 time to create 1 rle with old method : 0.0545651912689209 time for calcul the mask position with numpy : 0.03266596794128418 nb_pixel_total : 15332 time to create 1 rle with old method : 0.01788949966430664 time for calcul the mask position with numpy : 0.029453039169311523 nb_pixel_total : 24316 time to create 1 rle with old method : 0.02848029136657715 time for calcul the mask position with numpy : 0.02909374237060547 nb_pixel_total : 51015 time to create 1 rle with old method : 0.059411048889160156 time for calcul the mask position with numpy : 0.028796672821044922 nb_pixel_total : 58535 time to create 1 rle with old method : 0.06933307647705078 time for calcul the mask position with numpy : 0.029303312301635742 nb_pixel_total : 42589 time to create 1 rle with old method : 0.06905889511108398 time for calcul the mask position with numpy : 0.03201627731323242 nb_pixel_total : 24412 time to create 1 rle with old method : 0.028205156326293945 time for calcul the mask position with numpy : 0.028643369674682617 nb_pixel_total : 17270 time to create 1 rle with old method : 0.02008843421936035 time for calcul the mask position with numpy : 0.02838754653930664 nb_pixel_total : 27273 time to create 1 rle with old method : 0.031386613845825195 time for calcul the mask position with numpy : 0.02882075309753418 nb_pixel_total : 28404 time to create 1 rle with old method : 0.03198528289794922 time for calcul the mask position with numpy : 0.029230594635009766 nb_pixel_total : 45055 time to create 1 rle with old method : 0.05217576026916504 time for calcul the mask position with numpy : 0.02864837646484375 nb_pixel_total : 27403 time to create 1 rle with old method : 0.03148031234741211 time for calcul the mask position with numpy : 0.02943873405456543 nb_pixel_total : 92371 time to create 1 rle with old method : 0.13303589820861816 time for calcul the mask position with numpy : 0.0295865535736084 nb_pixel_total : 9518 time to create 1 rle with old method : 0.011148691177368164 time for calcul the mask position with numpy : 0.029787540435791016 nb_pixel_total : 25396 time to create 1 rle with old method : 0.0296783447265625 time for calcul the mask position with numpy : 0.029083251953125 nb_pixel_total : 15820 time to create 1 rle with old method : 0.018733739852905273 time for calcul the mask position with numpy : 0.02960944175720215 nb_pixel_total : 148187 time to create 1 rle with old method : 0.1980729103088379 time for calcul the mask position with numpy : 0.02956223487854004 nb_pixel_total : 25022 time to create 1 rle with old method : 0.03281092643737793 time for calcul the mask position with numpy : 0.031019926071166992 nb_pixel_total : 11568 time to create 1 rle with old method : 0.013569355010986328 time for calcul the mask position with numpy : 0.030406951904296875 nb_pixel_total : 14064 time to create 1 rle with old method : 0.01640772819519043 time for calcul the mask position with numpy : 0.02960681915283203 nb_pixel_total : 20839 time to create 1 rle with old method : 0.02677464485168457 time for calcul the mask position with numpy : 0.02914142608642578 nb_pixel_total : 19499 time to create 1 rle with old method : 0.02235579490661621 time for calcul the mask position with numpy : 0.02911663055419922 nb_pixel_total : 17253 time to create 1 rle with old method : 0.02021002769470215 time for calcul the mask position with numpy : 0.02921605110168457 nb_pixel_total : 9659 time to create 1 rle with old method : 0.011209726333618164 time for calcul the mask position with numpy : 0.031748294830322266 nb_pixel_total : 190519 time to create 1 rle with new method : 0.5444345474243164 time for calcul the mask position with numpy : 0.029650211334228516 nb_pixel_total : 7644 time to create 1 rle with old method : 0.008872032165527344 time for calcul the mask position with numpy : 0.030689716339111328 nb_pixel_total : 14992 time to create 1 rle with old method : 0.017608642578125 time for calcul the mask position with numpy : 0.029204130172729492 nb_pixel_total : 18261 time to create 1 rle with old method : 0.02128148078918457 time for calcul the mask position with numpy : 0.02951216697692871 nb_pixel_total : 37822 time to create 1 rle with old method : 0.044957637786865234 time for calcul the mask position with numpy : 0.029693126678466797 nb_pixel_total : 3616 time to create 1 rle with old method : 0.004232645034790039 time for calcul the mask position with numpy : 0.029519319534301758 nb_pixel_total : 8910 time to create 1 rle with old method : 0.013826370239257812 time for calcul the mask position with numpy : 0.03296327590942383 nb_pixel_total : 14950 time to create 1 rle with old method : 0.024441242218017578 time for calcul the mask position with numpy : 0.03230619430541992 nb_pixel_total : 35297 time to create 1 rle with old method : 0.0407564640045166 time for calcul the mask position with numpy : 0.02960991859436035 nb_pixel_total : 89617 time to create 1 rle with old method : 0.10329079627990723 time for calcul the mask position with numpy : 0.029620885848999023 nb_pixel_total : 12762 time to create 1 rle with old method : 0.015129804611206055 time for calcul the mask position with numpy : 0.02920985221862793 nb_pixel_total : 20078 time to create 1 rle with old method : 0.02366924285888672 time for calcul the mask position with numpy : 0.02947235107421875 nb_pixel_total : 71412 time to create 1 rle with old method : 0.081024169921875 time for calcul the mask position with numpy : 0.02844715118408203 nb_pixel_total : 14252 time to create 1 rle with old method : 0.016119956970214844 time for calcul the mask position with numpy : 0.028295516967773438 nb_pixel_total : 15494 time to create 1 rle with old method : 0.018001794815063477 time for calcul the mask position with numpy : 0.02907586097717285 nb_pixel_total : 4556 time to create 1 rle with old method : 0.005446672439575195 time for calcul the mask position with numpy : 0.029221057891845703 nb_pixel_total : 19720 time to create 1 rle with old method : 0.02671504020690918 create new chi : 5.386836290359497 time to delete rle : 0.01794147491455078 batch 1 Loaded 93 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24279 TO DO : save crop sub photo not yet done ! save time : 1.8751754760742188 nb_obj : 68 nb_hashtags : 5 time to prepare the origin masks : 4.47741961479187 time for calcul the mask position with numpy : 0.4947381019592285 nb_pixel_total : 5093237 time to create 1 rle with new method : 0.9108099937438965 time for calcul the mask position with numpy : 0.028338909149169922 nb_pixel_total : 28841 time to create 1 rle with old method : 0.033312082290649414 time for calcul the mask position with numpy : 0.028728485107421875 nb_pixel_total : 27318 time to create 1 rle with old method : 0.032302141189575195 time for calcul the mask position with numpy : 0.032990455627441406 nb_pixel_total : 175075 time to create 1 rle with new method : 0.9341208934783936 time for calcul the mask position with numpy : 0.0287473201751709 nb_pixel_total : 78064 time to create 1 rle with old method : 0.0902853012084961 time for calcul the mask position with numpy : 0.028845548629760742 nb_pixel_total : 3325 time to create 1 rle with old method : 0.004113674163818359 time for calcul the mask position with numpy : 0.02979111671447754 nb_pixel_total : 124829 time to create 1 rle with old method : 0.14440131187438965 time for calcul the mask position with numpy : 0.028870582580566406 nb_pixel_total : 7617 time to create 1 rle with old method : 0.008847236633300781 time for calcul the mask position with numpy : 0.0287625789642334 nb_pixel_total : 4462 time to create 1 rle with old method : 0.005251169204711914 time for calcul the mask position with numpy : 0.02968740463256836 nb_pixel_total : 117920 time to create 1 rle with old method : 0.13695001602172852 time for calcul the mask position with numpy : 0.02896904945373535 nb_pixel_total : 12171 time to create 1 rle with old method : 0.014321327209472656 time for calcul the mask position with numpy : 0.028832674026489258 nb_pixel_total : 15697 time to create 1 rle with old method : 0.018651485443115234 time for calcul the mask position with numpy : 0.0288388729095459 nb_pixel_total : 8393 time to create 1 rle with old method : 0.009908676147460938 time for calcul the mask position with numpy : 0.029160022735595703 nb_pixel_total : 36185 time to create 1 rle with old method : 0.05016064643859863 time for calcul the mask position with numpy : 0.03362751007080078 nb_pixel_total : 67286 time to create 1 rle with old method : 0.08286428451538086 time for calcul the mask position with numpy : 0.03168344497680664 nb_pixel_total : 30593 time to create 1 rle with old method : 0.04064750671386719 time for calcul the mask position with numpy : 0.029721736907958984 nb_pixel_total : 71198 time to create 1 rle with old method : 0.09573912620544434 time for calcul the mask position with numpy : 0.030231952667236328 nb_pixel_total : 9765 time to create 1 rle with old method : 0.014313220977783203 time for calcul the mask position with numpy : 0.029592275619506836 nb_pixel_total : 14847 time to create 1 rle with old method : 0.017317771911621094 time for calcul the mask position with numpy : 0.02938079833984375 nb_pixel_total : 16237 time to create 1 rle with old method : 0.01924610137939453 time for calcul the mask position with numpy : 0.02962470054626465 nb_pixel_total : 33623 time to create 1 rle with old method : 0.040415048599243164 time for calcul the mask position with numpy : 0.032507896423339844 nb_pixel_total : 10262 time to create 1 rle with old method : 0.014312744140625 time for calcul the mask position with numpy : 0.031922101974487305 nb_pixel_total : 15452 time to create 1 rle with old method : 0.018830537796020508 time for calcul the mask position with numpy : 0.030241727828979492 nb_pixel_total : 11698 time to create 1 rle with old method : 0.013776302337646484 time for calcul the mask position with numpy : 0.029389142990112305 nb_pixel_total : 16871 time to create 1 rle with old method : 0.019593000411987305 time for calcul the mask position with numpy : 0.02936863899230957 nb_pixel_total : 14273 time to create 1 rle with old method : 0.016734838485717773 time for calcul the mask position with numpy : 0.02957892417907715 nb_pixel_total : 50165 time to create 1 rle with old method : 0.06454229354858398 time for calcul the mask position with numpy : 0.03129148483276367 nb_pixel_total : 12399 time to create 1 rle with old method : 0.014721870422363281 time for calcul the mask position with numpy : 0.0290679931640625 nb_pixel_total : 18777 time to create 1 rle with old method : 0.021895408630371094 time for calcul the mask position with numpy : 0.02903914451599121 nb_pixel_total : 5998 time to create 1 rle with old method : 0.008096694946289062 time for calcul the mask position with numpy : 0.030362367630004883 nb_pixel_total : 9622 time to create 1 rle with old method : 0.011139631271362305 time for calcul the mask position with numpy : 0.029068946838378906 nb_pixel_total : 13400 time to create 1 rle with old method : 0.015530824661254883 time for calcul the mask position with numpy : 0.029045581817626953 nb_pixel_total : 27925 time to create 1 rle with old method : 0.03215169906616211 time for calcul the mask position with numpy : 0.029018640518188477 nb_pixel_total : 29945 time to create 1 rle with old method : 0.03503847122192383 time for calcul the mask position with numpy : 0.037085771560668945 nb_pixel_total : 34300 time to create 1 rle with old method : 0.05823945999145508 time for calcul the mask position with numpy : 0.031047344207763672 nb_pixel_total : 15340 time to create 1 rle with old method : 0.017792940139770508 time for calcul the mask position with numpy : 0.029226064682006836 nb_pixel_total : 32651 time to create 1 rle with old method : 0.037734270095825195 time for calcul the mask position with numpy : 0.02795267105102539 nb_pixel_total : 3928 time to create 1 rle with old method : 0.004651069641113281 time for calcul the mask position with numpy : 0.028597593307495117 nb_pixel_total : 33048 time to create 1 rle with old method : 0.03775310516357422 time for calcul the mask position with numpy : 0.0284116268157959 nb_pixel_total : 26331 time to create 1 rle with old method : 0.029894590377807617 time for calcul the mask position with numpy : 0.029339313507080078 nb_pixel_total : 27773 time to create 1 rle with old method : 0.0353543758392334 time for calcul the mask position with numpy : 0.030399799346923828 nb_pixel_total : 19332 time to create 1 rle with old method : 0.022494077682495117 time for calcul the mask position with numpy : 0.029055118560791016 nb_pixel_total : 6064 time to create 1 rle with old method : 0.007174491882324219 time for calcul the mask position with numpy : 0.02933502197265625 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0016016960144042969 time for calcul the mask position with numpy : 0.02871537208557129 nb_pixel_total : 9372 time to create 1 rle with old method : 0.011028528213500977 time for calcul the mask position with numpy : 0.028857946395874023 nb_pixel_total : 2646 time to create 1 rle with old method : 0.0031824111938476562 time for calcul the mask position with numpy : 0.028801679611206055 nb_pixel_total : 9946 time to create 1 rle with old method : 0.011618614196777344 time for calcul the mask position with numpy : 0.029034137725830078 nb_pixel_total : 5277 time to create 1 rle with old method : 0.006215333938598633 time for calcul the mask position with numpy : 0.028769969940185547 nb_pixel_total : 11351 time to create 1 rle with old method : 0.012952566146850586 time for calcul the mask position with numpy : 0.028741836547851562 nb_pixel_total : 15872 time to create 1 rle with old method : 0.0183560848236084 time for calcul the mask position with numpy : 0.03114771842956543 nb_pixel_total : 296723 time to create 1 rle with new method : 1.3050205707550049 time for calcul the mask position with numpy : 0.028165340423583984 nb_pixel_total : 25857 time to create 1 rle with old method : 0.029806137084960938 time for calcul the mask position with numpy : 0.028550386428833008 nb_pixel_total : 27300 time to create 1 rle with old method : 0.03138089179992676 time for calcul the mask position with numpy : 0.028527021408081055 nb_pixel_total : 6522 time to create 1 rle with old method : 0.00769352912902832 time for calcul the mask position with numpy : 0.02832341194152832 nb_pixel_total : 46901 time to create 1 rle with old method : 0.05364561080932617 time for calcul the mask position with numpy : 0.028577566146850586 nb_pixel_total : 15276 time to create 1 rle with old method : 0.01758122444152832 time for calcul the mask position with numpy : 0.028779983520507812 nb_pixel_total : 24559 time to create 1 rle with old method : 0.028586149215698242 time for calcul the mask position with numpy : 0.029089927673339844 nb_pixel_total : 22037 time to create 1 rle with old method : 0.025981426239013672 time for calcul the mask position with numpy : 0.02962970733642578 nb_pixel_total : 8344 time to create 1 rle with old method : 0.009844064712524414 time for calcul the mask position with numpy : 0.029436111450195312 nb_pixel_total : 21480 time to create 1 rle with old method : 0.0256044864654541 time for calcul the mask position with numpy : 0.029355287551879883 nb_pixel_total : 6630 time to create 1 rle with old method : 0.007758140563964844 time for calcul the mask position with numpy : 0.029079914093017578 nb_pixel_total : 4838 time to create 1 rle with old method : 0.0057027339935302734 time for calcul the mask position with numpy : 0.029491424560546875 nb_pixel_total : 29432 time to create 1 rle with old method : 0.03420233726501465 time for calcul the mask position with numpy : 0.03131675720214844 nb_pixel_total : 716 time to create 1 rle with old method : 0.0008952617645263672 time for calcul the mask position with numpy : 0.028995037078857422 nb_pixel_total : 14342 time to create 1 rle with old method : 0.016827106475830078 time for calcul the mask position with numpy : 0.02904367446899414 nb_pixel_total : 8185 time to create 1 rle with old method : 0.009548187255859375 time for calcul the mask position with numpy : 0.029198884963989258 nb_pixel_total : 7412 time to create 1 rle with old method : 0.0086669921875 time