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 : 306356 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 : ['2733691'] with mtr_portfolio_ids : ['22153644'] and first list_photo_ids : [] new path : /proc/306356/ 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 , BFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 11 ; length of list_pids : 11 ; length of list_args : 11 time to download the photos : 2.4788289070129395 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 Wed Apr 9 10:00:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-09 10:00:36.227125: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-09 10:00:36.259104: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-09 10:00:36.260774: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2e5c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-09 10:00:36.260800: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-09 10:00:36.264123: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-09 10:00:36.525663: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3f7b65b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-09 10:00:36.525709: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-09 10:00:36.526522: 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 2025-04-09 10:00:36.526879: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:00:36.529134: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:00:36.531382: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:00:36.531745: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:00:36.534163: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:00:36.535341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:00:36.540130: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:00:36.541610: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:00:36.541695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:00:36.542507: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 10:00:36.542523: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 10:00:36.542532: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 10:00:36.543950: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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. 2025-04-09 10:00:36.837175: 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 2025-04-09 10:00:36.837308: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:00:36.837351: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:00:36.837390: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:00:36.837428: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:00:36.837469: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:00:36.837509: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:00:36.837550: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:00:36.839951: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:00:36.841261: 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 2025-04-09 10:00:36.841288: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:00:36.841302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:00:36.841315: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:00:36.841328: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:00:36.841341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:00:36.841354: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:00:36.841367: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:00:36.842649: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:00:36.842674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 10:00:36.842682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 10:00:36.842689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 10:00:36.844065: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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 : [] 2025-04-09 10:00:47.095476: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:00:47.294384: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 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 : 11 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 : 94 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 : 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 : 94 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 : 59 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 : 40 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 : 51 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 : 70 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 : 88 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 : 81 Detection mask done ! Trying to reset tf kernel 312520 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 555 tf kernel not reseted sub process len(results) : 11 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 11 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 : 7037 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 : 0.07876443862915039 nb_pixel_total : 27065 time to create 1 rle with old method : 0.035608768463134766 length of segment : 204 time for calcul the mask position with numpy : 0.05662345886230469 nb_pixel_total : 27597 time to create 1 rle with old method : 0.03632664680480957 length of segment : 191 time for calcul the mask position with numpy : 0.2745382785797119 nb_pixel_total : 71462 time to create 1 rle with old method : 0.09295439720153809 length of segment : 399 time for calcul the mask position with numpy : 0.27292633056640625 nb_pixel_total : 86903 time to create 1 rle with old method : 0.09958839416503906 length of segment : 493 time for calcul the mask position with numpy : 0.0577850341796875 nb_pixel_total : 18119 time to create 1 rle with old method : 0.021951675415039062 length of segment : 178 time for calcul the mask position with numpy : 0.04212164878845215 nb_pixel_total : 12898 time to create 1 rle with old method : 0.028476715087890625 length of segment : 144 time for calcul the mask position with numpy : 0.06271815299987793 nb_pixel_total : 18724 time to create 1 rle with old method : 0.024738550186157227 length of segment : 233 time for calcul the mask position with numpy : 0.0241546630859375 nb_pixel_total : 5280 time to create 1 rle with old method : 0.007910013198852539 length of segment : 106 time for calcul the mask position with numpy : 0.05390501022338867 nb_pixel_total : 11598 time to create 1 rle with old method : 0.020051002502441406 length of segment : 110 time for calcul the mask position with numpy : 0.1216726303100586 nb_pixel_total : 44177 time to create 1 rle with old method : 0.05297732353210449 length of segment : 280 time for calcul the mask position with numpy : 0.1614975929260254 nb_pixel_total : 66537 time to create 1 rle with old method : 0.07744026184082031 length of segment : 379 time for calcul the mask position with numpy : 0.05330681800842285 nb_pixel_total : 14938 time to create 1 rle with old method : 0.018779993057250977 length of segment : 104 time for calcul the mask position with numpy : 0.06502771377563477 nb_pixel_total : 24945 time to create 1 rle with old method : 0.03367471694946289 length of segment : 135 time for calcul the mask position with numpy : 0.17427825927734375 nb_pixel_total : 66151 time to create 1 rle with old method : 0.10254335403442383 length of segment : 446 time for calcul the mask position with numpy : 0.0363461971282959 nb_pixel_total : 14626 time to create 1 rle with old method : 0.022509098052978516 length of segment : 207 time for calcul the mask position with numpy : 0.07587933540344238 nb_pixel_total : 22625 time to create 1 rle with old method : 0.03006434440612793 length of segment : 220 time for calcul the mask position with numpy : 0.057665348052978516 nb_pixel_total : 20872 time to create 1 rle with old method : 0.029370784759521484 length of segment : 220 time for calcul the mask position with numpy : 0.062406301498413086 nb_pixel_total : 16849 time to create 1 rle with old method : 0.022909879684448242 length of segment : 170 time for calcul the mask position with numpy : 0.0840754508972168 nb_pixel_total : 15793 time to create 1 rle with old method : 0.04275822639465332 length of segment : 172 time for calcul the mask position with numpy : 0.10062074661254883 nb_pixel_total : 34690 time to create 1 rle with old method : 0.05624675750732422 length of segment : 282 time for calcul the mask position with numpy : 0.06775164604187012 nb_pixel_total : 38367 time to create 1 rle with old method : 0.062059879302978516 length of segment : 204 time for calcul the mask position with numpy : 0.05654191970825195 nb_pixel_total : 28872 time to create 1 rle with old method : 0.0387575626373291 length of segment : 216 time for calcul the mask position with numpy : 0.03357505798339844 nb_pixel_total : 19903 time to create 1 rle with old method : 0.03126835823059082 length of segment : 145 time for calcul the mask position with numpy : 0.029272079467773438 nb_pixel_total : 8980 time to create 1 rle with old method : 0.015831708908081055 length of segment : 149 time for calcul the mask position with numpy : 0.03996586799621582 nb_pixel_total : 10000 time to create 1 rle with old method : 0.015393495559692383 length of segment : 133 time for calcul the mask position with numpy : 0.03293728828430176 nb_pixel_total : 10247 time to create 1 rle with old method : 0.016892433166503906 length of segment : 115 time for calcul the mask position with numpy : 0.17179107666015625 nb_pixel_total : 70080 time to create 1 rle with old method : 0.07967090606689453 length of segment : 298 time for calcul the mask position with numpy : 0.09908628463745117 nb_pixel_total : 26731 time to create 1 rle with old method : 0.03256106376647949 length of segment : 236 time for calcul the mask position with numpy : 0.03685712814331055 nb_pixel_total : 15272 time to create 1 rle with old method : 0.01937079429626465 length of segment : 92 time for calcul the mask position with numpy : 0.11443352699279785 nb_pixel_total : 34537 time to create 1 rle with old method : 0.04206228256225586 length of segment : 519 time for calcul the mask position with numpy : 0.011521100997924805 nb_pixel_total : 36419 time to create 1 rle with old method : 0.04077577590942383 length of segment : 331 time for calcul the mask position with numpy : 0.11623954772949219 nb_pixel_total : 45366 time to create 1 rle with old method : 0.05399203300476074 length of segment : 324 time for calcul the mask position with numpy : 0.11727094650268555 nb_pixel_total : 33149 time to create 1 rle with old method : 0.04205060005187988 length of segment : 244 time for calcul the mask position with numpy : 0.02328348159790039 nb_pixel_total : 10462 time to create 1 rle with old method : 0.01606607437133789 length of segment : 88 time for calcul the mask position with numpy : 0.019672870635986328 nb_pixel_total : 29918 time to create 1 rle with old method : 0.03754568099975586 length of segment : 211 time for calcul the mask position with numpy : 0.028341054916381836 nb_pixel_total : 9726 time to create 1 rle with old method : 0.013778448104858398 length of segment : 118 time for calcul the mask position with numpy : 0.05489826202392578 nb_pixel_total : 16580 time to create 1 rle with old method : 0.020440101623535156 length of segment : 226 time for calcul the mask position with numpy : 0.04023432731628418 nb_pixel_total : 13662 time to create 1 rle with old method : 0.01976156234741211 