for calcul the mask position with numpy : 0.028512954711914062 nb_pixel_total : 9181 time to create 1 rle with old method : 0.010587930679321289 time for calcul the mask position with numpy : 0.028525590896606445 nb_pixel_total : 6484 time to create 1 rle with old method : 0.007512569427490234 create new chi : 7.527769565582275 time to delete rle : 0.004025697708129883 batch 1 Loaded 137 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29083 TO DO : save crop sub photo not yet done ! save time : 2.519770860671997 nb_obj : 22 nb_hashtags : 4 time to prepare the origin masks : 8.36751675605774 time for calcul the mask position with numpy : 0.26404380798339844 nb_pixel_total : 6363885 time to create 1 rle with new method : 1.544693946838379 time for calcul the mask position with numpy : 0.021808147430419922 nb_pixel_total : 12616 time to create 1 rle with old method : 0.014701604843139648 time for calcul the mask position with numpy : 0.02195882797241211 nb_pixel_total : 9209 time to create 1 rle with old method : 0.01089024543762207 time for calcul the mask position with numpy : 0.03439807891845703 nb_pixel_total : 3664 time to create 1 rle with old method : 0.004588603973388672 time for calcul the mask position with numpy : 0.034070730209350586 nb_pixel_total : 26784 time to create 1 rle with old method : 0.03123950958251953 time for calcul the mask position with numpy : 0.03417396545410156 nb_pixel_total : 9835 time to create 1 rle with old method : 0.011498451232910156 time for calcul the mask position with numpy : 0.03435993194580078 nb_pixel_total : 25858 time to create 1 rle with old method : 0.030379056930541992 time for calcul the mask position with numpy : 0.03420734405517578 nb_pixel_total : 16902 time to create 1 rle with old method : 0.019829511642456055 time for calcul the mask position with numpy : 0.036286354064941406 nb_pixel_total : 31049 time to create 1 rle with old method : 0.05173969268798828 time for calcul the mask position with numpy : 0.03535795211791992 nb_pixel_total : 34439 time to create 1 rle with old method : 0.042070627212524414 time for calcul the mask position with numpy : 0.03491687774658203 nb_pixel_total : 111510 time to create 1 rle with old method : 0.12992334365844727 time for calcul the mask position with numpy : 0.03337359428405762 nb_pixel_total : 28313 time to create 1 rle with old method : 0.033429622650146484 time for calcul the mask position with numpy : 0.03214430809020996 nb_pixel_total : 19616 time to create 1 rle with old method : 0.022792339324951172 time for calcul the mask position with numpy : 0.021320819854736328 nb_pixel_total : 15110 time to create 1 rle with old method : 0.019489288330078125 time for calcul the mask position with numpy : 0.023107528686523438 nb_pixel_total : 24725 time to create 1 rle with old method : 0.04109358787536621 time for calcul the mask position with numpy : 0.02631211280822754 nb_pixel_total : 221047 time to create 1 rle with new method : 0.6012606620788574 time for calcul the mask position with numpy : 0.021481037139892578 nb_pixel_total : 8047 time to create 1 rle with old method : 0.00935816764831543 time for calcul the mask position with numpy : 0.021539688110351562 nb_pixel_total : 10318 time to create 1 rle with old method : 0.012119054794311523 time for calcul the mask position with numpy : 0.021462202072143555 nb_pixel_total : 25454 time to create 1 rle with old method : 0.02970433235168457 time for calcul the mask position with numpy : 0.022369384765625 nb_pixel_total : 6699 time to create 1 rle with old method : 0.008051872253417969 time for calcul the mask position with numpy : 0.02207469940185547 nb_pixel_total : 9933 time to create 1 rle with old method : 0.011965751647949219 time for calcul the mask position with numpy : 0.022226572036743164 nb_pixel_total : 11246 time to create 1 rle with old method : 0.013417482376098633 time for calcul the mask position with numpy : 0.02288055419921875 nb_pixel_total : 23981 time to create 1 rle with old method : 0.028071880340576172 create new chi : 3.6544578075408936 time to delete rle : 0.0015461444854736328 batch 1 Loaded 45 chid ids of type : 3594 +++++++++++++++++++++++++++++Number RLEs to save : 11660 TO DO : save crop sub photo not yet done ! save time : 0.8627078533172607 nb_obj : 31 nb_hashtags : 3 time to prepare the origin masks : 3.6259405612945557 time for calcul the mask position with numpy : 0.6822001934051514 nb_pixel_total : 6396541 time to create 1 rle with new method : 0.9165992736816406 time for calcul the mask position with numpy : 0.029547929763793945 nb_pixel_total : 9409 time to create 1 rle with old method : 0.011384725570678711 time for calcul the mask position with numpy : 0.030179500579833984 nb_pixel_total : 17739 time to create 1 rle with old method : 0.022240638732910156 time for calcul the mask position with numpy : 0.029658079147338867 nb_pixel_total : 20221 time to create 1 rle with old method : 0.0233461856842041 time for calcul the mask position with numpy : 0.029314756393432617 nb_pixel_total : 17754 time to create 1 rle with old method : 0.020664691925048828 time for calcul the mask position with numpy : 0.02947711944580078 nb_pixel_total : 10246 time to create 1 rle with old method : 0.012298107147216797 time for calcul the mask position with numpy : 0.02958965301513672 nb_pixel_total : 17926 time to create 1 rle with old method : 0.021482467651367188 time for calcul the mask position with numpy : 0.029267072677612305 nb_pixel_total : 112 time to create 1 rle with old method : 0.0002601146697998047 time for calcul the mask position with numpy : 0.031131267547607422 nb_pixel_total : 20341 time to create 1 rle with old method : 0.038498640060424805 time for calcul the mask position with numpy : 0.0338590145111084 nb_pixel_total : 9450 time to create 1 rle with old method : 0.012523174285888672 time for calcul the mask position with numpy : 0.043772220611572266 nb_pixel_total : 8016 time to create 1 rle with old method : 0.009466171264648438 time for calcul the mask position with numpy : 0.028875350952148438 nb_pixel_total : 9717 time to create 1 rle with old method : 0.011323690414428711 time for calcul the mask position with numpy : 0.02865433692932129 nb_pixel_total : 20659 time to create 1 rle with old method : 0.023697853088378906 time for calcul the mask position with numpy : 0.028962373733520508 nb_pixel_total : 70534 time to create 1 rle with old method : 0.08092546463012695 time for calcul the mask position with numpy : 0.028650999069213867 nb_pixel_total : 5242 time to create 1 rle with old method : 0.006174325942993164 time for calcul the mask position with numpy : 0.028338193893432617 nb_pixel_total : 42130 time to create 1 rle with old method : 0.04857778549194336 time for calcul the mask position with numpy : 0.029012203216552734 nb_pixel_total : 14130 time to create 1 rle with old method : 0.016616344451904297 time for calcul the mask position with numpy : 0.029865741729736328 nb_pixel_total : 30648 time to create 1 rle with old method : 0.036507606506347656 time for calcul the mask position with numpy : 0.028475522994995117 nb_pixel_total : 22762 time to create 1 rle with old method : 0.025829076766967773 time for calcul the mask position with numpy : 0.028209924697875977 nb_pixel_total : 62995 time to create 1 rle with old method : 0.07218337059020996 time for calcul the mask position with numpy : 0.028146982192993164 nb_pixel_total : 13637 time to create 1 rle with old method : 0.015571832656860352 time for calcul the mask position with numpy : 0.02831411361694336 nb_pixel_total : 6302 time to create 1 rle with old method : 0.00722956657409668 time for calcul the mask position with numpy : 0.027825355529785156 nb_pixel_total : 19551 time to create 1 rle with old method : 0.021575212478637695 time for calcul the mask position with numpy : 0.027523517608642578 nb_pixel_total : 14426 time to create 1 rle with old method : 0.016370773315429688 time for calcul the mask position with numpy : 0.027860164642333984 nb_pixel_total : 742 time to create 1 rle with old method : 0.0009036064147949219 time for calcul the mask position with numpy : 0.027706623077392578 nb_pixel_total : 3618 time to create 1 rle with old method : 0.004157304763793945 time for calcul the mask position with numpy : 0.028263568878173828 nb_pixel_total : 14689 time to create 1 rle with old method : 0.021573781967163086 time for calcul the mask position with numpy : 0.0328221321105957 nb_pixel_total : 46795 time to create 1 rle with old method : 0.06318855285644531 time for calcul the mask position with numpy : 0.028321266174316406 nb_pixel_total : 11333 time to create 1 rle with old method : 0.016828060150146484 time for calcul the mask position with numpy : 0.032828330993652344 nb_pixel_total : 49988 time to create 1 rle with old method : 0.06692028045654297 time for calcul the mask position with numpy : 0.027469396591186523 nb_pixel_total : 51344 time to create 1 rle with old method : 0.05620908737182617 time for calcul the mask position with numpy : 0.02810072898864746 nb_pixel_total : 11243 time to create 1 rle with old method : 0.01266789436340332 create new chi : 3.3527987003326416 time to delete rle : 0.0019571781158447266 batch 1 Loaded 63 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 13389 TO DO : save crop sub photo not yet done ! save time : 1.9650530815124512 nb_obj : 36 nb_hashtags : 4 time to prepare the origin masks : 3.8477494716644287 time for calcul the mask position with numpy : 0.5176529884338379 nb_pixel_total : 5822614 time to create 1 rle with new method : 1.6374027729034424 time for calcul the mask position with numpy : 0.029387950897216797 nb_pixel_total : 17957 time to create 1 rle with old method : 0.020558834075927734 time for calcul the mask position with numpy : 0.028077125549316406 nb_pixel_total : 47926 time to create 1 rle with old method : 0.05432462692260742 time for calcul the mask position with numpy : 0.0289766788482666 nb_pixel_total : 33757 time to create 1 rle with old method : 0.056288957595825195 time for calcul the mask position with numpy : 0.03242182731628418 nb_pixel_total : 21333 time to create 1 rle with old method : 0.026072263717651367 time for calcul the mask position with numpy : 0.028281450271606445 nb_pixel_total : 10834 time to create 1 rle with old method : 0.012378692626953125 time for calcul the mask position with numpy : 0.028112173080444336 nb_pixel_total : 23184 time to create 1 rle with old method : 0.025792837142944336 time for calcul the mask position with numpy : 0.027684926986694336 nb_pixel_total : 24062 time to create 1 rle with old method : 0.027206897735595703 time for calcul the mask position with numpy : 0.028642654418945312 nb_pixel_total : 10613 time to create 1 rle with old method : 0.012150049209594727 time for calcul the mask position with numpy : 0.028568506240844727 nb_pixel_total : 12521 time to create 1 rle with old method : 0.014644145965576172 time for calcul the mask position with numpy : 0.028285980224609375 nb_pixel_total : 18825 time to create 1 rle with old method : 0.021228551864624023 time for calcul the mask position with numpy : 0.028254032135009766 nb_pixel_total : 15954 time to create 1 rle with old method : 0.018656015396118164 time for calcul the mask position with numpy : 0.02933335304260254 nb_pixel_total : 153094 time to create 1 rle with new method : 0.5451154708862305 time for calcul the mask position with numpy : 0.02838587760925293 nb_pixel_total : 9820 time to create 1 rle with old method : 0.011368989944458008 time for calcul the mask position with numpy : 0.028343677520751953 nb_pixel_total : 55447 time to create 1 rle with old method : 0.0643925666809082 time for calcul the mask position with numpy : 0.02849555015563965 nb_pixel_total : 56296 time to create 1 rle with old method : 0.06076383590698242 time for calcul the mask position with numpy : 0.026723623275756836 nb_pixel_total : 23742 time to create 1 rle with old method : 0.02660965919494629 time for calcul the mask position with numpy : 0.02792668342590332 nb_pixel_total : 19920 time to create 1 rle with old method : 0.023059844970703125 time for calcul the mask position with numpy : 0.028733253479003906 nb_pixel_total : 19553 time to create 1 rle with old method : 0.0225830078125 time for calcul the mask position with numpy : 0.02849745750427246 nb_pixel_total : 44508 time to create 1 rle with old method : 0.051430463790893555 time for calcul the mask position with numpy : 0.02886676788330078 nb_pixel_total : 18645 time to create 1 rle with old method : 0.021596908569335938 time for calcul the mask position with numpy : 0.028972864151000977 nb_pixel_total : 18322 time to create 1 rle with old method : 0.02121591567993164 time for calcul the mask position with numpy : 0.02822399139404297 nb_pixel_total : 12275 time to create 1 rle with old method : 0.013831377029418945 time for calcul the mask position with numpy : 0.027469158172607422 nb_pixel_total : 38355 time to create 1 rle with old method : 0.04316520690917969 time for calcul the mask position with numpy : 0.02788543701171875 nb_pixel_total : 17304 time to create 1 rle with old method : 0.01971125602722168 time for calcul the mask position with numpy : 0.028252124786376953 nb_pixel_total : 51129 time to create 1 rle with old method : 0.05804920196533203 time for calcul the mask position with numpy : 0.028609514236450195 nb_pixel_total : 19642 time to create 1 rle with old method : 0.022716283798217773 time for calcul the mask position with numpy : 0.028571605682373047 nb_pixel_total : 12312 time to create 1 rle with old method : 0.014313220977783203 time for calcul the mask position with numpy : 0.02896428108215332 nb_pixel_total : 13000 time to create 1 rle with old method : 0.015330076217651367 time for calcul the mask position with numpy : 0.02863454818725586 nb_pixel_total : 43965 time to create 1 rle with old method : 0.04950451850891113 time for calcul the mask position with numpy : 0.028360605239868164 nb_pixel_total : 119872 time to create 1 rle with old method : 0.1322934627532959 time for calcul the mask position with numpy : 0.028345346450805664 nb_pixel_total : 72526 time to create 1 rle with old method : 0.08313965797424316 time for calcul the mask position with numpy : 0.02796030044555664 nb_pixel_total : 11608 time to create 1 rle with old method : 0.012903928756713867 time for calcul the mask position with numpy : 0.0276944637298584 nb_pixel_total : 60348 time to create 1 rle with old method : 0.0694725513458252 time for calcul the mask position with numpy : 0.029107093811035156 nb_pixel_total : 5320 time to create 1 rle with old method : 0.006383657455444336 time for calcul the mask position with numpy : 0.028877973556518555 nb_pixel_total : 58728 time to create 1 rle with old method : 0.0654911994934082 time for calcul the mask position with numpy : 0.027317285537719727 nb_pixel_total : 34929 time to create 1 rle with old method : 0.03814435005187988 create new chi : 5.022728681564331 time to delete rle : 0.0029726028442382812 batch 1 Loaded 73 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19791 TO DO : save crop sub photo not yet done ! save time : 1.2033543586730957 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 1.8472301959991455 time for calcul the mask position with numpy : 0.6392495632171631 nb_pixel_total : 6544242 time to create 1 rle with new method : 0.34502482414245605 time for calcul the mask position with numpy : 0.021604537963867188 nb_pixel_total : 23282 time to create 1 rle with old method : 0.026769399642944336 time for calcul the mask position with numpy : 0.021010398864746094 nb_pixel_total : 8601 time to create 1 rle with old method : 0.010168313980102539 time for calcul the mask position with numpy : 0.023973703384399414 nb_pixel_total : 449053 time to create 1 rle with new method : 0.63779616355896 time for calcul the mask position with numpy : 0.02424335479736328 nb_pixel_total : 25062 time to create 1 rle with old method : 0.03967452049255371 create new chi : 1.8458619117736816 time to delete rle : 0.0008282661437988281 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 4604 TO DO : save crop sub photo not yet done ! save time : 0.3282642364501953 nb_obj : 24 nb_hashtags : 5 time to prepare the origin masks : 8.482352018356323 time for calcul the mask position with numpy : 0.5750348567962646 nb_pixel_total : 6028981 time to create 1 rle with new method : 0.9896399974822998 time for calcul the mask position with numpy : 0.019488096237182617 nb_pixel_total : 8974 time to create 1 rle with old method : 0.009746551513671875 time for calcul the mask position with numpy : 0.02126598358154297 nb_pixel_total : 123536 time to create 1 rle with old method : 0.13540220260620117 time for calcul the mask position with numpy : 0.019806623458862305 nb_pixel_total : 20498 time to create 1 rle with old method : 0.02209019660949707 time for calcul the mask position with numpy : 0.021164655685424805 nb_pixel_total : 12510 time to create 1 rle with old method : 0.013737916946411133 time for calcul the mask position with numpy : 0.021385669708251953 nb_pixel_total : 17520 time to create 1 rle with old method : 0.01945209503173828 time for calcul the mask position with numpy : 0.021225690841674805 nb_pixel_total : 65300 time to create 1 rle with old method : 0.0729362964630127 time for calcul the mask position with numpy : 0.02050495147705078 nb_pixel_total : 7790 time to create 1 rle with old method : 0.008764505386352539 time for calcul the mask position with numpy : 0.020669221878051758 nb_pixel_total : 38641 time to create 1 rle with old method : 0.04353022575378418 time for calcul the mask position with numpy : 0.021336793899536133 nb_pixel_total : 23295 time to create 1 rle with old method : 0.025516271591186523 time for calcul the mask position with numpy : 0.02036142349243164 nb_pixel_total : 9548 time to create 1 rle with old method : 0.010881900787353516 time for calcul the mask position with numpy : 0.022473573684692383 nb_pixel_total : 36106 time to create 1 rle with old method : 0.041249990463256836 time for calcul the mask position with numpy : 0.021314144134521484 nb_pixel_total : 173670 time to create 1 rle with new method : 0.6982769966125488 time for calcul the mask position with numpy : 0.02116990089416504 nb_pixel_total : 14171 time to create 1 rle with old method : 0.016091346740722656 time for calcul the mask position with numpy : 0.021351099014282227 nb_pixel_total : 25287 time to create 1 rle with old method : 0.029152631759643555 time for calcul the mask position with numpy : 0.021726369857788086 nb_pixel_total : 8745 time to create 1 rle with old method : 0.011281728744506836 time for calcul the mask position with numpy : 0.025452375411987305 nb_pixel_total : 157135 time to create 1 rle with new method : 0.6724770069122314 time for calcul the mask position with numpy : 0.022111892700195312 nb_pixel_total : 21285 time to create 1 rle with old method : 0.024667739868164062 time for calcul the mask position with numpy : 0.022207260131835938 nb_pixel_total : 51071 time to create 1 rle with old method : 0.05783963203430176 time for calcul the mask position with numpy : 0.021796226501464844 nb_pixel_total : 30383 time to create 1 rle with old method : 0.034650325775146484 time for calcul the mask position with numpy : 0.021387815475463867 nb_pixel_total : 15896 time to create 1 rle with old method : 0.018204450607299805 time for calcul the mask position with numpy : 0.021921634674072266 nb_pixel_total : 91470 time to create 1 rle with old method : 0.10049915313720703 time for calcul the mask position with numpy : 0.020200252532958984 nb_pixel_total : 16396 time to create 1 rle with old method : 0.01754307746887207 time for calcul the mask position with numpy : 0.020374059677124023 nb_pixel_total : 15523 time to create 1 rle with old method : 0.016685009002685547 time for calcul the mask position with numpy : 0.020664453506469727 nb_pixel_total : 36509 time to create 1 rle with old method : 0.040273427963256836 create new chi : 4.297268390655518 time to delete rle : 0.001984119415283203 batch 1 Loaded 49 chid ids of type : 3594 ++++++++++++++++++++++++++++Number RLEs to save : 13174 TO DO : save crop sub photo not yet done ! save time : 0.9108719825744629 nb_obj : 12 nb_hashtags : 3 time to prepare the origin masks : 4.414781332015991 time for calcul the mask position with numpy : 0.5684771537780762 nb_pixel_total : 5904677 time to create 1 rle with new method : 1.3471243381500244 time for calcul the mask position with numpy : 0.022552013397216797 nb_pixel_total : 9652 time to create 1 rle with old method : 0.011434078216552734 time for calcul the mask position with numpy : 0.023433208465576172 nb_pixel_total : 293718 time to create 1 rle with new method : 0.8559820652008057 time for calcul the mask position with numpy : 0.021944522857666016 nb_pixel_total : 76140 time to create 1 rle with old method : 0.09296655654907227 time for calcul the mask position with numpy : 0.02199077606201172 nb_pixel_total : 32816 time to create 1 rle with old method : 0.039420127868652344 time for calcul the mask position with numpy : 0.022141695022583008 nb_pixel_total : 28295 time to create 1 rle with old method : 0.03227400779724121 time for calcul the mask position with numpy : 0.02120232582092285 nb_pixel_total : 59837 time to create 1 rle with old method : 0.07449936866760254 time for calcul the mask position with numpy : 0.02248978614807129 nb_pixel_total : 15461 time to create 1 rle with old method : 0.018361330032348633 time for calcul the mask position with numpy : 0.02680516242980957 nb_pixel_total : 122204 time to create 1 rle with old method : 0.15832805633544922 time for calcul the mask position with numpy : 0.026525020599365234 nb_pixel_total : 229185 time to create 1 rle with new method : 0.8760359287261963 time for calcul the mask position with numpy : 0.0222170352935791 nb_pixel_total : 26042 time to create 1 rle with old method : 0.030214786529541016 time for calcul the mask position with numpy : 0.02243947982788086 nb_pixel_total : 163640 time to create 1 rle with new method : 1.0284874439239502 time for calcul the mask position with numpy : 0.022928714752197266 nb_pixel_total : 88573 time to create 1 rle with old method : 0.10136961936950684 create new chi : 5.624728202819824 time to delete rle : 0.002105236053466797 batch 1 Loaded 25 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 13856 TO DO : save crop sub photo not yet done ! save time : 0.8760013580322266 nb_obj : 20 nb_hashtags : 6 time to prepare the origin masks : 8.8843252658844 time for calcul the mask position with numpy : 0.3645904064178467 nb_pixel_total : 5846190 time to create 1 rle with new method : 0.7953605651855469 time for calcul the mask position with numpy : 0.03585505485534668 nb_pixel_total : 26096 time to create 1 rle with old method : 0.030234098434448242 time for calcul the mask position with numpy : 0.038251399993896484 nb_pixel_total : 58848 time to create 1 rle with old method : 0.06825685501098633 time for calcul the mask position with numpy : 0.04022717475891113 nb_pixel_total : 22146 time to create 1 rle with old method : 0.025787830352783203 time for calcul the mask position with numpy : 0.036247968673706055 nb_pixel_total : 135288 time to create 1 rle with old method : 0.16341567039489746 time for calcul the mask position with numpy : 0.04082894325256348 nb_pixel_total : 51079 time to create 1 rle with old method : 0.06477499008178711 time for calcul the mask position with numpy : 0.03476834297180176 nb_pixel_total : 118912 time to create 1 rle with old method : 0.13735222816467285 time for calcul the mask position with numpy : 0.035004615783691406 nb_pixel_total : 18782 time to create 1 rle with old method : 0.021979331970214844 time for calcul the mask position with numpy : 0.03591656684875488 nb_pixel_total : 24672 time to create 1 rle with old method : 0.02863931655883789 time for calcul the mask position with numpy : 0.034447669982910156 nb_pixel_total : 137884 time to create 1 rle with old method : 0.16198348999023438 time for calcul the mask position with numpy : 0.026136398315429688 nb_pixel_total : 7877 time to create 1 rle with old method : 0.009232044219970703 time for calcul the mask position with numpy : 0.027963876724243164 nb_pixel_total : 11043 time to create 1 rle with old method : 0.013221502304077148 time for calcul the mask position with numpy : 0.023150205612182617 nb_pixel_total : 100529 time to create 1 rle with old method : 0.11810874938964844 time for calcul the mask position with numpy : 0.02261209487915039 nb_pixel_total : 47011 time to create 1 rle with old method : 0.07630753517150879 time for calcul the mask position with numpy : 0.02341175079345703 nb_pixel_total : 106116 time to create 1 rle with old method : 0.12281632423400879 time for calcul the mask position with numpy : 0.023037195205688477 nb_pixel_total : 56790 time to create 1 rle with old method : 0.0702970027923584 time for calcul the mask position with numpy : 0.03473544120788574 nb_pixel_total : 33902 time to create 1 rle with old method : 0.042343854904174805 time for calcul the mask position with numpy : 0.03551936149597168 nb_pixel_total : 82079 time to create 1 rle with old method : 0.09492683410644531 time for calcul the mask position with numpy : 0.03435993194580078 nb_pixel_total : 44339 time to create 1 rle with old method : 0.05196213722229004 time for calcul the mask position with numpy : 0.03551197052001953 nb_pixel_total : 85972 time to create 1 rle with old method : 0.09994888305664062 time for calcul the mask position with numpy : 0.03315854072570801 nb_pixel_total : 34685 time to create 1 rle with old method : 0.04036402702331543 create new chi : 3.291961908340454 time to delete rle : 0.002681255340576172 batch 1 Loaded 41 chid ids of type : 3594 ++++++++++++++++++++++++++++++Number RLEs to save : 15368 TO DO : save crop sub photo not yet done ! save time : 1.0104360580444336 nb_obj : 18 nb_hashtags : 4 time to prepare the origin masks : 7.667216062545776 time for calcul the mask position with numpy : 0.4488213062286377 nb_pixel_total : 5044727 time to create 1 rle with new method : 1.958857536315918 time for calcul the mask position with numpy : 0.03212308883666992 nb_pixel_total : 60742 time to create 1 rle with old method : 0.07569718360900879 time for calcul the mask position with numpy : 0.03571319580078125 nb_pixel_total : 43788 time to create 1 rle with old method : 0.05048394203186035 time for calcul the mask position with numpy : 0.03651833534240723 nb_pixel_total : 19179 time to create 1 rle with old method : 0.031152725219726562 time for calcul the mask position with numpy : 0.03992772102355957 nb_pixel_total : 29612 time to create 1 rle with old method : 0.034308671951293945 time for calcul the mask position with numpy : 0.034493446350097656 nb_pixel_total : 3173 time to create 1 rle with old method : 0.003793954849243164 time for calcul the mask position with numpy : 0.037358999252319336 nb_pixel_total : 30885 time to create 1 rle with old method : 0.03793978691101074 time for calcul the mask position with numpy : 0.03734421730041504 nb_pixel_total : 15773 time to create 1 rle with old method : 0.01851034164428711 time for calcul the mask position with numpy : 0.03518509864807129 nb_pixel_total : 26227 time to create 1 rle with old method : 0.03086400032043457 time for calcul the mask position with numpy : 0.03935861587524414 nb_pixel_total : 255599 time to create 1 rle with new method : 1.0774056911468506 time for calcul the mask position with numpy : 0.036724090576171875 nb_pixel_total : 134415 time to create 1 rle with old method : 0.1684563159942627 time for calcul the mask position with numpy : 0.03677773475646973 nb_pixel_total : 24044 time to create 1 rle with old method : 0.029437541961669922 time for calcul the mask position with numpy : 0.041481733322143555 nb_pixel_total : 31244 time to create 1 rle with old method : 0.04755234718322754 time for calcul the mask position with numpy : 0.03812551498413086 nb_pixel_total : 266983 time to create 1 rle with new method : 1.4161834716796875 time for calcul the mask position with numpy : 0.032655954360961914 nb_pixel_total : 25239 time to create 1 rle with old method : 0.028499603271484375 time for calcul the mask position with numpy : 0.031357765197753906 nb_pixel_total : 147967 time to create 1 rle with old method : 0.16513276100158691 time for calcul the mask position with numpy : 0.031737565994262695 nb_pixel_total : 130884 time to create 1 rle with old method : 0.1753087043762207 time for calcul the mask position with numpy : 0.036930084228515625 nb_pixel_total : 267788 time to create 1 rle with new method : 1.564079761505127 time for calcul the mask position with numpy : 0.039057016372680664 nb_pixel_total : 491971 time to create 1 rle with new method : 0.46434807777404785 create new chi : 8.624396324157715 time to delete rle : 0.00410914421081543 batch 1 Loaded 37 chid ids of type : 3594 ++++++++++++++++++++++++++Number RLEs to save : 16611 TO DO : save crop sub photo not yet done ! save time : 1.0611796379089355 nb_obj : 61 nb_hashtags : 4 time to prepare the origin masks : 4.31372594833374 time for calcul the mask position with numpy : 0.7052161693572998 nb_pixel_total : 5408340 time to create 1 rle with new method : 1.7034482955932617 time for calcul the mask position with numpy : 0.028978824615478516 nb_pixel_total : 4871 time to create 1 rle with old method : 0.005772590637207031 time for calcul the mask position with numpy : 0.029204130172729492 nb_pixel_total : 20428 time to create 1 rle with old method : 0.023631572723388672 time for calcul the mask position with numpy : 0.028838396072387695 nb_pixel_total : 10277 time to create 1 rle with old method : 0.012058496475219727 time for calcul the mask position with numpy : 0.028124332427978516 nb_pixel_total : 19231 time to create 1 rle with old method : 0.021724939346313477 time for calcul the mask position with numpy : 0.029013872146606445 nb_pixel_total : 9178 time to create 1 rle with old method : 0.010921239852905273 time for calcul the mask position with numpy : 0.02934885025024414 nb_pixel_total : 80153 time to create 1 rle with old method : 0.09207534790039062 time for calcul the mask position with numpy : 0.0284421443939209 nb_pixel_total : 25504 time to create 1 rle with old method : 0.02964186668395996 time for calcul the mask position with numpy : 0.02991461753845215 nb_pixel_total : 7763 time to create 1 rle with old method : 0.009014129638671875 time for calcul the mask position with numpy : 0.02944207191467285 nb_pixel_total : 35999 time to create 1 rle with old method : 0.0413360595703125 time for calcul the mask position with numpy : 0.029245615005493164 nb_pixel_total : 4140 time to create 1 rle with old method : 0.004833221435546875 time for calcul the mask position with numpy : 0.028812885284423828 nb_pixel_total : 41668 time to create 1 rle with old method : 0.04819059371948242 time for calcul the mask position with numpy : 0.031035184860229492 nb_pixel_total : 15166 time to create 1 rle with old method : 0.017474651336669922 time for calcul the mask position with numpy : 0.02907705307006836 nb_pixel_total : 6194 time to create 1 rle with old method : 0.007550239562988281 time for calcul the mask position with numpy : 0.0307769775390625 nb_pixel_total : 33046 time to create 1 rle with old method : 0.04208230972290039 time for calcul the mask position with numpy : 0.029410123825073242 nb_pixel_total : 33124 time to create 1 rle with old method : 0.03841829299926758 time for calcul the mask position with numpy : 0.029138803482055664 nb_pixel_total : 8573 time to create 1 rle with old method : 0.009920597076416016 time for calcul the mask position with numpy : 0.028815507888793945 nb_pixel_total : 8359 time to create 1 rle with old method : 0.009629964828491211 time for calcul the mask position with numpy : 0.028412580490112305 nb_pixel_total : 2779 time to create 1 rle with old method : 0.003347635269165039 time for calcul the mask position with numpy : 0.028527498245239258 nb_pixel_total : 45096 time to create 1 rle with old method : 0.05328226089477539 time for calcul the mask position with numpy : 0.02919769287109375 nb_pixel_total : 20868 time to create 1 rle with old method : 0.02463221549987793 time for calcul the mask position with numpy : 0.02841043472290039 nb_pixel_total : 12449 time to create 1 rle with old method : 0.01407766342163086 time for calcul the mask position with numpy : 0.02834320068359375 nb_pixel_total : 