length of segment : 183 time for calcul the mask position with numpy : 0.048127174377441406 nb_pixel_total : 41456 time to create 1 rle with old method : 0.05043506622314453 length of segment : 225 time for calcul the mask position with numpy : 0.08544492721557617 nb_pixel_total : 59960 time to create 1 rle with old method : 0.06665611267089844 length of segment : 316 time for calcul the mask position with numpy : 0.03186917304992676 nb_pixel_total : 6002 time to create 1 rle with old method : 0.008916616439819336 length of segment : 107 time for calcul the mask position with numpy : 0.024489879608154297 nb_pixel_total : 15243 time to create 1 rle with old method : 0.02386474609375 length of segment : 203 time for calcul the mask position with numpy : 0.00443577766418457 nb_pixel_total : 6611 time to create 1 rle with old method : 0.00991964340209961 length of segment : 63 time for calcul the mask position with numpy : 0.02674388885498047 nb_pixel_total : 22602 time to create 1 rle with old method : 0.027868986129760742 length of segment : 167 time for calcul the mask position with numpy : 0.08452081680297852 nb_pixel_total : 48436 time to create 1 rle with old method : 0.05716419219970703 length of segment : 274 time for calcul the mask position with numpy : 0.0179443359375 nb_pixel_total : 16514 time to create 1 rle with old method : 0.021683931350708008 length of segment : 169 time for calcul the mask position with numpy : 0.03168296813964844 nb_pixel_total : 17869 time to create 1 rle with old method : 0.022492170333862305 length of segment : 168 time for calcul the mask position with numpy : 0.03292989730834961 nb_pixel_total : 13177 time to create 1 rle with old method : 0.018787384033203125 length of segment : 161 time for calcul the mask position with numpy : 0.1771092414855957 nb_pixel_total : 89802 time to create 1 rle with old method : 0.10968685150146484 length of segment : 601 time for calcul the mask position with numpy : 0.03600716590881348 nb_pixel_total : 22945 time to create 1 rle with old method : 0.02904486656188965 length of segment : 161 time for calcul the mask position with numpy : 0.05177450180053711 nb_pixel_total : 17245 time to create 1 rle with old method : 0.025109291076660156 length of segment : 180 time for calcul the mask position with numpy : 0.0040814876556396484 nb_pixel_total : 12079 time to create 1 rle with old method : 0.015855789184570312 length of segment : 153 time for calcul the mask position with numpy : 0.06476998329162598 nb_pixel_total : 31142 time to create 1 rle with old method : 0.04106497764587402 length of segment : 143 time for calcul the mask position with numpy : 0.07962393760681152 nb_pixel_total : 22660 time to create 1 rle with old method : 0.03118300437927246 length of segment : 141 time for calcul the mask position with numpy : 0.05538344383239746 nb_pixel_total : 10790 time to create 1 rle with old method : 0.019719362258911133 length of segment : 151 time for calcul the mask position with numpy : 0.1672074794769287 nb_pixel_total : 122401 time to create 1 rle with old method : 0.17488646507263184 length of segment : 242 time for calcul the mask position with numpy : 0.119781494140625 nb_pixel_total : 37696 time to create 1 rle with old method : 0.046845436096191406 length of segment : 260 time for calcul the mask position with numpy : 0.028522491455078125 nb_pixel_total : 9020 time to create 1 rle with old method : 0.013846158981323242 length of segment : 127 time for calcul the mask position with numpy : 0.06428265571594238 nb_pixel_total : 17518 time to create 1 rle with old method : 0.024501562118530273 length of segment : 159 time for calcul the mask position with numpy : 0.06238389015197754 nb_pixel_total : 10513 time to create 1 rle with old method : 0.015511751174926758 length of segment : 151 time for calcul the mask position with numpy : 0.15325284004211426 nb_pixel_total : 52674 time to create 1 rle with old method : 0.05861067771911621 length of segment : 418 time for calcul the mask position with numpy : 0.12050461769104004 nb_pixel_total : 21808 time to create 1 rle with old method : 0.028064727783203125 length of segment : 388 time for calcul the mask position with numpy : 0.21556878089904785 nb_pixel_total : 80377 time to create 1 rle with old method : 0.09493279457092285 length of segment : 480 time for calcul the mask position with numpy : 0.08040642738342285 nb_pixel_total : 21218 time to create 1 rle with old method : 0.029316425323486328 length of segment : 165 time for calcul the mask position with numpy : 0.19795823097229004 nb_pixel_total : 81614 time to create 1 rle with old method : 0.0990455150604248 length of segment : 471 time for calcul the mask position with numpy : 0.05121469497680664 nb_pixel_total : 10311 time to create 1 rle with old method : 0.01852107048034668 length of segment : 195 time for calcul the mask position with numpy : 0.1413111686706543 nb_pixel_total : 54552 time to create 1 rle with old method : 0.06566691398620605 length of segment : 328 time for calcul the mask position with numpy : 0.0177459716796875 nb_pixel_total : 6982 time to create 1 rle with old method : 0.011564254760742188 length of segment : 74 time for calcul the mask position with numpy : 0.2692530155181885 nb_pixel_total : 115375 time to create 1 rle with old method : 0.1434776782989502 length of segment : 583 time for calcul the mask position with numpy : 0.053314208984375 nb_pixel_total : 15104 time to create 1 rle with old method : 0.01948261260986328 length of segment : 154 time for calcul the mask position with numpy : 0.16451478004455566 nb_pixel_total : 65070 time to create 1 rle with old method : 0.07591652870178223 length of segment : 232 time for calcul the mask position with numpy : 0.03137803077697754 nb_pixel_total : 29816 time to create 1 rle with old method : 0.03845548629760742 length of segment : 271 time for calcul the mask position with numpy : 0.007913589477539062 nb_pixel_total : 5251 time to create 1 rle with old method : 0.007680177688598633 length of segment : 88 time for calcul the mask position with numpy : 0.06297135353088379 nb_pixel_total : 37306 time to create 1 rle with old method : 0.043459177017211914 length of segment : 304 time for calcul the mask position with numpy : 0.12175941467285156 nb_pixel_total : 20721 time to create 1 rle with old method : 0.026271820068359375 length of segment : 232 time for calcul the mask position with numpy : 0.08163118362426758 nb_pixel_total : 37238 time to create 1 rle with old method : 0.05128836631774902 length of segment : 311 time for calcul the mask position with numpy : 0.023738861083984375 nb_pixel_total : 9818 time to create 1 rle with old method : 0.015194892883300781 length of segment : 80 time for calcul the mask position with numpy : 0.03546786308288574 nb_pixel_total : 8588 time to create 1 rle with old method : 0.009856700897216797 length of segment : 136 time for calcul the mask position with numpy : 0.019485950469970703 nb_pixel_total : 23026 time to create 1 rle with old method : 0.03129744529724121 length of segment : 224 time for calcul the mask position with numpy : 0.035637617111206055 nb_pixel_total : 23945 time to create 1 rle with old method : 0.02847766876220703 length of segment : 196 time for calcul the mask position with numpy : 0.07272005081176758 nb_pixel_total : 48261 time to create 1 rle with old method : 0.06071901321411133 length of segment : 330 time for calcul the mask position with numpy : 0.1644129753112793 nb_pixel_total : 38039 time to create 1 rle with old method : 0.04505753517150879 length of segment : 325 time for calcul the mask position with numpy : 0.04809165000915527 nb_pixel_total : 30705 time to create 1 rle with old method : 0.03588461875915527 length of segment : 350 time for calcul the mask position with numpy : 0.19288921356201172 nb_pixel_total : 108748 time to create 1 rle with old method : 0.1162879467010498 length of segment : 361 time for calcul the mask position with numpy : 0.04747128486633301 nb_pixel_total : 12226 time to create 1 rle with old method : 0.018556833267211914 length of segment : 110 time for calcul the mask position with numpy : 0.042813777923583984 nb_pixel_total : 15042 time to create 1 rle with old method : 0.02066326141357422 length of segment : 171 time for calcul the mask position with numpy : 0.033745765686035156 nb_pixel_total : 7550 time to create 1 rle with old method : 0.013059139251708984 length of segment : 98 time for calcul the mask position with numpy : 0.028552532196044922 nb_pixel_total : 10379 time to create 1 rle with old method : 0.016698360443115234 length of segment : 115 time for calcul the mask position with numpy : 0.013118267059326172 nb_pixel_total : 10076 time to create 1 rle with old method : 0.014299154281616211 length of segment : 110 time for calcul the mask position with numpy : 0.33467841148376465 nb_pixel_total : 113187 time to create 1 rle with old method : 0.12328362464904785 length of segment : 484 time for calcul the mask position with numpy : 0.020128488540649414 nb_pixel_total : 16199 time to create 1 rle with old method : 0.02077937126159668 length of segment : 196 time for calcul the mask position with numpy : 0.07718610763549805 nb_pixel_total : 31862 time to create 1 rle with old method : 0.04194498062133789 length of segment : 202 time for calcul the mask position with numpy : 0.04247641563415527 nb_pixel_total : 26854 time to create 1 rle with old method : 0.037053585052490234 length of segment : 185 time for calcul the mask position with numpy : 0.009117603302001953 nb_pixel_total : 18418 time to create 1 rle with old method : 0.02549600601196289 length of segment : 135 time for calcul the mask position with numpy : 0.06555771827697754 nb_pixel_total : 14887 time to create 1 rle with old method : 0.022318601608276367 length of segment : 207 time for calcul the mask position with numpy : 0.0897216796875 nb_pixel_total : 37477 time to create 1 rle with old method : 0.04366350173950195 length of segment : 242 time for calcul the mask position with numpy : 0.014683961868286133 nb_pixel_total : 27340 time to create 1 rle with old method : 0.03310680389404297 length of segment : 363 time for calcul the mask position with numpy : 0.11150360107421875 nb_pixel_total : 44626 time to create 1 rle with old method : 0.05257439613342285 length of segment : 238 time for calcul the mask position with numpy : 0.013679981231689453 nb_pixel_total : 4929 time to create 1 rle with old method : 0.010180473327636719 length of segment : 78 time for calcul the mask position with numpy : 0.0006489753723144531 nb_pixel_total : 8631 time to create 1 rle with old method : 0.010439395904541016 length of segment : 297 time for calcul the mask position with numpy : 0.0016562938690185547 nb_pixel_total : 20923 time to create 1 rle with old method : 0.023372411727905273 length of segment : 289 time for calcul the mask position with numpy : 0.017026185989379883 nb_pixel_total : 4440 time to create 1 rle with old method : 0.008976221084594727 length of segment : 73 time for calcul the mask position with numpy : 0.03562164306640625 nb_pixel_total : 24355 time to create 1 rle with old method : 0.031071186065673828 length of segment : 260 time for calcul the mask position with numpy : 0.03817319869995117 nb_pixel_total : 16273 time to create 1 rle with old method : 0.020958900451660156 length of segment : 159 time for calcul the mask position with numpy : 0.06296896934509277 nb_pixel_total : 37540 time to create 1 rle with old method : 0.04649639129638672 length of segment : 167 time for calcul the mask position with numpy : 0.05978870391845703 nb_pixel_total : 30023 time to create 1 rle with old method : 0.042406558990478516 length of segment : 184 time for calcul the mask position with numpy : 0.05976057052612305 nb_pixel_total : 15696 time to create 1 rle with old method : 0.0461421012878418 length of segment : 178 time for calcul the mask position with numpy : 0.9490022659301758 nb_pixel_total : 437832 time to create 1 rle with new method : 0.6434264183044434 length of segment : 1258 time for calcul the mask position with numpy : 0.48293209075927734 nb_pixel_total : 582061 time to create 1 rle with new method : 0.4188873767852783 length of segment : 813 time for calcul the mask position with numpy : 0.08701944351196289 nb_pixel_total : 39580 time to create 1 rle with old method : 0.05308198928833008 length of segment : 298 time for calcul the mask position with numpy : 0.0396726131439209 nb_pixel_total : 90373 time to create 1 rle with old method : 0.11324787139892578 length of segment : 673 time for calcul the mask position with numpy : 0.17378687858581543 nb_pixel_total : 384200 time to create 1 rle with new method : 0.03625822067260742 length of segment : 1389 time for calcul the mask position with numpy : 0.009892940521240234 nb_pixel_total : 19661 time to create 1 rle with old method : 0.027448177337646484 length of segment : 128 time for calcul the mask position with numpy : 0.0684967041015625 nb_pixel_total : 47916 time to create 1 rle with old method : 0.06270742416381836 length of segment : 362 time for calcul the mask position with numpy : 0.0006973743438720703 nb_pixel_total : 27778 time to create 1 rle with old method : 0.03393220901489258 length of segment : 232 time for calcul the mask position with numpy : 0.0013875961303710938 nb_pixel_total : 39406 time to create 1 rle with old method : 0.049887657165527344 length of segment : 188 time for calcul the mask position with numpy : 0.07478737831115723 nb_pixel_total : 57622 time to create 1 rle with old method : 0.07233500480651855 length of segment : 227 time for calcul the mask position with numpy : 0.16463971138000488 nb_pixel_total : 319512 time to create 1 rle with new method : 0.014898538589477539 length of segment : 873 time for calcul the mask position with numpy : 0.0012090206146240234 nb_pixel_total : 50570 time to create 1 rle with old method : 0.0579066276550293 length of segment : 326 time for calcul the mask position with numpy : 0.04361081123352051 nb_pixel_total : 39285 time to create 1 rle with old method : 0.05079936981201172 length of segment : 326 time for calcul the mask position with numpy : 0.0002028942108154297 nb_pixel_total : 8444 time to create 1 rle with old method : 0.01004791259765625 length of segment : 101 time for calcul the mask position with numpy : 0.07100462913513184 nb_pixel_total : 152741 time to create 1 rle with new method : 0.01063990592956543 length of segment : 693 time for calcul the mask position with numpy : 0.009951114654541016 nb_pixel_total : 147561 time to create 1 rle with old method : 0.1703178882598877 length of segment : 439 time for calcul the mask position with numpy : 0.01756763458251953 nb_pixel_total : 132906 time to create 1 rle with old method : 0.1593306064605713 length of segment : 478 time for calcul the mask position with numpy : 0.0019526481628417969 nb_pixel_total : 25481 time to create 1 rle with old method : 0.031133651733398438 length of segment : 244 time for calcul the mask position with numpy : 0.0021963119506835938 nb_pixel_total : 7344 time to create 1 rle with old method : 0.00841379165649414 length of segment : 134 time for calcul the mask position with numpy : 0.000377655029296875 nb_pixel_total : 7076 time to create 1 rle with old method : 0.009250640869140625 length of segment : 58 time for calcul the mask position with numpy : 0.010601282119750977 nb_pixel_total : 43026 time to create 1 rle with old method : 0.056458234786987305 length of segment : 241 time for calcul the mask position with numpy : 0.0010325908660888672 nb_pixel_total : 8181 time to create 1 rle with old method : 0.009702444076538086 length of segment : 117 time for calcul the mask position with numpy : 0.008944272994995117 nb_pixel_total : 101545 time to create 1 rle with old method : 0.11995220184326172 length of segment : 436 time for calcul the mask position with numpy : 0.0015189647674560547 nb_pixel_total : 23279 time to create 1 rle with old method : 0.026489973068237305 length of segment : 142 time for calcul the mask position with numpy : 0.009691715240478516 nb_pixel_total : 144791 time to create 1 rle with old method : 0.16281676292419434 length of segment : 803 time for calcul the mask position with numpy : 0.0014624595642089844 nb_pixel_total : 17026 time to create 1 rle with old method : 0.01932835578918457 length of segment : 151 time for calcul the mask position with numpy : 0.002669811248779297 nb_pixel_total : 21780 time to create 1 rle with old method : 0.025053024291992188 length of segment : 173 time for calcul the mask position with numpy : 0.0036988258361816406 nb_pixel_total : 48796 time to create 1 rle with old method : 0.055365562438964844 length of segment : 243 time for calcul the mask position with numpy : 0.00048828125 nb_pixel_total : 7285 time to create 1 rle with old method : 0.008828163146972656 length of segment : 70 time for calcul the mask position with numpy : 0.0005140304565429688 nb_pixel_total : 6886 time to create 1 rle with old method : 0.008236408233642578 length of segment : 107 time for calcul the mask position with numpy : 0.0023283958435058594 nb_pixel_total : 34510 time to create 1 rle with old method : 0.03881478309631348 length of segment : 217 time for calcul the mask position with numpy : 0.0062105655670166016 nb_pixel_total : 60622 time to create 1 rle with old method : 0.06627202033996582 length of segment : 330 time for calcul the mask position with numpy : 0.01234292984008789 nb_pixel_total : 51620 time to create 1 rle with old method : 0.06871676445007324 length of segment : 286 time for calcul the mask position with numpy : 0.0003020763397216797 nb_pixel_total : 15023 time to create 1 rle with old method : 0.017272472381591797 length of segment : 80 time for calcul the mask position with numpy : 0.0029938220977783203 nb_pixel_total : 61034 time to create 1 rle with old method : 0.06849312782287598 length of segment : 317 time for calcul the mask position with numpy : 0.0019447803497314453 nb_pixel_total : 10744 time to create 1 rle with old method : 0.012199163436889648 length of segment : 139 time for calcul the mask position with numpy : 0.01555943489074707 nb_pixel_total : 292802 time to create 1 rle with new method : 0.020568370819091797 length of segment : 603 time for calcul the mask position with numpy : 0.012823343276977539 nb_pixel_total : 73048 time to create 1 rle with old method : 0.08492350578308105 length of segment : 347 time for calcul the mask position with numpy : 0.004918813705444336 nb_pixel_total : 16147 time to create 1 rle with old method : 0.021074771881103516 length of segment : 178 time for calcul the mask position with numpy : 0.006087303161621094 nb_pixel_total : 71314 time to create 1 rle with old method : 0.08127212524414062 length of segment : 302 time for calcul the mask position with numpy : 0.0015454292297363281 nb_pixel_total : 15991 time to create 1 rle with old method : 0.018218278884887695 length of segment : 143 time for calcul the mask position with numpy : 0.003701925277709961 nb_pixel_total : 45518 time to create 1 rle with old method : 0.05223536491394043 length of segment : 402 time for calcul the mask position with numpy : 0.000988006591796875 nb_pixel_total : 14650 time to create 1 rle with old method : 0.02156853675842285 length of segment : 133 time for calcul the mask position with numpy : 0.015447854995727539 nb_pixel_total : 105273 time to create 1 rle with old method : 0.1276395320892334 length of segment : 379 time for calcul the mask position with numpy : 0.004950761795043945 nb_pixel_total : 41870 time to create 1 rle with old method : 0.05295157432556152 length of segment : 187 time for calcul the mask position with numpy : 0.0052607059478759766 nb_pixel_total : 39924 time to create 1 rle with old method : 0.051412343978881836 length of segment : 288 time for calcul the mask position with numpy : 0.0010113716125488281 nb_pixel_total : 11735 time to create 1 rle with old method : 0.01336216926574707 length of segment : 204 time for calcul the mask position with numpy : 0.0013003349304199219 nb_pixel_total : 7690 time to create 1 rle with old method : 0.009000301361083984 length of segment : 240 time for calcul the mask position with numpy : 0.00792551040649414 nb_pixel_total : 95819 time to create 1 rle with old method : 0.11474919319152832 length of segment : 255 time for calcul the mask position with numpy : 0.01603412628173828 nb_pixel_total : 222123 time to create 1 rle with new method : 0.022171974182128906 length of segment : 768 time for calcul the mask position with numpy : 0.011471986770629883 nb_pixel_total : 96593 time to create 1 rle with old method : 0.13117170333862305 length of segment : 545 time for calcul the mask position with numpy : 0.0016026496887207031 nb_pixel_total : 12080 time to create 1 rle with old method : 0.01888751983642578 length of segment : 163 time for calcul the mask position with numpy : 0.0005612373352050781 nb_pixel_total : 20323 time to create 1 rle with old method : 0.023116588592529297 length of segment : 158 time for calcul the mask position with numpy : 0.02364802360534668 nb_pixel_total : 386624 time to create 1 rle with new method : 0.022830724716186523 length of segment : 944 time for calcul the mask position with numpy : 0.0012514591217041016 nb_pixel_total : 36581 time to create 1 rle with old method : 0.04667091369628906 length of segment : 205 time for calcul the mask position with numpy : 0.0038299560546875 nb_pixel_total : 50302 time to create 1 rle with old method : 0.06995487213134766 length of segment : 389 time for calcul the mask position with numpy : 0.010356664657592773 nb_pixel_total : 261758 time to create 1 rle with new method : 0.01455545425415039 length of segment : 434 time for calcul the mask position with numpy : 0.005615711212158203 nb_pixel_total : 70743 time to create 1 rle with old method : 0.0805666446685791 length of segment : 375 time for calcul the mask position with numpy : 0.01458120346069336 nb_pixel_total : 230758 time to create 1 rle with new method : 0.013251781463623047 length of segment : 348 time for calcul the mask position with numpy : 0.002547025680541992 nb_pixel_total : 34147 time to create 1 rle with old method : 0.03834104537963867 length of segment : 325 time for calcul the mask position with numpy : 0.0034208297729492188 nb_pixel_total : 74923 time to create 1 rle with old method : 0.08408260345458984 length of segment : 382 time for calcul the mask position with numpy : 0.003595113754272461 nb_pixel_total : 63524 time to create 1 rle with old method : 0.07509469985961914 length of segment : 306 time for calcul the mask position with numpy : 0.0015010833740234375 nb_pixel_total : 34281 time to create 1 rle with old method : 0.039925575256347656 length of segment : 228 time for calcul the mask position with numpy : 0.0008563995361328125 nb_pixel_total : 15589 time to create 1 rle with old method : 0.018830537796020508 length of segment : 159 time for calcul the mask position with numpy : 0.0025310516357421875 nb_pixel_total : 62154 time to create 1 rle with old method : 0.07518553733825684 length of segment : 231 time for calcul the mask position with numpy : 0.0036962032318115234 nb_pixel_total : 55813 time to create 1 rle with old method : 0.0768280029296875 length of segment : 268 time for calcul the mask position with numpy : 0.012380599975585938 nb_pixel_total : 126248 time to create 1 rle with old method : 0.17660117149353027 length of segment : 523 time for calcul the mask position with numpy : 0.0029480457305908203 nb_pixel_total : 85605 time to create 1 rle with old method : 0.09626603126525879 length of segment : 350 time for calcul the mask position with numpy : 0.002496480941772461 nb_pixel_total : 76628 time to create 1 rle with old method : 0.1046149730682373 length of segment : 390 time for calcul the mask position with numpy : 0.0025038719177246094 nb_pixel_total : 44487 time to create 1 rle with old method : 0.04980611801147461 length of segment : 269 time for calcul the mask position with numpy : 0.003225088119506836 nb_pixel_total : 36973 time to create 1 rle with old method : 0.041372060775756836 length of segment : 243 time for calcul the mask position with numpy : 0.0028836727142333984 nb_pixel_total : 55759 time to create 1 rle with old method : 0.0649869441986084 length of segment : 348 time for calcul the mask position with numpy : 0.005793333053588867 nb_pixel_total : 111219 time to create 1 rle with old method : 0.12148118019104004 length of segment : 299 time for calcul the mask position with numpy : 0.0005104541778564453 nb_pixel_total : 10680 time to create 1 rle with old method : 0.012477397918701172 length of segment : 115 time for calcul the mask position with numpy : 0.0006115436553955078 nb_pixel_total : 12288 time to create 1 rle with old method : 0.013872146606445312 length of segment : 152 time for calcul the mask position with numpy : 0.0038738250732421875 nb_pixel_total : 86392 time to create 1 rle with old method : 0.09470295906066895 length of segment : 493 time for calcul the mask position with numpy : 0.0034542083740234375 nb_pixel_total : 32676 time to create 1 rle with old method : 0.03534245491027832 length of segment : 239 time for calcul the mask position with numpy : 0.0031147003173828125 nb_pixel_total : 49944 time to create 1 rle with old method : 0.05536484718322754 length of segment : 353 time for calcul the mask position with numpy : 0.0012445449829101562 nb_pixel_total : 28539 time to create 1 rle with old method : 0.031116247177124023 length of segment : 293 time for calcul the mask position with numpy : 0.0021924972534179688 nb_pixel_total : 63948 time to create 1 rle with old method : 0.07062053680419922 length of segment : 311 time for calcul the mask position with numpy : 0.0007183551788330078 nb_pixel_total : 26154 time to create 1 rle with old method : 0.0299530029296875 length of segment : 191 time for calcul the mask position with numpy : 0.0013382434844970703 nb_pixel_total : 42875 time to create 1 rle with old method : 0.04788398742675781 length of segment : 181 time for calcul the mask position with numpy : 0.0006020069122314453 nb_pixel_total : 13971 time to create 1 rle with old method : 0.015355825424194336 length of segment : 132 time for calcul the mask position with numpy : 0.0011413097381591797 nb_pixel_total : 30279 time to create 1 rle with old method : 0.03332781791687012 length of segment : 220 time for calcul the mask position with numpy : 0.001127481460571289 nb_pixel_total : 16149 time to create 1 rle with old method : 0.020000934600830078 length of segment : 222 time for calcul the mask position with numpy : 0.0074193477630615234 nb_pixel_total : 222069 time to create 1 rle with new method : 0.011755704879760742 length of segment : 474 time for calcul the mask position with numpy : 0.0008747577667236328 nb_pixel_total : 17149 time to create 1 rle with old method : 0.018080711364746094 length of segment : 204 time for calcul the mask position with numpy : 0.004268646240234375 nb_pixel_total : 106611 time to create 1 rle with old method : 0.11658430099487305 length of segment : 388 time for calcul the mask position with numpy : 0.0014388561248779297 nb_pixel_total : 33574 time to create 1 rle with old method : 0.038103342056274414 length of segment : 187 time for calcul the mask position with numpy : 0.0023353099822998047 nb_pixel_total : 64433 time to create 1 rle with old method : 0.07104206085205078 length of segment : 436 time for calcul the mask position with numpy : 0.0073773860931396484 nb_pixel_total : 195972 time to create 1 rle with new method : 0.008758306503295898 length of segment : 461 time for calcul the mask position with numpy : 0.001367807388305664 nb_pixel_total : 28943 time to create 1 rle with old method : 0.032570600509643555 length of segment : 235 time for calcul the mask position with numpy : 0.0013580322265625 nb_pixel_total : 30979 time to create 1 rle with old method : 0.034149885177612305 length of segment : 264 time for calcul the mask position with numpy : 0.0005452632904052734 nb_pixel_total : 11562 time to create 1 rle with old method : 0.013412714004516602 length of segment : 107 time for calcul the mask position with numpy : 0.0020444393157958984 nb_pixel_total : 64692 time to create 1 rle with old method : 0.07166194915771484 length of segment : 208 time for calcul the mask position with numpy : 0.0006990432739257812 nb_pixel_total : 16687 time to create 1 rle with old method : 0.019104957580566406 length of segment : 145 time for calcul the mask position with numpy : 0.0011036396026611328 nb_pixel_total : 29370 time to create 1 rle with old method : 0.032750844955444336 length of segment : 288 time for calcul the mask position with numpy : 0.0009548664093017578 nb_pixel_total : 23162 time to create 1 rle with old method : 0.025362730026245117 length of segment : 216 time for calcul the mask position with numpy : 0.0040340423583984375 nb_pixel_total : 131576 time to create 1 rle with old method : 0.16504716873168945 length of segment : 378 time for calcul the mask position with numpy : 0.0024645328521728516 nb_pixel_total : 74524 time to create 1 rle with old method : 0.08301281929016113 length of segment : 365 time for calcul the mask position with numpy : 0.0009920597076416016 nb_pixel_total : 37550 time to create 1 rle with old method : 0.042243242263793945 length of segment : 148 time for calcul the mask position with numpy : 0.005405902862548828 nb_pixel_total : 134455 time to create 1 rle with old method : 0.15071344375610352 length of segment : 892 time for calcul the mask position with numpy : 0.002068042755126953 nb_pixel_total : 46495 time to create 1 rle with old method : 0.05265331268310547 length of segment : 366 time for calcul the mask position with numpy : 0.0003848075866699219 nb_pixel_total : 15891 time to create 1 rle with old method : 0.01823902130126953 length of segment : 199 time for calcul the mask position with numpy : 0.003043651580810547 nb_pixel_total : 69495 time to create 1 rle with old method : 0.09435486793518066 length of segment : 388 time for calcul the mask position with numpy : 0.0009021759033203125 nb_pixel_total : 28673 time to create 1 rle with old method : 0.03188943862915039 length of segment : 257 time for calcul the mask position with numpy : 0.0046460628509521484 nb_pixel_total : 218791 time to create 1 rle with new method : 0.010574817657470703 length of segment : 497 time for calcul the mask position with numpy : 0.0011019706726074219 nb_pixel_total : 26799 time to create 1 rle with old method : 0.029848575592041016 length of segment : 196 time for calcul the mask position with numpy : 0.0010023117065429688 nb_pixel_total : 17035 time to create 1 rle with old method : 0.019776105880737305 length of segment : 196 time for calcul the mask position with numpy : 0.0010297298431396484 nb_pixel_total : 19325 time to create 1 rle with old method : 0.022819042205810547 length of segment : 170 time for calcul the mask position with numpy : 0.0006742477416992188 nb_pixel_total : 11371 time to create 1 rle with old method : 0.020424842834472656 length of segment : 69 time for calcul the mask position with numpy : 0.008783102035522461 nb_pixel_total : 203201 time to create 1 rle with new method : 0.009971380233764648 length of segment : 407 time for calcul the mask position with numpy : 0.0010907649993896484 nb_pixel_total : 22188 time to create 1 rle with old method : 0.027675390243530273 length of segment : 177 time for calcul the mask position with numpy : 0.006262540817260742 nb_pixel_total : 50738 time to create 1 rle with old method : 0.07309222221374512 length of segment : 296 time for calcul the mask position with numpy : 0.006032466888427734 nb_pixel_total : 84522 time to create 1 rle with old method : 0.10467648506164551 length of segment : 589 time for calcul the mask position with numpy : 0.0015401840209960938 nb_pixel_total : 34694 time to create 1 rle with old method : 0.043511152267456055 length of segment : 140 time for calcul the mask position with numpy : 0.021979570388793945 nb_pixel_total : 222585 time to create 1 rle with new method : 0.17092204093933105 length of segment : 727 time for calcul the mask position with numpy : 0.0021080970764160156 nb_pixel_total : 46936 time to create 1 rle with old method : 0.05398726463317871 length of segment : 360 time for calcul the mask position with numpy : 0.0019600391387939453 nb_pixel_total : 48207 time to create 1 rle with old method : 0.053315162658691406 length of segment : 264 time for calcul the mask position with numpy : 0.002804994583129883 nb_pixel_total : 49569 time to create 1 rle with old method : 0.05514788627624512 length of segment : 367 time for calcul the mask position with numpy : 0.002184629440307617 nb_pixel_total : 59959 time to create 1 rle with old method : 0.07151293754577637 length of segment : 534 time for calcul the mask position with numpy : 0.012597322463989258 nb_pixel_total : 399709 time to create 1 rle with new method : 0.03585672378540039 length of segment : 828 time for calcul the mask position with numpy : 0.0007159709930419922 nb_pixel_total : 21714 time to create 1 rle with old method : 0.02438044548034668 length of segment : 197 time for calcul the mask position with numpy : 0.0009546279907226562 nb_pixel_total : 13968 time to create 1 rle with old method : 0.016003131866455078 length of segment : 141 time for calcul the mask position with numpy : 0.005606412887573242 nb_pixel_total : 168556 time to create 1 rle with new method : 0.00874185562133789 length of segment : 440 time for calcul the mask position with numpy : 0.019347667694091797 nb_pixel_total : 704282 time to create 1 rle with new method : 0.046625375747680664 length of segment : 1180 time for calcul the mask position with numpy : 0.0011551380157470703 nb_pixel_total : 49069 time to create 1 