8786 time to create 1 rle with old method : 0.009933233261108398 time for calcul the mask position with numpy : 0.029133319854736328 nb_pixel_total : 5482 time to create 1 rle with old method : 0.006460666656494141 time for calcul the mask position with numpy : 0.029238224029541016 nb_pixel_total : 13102 time to create 1 rle with old method : 0.015378952026367188 time for calcul the mask position with numpy : 0.029175758361816406 nb_pixel_total : 22377 time to create 1 rle with old method : 0.026194095611572266 time for calcul the mask position with numpy : 0.02933192253112793 nb_pixel_total : 36608 time to create 1 rle with old method : 0.05989956855773926 time for calcul the mask position with numpy : 0.03265881538391113 nb_pixel_total : 5715 time to create 1 rle with old method : 0.0068361759185791016 time for calcul the mask position with numpy : 0.029126405715942383 nb_pixel_total : 25661 time to create 1 rle with old method : 0.029509544372558594 time for calcul the mask position with numpy : 0.02883315086364746 nb_pixel_total : 38535 time to create 1 rle with old method : 0.04669475555419922 time for calcul the mask position with numpy : 0.03328275680541992 nb_pixel_total : 10972 time to create 1 rle with old method : 0.017928600311279297 time for calcul the mask position with numpy : 0.03235483169555664 nb_pixel_total : 22283 time to create 1 rle with old method : 0.025716304779052734 time for calcul the mask position with numpy : 0.028522491455078125 nb_pixel_total : 19392 time to create 1 rle with old method : 0.02203679084777832 time for calcul the mask position with numpy : 0.03183794021606445 nb_pixel_total : 38219 time to create 1 rle with old method : 0.055756330490112305 time for calcul the mask position with numpy : 0.033539533615112305 nb_pixel_total : 18085 time to create 1 rle with old method : 0.023348569869995117 time for calcul the mask position with numpy : 0.029389619827270508 nb_pixel_total : 15030 time to create 1 rle with old method : 0.01757645606994629 time for calcul the mask position with numpy : 0.028963327407836914 nb_pixel_total : 29785 time to create 1 rle with old method : 0.034334659576416016 time for calcul the mask position with numpy : 0.0292356014251709 nb_pixel_total : 19419 time to create 1 rle with old method : 0.02208733558654785 time for calcul the mask position with numpy : 0.029268264770507812 nb_pixel_total : 43471 time to create 1 rle with old method : 0.050112009048461914 time for calcul the mask position with numpy : 0.02909255027770996 nb_pixel_total : 88851 time to create 1 rle with old method : 0.10117435455322266 time for calcul the mask position with numpy : 0.03061389923095703 nb_pixel_total : 7333 time to create 1 rle with old method : 0.008710861206054688 time for calcul the mask position with numpy : 0.030057191848754883 nb_pixel_total : 40292 time to create 1 rle with old method : 0.04677081108093262 time for calcul the mask position with numpy : 0.030588626861572266 nb_pixel_total : 108091 time to create 1 rle with old method : 0.1258409023284912 time for calcul the mask position with numpy : 0.0292360782623291 nb_pixel_total : 20832 time to create 1 rle with old method : 0.02587270736694336 time for calcul the mask position with numpy : 0.029126644134521484 nb_pixel_total : 25891 time to create 1 rle with old method : 0.029998064041137695 time for calcul the mask position with numpy : 0.0292203426361084 nb_pixel_total : 23974 time to create 1 rle with old method : 0.02815723419189453 time for calcul the mask position with numpy : 0.029340267181396484 nb_pixel_total : 28069 time to create 1 rle with old method : 0.045943260192871094 time for calcul the mask position with numpy : 0.033350467681884766 nb_pixel_total : 58422 time to create 1 rle with old method : 0.06989479064941406 time for calcul the mask position with numpy : 0.02953052520751953 nb_pixel_total : 15633 time to create 1 rle with old method : 0.018358230590820312 time for calcul the mask position with numpy : 0.029341697692871094 nb_pixel_total : 30677 time to create 1 rle with old method : 0.03524589538574219 time for calcul the mask position with numpy : 0.02913951873779297 nb_pixel_total : 77877 time to create 1 rle with old method : 0.08960938453674316 time for calcul the mask position with numpy : 0.029430866241455078 nb_pixel_total : 63284 time to create 1 rle with old method : 0.0726175308227539 time for calcul the mask position with numpy : 0.02889728546142578 nb_pixel_total : 6832 time to create 1 rle with old method : 0.008217811584472656 time for calcul the mask position with numpy : 0.029614686965942383 nb_pixel_total : 131362 time to create 1 rle with old method : 0.15297842025756836 time for calcul the mask position with numpy : 0.02884197235107422 nb_pixel_total : 17643 time to create 1 rle with old method : 0.020177125930786133 time for calcul the mask position with numpy : 0.028551578521728516 nb_pixel_total : 7688 time to create 1 rle with old method : 0.008781194686889648 time for calcul the mask position with numpy : 0.02847576141357422 nb_pixel_total : 40876 time to create 1 rle with old method : 0.047255516052246094 time for calcul the mask position with numpy : 0.03170156478881836 nb_pixel_total : 2754 time to create 1 rle with old method : 0.0033080577850341797 time for calcul the mask position with numpy : 0.0290372371673584 nb_pixel_total : 3914 time to create 1 rle with old method : 0.004662990570068359 time for calcul the mask position with numpy : 0.029878854751586914 nb_pixel_total : 6482 time to create 1 rle with old method : 0.007891654968261719 time for calcul the mask position with numpy : 0.03180575370788574 nb_pixel_total : 11755 time to create 1 rle with old method : 0.014662027359008789 time for calcul the mask position with numpy : 0.029254913330078125 nb_pixel_total : 5580 time to create 1 rle with old method : 0.006579399108886719 create new chi : 6.218061685562134 time to delete rle : 0.004999399185180664 batch 1 Loaded 123 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28322 TO DO : save crop sub photo not yet done ! save time : 1.7873222827911377 nb_obj : 67 nb_hashtags : 3 time to prepare the origin masks : 4.537168979644775 time for calcul the mask position with numpy : 1.134591817855835 nb_pixel_total : 5369179 time to create 1 rle with new method : 0.5290038585662842 time for calcul the mask position with numpy : 0.02967095375061035 nb_pixel_total : 14723 time to create 1 rle with old method : 0.017203569412231445 time for calcul the mask position with numpy : 0.029050588607788086 nb_pixel_total : 5744 time to create 1 rle with old method : 0.006742715835571289 time for calcul the mask position with numpy : 0.029036998748779297 nb_pixel_total : 5411 time to create 1 rle with old method : 0.00638270378112793 time for calcul the mask position with numpy : 0.02910137176513672 nb_pixel_total : 17750 time to create 1 rle with old method : 0.020574092864990234 time for calcul the mask position with numpy : 0.029242277145385742 nb_pixel_total : 35190 time to create 1 rle with old method : 0.04029345512390137 time for calcul the mask position with numpy : 0.032715559005737305 nb_pixel_total : 32521 time to create 1 rle with old method : 0.04165530204772949 time for calcul the mask position with numpy : 0.03232979774475098 nb_pixel_total : 11160 time to create 1 rle with old method : 0.014379262924194336 time for calcul the mask position with numpy : 0.029321908950805664 nb_pixel_total : 12171 time to create 1 rle with old method : 0.014182090759277344 time for calcul the mask position with numpy : 0.029067039489746094 nb_pixel_total : 13877 time to create 1 rle with old method : 0.019954681396484375 time for calcul the mask position with numpy : 0.031002521514892578 nb_pixel_total : 45166 time to create 1 rle with old method : 0.053153276443481445 time for calcul the mask position with numpy : 0.02957773208618164 nb_pixel_total : 59938 time to create 1 rle with old method : 0.07341480255126953 time for calcul the mask position with numpy : 0.029526472091674805 nb_pixel_total : 23252 time to create 1 rle with old method : 0.026881933212280273 time for calcul the mask position with numpy : 0.02964615821838379 nb_pixel_total : 30718 time to create 1 rle with old method : 0.03698897361755371 time for calcul the mask position with numpy : 0.030345439910888672 nb_pixel_total : 11230 time to create 1 rle with old method : 0.013721942901611328 time for calcul the mask position with numpy : 0.030417203903198242 nb_pixel_total : 24657 time to create 1 rle with old method : 0.03280234336853027 time for calcul the mask position with numpy : 0.034352779388427734 nb_pixel_total : 15109 time to create 1 rle with old method : 0.022091150283813477 time for calcul the mask position with numpy : 0.032637596130371094 nb_pixel_total : 13276 time to create 1 rle with old method : 0.015444278717041016 time for calcul the mask position with numpy : 0.029162168502807617 nb_pixel_total : 16130 time to create 1 rle with old method : 0.018770933151245117 time for calcul the mask position with numpy : 0.02992105484008789 nb_pixel_total : 40611 time to create 1 rle with old method : 0.06777334213256836 time for calcul the mask position with numpy : 0.030029296875 nb_pixel_total : 25807 time to create 1 rle with old method : 0.03078627586364746 time for calcul the mask position with numpy : 0.02917027473449707 nb_pixel_total : 27101 time to create 1 rle with old method : 0.03350496292114258 time for calcul the mask position with numpy : 0.02942681312561035 nb_pixel_total : 39811 time to create 1 rle with old method : 0.047583818435668945 time for calcul the mask position with numpy : 0.029311656951904297 nb_pixel_total : 77507 time to create 1 rle with old method : 0.10137939453125 time for calcul the mask position with numpy : 0.05285358428955078 nb_pixel_total : 15616 time to create 1 rle with old method : 0.036763668060302734 time for calcul the mask position with numpy : 0.0468907356262207 nb_pixel_total : 23179 time to create 1 rle with old method : 0.028210163116455078 time for calcul the mask position with numpy : 0.029309988021850586 nb_pixel_total : 78331 time to create 1 rle with old method : 0.0969851016998291 time for calcul the mask position with numpy : 0.02914261817932129 nb_pixel_total : 10977 time to create 1 rle with old method : 0.020409107208251953 time for calcul the mask position with numpy : 0.0333399772644043 nb_pixel_total : 21286 time to create 1 rle with old method : 0.031195402145385742 time for calcul the mask position with numpy : 0.02928018569946289 nb_pixel_total : 44331 time to create 1 rle with old method : 0.053150177001953125 time for calcul the mask position with numpy : 0.030583620071411133 nb_pixel_total : 42124 time to create 1 rle with old method : 0.05504751205444336 time for calcul the mask position with numpy : 0.029282331466674805 nb_pixel_total : 24675 time to create 1 rle with old method : 0.028946399688720703 time for calcul the mask position with numpy : 0.029696226119995117 nb_pixel_total : 23440 time to create 1 rle with old method : 0.03263378143310547 time for calcul the mask position with numpy : 0.03570723533630371 nb_pixel_total : 34725 time to create 1 rle with old method : 0.05439043045043945 time for calcul the mask position with numpy : 0.03674173355102539 nb_pixel_total : 15292 time to create 1 rle with old method : 0.02338862419128418 time for calcul the mask position with numpy : 0.03660106658935547 nb_pixel_total : 11356 time to create 1 rle with old method : 0.016816377639770508 time for calcul the mask position with numpy : 0.028786420822143555 nb_pixel_total : 10718 time to create 1 rle with old method : 0.012636184692382812 time for calcul the mask position with numpy : 0.029633760452270508 nb_pixel_total : 40244 time to create 1 rle with old method : 0.04933953285217285 time for calcul the mask position with numpy : 0.03400897979736328 nb_pixel_total : 39795 time to create 1 rle with old method : 0.06007838249206543 time for calcul the mask position with numpy : 0.03266501426696777 nb_pixel_total : 13889 time to create 1 rle with old method : 0.016396045684814453 time for calcul the mask position with numpy : 0.029767990112304688 nb_pixel_total : 5117 time to create 1 rle with old method : 0.00595855712890625 time for calcul the mask position with numpy : 0.029277801513671875 nb_pixel_total : 9583 time to create 1 rle with old method : 0.011161565780639648 time for calcul the mask position with numpy : 0.029323816299438477 nb_pixel_total : 18588 time to create 1 rle with old method : 0.021630525588989258 time for calcul the mask position with numpy : 0.029669523239135742 nb_pixel_total : 61203 time to create 1 rle with old method : 0.07419276237487793 time for calcul the mask position with numpy : 0.02967381477355957 nb_pixel_total : 11355 time to create 1 rle with old method : 0.01339101791381836 time for calcul the mask position with numpy : 0.02946329116821289 nb_pixel_total : 19540 time to create 1 rle with old method : 0.0231630802154541 time for calcul the mask position with numpy : 0.029307126998901367 nb_pixel_total : 26418 time to create 1 rle with old method : 0.03130221366882324 time for calcul the mask position with numpy : 0.02928757667541504 nb_pixel_total : 17793 time to create 1 rle with old method : 0.020879745483398438 time for calcul the mask position with numpy : 0.029449939727783203 nb_pixel_total : 13993 time to create 1 rle with old method : 0.01651167869567871 time for calcul the mask position with numpy : 0.03190469741821289 nb_pixel_total : 21881 time to create 1 rle with old method : 0.03763294219970703 time for calcul the mask position with numpy : 0.04224658012390137 nb_pixel_total : 10538 time to create 1 rle with old method : 0.012722492218017578 time for calcul the mask position with numpy : 0.0317840576171875 nb_pixel_total : 12023 time to create 1 rle with old method : 0.015196561813354492 time for calcul the mask position with numpy : 0.0295102596282959 nb_pixel_total : 6710 time to create 1 rle with old method : 0.007923126220703125 time for calcul the mask position with numpy : 0.029174089431762695 nb_pixel_total : 17670 time to create 1 rle with old method : 0.020501375198364258 time for calcul the mask position with numpy : 0.030635595321655273 nb_pixel_total : 27195 time to create 1 rle with old method : 0.04674792289733887 time for calcul the mask position with numpy : 0.03821063041687012 nb_pixel_total : 52482 time to create 1 rle with old method : 0.07167434692382812 time for calcul the mask position with numpy : 0.028858423233032227 nb_pixel_total : 3448 time to create 1 rle with old method : 0.004030942916870117 time for calcul the mask position with numpy : 0.03203606605529785 nb_pixel_total : 9868 time to create 1 rle with old method : 0.016983509063720703 time for calcul the mask position with numpy : 0.03472089767456055 nb_pixel_total : 70553 time to create 1 rle with old method : 0.10277152061462402 time for calcul the mask position with numpy : 0.029458045959472656 nb_pixel_total : 21130 time to create 1 rle with old method : 0.026317596435546875 time for calcul the mask position with numpy : 0.0314631462097168 nb_pixel_total : 27021 time to create 1 rle with old method : 0.03335404396057129 time for calcul the mask position with numpy : 0.02902388572692871 nb_pixel_total : 24110 time to create 1 rle with old method : 0.027956485748291016 time for calcul the mask position with numpy : 0.02869248390197754 nb_pixel_total : 15950 time to create 1 rle with old method : 0.018642425537109375 time for calcul the mask position with numpy : 0.03038311004638672 nb_pixel_total : 17631 time to create 1 rle with old method : 0.020667552947998047 time for calcul the mask position with numpy : 0.029312610626220703 nb_pixel_total : 10345 time to create 1 rle with old method : 0.01285696029663086 time for calcul the mask position with numpy : 0.03045797348022461 nb_pixel_total : 40409 time to create 1 rle with old method : 0.04723381996154785 time for calcul the mask position with numpy : 0.030310869216918945 nb_pixel_total : 51524 time to create 1 rle with old method : 0.06115531921386719 time for calcul the mask position with numpy : 0.03428030014038086 nb_pixel_total : 8145 time to create 1 rle with old method : 0.010215282440185547 create new chi : 5.997267961502075 time to delete rle : 0.0069065093994140625 batch 1 Loaded 135 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 31953 TO DO : save crop sub photo not yet done ! save time : 4.134092569351196 nb_obj : 26 nb_hashtags : 3 time to prepare the origin masks : 4.874963283538818 time for calcul the mask position with numpy : 0.7448585033416748 nb_pixel_total : 5304602 time to create 1 rle with new method : 0.6211602687835693 time for calcul the mask position with numpy : 0.04148435592651367 nb_pixel_total : 313588 time to create 1 rle with new method : 0.3989238739013672 time for calcul the mask position with numpy : 0.02931690216064453 nb_pixel_total : 6585 time to create 1 rle with old method : 0.007760286331176758 time for calcul the mask position with numpy : 0.030933380126953125 nb_pixel_total : 288130 time to create 1 rle with new method : 0.8877477645874023 time for calcul the mask position with numpy : 0.029971837997436523 nb_pixel_total : 20902 time to create 1 rle with old method : 0.02478957176208496 time for calcul the mask position with numpy : 0.030519962310791016 nb_pixel_total : 82214 time to create 1 rle with old method : 0.09874773025512695 time for calcul the mask position with numpy : 0.02992725372314453 nb_pixel_total : 63497 time to create 1 rle with old method : 0.0761880874633789 time for calcul the mask position with numpy : 0.029137134552001953 nb_pixel_total : 15722 time to create 1 rle with old method : 0.018439054489135742 time for calcul the mask position with numpy : 0.03167867660522461 nb_pixel_total : 60166 time to create 1 rle with old method : 0.0718536376953125 time for calcul the mask position with numpy : 0.03617453575134277 nb_pixel_total : 189910 time to create 1 rle with new method : 1.1765179634094238 time for calcul the mask position with numpy : 0.0475306510925293 nb_pixel_total : 21534 time to create 1 rle with old method : 0.02836441993713379 time for calcul the mask position with numpy : 0.033143043518066406 nb_pixel_total : 13071 time to create 1 rle with old method : 0.016950130462646484 time for calcul the mask position with numpy : 0.03018045425415039 nb_pixel_total : 6588 time to create 1 rle with old method : 0.008215904235839844 time for calcul the mask position with numpy : 0.030715227127075195 nb_pixel_total : 66812 time to create 1 rle with old method : 0.09842681884765625 time for calcul the mask position with numpy : 0.04076194763183594 nb_pixel_total : 202752 time to create 1 rle with new method : 1.3591337203979492 time for calcul the mask position with numpy : 0.03088521957397461 nb_pixel_total : 32172 time to create 1 rle with old method : 0.03754854202270508 time for calcul the mask position with numpy : 0.030905485153198242 nb_pixel_total : 112383 time to create 1 rle with old method : 0.13602089881896973 time for calcul the mask position with numpy : 0.029233694076538086 nb_pixel_total : 28986 time to create 1 rle with old method : 0.033846378326416016 time for calcul the mask position with numpy : 0.029829025268554688 nb_pixel_total : 40282 time to create 1 rle with old method : 0.04784846305847168 time for calcul the mask position with numpy : 0.03024601936340332 nb_pixel_total : 26910 time to create 1 rle with old method : 0.03681325912475586 time for calcul the mask position with numpy : 0.03060436248779297 nb_pixel_total : 43212 time to create 1 rle with old method : 0.05004143714904785 time for calcul the mask position with numpy : 0.02943277359008789 nb_pixel_total : 16903 time to create 1 rle with old method : 0.01994919776916504 time for calcul the mask position with numpy : 0.029415130615234375 nb_pixel_total : 33604 time to create 1 rle with old method : 0.039182424545288086 time for calcul the mask position with numpy : 0.02927112579345703 nb_pixel_total : 24270 time to create 1 rle with old method : 0.028316736221313477 time for calcul the mask position with numpy : 0.032961368560791016 nb_pixel_total : 8081 time to create 1 rle with old method : 0.00943756103515625 time for calcul the mask position with numpy : 0.029349803924560547 nb_pixel_total : 16074 time to create 1 rle with old method : 0.01877570152282715 time for calcul the mask position with numpy : 0.029300928115844727 nb_pixel_total : 11290 time to create 1 rle with old method : 0.01307058334350586 create new chi : 7.104643106460571 time to delete rle : 0.002821683883666992 batch 1 Loaded 53 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++Number RLEs to save : 16704 TO DO : save crop sub photo not yet done ! save time : 2.188431978225708 nb_obj : 25 nb_hashtags : 3 time to prepare the origin masks : 11.576719284057617 time for calcul the mask position with numpy : 0.8409943580627441 nb_pixel_total : 6233346 time to create 1 rle with new method : 0.8139431476593018 time for calcul the mask position with numpy : 0.033866167068481445 nb_pixel_total : 39847 time to create 1 rle with old method : 0.043725013732910156 time for calcul the mask position with numpy : 0.033087730407714844 nb_pixel_total : 17715 time to create 1 rle with old method : 0.02057480812072754 time for calcul the mask position with numpy : 0.033858537673950195 nb_pixel_total : 4843 time to create 1 rle with old method : 0.005538225173950195 time for calcul the mask position with numpy : 0.03290390968322754 nb_pixel_total : 61448 time to create 1 rle with old method : 0.06508016586303711 time for calcul the mask position with numpy : 0.036647796630859375 nb_pixel_total : 125388 time to create 1 rle with old method : 0.1623859405517578 time for calcul the mask position with numpy : 0.03900909423828125 nb_pixel_total : 3355 time to create 1 rle with old method : 0.00605010986328125 time for calcul the mask position with numpy : 0.039965152740478516 nb_pixel_total : 52007 time to create 1 rle with old method : 0.06047701835632324 time for calcul the mask position with numpy : 0.0337071418762207 nb_pixel_total : 9710 time to create 1 rle with old method : 0.011133909225463867 time for calcul the mask position with numpy : 0.034173011779785156 nb_pixel_total : 29206 time to create 1 rle with old method : 0.03343486785888672 time for calcul the mask position with numpy : 0.03285574913024902 nb_pixel_total : 6340 time to create 1 rle with old method : 0.007306575775146484 time for calcul the mask position with numpy : 0.03421521186828613 nb_pixel_total : 5098 time to create 1 rle with old method : 0.006024599075317383 time for calcul the mask position with numpy : 0.035376548767089844 nb_pixel_total : 7785 time to create 1 rle with old method : 0.009194612503051758 time for calcul the mask position with numpy : 0.037014007568359375 nb_pixel_total : 34764 time to create 1 rle with old method : 0.0413515567779541 time for calcul the mask position with numpy : 0.034644365310668945 nb_pixel_total : 8443 time to create 1 rle with old method : 0.010473966598510742 time for calcul the mask position with numpy : 0.03350329399108887 nb_pixel_total : 16944 time to create 1 rle with old method : 0.020450830459594727 time for calcul the mask position with numpy : 0.026285409927368164 nb_pixel_total : 26815 time to create 1 rle with old method : 0.03157520294189453 time for calcul the mask position with numpy : 0.022243499755859375 nb_pixel_total : 14146 time to create 1 rle with old method : 0.016231775283813477 time for calcul the mask position with numpy : 0.02172708511352539 nb_pixel_total : 16991 time to create 1 rle with old method : 0.019699811935424805 time for calcul the mask position with numpy : 0.02253437042236328 nb_pixel_total : 36111 time to create 1 rle with old method : 0.04383993148803711 time for calcul the mask position with numpy : 0.02338552474975586 nb_pixel_total : 16687 time to create 1 rle with old method : 0.019434452056884766 time for calcul the mask position with numpy : 0.02276468276977539 nb_pixel_total : 33861 time to create 1 rle with old method : 0.03904533386230469 time for calcul the mask position with numpy : 0.03095245361328125 nb_pixel_total : 18449 time to create 1 rle with old method : 0.030627012252807617 time for calcul the mask position with numpy : 0.038501739501953125 nb_pixel_total : 103107 time to create 1 rle with old method : 0.12158203125 time for calcul the mask position with numpy : 0.03549385070800781 nb_pixel_total : 16926 time to create 1 rle with old method : 0.019028663635253906 time for calcul the mask position with numpy : 0.03872275352478027 nb_pixel_total : 110908 time to create 1 rle with old method : 0.12814569473266602 create new chi : 3.470271587371826 time to delete rle : 0.00211334228515625 batch 1 Loaded 51 chid ids of type : 3594 ++++++++++++++++++++++++++++++Number RLEs to save : 12721 TO DO : save crop sub photo not yet done ! save time : 1.2109863758087158 nb_obj : 10 nb_hashtags : 1 time to prepare the origin masks : 5.6032328605651855 time for calcul the mask position with numpy : 0.8258671760559082 nb_pixel_total : 6692874 time to create 1 rle with new method : 1.1441383361816406 time for calcul the mask position with numpy : 0.022064208984375 nb_pixel_total : 11320 time to create 1 rle with old method : 0.013824939727783203 time for calcul the mask position with numpy : 0.022172212600708008 nb_pixel_total : 76944 time to create 1 rle with old method : 0.09094619750976562 time for calcul the mask position with numpy : 0.023978471755981445 nb_pixel_total : 16520 time to create 1 rle with old method : 0.019487619400024414 time for calcul the mask position with numpy : 0.023474931716918945 nb_pixel_total : 46809 time to create 1 rle with old method : 0.0550079345703125 time for calcul the mask position with numpy : 0.022375106811523438 nb_pixel_total : 35080 time to create 1 rle with old method : 0.04189753532409668 time for calcul the mask position with numpy : 0.023659467697143555 nb_pixel_total : 103821 time to create 1 rle with old method : 0.1233823299407959 time for calcul the mask position with numpy : 0.022929906845092773 nb_pixel_total : 3855 time to create 1 rle with old method : 0.004956722259521484 time for calcul the mask position with numpy : 0.024466991424560547 nb_pixel_total : 42870 time to create 1 rle with old method : 0.05034017562866211 time for calcul the mask position with numpy : 0.02244091033935547 nb_pixel_total : 8092 time to create 1 rle with old method : 0.009574651718139648 time for calcul the mask position with numpy : 0.021760225296020508 nb_pixel_total : 12055 time to create 1 rle with old method : 0.015640974044799805 create new chi : 2.666637659072876 time to delete rle : 0.0013353824615478516 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++Number RLEs to save : 7012 TO DO : save crop sub photo not yet done ! save time : 0.5145001411437988 map_output_result : {1350595105: (0.0, 'Should be the crop_list due to order', 0), 1350595100: (0.0, 'Should be the crop_list due to order', 0), 1350595098: (0.0, 'Should be the crop_list due to order', 0), 1350595092: (0.0, 'Should be the crop_list due to order', 0), 1350595000: (0.0, 'Should be the crop_list due to order', 0), 1350594997: (0.0, 'Should be the crop_list due to order', 0), 1350594994: (0.0, 'Should be the crop_list due to order', 0), 1350594917: (0.0, 'Should be the crop_list due to order', 0), 1350594839: (0.0, 'Should be the crop_list due to order', 0), 1350594822: (0.0, 'Should be the crop_list due to order', 0), 1350594566: (0.0, 'Should be the crop_list due to order', 0), 1350594240: (0.0, 'Should be the crop_list due to order', 0), 1350594232: (0.0, 'Should be the crop_list due to order', 0), 1350594226: (0.0, 'Should be the crop_list due to order', 0), 1350594216: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1350595105, 1350595100, 1350595098, 1350595092, 1350595000, 1350594997, 1350594994, 1350594917, 1350594839, 1350594822, 1350594566, 1350594240, 1350594232, 1350594226, 1350594216] Looping around the photos to save general results len do output : 15 /1350595105.Didn't retrieve data . /1350595100.Didn't retrieve data . /1350595098.Didn't retrieve data . /1350595092.Didn't retrieve data . /1350595000.Didn't retrieve data . /1350594997.Didn't retrieve data . /1350594994.Didn't retrieve data . /1350594917.Didn't retrieve data . /1350594839.Didn't retrieve data . /1350594822.Didn't retrieve data . /1350594566.Didn't retrieve data . /1350594240.Didn't retrieve data . /1350594232.Didn't retrieve data . /1350594226.Didn't retrieve data . /1350594216.Didn't retrieve data . before output type Used above Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595105', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595100', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595098', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595092', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595000', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594997', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594994', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594917', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594839', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594822', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594566', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594240', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594232', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594226', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594216', None, None, None, None, None, '2732571') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 45 time used for this insertion : 0.013946771621704102 save_final save missing photos in datou_result : time spend for datou_step_exec : 185.7402675151825 time spend to save output : 0.014663457870483398 total time spend for step 3 : 185.75493097305298 step4:ventilate_hashtags_in_portfolio Tue Apr 8 14:21:45 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 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 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 ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 22142991 get user id for portfolio 22142991 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22142991 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','pet_fonce','autre','mal_croppe','flou','environnement','papier','background','metal','pehd')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22142991 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','pet_fonce','autre','mal_croppe','flou','environnement','papier','background','metal','pehd')) AND mptpi.`min_score`=0.5 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22142991 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','pet_fonce','autre','mal_croppe','flou','environnement','papier','background','metal','pehd')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22145071,22145072,22145073,22145074,22145075,22145076,22145077,22145078,22145079,22145080,22145081?tags=carton,pet_clair,pet_fonce,autre,mal_croppe,flou,environnement,papier,background,metal,pehd Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350595105, 1350595100, 1350595098, 1350595092, 1350595000, 1350594997, 1350594994, 1350594917, 1350594839, 1350594822, 1350594566, 1350594240, 1350594232, 1350594226, 1350594216] Looping around the photos to save general results len do output : 1 /22142991. 