rle with old method : 0.05402112007141113 length of segment : 276 time for calcul the mask position with numpy : 0.0007221698760986328 nb_pixel_total : 20888 time to create 1 rle with old method : 0.024530887603759766 length of segment : 171 time for calcul the mask position with numpy : 0.0013916492462158203 nb_pixel_total : 33245 time to create 1 rle with old method : 0.038411855697631836 length of segment : 255 time for calcul the mask position with numpy : 0.0013179779052734375 nb_pixel_total : 40539 time to create 1 rle with old method : 0.048248291015625 length of segment : 356 time for calcul the mask position with numpy : 0.005840778350830078 nb_pixel_total : 136155 time to create 1 rle with old method : 0.15729498863220215 length of segment : 495 time for calcul the mask position with numpy : 0.014589309692382812 nb_pixel_total : 480776 time to create 1 rle with new method : 0.055985212326049805 length of segment : 1438 time for calcul the mask position with numpy : 0.0004897117614746094 nb_pixel_total : 15994 time to create 1 rle with old method : 0.018851757049560547 length of segment : 196 time for calcul the mask position with numpy : 0.0005211830139160156 nb_pixel_total : 7741 time to create 1 rle with old method : 0.00905156135559082 length of segment : 115 time for calcul the mask position with numpy : 0.0026731491088867188 nb_pixel_total : 55369 time to create 1 rle with old method : 0.061487674713134766 length of segment : 226 time for calcul the mask position with numpy : 0.001766204833984375 nb_pixel_total : 27258 time to create 1 rle with old method : 0.03086543083190918 length of segment : 251 time for calcul the mask position with numpy : 0.0031676292419433594 nb_pixel_total : 77768 time to create 1 rle with old method : 0.08705329895019531 length of segment : 419 time for calcul the mask position with numpy : 0.0013031959533691406 nb_pixel_total : 30118 time to create 1 rle with old method : 0.036958932876586914 length of segment : 175 time for calcul the mask position with numpy : 0.0005297660827636719 nb_pixel_total : 8966 time to create 1 rle with old method : 0.011147260665893555 length of segment : 113 time for calcul the mask position with numpy : 0.0033767223358154297 nb_pixel_total : 52402 time to create 1 rle with old method : 0.061043739318847656 length of segment : 407 time for calcul the mask position with numpy : 0.0016148090362548828 nb_pixel_total : 29124 time to create 1 rle with old method : 0.03912639617919922 length of segment : 214 time for calcul the mask position with numpy : 0.0010385513305664062 nb_pixel_total : 19437 time to create 1 rle with old method : 0.023078441619873047 length of segment : 205 time for calcul the mask position with numpy : 0.001033782958984375 nb_pixel_total : 18649 time to create 1 rle with old method : 0.02174544334411621 length of segment : 147 time for calcul the mask position with numpy : 0.0006802082061767578 nb_pixel_total : 9633 time to create 1 rle with old method : 0.011292695999145508 length of segment : 214 time for calcul the mask position with numpy : 0.0010840892791748047 nb_pixel_total : 22586 time to create 1 rle with old method : 0.026133298873901367 length of segment : 200 time for calcul the mask position with numpy : 0.0004868507385253906 nb_pixel_total : 9786 time to create 1 rle with old method : 0.011649847030639648 length of segment : 87 time for calcul the mask position with numpy : 0.003988742828369141 nb_pixel_total : 80190 time to create 1 rle with old method : 0.09409379959106445 length of segment : 431 time for calcul the mask position with numpy : 0.0011992454528808594 nb_pixel_total : 24144 time to create 1 rle with old method : 0.02715778350830078 length of segment : 202 time for calcul the mask position with numpy : 0.0004677772521972656 nb_pixel_total : 17061 time to create 1 rle with old method : 0.019565343856811523 length of segment : 181 time for calcul the mask position with numpy : 0.001138925552368164 nb_pixel_total : 30469 time to create 1 rle with old method : 0.035852909088134766 length of segment : 236 time for calcul the mask position with numpy : 0.0005571842193603516 nb_pixel_total : 10802 time to create 1 rle with old method : 0.017785072326660156 length of segment : 127 time for calcul the mask position with numpy : 0.0008108615875244141 nb_pixel_total : 17447 time to create 1 rle with old method : 0.033014774322509766 length of segment : 131 time for calcul the mask position with numpy : 0.0003497600555419922 nb_pixel_total : 4467 time to create 1 rle with old method : 0.005365848541259766 length of segment : 103 time for calcul the mask position with numpy : 0.0006105899810791016 nb_pixel_total : 9641 time to create 1 rle with old method : 0.011220216751098633 length of segment : 139 time for calcul the mask position with numpy : 0.0003147125244140625 nb_pixel_total : 5829 time to create 1 rle with old method : 0.006836652755737305 length of segment : 100 time for calcul the mask position with numpy : 0.0006234645843505859 nb_pixel_total : 13275 time to create 1 rle with old method : 0.016024351119995117 length of segment : 92 time for calcul the mask position with numpy : 0.0012586116790771484 nb_pixel_total : 24864 time to create 1 rle with old method : 0.031067609786987305 length of segment : 273 time for calcul the mask position with numpy : 0.0011444091796875 nb_pixel_total : 22152 time to create 1 rle with old method : 0.03250765800476074 length of segment : 246 time for calcul the mask position with numpy : 0.0006134510040283203 nb_pixel_total : 15600 time to create 1 rle with old method : 0.017705440521240234 length of segment : 229 time for calcul the mask position with numpy : 0.004431009292602539 nb_pixel_total : 151916 time to create 1 rle with new method : 0.009386777877807617 length of segment : 992 time for calcul the mask position with numpy : 0.0015056133270263672 nb_pixel_total : 39839 time to create 1 rle with old method : 0.04459190368652344 length of segment : 270 time for calcul the mask position with numpy : 0.0003895759582519531 nb_pixel_total : 18213 time to create 1 rle with old method : 0.02067279815673828 length of segment : 169 time for calcul the mask position with numpy : 0.006277799606323242 nb_pixel_total : 120488 time to create 1 rle with old method : 0.13592267036437988 length of segment : 604 time for calcul the mask position with numpy : 0.00039958953857421875 nb_pixel_total : 8086 time to create 1 rle with old method : 0.009254217147827148 length of segment : 221 time for calcul the mask position with numpy : 0.004807710647583008 nb_pixel_total : 95578 time to create 1 rle with old method : 0.10914731025695801 length of segment : 433 time for calcul the mask position with numpy : 0.0028526782989501953 nb_pixel_total : 53191 time to create 1 rle with old method : 0.061974525451660156 length of segment : 310 time for calcul the mask position with numpy : 0.004087686538696289 nb_pixel_total : 74074 time to create 1 rle with old method : 0.08489346504211426 length of segment : 381 time for calcul the mask position with numpy : 0.0017232894897460938 nb_pixel_total : 30425 time to create 1 rle with old method : 0.0346376895904541 length of segment : 299 time for calcul the mask position with numpy : 0.0010421276092529297 nb_pixel_total : 15970 time to create 1 rle with old method : 0.018314838409423828 length of segment : 263 time for calcul the mask position with numpy : 0.001241445541381836 nb_pixel_total : 19966 time to create 1 rle with old method : 0.023190975189208984 length of segment : 178 time for calcul the mask position with numpy : 0.0014896392822265625 nb_pixel_total : 22507 time to create 1 rle with old method : 0.025612354278564453 length of segment : 231 time for calcul the mask position with numpy : 0.00040650367736816406 nb_pixel_total : 9938 time to create 1 rle with old method : 0.011511802673339844 length of segment : 136 time for calcul the mask position with numpy : 0.0017783641815185547 nb_pixel_total : 25442 time to create 1 rle with old method : 0.029775142669677734 length of segment : 151 time for calcul the mask position with numpy : 0.001828908920288086 nb_pixel_total : 32699 time to create 1 rle with old method : 0.03728342056274414 length of segment : 172 time for calcul the mask position with numpy : 0.0003581047058105469 nb_pixel_total : 5987 time to create 1 rle with old method : 0.007063627243041992 length of segment : 109 time for calcul the mask position with numpy : 0.001733541488647461 nb_pixel_total : 27858 time to create 1 rle with old method : 0.03161931037902832 length of segment : 227 time for calcul the mask position with numpy : 0.0010302066802978516 nb_pixel_total : 15567 time to create 1 rle with old method : 0.017862319946289062 length of segment : 196 time for calcul the mask position with numpy : 0.0026903152465820312 nb_pixel_total : 28045 time to create 1 rle with old method : 0.03235816955566406 length of segment : 188 time for calcul the mask position with numpy : 0.005594730377197266 nb_pixel_total : 117755 time to create 1 rle with old method : 0.13135051727294922 length of segment : 554 time for calcul the mask position with numpy : 0.0023987293243408203 nb_pixel_total : 44850 time to create 1 rle with old method : 0.051316022872924805 length of segment : 286 time for calcul the mask position with numpy : 0.015154838562011719 nb_pixel_total : 422467 time to create 1 rle with new method : 0.03473687171936035 length of segment : 961 time for calcul the mask position with numpy : 0.0009036064147949219 nb_pixel_total : 30452 time to create 1 rle with old method : 0.034418582916259766 length of segment : 189 time for calcul the mask position with numpy : 0.0004985332489013672 nb_pixel_total : 21684 time to create 1 rle with old method : 0.02546548843383789 length of segment : 210 time for calcul the mask position with numpy : 0.0012841224670410156 nb_pixel_total : 9561 time to create 1 rle with old method : 0.0114593505859375 length of segment : 152 time for calcul the mask position with numpy : 0.0008091926574707031 nb_pixel_total : 11559 time to create 1 rle with old method : 0.013439416885375977 length of segment : 200 time for calcul the mask position with numpy : 0.0010349750518798828 nb_pixel_total : 15834 time to create 1 rle with old method : 0.018279075622558594 length of segment : 181 time for calcul the mask position with numpy : 0.0022695064544677734 nb_pixel_total : 17533 time to create 1 rle with old method : 0.020670413970947266 length of segment : 272 time for calcul the mask position with numpy : 0.0070192813873291016 nb_pixel_total : 105506 time to create 1 rle with old method : 0.12027120590209961 length of segment : 560 time for calcul the mask position with numpy : 0.0011031627655029297 nb_pixel_total : 16432 time to create 1 rle with old method : 0.019014835357666016 length of segment : 151 time for calcul the mask position with numpy : 0.0026564598083496094 nb_pixel_total : 44004 time to create 1 rle with old method : 0.051305294036865234 length of segment : 272 time for calcul the mask position with numpy : 0.0015900135040283203 nb_pixel_total : 25617 time to create 1 rle with old method : 0.029706239700317383 length of segment : 223 time for calcul the mask position with numpy : 0.0014328956604003906 nb_pixel_total : 61144 time to create 1 rle with old method : 0.06866312026977539 length of segment : 361 time for calcul the mask position with numpy : 0.00018525123596191406 nb_pixel_total : 3838 time to create 1 rle with old method : 0.0045166015625 length of segment : 87 time for calcul the mask position with numpy : 0.002345561981201172 nb_pixel_total : 49459 time to create 1 rle with old method : 0.056205034255981445 length of segment : 245 time for calcul the mask position with numpy : 0.0025310516357421875 nb_pixel_total : 38703 time to create 1 rle with old method : 0.047118425369262695 length of segment : 255 time for calcul the mask position with numpy : 0.0013537406921386719 nb_pixel_total : 15698 time to create 1 rle with old method : 0.01811361312866211 length of segment : 203 time for calcul the mask position with numpy : 0.0014820098876953125 nb_pixel_total : 21617 time to create 1 rle with old method : 0.024592161178588867 length of segment : 219 time for calcul the mask position with numpy : 0.003373384475708008 nb_pixel_total : 39270 time to create 1 rle with old method : 0.04559659957885742 length of segment : 294 time for calcul the mask position with numpy : 0.0004165172576904297 nb_pixel_total : 5578 time to create 1 rle with old method : 0.006544351577758789 length of segment : 62 time for calcul the mask position with numpy : 0.0027170181274414062 nb_pixel_total : 40888 time to create 1 rle with old method : 0.04617595672607422 length of segment : 360 time for calcul the mask position with numpy : 0.0008342266082763672 nb_pixel_total : 12133 time to create 1 rle with old method : 0.015102148056030273 length of segment : 80 time for calcul the mask position with numpy : 0.0007586479187011719 nb_pixel_total : 10084 time to create 1 rle with old method : 0.011746406555175781 length of segment : 143 time for calcul the mask position with numpy : 0.0030820369720458984 nb_pixel_total : 32763 time to create 1 rle with old method : 0.038901567459106445 length of segment : 403 time for calcul the mask position with numpy : 0.0016782283782958984 nb_pixel_total : 20684 time to create 1 rle with old method : 0.023660659790039062 length of segment : 194 time for calcul the mask position with numpy : 0.0017478466033935547 nb_pixel_total : 24350 time to create 1 rle with old method : 0.028071880340576172 length of segment : 218 time for calcul the mask position with numpy : 0.0012059211730957031 nb_pixel_total : 22991 time to create 1 rle with old method : 0.02691340446472168 length of segment : 110 time for calcul the mask position with numpy : 0.0014128684997558594 nb_pixel_total : 19530 time to create 1 rle with old method : 0.02388310432434082 length of segment : 185 time for calcul the mask position with numpy : 0.0018246173858642578 nb_pixel_total : 28997 time to create 1 rle with old method : 0.03311038017272949 length of segment : 184 time for calcul the mask position with numpy : 0.007562875747680664 nb_pixel_total : 100532 time to create 1 rle with old method : 0.11617517471313477 length of segment : 357 time for calcul the mask position with numpy : 0.0005850791931152344 nb_pixel_total : 6166 time to create 1 rle with old method : 0.007265567779541016 length of segment : 114 time for calcul the mask position with numpy : 0.0019412040710449219 nb_pixel_total : 18458 time to create 1 rle with old method : 0.021315813064575195 length of segment : 214 time for calcul the mask position with numpy : 0.0004305839538574219 nb_pixel_total : 5848 time to create 1 rle with old method : 0.007209062576293945 length of segment : 58 time for calcul the mask position with numpy : 0.010069847106933594 nb_pixel_total : 120220 time to create 1 rle with old method : 0.1599271297454834 length of segment : 660 time for calcul the mask position with numpy : 0.0004954338073730469 nb_pixel_total : 5850 time to create 1 rle with old method : 0.007035255432128906 length of segment : 93 time for calcul the mask position with numpy : 0.006357908248901367 nb_pixel_total : 86384 time to create 1 rle with old method : 0.11927914619445801 length of segment : 400 time for calcul the mask position with numpy : 0.0005526542663574219 nb_pixel_total : 3939 time to create 1 rle with old method : 0.0050356388092041016 length of segment : 56 time for calcul the mask position with numpy : 0.0016715526580810547 nb_pixel_total : 26969 time to create 1 rle with old method : 0.03242921829223633 length of segment : 173 time for calcul the mask position with numpy : 0.005891561508178711 nb_pixel_total : 82951 time to create 1 rle with old method : 0.09363031387329102 length of segment : 347 time for calcul the mask position with numpy : 0.003329038619995117 nb_pixel_total : 54759 time to create 1 rle with old method : 0.06069517135620117 length of segment : 544 time for calcul the mask position with numpy : 0.0005557537078857422 nb_pixel_total : 9879 time to create 1 rle with old method : 0.01159977912902832 length of segment : 77 time for calcul the mask position with numpy : 0.0015935897827148438 nb_pixel_total : 31405 time to create 1 rle with old method : 0.03614497184753418 length of segment : 245 time for calcul the mask position with numpy : 0.0005409717559814453 nb_pixel_total : 4134 time to create 1 rle with old method : 0.004956245422363281 length of segment : 89 time for calcul the mask position with numpy : 0.0001914501190185547 nb_pixel_total : 2643 time to create 1 rle with old method : 0.00339508056640625 length of segment : 56 time for calcul the mask position with numpy : 0.002837657928466797 nb_pixel_total : 59513 time to create 1 rle with old method : 0.07381391525268555 length of segment : 358 time for calcul the mask position with numpy : 0.0019474029541015625 nb_pixel_total : 32979 time to create 1 rle with old method : 0.037613868713378906 length of segment : 236 time for calcul the mask position with numpy : 0.0023543834686279297 nb_pixel_total : 44326 time to create 1 rle with old method : 0.04967927932739258 length of segment : 354 time for calcul the mask position with numpy : 0.0004909038543701172 nb_pixel_total : 11431 time to create 1 rle with old method : 0.013182878494262695 length of segment : 119 time for calcul the mask position with numpy : 0.0075244903564453125 nb_pixel_total : 137471 time to create 1 rle with old method : 0.15405917167663574 length of segment : 560 time for calcul the mask position with numpy : 0.000774383544921875 nb_pixel_total : 8614 time to create 1 rle with old method : 0.009927749633789062 length of segment : 109 time for calcul the mask position with numpy : 0.0008709430694580078 nb_pixel_total : 18260 time to create 1 rle with old method : 0.020936965942382812 length of segment : 231 time for calcul the mask position with numpy : 0.001699209213256836 nb_pixel_total : 9961 time to create 1 rle with old method : 0.011846065521240234 length of segment : 195 time for calcul the mask position with numpy : 0.0005087852478027344 nb_pixel_total : 9961 time to create 1 rle with old method : 0.011806488037109375 length of segment : 95 time for calcul the mask position with numpy : 0.002885103225708008 nb_pixel_total : 37059 time to create 1 rle with old method : 0.04193115234375 length of segment : 263 time for calcul the mask position with numpy : 0.002841949462890625 nb_pixel_total : 43940 time to create 1 rle with old method : 0.05009031295776367 length of segment : 256 time for calcul the mask position with numpy : 0.0040814876556396484 nb_pixel_total : 47749 time to create 1 rle with old method : 0.05383777618408203 length of segment : 361 time for calcul the mask position with numpy : 0.003151416778564453 nb_pixel_total : 20319 time to create 1 rle with old method : 0.022786617279052734 length of segment : 504 time for calcul the mask position with numpy : 0.0019445419311523438 nb_pixel_total : 22353 time to create 1 rle with old method : 0.025582075119018555 length of segment : 174 time for calcul the mask position with numpy : 0.004109382629394531 nb_pixel_total : 37758 time to create 1 rle with old method : 0.0438845157623291 length of segment : 308 time for calcul the mask position with numpy : 0.0005383491516113281 nb_pixel_total : 6335 time to create 1 rle with old method : 0.007559061050415039 length of segment : 100 time for calcul the mask position with numpy : 0.0015914440155029297 nb_pixel_total : 26327 time to create 1 rle with old method : 0.031189680099487305 length of segment : 159 time for calcul the mask position with numpy : 0.0008554458618164062 nb_pixel_total : 11623 time to create 1 rle with old method : 0.01382136344909668 length of segment : 137 time for calcul the mask position with numpy : 0.0011258125305175781 nb_pixel_total : 18135 time to create 1 rle with old method : 0.02112436294555664 length of segment : 163 time for calcul the mask position with numpy : 0.003487110137939453 nb_pixel_total : 47498 time to create 1 rle with old method : 0.05397486686706543 length of segment : 295 time for calcul the mask position with numpy : 0.0013425350189208984 nb_pixel_total : 14816 time to create 1 rle with old method : 0.01824021339416504 length of segment : 176 time for calcul the mask position with numpy : 0.0012297630310058594 nb_pixel_total : 16026 time to create 1 rle with old method : 0.018598318099975586 length of segment : 176 time for calcul the mask position with numpy : 0.0013489723205566406 nb_pixel_total : 10219 time to create 1 rle with old method : 0.012142181396484375 length of segment : 91 time for calcul the mask position with numpy : 0.002866506576538086 nb_pixel_total : 39384 time to create 1 rle with old method : 0.044776201248168945 length of segment : 309 time for calcul the mask position with numpy : 0.0011801719665527344 nb_pixel_total : 12055 time to create 1 rle with old method : 0.014045238494873047 length of segment : 147 time for calcul the mask position with numpy : 0.0008726119995117188 nb_pixel_total : 11833 time to create 1 rle with old method : 0.014145612716674805 length of segment : 110 time for calcul the mask position with numpy : 0.0014705657958984375 nb_pixel_total : 19780 time to create 1 rle with old method : 0.022641897201538086 length of segment : 254 time for calcul the mask position with numpy : 0.0012905597686767578 nb_pixel_total : 14009 time to create 1 rle with old method : 0.016093015670776367 length of segment : 192 time for calcul the mask position with numpy : 0.004176139831542969 nb_pixel_total : 60602 time to create 1 rle with old method : 0.06864380836486816 length of segment : 370 time for calcul the mask position with numpy : 0.0012383460998535156 nb_pixel_total : 9966 time to create 1 rle with old method : 0.01602029800415039 length of segment : 132 time for calcul the mask position with numpy : 0.001295328140258789 nb_pixel_total : 20479 time to create 1 rle with old method : 0.02391362190246582 length of segment : 135 time for calcul the mask position with numpy : 0.003422260284423828 nb_pixel_total : 62830 time to create 1 rle with old method : 0.06940484046936035 length of segment : 438 time for calcul the mask position with numpy : 0.0008177757263183594 nb_pixel_total : 11684 time to create 1 rle with old method : 0.01317906379699707 length of segment : 149 time for calcul the mask position with numpy : 0.0029458999633789062 nb_pixel_total : 45091 time to create 