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 ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595105', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595100', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595098', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595092', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595000', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594997', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594994', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594917', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594839', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594822', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594566', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594240', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594232', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594226', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594216', None, None, None, None, None, '2732571') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.014161348342895508 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.5554087162017822 time spend to save output : 0.014578819274902344 total time spend for step 4 : 2.5699875354766846 step5:final Tue Apr 8 14:21:48 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 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 ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1350595105: ('0.17645295290183974',), 1350595100: ('0.17645295290183974',), 1350595098: ('0.17645295290183974',), 1350595092: ('0.17645295290183974',), 1350595000: ('0.17645295290183974',), 1350594997: ('0.17645295290183974',), 1350594994: ('0.17645295290183974',), 1350594917: ('0.17645295290183974',), 1350594839: ('0.17645295290183974',), 1350594822: ('0.17645295290183974',), 1350594566: ('0.17645295290183974',), 1350594240: ('0.17645295290183974',), 1350594232: ('0.17645295290183974',), 1350594226: ('0.17645295290183974',), 1350594216: ('0.17645295290183974',)} new output for save of step final : {1350595105: ('0.17645295290183974',), 1350595100: ('0.17645295290183974',), 1350595098: ('0.17645295290183974',), 1350595092: ('0.17645295290183974',), 1350595000: ('0.17645295290183974',), 1350594997: ('0.17645295290183974',), 1350594994: ('0.17645295290183974',), 1350594917: ('0.17645295290183974',), 1350594839: ('0.17645295290183974',), 1350594822: ('0.17645295290183974',), 1350594566: ('0.17645295290183974',), 1350594240: ('0.17645295290183974',), 1350594232: ('0.17645295290183974',), 1350594226: ('0.17645295290183974',), 1350594216: ('0.17645295290183974',)} [1350595105, 1350595100, 1350595098, 1350595092, 1350595000, 1350594997, 1350594994, 1350594917, 1350594839, 1350594822, 1350594566, 1350594240, 1350594232, 1350594226, 1350594216] Looping around the photos to save general results len do output : 15 /1350595105.Didn't retrieve data . /1350595100.Didn't retrieve data . /1350595098.Didn't retrieve data . /1350595092.Didn't retrieve data . /1350595000.Didn't retrieve data . /1350594997.Didn't retrieve data . /1350594994.Didn't retrieve data . /1350594917.Didn't retrieve data . /1350594839.Didn't retrieve data . /1350594822.Didn't retrieve data . /1350594566.Didn't retrieve data . /1350594240.Didn't retrieve data . /1350594232.Didn't retrieve data . /1350594226.Didn't retrieve data . /1350594216.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595105', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595100', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595098', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595092', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595000', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594997', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594994', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594917', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594839', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594822', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594566', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594240', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594232', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594226', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594216', None, None, None, None, None, '2732571') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 45 time used for this insertion : 0.015001296997070312 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.4777381420135498 time spend to save output : 0.015445709228515625 total time spend for step 5 : 1.4931838512420654 step6:blur_detection Tue Apr 8 14:21:49 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 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 inside step blur_detection methode: ratio et variance treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051.jpg resize: (2160, 3264) 1350595105 -4.33455676613796 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7.jpg resize: (2160, 3264) 1350595100 -7.2190430805309935 treat image : temp/1744114229_1619204_1350595098_3ddbd4ead9d6eb6efe98ccd8da6c9119.jpg resize: (2160, 3264) 1350595098 -3.4647614469885113 treat image : temp/1744114229_1619204_1350595092_f59fedfb4d4b01a2c75b956a84583997.jpg resize: (2160, 3264) 1350595092 -3.9594842667982024 treat image : temp/1744114229_1619204_1350595000_87b8b89c12eb7f5c6294c1ef0ed4b618.jpg resize: (2160, 3264) 1350595000 -4.590495041527256 treat image : temp/1744114229_1619204_1350594997_d37e34b05205e7c13471f9c52daefc16.jpg resize: (2160, 3264) 1350594997 -1.6513528087359037 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11.jpg resize: (2160, 3264) 1350594994 -3.4995529324082963 treat image : temp/1744114229_1619204_1350594917_0f4458fb90fec44aba897e5d1fe7765f.jpg resize: (2160, 3264) 1350594917 -3.2981679954399854 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55.jpg resize: (2160, 3264) 1350594839 -3.7179714036359397 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d.jpg resize: (2160, 3264) 1350594822 -2.657149466640577 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e.jpg resize: (2160, 3264) 1350594566 -4.667818281547124 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702.jpg resize: (2160, 3264) 1350594240 -3.7898421578175294 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e.jpg resize: (2160, 3264) 1350594232 -4.257810220874119 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e.jpg resize: (2160, 3264) 1350594226 -2.3485083295049676 treat image : temp/1744114229_1619204_1350594216_851b6338e5b83369bf46f1a7ef05ed9c.jpg resize: (2160, 3264) 1350594216 -0.7895990378759897 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812015_0.png resize: (186, 57) 1350624492 -2.31485954223484 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811997_0.png resize: (506, 247) 1350624493 -3.002957243991985 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811988_0.png resize: (200, 129) 1350624494 -1.459012843872324 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812009_0.png resize: (308, 207) 1350624496 -2.8412571191865843 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812000_0.png resize: (174, 138) 1350624497 -2.5655939830384646 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812004_0.png resize: (93, 128) 1350624498 -2.2441633768641127 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812003_0.png resize: (136, 194) 1350624499 -2.506867840468659 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811994_0.png resize: (249, 311) 1350624500 -2.722862435249242 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812023_0.png resize: (152, 171) 1350624501 -2.872566846578013 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812002_0.png resize: (243, 386) 1350624502 -2.78845729017698 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812016_0.png resize: (114, 171) 1350624503 -2.2719929487780663 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812007_0.png resize: (179, 103) 1350624504 -2.4673989024450877 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812020_0.png resize: (61, 74) 1350624505 2.0128611942995356 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812011_0.png resize: (148, 147) 1350624506 -1.4262162078993763 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812005_0.png resize: (352, 257) 1350624507 -2.9721508670759875 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812019_0.png resize: (124, 93) 1350624508 -2.416876554683943 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811990_0.png resize: (145, 151) 1350624509 -1.6681140986852516 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812025_0.png resize: (117, 116) 1350624510 -2.0195302962866104 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812018_0.png resize: (190, 125) 1350624511 -2.5321836298222618 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811987_0.png resize: (165, 161) 1350624512 -0.6909385360742633 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811992_0.png resize: (239, 103) 1350624513 -2.4767564218520572 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812010_0.png resize: (271, 204) 1350624514 -2.376800435328432 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812030_0.png resize: (374, 482) 1350624515 -2.556821509078774 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812001_0.png resize: (206, 261) 1350624516 -2.9880573317353947 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812006_0.png resize: (76, 100) 1350624517 -2.1286780478353897 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812026_0.png resize: (332, 350) 1350624518 -2.057418172953231 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812013_0.png resize: (115, 156) 1350624519 -1.9405095924088502 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812027_0.png resize: (430, 553) 1350624520 -3.238168934776231 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811995_0.png resize: (147, 238) 1350624521 -2.7480668377907302 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812021_0.png resize: (122, 201) 1350624522 -3.114075491740011 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811999_0.png resize: (618, 449) 1350624523 -3.029968925839859 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812008_0.png resize: (292, 245) 1350624524 -3.892897762623439 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812014_0.png resize: (124, 318) 1350624526 -1.293283117285672 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811996_0.png resize: (177, 165) 1350624527 -3.7463543169380014 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811993_0.png resize: (353, 357) 1350624528 -1.4613217102897251 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812028_0.png resize: (163, 192) 1350624529 -2.1424923569360126 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812022_0.png resize: (172, 225) 1350624530 -2.6959844475471697 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811991_0.png resize: (168, 244) 1350624531 -2.5546010609921312 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750811998_0.png resize: (286, 252) 1350624532 -1.888837853750212 treat image : temp/1744114229_1619204_1350595105_092b2dbb95baba16145abbd783380051_rle_crop_3750812032_0.png resize: (326, 228) 1350624533 -3.4913827628203076 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812034_0.png resize: (510, 329) 1350624534 -3.8842279452719466 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812074_0.png resize: (137, 216) 1350624535 -3.0082721221769444 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812062_0.png resize: (204, 160) 1350624536 -3.0532860449790653 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812088_0.png resize: (131, 170) 1350624537 -2.58593509780129 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812066_0.png resize: (183, 115) 1350624538 -1.4050751441287317 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812052_0.png resize: (113, 215) 1350624539 -2.83072632096605 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812072_0.png resize: (133, 187) 1350624540 -3.064854069418608 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812050_0.png resize: (176, 211) 1350624541 -3.140860472429979 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812089_0.png resize: (427, 362) 1350624542 -4.28580506709168 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812049_0.png resize: (123, 95) 1350624543 -4.225443224385552 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812076_0.png resize: (218, 233) 1350624544 -4.985549023790636 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812073_0.png resize: (69, 73) 1350624545 -1.8987068507537772 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812051_0.png resize: (130, 148) 1350624546 -2.0009695224506636 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812035_0.png resize: (59, 114) 1350624547 -1.3460343155584709 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812037_0.png resize: (201, 209) 1350624548 -3.079292098437228 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812085_0.png resize: (131, 188) 1350624549 -5.122046534570331 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812092_0.png resize: (118, 109) 1350624550 -3.9235135379526676 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812077_0.png resize: (88, 107) 1350624551 -4.340669496994668 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812081_0.png resize: (231, 237) 1350624552 -4.776979005898513 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812095_0.png resize: (43, 52) 1350624553 -2.8904337192843066 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812090_0.png resize: (149, 142) 1350624554 -3.8490421964284685 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812065_0.png resize: (251, 215) 1350624555 -5.154724194630516 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812084_0.png resize: (72, 96) 1350624556 -2.376124632099328 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812098_0.png resize: (443, 321) 1350624557 -4.416680009820675 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812043_0.png resize: (160, 168) 1350624558 -3.9899074485217447 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812033_0.png resize: (467, 608) 1350624559 -4.102521392699234 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812078_0.png resize: (262, 236) 1350624560 -0.9140625424844491 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812070_0.png resize: (184, 284) 1350624561 -4.190827723566882 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812086_0.png resize: (231, 171) 1350624562 -2.6436337046601563 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812041_0.png resize: (118, 167) 1350624563 -4.7222518664056325 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812059_0.png resize: (346, 311) 1350624564 -5.566958423860182 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812046_0.png resize: (92, 103) 1350624565 -2.0995860147462024 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812047_0.png resize: (233, 215) 1350624566 -4.467467367752135 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812099_0.png resize: (107, 105) 1350624567 -4.292472066670829 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812082_0.png resize: (588, 697) 1350624568 -2.604484201881557 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812100_0.png resize: (58, 37) 1350624569 -1.4151183074412825 treat image : temp/1744114229_1619204_1350595100_20e735cca2195940e00682fa2f3df0d7_rle_crop_3750812039_0.png resize: (236, 280) 1350624570 -5.134829459800236 treat image : 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temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812203_0.png resize: (98, 138) 1350624671 -1.2932960095025936 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812206_0.png resize: (462, 563) 1350624672 -1.7341147998888515 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812198_0.png resize: (187, 187) 1350624673 -2.2037869001620893 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812207_0.png resize: (211, 251) 1350624674 -0.9648740064263114 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812199_0.png resize: (176, 256) 1350624675 -1.5811923227780837 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812200_0.png resize: (327, 340) 1350624676 0.490326005668057 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812214_0.png resize: (114, 163) 1350624677 -2.4890279992303412 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812208_0.png resize: (95, 138) 1350624678 -1.4748111356036708 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812209_0.png resize: (305, 124) 1350624679 -1.6173639997077458 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812201_0.png resize: (148, 340) 1350624680 -2.262677663049291 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812211_0.png resize: (96, 106) 1350624681 -0.605956515430953 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812213_0.png resize: (163, 220) 1350624682 -2.7754938314958664 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812212_0.png resize: (252, 429) 1350624683 -2.9877556261599065 treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812217_0.png resize: (129, 105) 1350624684 -3.0377245104352157 treat image : temp/1744114229_1619204_1350594917_0f4458fb90fec44aba897e5d1fe7765f_rle_crop_3750812219_0.png resize: (495, 407) 1350624685 -1.3775669829854698 treat image : temp/1744114229_1619204_1350594917_0f4458fb90fec44aba897e5d1fe7765f_rle_crop_3750812225_0.png resize: (204, 242) 1350624687 -2.137479426268485 treat image : temp/1744114229_1619204_1350594917_0f4458fb90fec44aba897e5d1fe7765f_rle_crop_3750812220_0.png resize: (206, 218) 1350624688 -1.8905006903952881 treat image : temp/1744114229_1619204_1350594917_0f4458fb90fec44aba897e5d1fe7765f_rle_crop_3750812224_0.png resize: (416, 288) 1350624689 -3.3669081683023467 treat image : temp/1744114229_1619204_1350594917_0f4458fb90fec44aba897e5d1fe7765f_rle_crop_3750812226_0.png resize: (143, 381) 1350624690 -2.1367890542966275 treat image : temp/1744114229_1619204_1350594917_0f4458fb90fec44aba897e5d1fe7765f_rle_crop_3750812218_0.png resize: (673, 358) 1350624691 -2.638470209071097 treat image : temp/1744114229_1619204_1350594917_0f4458fb90fec44aba897e5d1fe7765f_rle_crop_3750812228_0.png resize: (672, 840) 1350624692 -2.1149773290636382 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812245_0.png resize: (142, 452) 1350624693 -1.6431017453634238 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812246_0.png resize: (704, 591) 1350624694 -2.9423625402235016 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812244_0.png resize: (498, 289) 1350624695 -2.8031465332932815 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812231_0.png resize: (356, 381) 1350624696 -2.785567466554189 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812232_0.png resize: (223, 340) 1350624697 -3.4851588155234974 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812240_0.png resize: (152, 90) 1350624698 -2.9568960396582074 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812237_0.png resize: (275, 242) 1350624699 -3.5876214266550814 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812234_0.png resize: (282, 198) 1350624700 -1.7393226905202288 treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812243_0.png resize: (159, 156) 1350624701 -0.8562339626589872 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812253_0.png resize: (546, 512) 1350624702 -3.1914897405100935 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812264_0.png resize: (208, 288) 1350624703 -2.1794420908409124 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812258_0.png resize: (407, 598) 1350624704 -1.4138718479542078 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812254_0.png resize: (246, 192) 1350624705 -2.46666275418095 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812262_0.png resize: (317, 140) 1350624706 -3.0502212017204404 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812266_0.png resize: (309, 169) 1350624707 -3.26105379056077 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812267_0.png resize: (175, 504) 1350624708 -0.1939290927378359 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812251_0.png resize: (598, 837) 1350624710 -1.864518743156269 treat image : temp/1744114229_1619204_1350594822_76037bc07415a0c36605ea8df792ab9d_rle_crop_3750812263_0.png resize: (54, 79) 1350624711 0.37306839583391976 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812278_0.png resize: (188, 301) 1350624712 -2.964934802144915 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812273_0.png resize: (186, 180) 1350624713 -2.6806425260570834 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812318_0.png resize: (222, 236) 1350624714 -2.7570189595176373 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812300_0.png resize: (232, 234) 1350624715 -3.2518836265219875 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812303_0.png resize: (89, 66) 1350624716 -1.9971437092581044 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812289_0.png resize: (91, 110) 1350624717 -1.3875280677584916 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812271_0.png resize: (144, 139) 1350624718 0.8701627102370896 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812316_0.png resize: (147, 172) 1350624719 -2.4020261511522145 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812299_0.png resize: (351, 384) 1350624720 -3.1238911621029257 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812306_0.png resize: (255, 298) 1350624721 -3.3958615207769456 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812275_0.png resize: (178, 226) 1350624722 -3.259319484600726 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812276_0.png resize: (175, 182) 1350624723 -2.8212198801861907 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812294_0.png resize: (311, 321) 1350624724 -3.331533221204361 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812269_0.png resize: (282, 199) 1350624725 -1.8571374599503758 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812327_0.png resize: (63, 118) 1350624726 0.7479753569520882 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812295_0.png resize: (380, 196) 1350624727 -2.502855680681438 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812296_0.png resize: (197, 150) 1350624729 -4.600466918110814 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812319_0.png resize: (240, 213) 1350624730 -4.311196693294312 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812304_0.png resize: (157, 81) 1350624731 -0.8363284869357495 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812284_0.png resize: (389, 116) 1350624732 -3.25625122675914 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812297_0.png resize: (313, 262) 1350624733 -2.9262271698954394 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812282_0.png resize: (346, 254) 1350624735 -2.2699899918500175 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812280_0.png resize: (368, 684) 1350624736 -1.7056722874923245 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812314_0.png resize: (74, 100) 1350624737 -1.2959184613469772 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812268_0.png resize: (344, 474) 1350624738 -3.5812375420525133 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812281_0.png resize: (106, 122) 1350624739 -1.0572579312533243 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812302_0.png resize: (203, 162) 1350624740 -1.4496657760966878 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812323_0.png resize: (271, 253) 1350624741 -3.2011369665222746 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812311_0.png resize: (212, 67) 1350624743 -1.4146947613040117 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812321_0.png resize: (65, 100) 1350624744 -2.783868122181558 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812312_0.png resize: (125, 71) 1350624745 -0.09788828298492744 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812307_0.png resize: (70, 90) 1350624746 -1.3358918976833607 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812320_0.png resize: (42, 138) 1350624747 -3.6370639724502616 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812288_0.png resize: (141, 119) 1350624748 -2.847117711764948 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812272_0.png resize: (99, 152) 1350624749 -2.1965796699488367 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812325_0.png resize: (100, 139) 1350624750 -1.5892522744354882 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812315_0.png resize: (137, 113) 1350624751 -2.8380442679211044 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812310_0.png resize: (386, 383) 1350624752 -3.4256433691333896 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812301_0.png resize: (101, 148) 1350624753 -1.9463934916240224 treat image : 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temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812377_0.png resize: (122, 117) 1350624785 -0.077523927045163 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812391_0.png resize: (164, 208) 1350624786 -1.1704577319374403 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812383_0.png resize: (273, 156) 1350624788 -0.8650073767052742 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812386_0.png resize: (300, 309) 1350624789 -3.189353791438033 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812392_0.png resize: (191, 99) 1350624790 -2.3342865787892904 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812347_0.png resize: (190, 290) 1350624791 -3.782194848344481 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812352_0.png resize: (134, 111) 1350624792 -2.0664422136994363 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812353_0.png resize: (422, 488) 1350624793 -1.8861810959661562 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812366_0.png resize: (166, 55) 1350624794 -0.7629350752494838 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812378_0.png resize: (68, 91) 1350624795 -3.3268351858084575 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812385_0.png resize: (158, 173) 1350624796 -2.26107899302281 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812364_0.png resize: (206, 133) 1350624797 -1.603055184529721 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812380_0.png resize: (231, 298) 1350624798 -0.6252193655135925 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812372_0.png resize: (97, 125) 1350624799 -3.4757733571932428 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812337_0.png resize: (222, 145) 1350624800 -2.720874546202167 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812368_0.png resize: (439, 289) 1350624801 -1.9809819047537824 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812382_0.png resize: (309, 132) 1350624802 -1.9153607665950843 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812345_0.png resize: (279, 246) 1350624804 -2.6053880419372146 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812384_0.png resize: (152, 163) 1350624805 -2.1436444734669697 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812369_0.png resize: (137, 139) 1350624806 -0.340528436525001 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812346_0.png resize: (194, 370) 1350624807 -3.2087648904705404 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812379_0.png resize: (155, 106) 1350624808 -3.1908974979012843 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812371_0.png resize: (132, 230) 1350624809 -3.1432187017448525 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812329_0.png resize: (196, 203) 1350624810 -2.182733946925149 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812388_0.png resize: (368, 199) 1350624811 -4.080027507616377 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812387_0.png resize: (145, 174) 1350624812 -3.1730885890451717 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812348_0.png resize: (180, 104) 1350624813 -1.9618671814632664 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812409_0.png resize: (382, 255) 1350624814 -1.0298514504500567 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812417_0.png resize: (340, 309) 1350624815 -2.361063188712338 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812399_0.png resize: (125, 185) 1350624816 -1.9608281859638859 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812404_0.png resize: (117, 75) 1350624818 -2.6304854006794396 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812403_0.png resize: (106, 148) 1350624819 -1.8695890188445234 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812413_0.png resize: (266, 359) 1350624820 -3.5839692006781343 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812408_0.png resize: (238, 246) 1350624821 -2.112638581455171 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812410_0.png resize: (105, 166) 1350624822 -0.18584383395099763 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812400_0.png resize: (197, 316) 1350624824 -2.964123761485559 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812407_0.png resize: (142, 116) 1350624825 -2.260227619758036 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812405_0.png resize: (420, 649) 1350624826 -4.118767072793295 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812406_0.png resize: (200, 217) 1350624827 -3.510866924805301 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812398_0.png resize: (104, 106) 1350624828 -2.3054367259912905 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812416_0.png resize: (214, 209) 1350624829 -3.2920415636526505 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812415_0.png resize: (176, 487) 1350624830 -3.2916802139304915 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812396_0.png resize: (250, 107) 1350624831 -2.1426468529781437 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812411_0.png resize: (159, 167) 1350624832 -3.316219963051211 treat image : temp/1744114229_1619204_1350594232_b4f434b30589f64c36043624b355338e_rle_crop_3750812412_0.png resize: (234, 198) 1350624833 -3.0346020485009277 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812445_0.png resize: (107, 250) 1350624834 -2.327915525634098 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812440_0.png resize: (308, 331) 1350624835 -2.4578183192646477 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812427_0.png resize: (152, 172) 1350624836 -1.9015962518111422 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812437_0.png resize: (148, 47) 1350624837 2.8372143278987254 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812429_0.png resize: (156, 183) 1350624838 -3.150417459866137 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812438_0.png resize: (240, 195) 1350624839 -2.762013951847791 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812428_0.png resize: (231, 318) 1350624840 -1.4466911738249697 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812424_0.png resize: (457, 454) 1350624841 -2.508596723380445 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812435_0.png resize: (86, 136) 1350624842 -1.9029649565027655 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812446_0.png resize: (232, 273) 1350624843 -0.7096178898379729 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812430_0.png resize: (94, 187) 1350624844 -1.1443020318469088 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812433_0.png resize: (116, 94) 1350624845 -1.5868918343699046 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812431_0.png resize: (226, 152) 1350624846 -0.02882838296914778 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812422_0.png resize: (417, 381) 1350624847 -3.134371176398026 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812434_0.png resize: (238, 255) 1350624848 -1.6963510899119905 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812426_0.png resize: (208, 278) 1350624849 -2.0483039437826265 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812444_0.png resize: (148, 92) 1350624850 -1.1972579151185208 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812443_0.png resize: (277, 424) 1350624851 -3.184264514901961 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812425_0.png resize: (144, 170) 1350624852 0.20414091716116584 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812423_0.png resize: (166, 164) 1350624854 0.10305421312229267 treat image : temp/1744114229_1619204_1350594226_0b7416aba620288cabaf3a24be91a88e_rle_crop_3750812436_0.png resize: (73, 93) 1350624855 1.5049752889947157 treat image : 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temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812332_0.png resize: (194, 216) 1350624958 0.3802637651594576 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812341_0.png resize: (70, 114) 1350624959 -0.4386156964375852 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812356_0.png resize: (58, 115) 1350624960 1.3087096970749001 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812393_0.png resize: (152, 173) 1350624961 -1.5034260435549096 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812340_0.png resize: (243, 247) 1350624962 -1.7018561295227468 treat image : temp/1744114229_1619204_1350594240_3262b4dc4922ed5bbcc7a8e34cb3a702_rle_crop_3750812359_0.png resize: (144, 217) 1350624963 -2.089692649755853 treat image : 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temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812238_0.png resize: (270, 553) 1350625095 -2.6548905703511303 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812308_0.png resize: (156, 185) 1350625096 -3.63347434069423 treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812317_0.png resize: (146, 168) 1350625098 -1.6832407468709725 Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 485 time used for this insertion : 0.03406643867492676 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 485 time used for this insertion : 0.08614158630371094 save missing photos in datou_result : time spend for datou_step_exec : 63.96934199333191 time spend to save output : 0.12793612480163574 total time spend for step 6 : 64.09727811813354 step7:brightness Tue Apr 8 14:22:53 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 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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temp/1744114229_1619204_1350595098_3ddbd4ead9d6eb6efe98ccd8da6c9119_rle_crop_3750812119_0.png treat image : temp/1744114229_1619204_1350595000_87b8b89c12eb7f5c6294c1ef0ed4b618_rle_crop_3750812185_0.png treat image : temp/1744114229_1619204_1350594994_fdff88ef7f3abc016e8dca3dc6361c11_rle_crop_3750812216_0.png treat image : temp/1744114229_1619204_1350594839_2fc5c060dcc65c9c25dc16c8b999fe55_rle_crop_3750812238_0.png treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812308_0.png treat image : temp/1744114229_1619204_1350594566_b76f5f825491d5b1437100c7be5a3f1e_rle_crop_3750812317_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 485 time used for this insertion : 0.09865403175354004 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 485 time used for this insertion : 0.12517929077148438 save missing photos in datou_result : time spend for datou_step_exec : 14.75127100944519 time spend to save output : 0.23063349723815918 total time spend for step 7 : 14.98190450668335 step8:velours_tree Tue Apr 8 14:23:08 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 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.3642761707305908 time spend to save output : 4.553794860839844e-05 total time spend for step 8 : 0.3643217086791992 step9:send_mail_cod Tue Apr 8 14:23:08 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 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 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 cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P22142991_08-04-2025_14_23_09.pdf 22145071 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221450711744114989 22145072 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221450721744114990 22145073 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221450731744114991 22145074 change filename to text .change filename to text .change filename to text .change filename to text .imagette221450741744114992 22145075 imagette221450751744114992 22145076 imagette221450761744114992 22145078 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221450781744114992 22145079 imagette221450791744114993 22145080 change filename to text .change filename to text .imagette221450801744114993 22145081 change filename to text .change filename to text .change filename to text .imagette221450811744114994 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22142991 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22145071,22145072,22145073,22145074,22145075,22145076,22145077,22145078,22145079,22145080,22145081?tags=carton,pet_clair,pet_fonce,autre,mal_croppe,flou,environnement,papier,background,metal,pehd args[1350595105] : ((1350595105, -4.33455676613796, 492609224), (1350595105, -0.08538767908468543, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350595100] : ((1350595100, -7.2190430805309935, 492609224), (1350595100, -0.20122043157691022, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350595098] : ((1350595098, -3.4647614469885113, 492609224), (1350595098, -0.23868895826241293, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350595092] : ((1350595092, -3.9594842667982024, 492609224), (1350595092, -0.2746267214855876, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350595000] : ((1350595000, -4.590495041527256, 492609224), (1350595000, -0.31575107229627536, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594997] : ((1350594997, -1.6513528087359037, 492688767), (1350594997, 0.041070341275052644, 2107752395), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594994] : ((1350594994, -3.4995529324082963, 492609224), (1350594994, -0.005111523289885565, 2107752395), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594917] : ((1350594917, -3.2981679954399854, 492609224), (1350594917, 0.07861384645607143, 2107752395), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594839] : ((1350594839, -3.7179714036359397, 492609224), (1350594839, -0.1005493916727599, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594822] : ((1350594822, -2.657149466640577, 492609224), (1350594822, -0.30336082029072775, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594566] : ((1350594566, -4.667818281547124, 492609224), (1350594566, -0.09764442152581783, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594240] : ((1350594240, -3.7898421578175294, 492609224), (1350594240, -0.11944122241976773, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594232] : ((1350594232, -4.257810220874119, 492609224), (1350594232, -0.1335247417994205, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594226] : ((1350594226, -2.3485083295049676, 492609224), (1350594226, -0.17339000162465013, 496442774), '0.17645295290183974') We are sending mail with results at report@fotonower.com args[1350594216] : ((1350594216, -0.7895990378759897, 492688767), (1350594216, 0.2564943296499999, 2107752395), '0.17645295290183974') We are sending mail with results at report@fotonower.com refus_total : 0.17645295290183974 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=22142991 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350594822,1350594839,1350594917,1350594240,1350594566,1350594216,1350594226,1350594232,1350594994,1350594997,1350595000,1350595092,1350595098,1350595100,1350595105) Found this number of photos: 15 begin to download photo : 1350594822 begin to download photo : 1350594566 begin to download photo : 1350594994 begin to download photo : 1350595098 download finish for photo 1350595098 begin to download photo : 1350595100 download finish for photo 1350594994 begin to download photo : 1350594997 download finish for photo 1350594566 begin to download photo : 1350594216 download finish for photo 1350594822 begin to download photo : 1350594839 download finish for photo 1350594997 begin to download photo : 1350595000 download finish for photo 1350594216 begin to download photo : 1350594226 download finish for photo 1350595100 begin to download photo : 1350595105 download finish for photo 1350594839 begin to download photo : 1350594917 download finish for photo 1350595000 begin to download photo : 1350595092 download finish for photo 1350594226 begin to download photo : 1350594232 download finish for photo 1350595105 download finish for photo 1350594917 begin to download photo : 1350594240 download finish for photo 1350595092 download finish for photo 1350594232 download finish for photo 1350594240 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22142991_08-04-2025_14_23_09.pdf results_Auto_P22142991_08-04-2025_14_23_09.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22142991_08-04-2025_14_23_09.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3318','22142991','results_Auto_P22142991_08-04-2025_14_23_09.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22142991_08-04-2025_14_23_09.pdf','pdf','','0.98','0.17645295290183974') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22142991

https://www.fotonower.com/image?json=false&list_photos_id=1350595105
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350595100
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350595098
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350595092
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350595000
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594997
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594994
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594917
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594839
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594822
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594566
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594240
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594232
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594226
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350594216
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 17.65%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/22145071?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22145072?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22145073?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22145074?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22145078?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22145080?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22145081?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22142991_08-04-2025_14_23_09.pdf.

Lien vers velours :https://www.fotonower.com/velours/22145071,22145072,22145073,22145074,22145075,22145076,22145077,22145078,22145079,22145080,22145081?tags=carton,pet_clair,pet_fonce,autre,mal_croppe,flou,environnement,papier,background,metal,pehd.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 08 Apr 2025 12:23:19 GMT Content-Length: 0 Connection: close X-Message-Id: m9piywYiQWyRPfC2Gkfjfw Access-Control-Allow-Origin: https://sendgrid.api-docs.io Access-Control-Allow-Methods: POST Access-Control-Allow-Headers: Authorization, Content-Type, On-behalf-of, x-sg-elas-acl Access-Control-Max-Age: 600 X-No-CORS-Reason: https://sendgrid.com/docs/Classroom/Basics/API/cors.html Strict-Transport-Security: max-age=31536000; includeSubDomains Content-Security-Policy: frame-ancestors 'none' Cache-Control: no-cache X-Content-Type-Options: no-sniff Referrer-Policy: strict-origin-when-cross-origin Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1350595105, 1350595100, 1350595098, 1350595092, 1350595000, 1350594997, 1350594994, 1350594917, 1350594839, 1350594822, 1350594566, 1350594240, 1350594232, 1350594226, 1350594216] Looping around the photos to save general results len do output : 0 before output type Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595105', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595100', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595098', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595092', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595000', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594997', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594994', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594917', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594839', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594822', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594566', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594240', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594232', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594226', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594216', None, None, None, None, None, '2732571') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.017966508865356445 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.26668667793274 time spend to save output : 0.018283843994140625 total time spend for step 9 : 10.28497052192688 step10:split_time_score Tue Apr 8 14:23:19 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 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('11', 15),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 08042025 22142991 Nombre de photos uploadées : 15 / 23040 (0%) 08042025 22142991 Nombre de photos taguées (types de déchets): 0 / 15 (0%) 08042025 22142991 Nombre de photos taguées (volume) : 0 / 15 (0%) elapsed_time : load_data_split_time_score 3.0994415283203125e-06 elapsed_time : order_list_meta_photo_and_scores 9.059906005859375e-06 ??????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0006079673767089844 elapsed_time : insert_dashboard_record_day_entry 0.03043961524963379 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.07306878209413326 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22138943_08-04-2025_09_19_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22138943 order by id desc limit 1 # 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 ! 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 ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod 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 : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22138943 AND mptpi.`type`=3726 To do Qualite : 0.14044295848623672 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22142051_08-04-2025_12_03_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22142051 order by id desc limit 1 # 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 ! 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 ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod 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 : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22142051 AND mptpi.`type`=3726 To do Qualite : 0.17645295290183974 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22142991_08-04-2025_14_23_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22142991 order by id desc limit 1 # 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 ! 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 ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : 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 ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22142991 AND mptpi.`type`=3594 To do Qualite : 0.06703062722463386 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22143466_08-04-2025_13_22_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22143466 order by id desc limit 1 # 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 ! 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 ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod 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 : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22143466 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'08042025': {'nb_upload': 15, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1350595105, 1350595100, 1350595098, 1350595092, 1350595000, 1350594997, 1350594994, 1350594917, 1350594839, 1350594822, 1350594566, 1350594240, 1350594232, 1350594226, 1350594216] Looping around the photos to save general results len do output : 1 /22142991Didn'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 ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595105', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595100', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595098', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595092', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350595000', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594997', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594994', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594917', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594839', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594822', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594566', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594240', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594232', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594226', None, None, None, None, None, '2732571') ('3318', None, None, None, None, None, None, None, '2732571') ('3318', '22142991', '1350594216', None, None, None, None, None, '2732571') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.022512197494506836 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.43566370010375977 time spend to save output : 0.022840023040771484 total time spend for step 10 : 0.45850372314453125 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 15 set_done_treatment 352.80user 222.07system 12:55.03elapsed 74%CPU (0avgtext+0avgdata 8387972maxresident)k 2605784inputs+241528outputs (88808major+33298020minor)pagefaults 0swaps