1 rle with old method : 0.05112147331237793 length of segment : 316 time for calcul the mask position with numpy : 0.0011146068572998047 nb_pixel_total : 12110 time to create 1 rle with old method : 0.014123678207397461 length of segment : 138 time for calcul the mask position with numpy : 0.004454612731933594 nb_pixel_total : 64635 time to create 1 rle with old method : 0.07457566261291504 length of segment : 384 time for calcul the mask position with numpy : 0.006270170211791992 nb_pixel_total : 98909 time to create 1 rle with old method : 0.10927629470825195 length of segment : 466 time for calcul the mask position with numpy : 0.0014345645904541016 nb_pixel_total : 22856 time to create 1 rle with old method : 0.031202316284179688 length of segment : 209 time for calcul the mask position with numpy : 0.0007560253143310547 nb_pixel_total : 11676 time to create 1 rle with old method : 0.01925063133239746 length of segment : 130 time for calcul the mask position with numpy : 0.0020868778228759766 nb_pixel_total : 37706 time to create 1 rle with old method : 0.04242348670959473 length of segment : 294 time spent for convertir_results : 43.64289999008179 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 370 chid ids of type : 3594 Number RLEs to save : 101620 save missing photos in datou_result : time spend for datou_step_exec : 232.28540682792664 time spend to save output : 12.85714077949524 total time spend for step 1 : 245.14254760742188 step2:crop_condition Wed Apr 9 10:04:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 11 ! batch 1 Loaded 370 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 ! map_result returned by crop_photo_return_map_crop : length : 279 About to insert : list_path_to_insert length 279 new photo from crops ! About to upload 279 photos upload in portfolio : 3736932 init cache_photo without model_param we have 279 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744185940_306356 we have uploaded 279 photos in the portfolio 3736932 time of upload the photos Elapsed time : 81.7842538356781 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 ! map_result returned by crop_photo_return_map_crop : length : 51 About to insert : list_path_to_insert length 51 new photo from crops ! About to upload 51 photos upload in portfolio : 3736932 init cache_photo without model_param we have 51 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186038_306356 we have uploaded 51 photos in the portfolio 3736932 time of upload the photos Elapsed time : 14.217000007629395 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 ! 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/1744186054_306356 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.1235229969024658 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 ! map_result returned by crop_photo_return_map_crop : length : 26 About to insert : list_path_to_insert length 26 new photo from crops ! About to upload 26 photos upload in portfolio : 3736932 init cache_photo without model_param we have 26 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186071_306356 we have uploaded 26 photos in the portfolio 3736932 time of upload the photos Elapsed time : 6.409569978713989 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 ! we have both polygon and rles Next one ! 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 : 8 About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos upload in portfolio : 3736932 init cache_photo without model_param we have 8 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186080_306356 we have uploaded 8 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.2156260013580322 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/1744186085_306356 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 12.283228397369385 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 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350433591, 1350433508, 1350433246, 1350433188, 1350382174, 1350382169, 1350382164, 1350382052, 1350382045, 1350382041, 1350382029] Looping around the photos to save general results len do output : 370 /1350694668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694907Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694917Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694931Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694933Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694941Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350694946Didn't retrieve data .Didn't retrieve 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.Didn't retrieve data .Didn't retrieve data . /1350696383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350696385Didn'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, '2733691') ('3318', '22153644', '1350433591', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433508', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433246', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433188', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382174', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382169', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382164', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382052', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382045', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382041', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382029', None, None, None, None, None, '2733691') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1121 time used for this insertion : 0.11584949493408203 save_final save missing photos in datou_result : time spend for datou_step_exec : 218.91049456596375 time spend to save output : 0.12753582000732422 total time spend for step 2 : 219.03803038597107 step3:rle_unique_nms_with_priority Wed Apr 9 10:08:17 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 370 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 52 nb_hashtags : 4 time to prepare the origin masks : 3.9526712894439697 time for calcul the mask position with numpy : 0.3029334545135498 nb_pixel_total : 5614132 time to create 1 rle with new method : 0.7822365760803223 time for calcul the mask position with numpy : 0.027799606323242188 nb_pixel_total : 5280 time to create 1 rle with old method : 0.005686521530151367 time for calcul the mask position with numpy : 0.027277469635009766 nb_pixel_total : 27065 time to create 1 rle with old method : 0.02866053581237793 time for calcul the mask position with numpy : 0.027458667755126953 nb_pixel_total : 6002 time to create 1 rle with old method : 0.006711483001708984 time for calcul the mask position with numpy : 0.027501583099365234 nb_pixel_total : 15272 time to create 1 rle with old method : 0.017006635665893555 time for calcul the mask position with numpy : 0.028095722198486328 nb_pixel_total : 10247 time to create 1 rle with old method : 0.011355161666870117 time for calcul the mask position with numpy : 0.02800893783569336 nb_pixel_total : 22945 time to create 1 rle with old method : 0.02493906021118164 time for calcul the mask position with numpy : 0.028130531311035156 nb_pixel_total : 17245 time to create 1 rle with old method : 0.019619464874267578 time for calcul the mask position with numpy : 0.028442859649658203 nb_pixel_total : 16580 time to create 1 rle with old method : 0.01832294464111328 time for calcul the mask position with numpy : 0.029795408248901367 nb_pixel_total : 10000 time to create 1 rle with old method : 0.011807680130004883 time for calcul the mask position with numpy : 0.029746294021606445 nb_pixel_total : 71462 time to create 1 rle with old method : 0.07889652252197266 time for calcul the mask position with numpy : 0.029010295867919922 nb_pixel_total : 27597 time to create 1 rle with old method : 0.031103849411010742 time for calcul the mask position with numpy : 0.028728961944580078 nb_pixel_total : 19903 time to create 1 rle with old method : 0.021962642669677734 time for calcul the mask position with numpy : 0.028499841690063477 nb_pixel_total : 28872 time to create 1 rle with old method : 0.03195071220397949 time for calcul the mask position with numpy : 0.029029130935668945 nb_pixel_total : 22602 time to create 1 rle with old method : 0.0254058837890625 time for calcul the mask position with numpy : 0.028871774673461914 nb_pixel_total : 34690 time to create 1 rle with old method : 0.03751802444458008 time for calcul the mask position with numpy : 0.02887272834777832 nb_pixel_total : 17869 time to create 1 rle with old method : 0.019707679748535156 time for calcul the mask position with numpy : 0.028606414794921875 nb_pixel_total : 35782 time to create 1 rle with old method : 0.039465904235839844 time for calcul the mask position with numpy : 0.028285741806030273 nb_pixel_total : 22625 time to create 1 rle with old method : 0.02508258819580078 time for calcul the mask position with numpy : 0.027703046798706055 nb_pixel_total : 18119 time to create 1 rle with old method : 0.02029895782470703 time for calcul the mask position with numpy : 0.028496980667114258 nb_pixel_total : 45366 time to create 1 rle with old method : 0.048870086669921875 time for calcul the mask position with numpy : 0.02830052375793457 nb_pixel_total : 14246 time to create 1 rle with old method : 0.01557779312133789 time for calcul the mask position with numpy : 0.028458595275878906 nb_pixel_total : 10462 time to create 1 rle with old method : 0.011713504791259766 time for calcul the mask position with numpy : 0.02874612808227539 nb_pixel_total : 48436 time to create 1 rle with old method : 0.07386255264282227 time for calcul the mask position with numpy : 0.031755924224853516 nb_pixel_total : 16849 time to create 1 rle with old method : 0.018425941467285156 time for calcul the mask position with numpy : 0.02778029441833496 nb_pixel_total : 70080 time to create 1 rle with old method : 0.07635903358459473 time for calcul the mask position with numpy : 0.028293848037719727 nb_pixel_total : 41456 time to create 1 rle with old method : 0.04534459114074707 time for calcul the mask position with numpy : 0.028455018997192383 nb_pixel_total : 38367 time to create 1 rle with old method : 0.04838252067565918 time for calcul the mask position with numpy : 0.029795408248901367 nb_pixel_total : 15793 time to create 1 rle with old method : 0.017844676971435547 time for calcul the mask position with numpy : 0.028276681900024414 nb_pixel_total : 29918 time to create 1 rle with old method : 0.03277158737182617 time for calcul the mask position with numpy : 0.028813838958740234 nb_pixel_total : 16509 time to create 1 rle with old method : 0.01749110221862793 time for calcul the mask position with numpy : 0.027547836303710938 nb_pixel_total : 13662 time to create 1 rle with old method : 0.01540231704711914 time for calcul the mask position with numpy : 0.02850198745727539 nb_pixel_total : 44177 time to create 1 rle with old method : 0.05025315284729004 time for calcul the mask position with numpy : 0.028407812118530273 nb_pixel_total : 34537 time to create 1 rle with old method : 0.03860759735107422 time for calcul the mask position with numpy : 0.028606414794921875 nb_pixel_total : 11598 time to create 1 rle with old method : 0.013028621673583984 time for calcul the mask position with numpy : 0.02890753746032715 nb_pixel_total : 27646 time to create 1 rle with old method : 0.03151869773864746 time for calcul the mask position with numpy : 0.028748273849487305 nb_pixel_total : 86903 time to create 1 rle with old method : 0.09474015235900879 time for calcul the mask position with numpy : 0.028455018997192383 nb_pixel_total : 14626 time to create 1 rle with old method : 0.015572547912597656 time for calcul the mask position with numpy : 0.027812480926513672 nb_pixel_total : 12079 time to create 1 rle with old method : 0.012984037399291992 time for calcul the mask position with numpy : 0.02817392349243164 nb_pixel_total : 12898 time to create 1 rle with old method : 0.014232158660888672 time for calcul the mask position with numpy : 0.028660058975219727 nb_pixel_total : 66537 time to create 1 rle with old method : 0.07367420196533203 time for calcul the mask position with numpy : 0.02826237678527832 nb_pixel_total : 18724 time to create 1 rle with old method : 0.02060723304748535 time for calcul the mask position with numpy : 0.028129100799560547 nb_pixel_total : 8980 time to create 1 rle with old method : 0.011442184448242188 time for calcul the mask position with numpy : 0.02842402458190918 nb_pixel_total : 24945 time to create 1 rle with old method : 0.026925325393676758 time for calcul the mask position with numpy : 0.027907133102416992 nb_pixel_total : 13177 time to create 1 rle with old method : 0.014304876327514648 time for calcul the mask position with numpy : 0.029096603393554688 nb_pixel_total : 89802 time to create 1 rle with old method : 0.0972590446472168 time for calcul the mask position with numpy : 0.02747035026550293 nb_pixel_total : 66151 time to create 1 rle with old method : 0.07180166244506836 time for calcul the mask position with numpy : 0.02852654457092285 nb_pixel_total : 14938 time to create 1 rle with old method : 0.016674280166625977 time for calcul the mask position with numpy : 0.02873063087463379 nb_pixel_total : 20872 time to create 1 rle with old method : 0.022372722625732422 time for calcul the mask position with numpy : 0.02870798110961914 nb_pixel_total : 33149 time to create 1 rle with old method : 0.03631281852722168 time for calcul the mask position with numpy : 0.028470277786254883 nb_pixel_total : 26731 time to create 1 rle with old method : 0.029593467712402344 time for calcul the mask position with numpy : 0.029891490936279297 nb_pixel_total : 9726 time to create 1 rle with old method : 0.012818336486816406 time for calcul the mask position with numpy : 0.05450272560119629 nb_pixel_total : 6611 time to create 1 rle with old method : 0.06698894500732422 create new chi : 4.296391010284424 time to delete rle : 0.021361589431762695 batch 1 Loaded 105 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24695 TO DO : save crop sub photo not yet done ! save time : 5.536365270614624 nb_obj : 52 nb_hashtags : 5 time to prepare the origin masks : 4.41903281211853 time for calcul the mask position with numpy : 0.39144110679626465 nb_pixel_total : 5396077 time to create 1 rle with new method : 0.5778930187225342 time for calcul the mask position with numpy : 0.029288291931152344 nb_pixel_total : 10076 time to create 1 rle with old method : 0.011330604553222656 time for calcul the mask position with numpy : 0.029087305068969727 nb_pixel_total : 122401 time to create 1 rle with old method : 0.14635848999023438 time for calcul the mask position with numpy : 0.029084205627441406 nb_pixel_total : 8588 time to create 1 rle with old method : 0.009635210037231445 time for calcul the mask position with numpy : 0.029198884963989258 nb_pixel_total : 18418 time to create 1 rle with old method : 0.020552635192871094 time for calcul the mask position with numpy : 0.029036521911621094 nb_pixel_total : 4440 time to create 1 rle with old method : 0.0049686431884765625 time for calcul the mask position with numpy : 0.029184818267822266 nb_pixel_total : 10790 time to create 1 rle with old method : 0.014485836029052734 time for calcul the mask position with numpy : 0.02899909019470215 nb_pixel_total : 17518 time to create 1 rle with old method : 0.019481420516967773 time for calcul the mask position with numpy : 0.02910590171813965 nb_pixel_total : 23945 time to create 1 rle with old method : 0.02666640281677246 time for calcul the mask position with numpy : 0.029112577438354492 nb_pixel_total : 65070 time to create 1 rle with old method : 0.07191348075866699 time for calcul the mask position with numpy : 0.02904677391052246 nb_pixel_total : 27340 time to create 1 rle with old method : 0.030352115631103516 time for calcul the mask position with numpy : 0.028923749923706055 nb_pixel_total : 22660 time to create 1 rle with old method : 0.025346755981445312 time for calcul the mask position with numpy : 0.029046058654785156 nb_pixel_total : 44626 time to create 1 rle with old method : 0.049455881118774414 time for calcul the mask position with numpy : 0.02917790412902832 nb_pixel_total : 81614 time to create 1 rle with old method : 0.0906059741973877 time for calcul the mask position with numpy : 0.029023408889770508 nb_pixel_total : 2751 time to create 1 rle with old method : 0.0031328201293945312 time for calcul the mask position with numpy : 0.03005361557006836 nb_pixel_total : 29816 time to create 1 rle with old method : 0.03350996971130371 time for calcul the mask position with numpy : 0.02929067611694336 nb_pixel_total : 15042 time to create 1 rle with old method : 0.017039060592651367 time for calcul the mask position with numpy : 0.028696298599243164 nb_pixel_total : 8627 time to create 1 rle with old method : 0.00969076156616211 time for calcul the mask position with numpy : 0.02888631820678711 nb_pixel_total : 14887 time to create 1 rle with old method : 0.01615118980407715 time for calcul the mask position with numpy : 0.028989315032958984 nb_pixel_total : 4619 time to create 1 rle with old method : 0.005033254623413086 time for calcul the mask position with numpy : 0.028537750244140625 nb_pixel_total : 9020 time to create 1 rle with old method : 0.009703636169433594 time for calcul the mask position with numpy : 0.0280454158782959 nb_pixel_total : 10379 time to create 1 rle with old method : 0.011398792266845703 time for calcul the mask position with numpy : 0.028373241424560547 nb_pixel_total : 31142 time to create 1 rle with old method : 0.03471636772155762 time for calcul the mask position with numpy : 0.02817225456237793 nb_pixel_total : 21808 time to create 1 rle with old method : 0.023984670639038086 time for calcul the mask position with numpy : 0.028569698333740234 nb_pixel_total : 80377 time to create 1 rle with old method : 0.08708691596984863 time for calcul the mask position with numpy : 0.02896857261657715 nb_pixel_total : 113187 time to create 1 rle with old method : 0.12304854393005371 time for calcul the mask position with numpy : 0.028515100479125977 nb_pixel_total : 24355 time to create 1 rle with old method : 0.026675939559936523 time for calcul the mask position with numpy : 0.02811145782470703 nb_pixel_total : 48261 time to create 1 rle with old method : 0.05311751365661621 time for calcul the mask position with numpy : 0.028556346893310547 nb_pixel_total : 38039 time to create 1 rle with old method : 0.041961669921875 time for calcul the mask position with numpy : 0.028849363327026367 nb_pixel_total : 37238 time to create 1 rle with old method : 0.042229413986206055 time for calcul the mask position with numpy : 0.029168367385864258 nb_pixel_total : 26854 time to create 1 rle with old method : 0.02984333038330078 time for calcul the mask position with numpy : 0.02866530418395996 nb_pixel_total : 7550 time to create 1 rle with old method : 0.008623123168945312 time for calcul the mask position with numpy : 0.029916048049926758 nb_pixel_total : 16273 time to create 1 rle with old method : 0.021467208862304688 time for calcul the mask position with numpy : 0.029653549194335938 nb_pixel_total : 29715 time to create 1 rle with old method : 0.03436398506164551 time for calcul the mask position with numpy : 0.029286861419677734 nb_pixel_total : 15104 time to create 1 rle with old method : 0.01843857765197754 time for calcul the mask position with numpy : 0.029166221618652344 nb_pixel_total : 37696 time to create 1 rle with old method : 0.04127049446105957 time for calcul the mask position with numpy : 0.02921009063720703 nb_pixel_total : 52674 time to create 1 rle with old method : 0.06032133102416992 time for calcul the mask position with numpy : 0.029674053192138672 nb_pixel_total : 108748 time to create 1 rle with old method : 0.14861273765563965 time for calcul the mask position with numpy : 0.02967667579650879 nb_pixel_total : 12226 time to create 1 rle with old method : 0.013695240020751953 time for calcul the mask position with numpy : 0.029323816299438477 nb_pixel_total : 31862 time to create 1 rle with old method : 0.035585641860961914 time for calcul the mask position with numpy : 0.030392885208129883 nb_pixel_total : 10513 time to create 1 rle with old method : 0.011917829513549805 time for calcul the mask position with numpy : 0.029381990432739258 nb_pixel_total : 20721 time to create 1 rle with old method : 0.02392864227294922 time for calcul the mask position with numpy : 0.029329538345336914 nb_pixel_total : 16199 time to create 1 rle with old method : 0.01834845542907715 time for calcul the mask position with numpy : 0.02928757667541504 nb_pixel_total : 115375 time to create 1 rle with old method : 0.12783145904541016 time for calcul the mask position with numpy : 0.029626131057739258 nb_pixel_total : 10311 time to create 1 rle with old method : 0.013497352600097656 time for calcul the mask position with numpy : 0.030028343200683594 nb_pixel_total : 37306 time to create 1 rle with old method : 0.04192328453063965 time for calcul the mask position with numpy : 0.028687238693237305 nb_pixel_total : 54552 time to create 1 rle with old method : 0.060195207595825195 time for calcul the mask position with numpy : 0.02870011329650879 nb_pixel_total : 6982 time to create 1 rle with old method : 0.007668733596801758 time for calcul the mask position with numpy : 0.028248071670532227 nb_pixel_total : 21218 time to create 1 rle with old method : 0.02314162254333496 time for calcul the mask position with numpy : 0.028425931930541992 nb_pixel_total : 23026 time to create 1 rle with old method : 0.02558135986328125 time for calcul the mask position with numpy : 0.029015779495239258 nb_pixel_total : 9818 time to create 1 rle with old method : 0.011034011840820312 time for calcul the mask position with numpy : 0.03227543830871582 nb_pixel_total : 37477 time to create 1 rle with old method : 0.04373764991760254 time for calcul the mask position with numpy : 0.02859783172607422 nb_pixel_total : 4929 time to create 1 rle with old method : 0.006234407424926758 create new chi : 4.408862352371216 time to delete rle : 0.0033674240112304688 batch 1 Loaded 105 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25773 TO DO : save crop sub photo not yet done ! save time : 5.433666467666626 nb_obj : 18 nb_hashtags : 4 time to prepare the origin masks : 6.220155477523804 time for calcul the mask position with numpy : 0.3490898609161377 nb_pixel_total : 4694645 time to create 1 rle with new method : 0.506476640701294 time for calcul the mask position with numpy : 0.028568506240844727 nb_pixel_total : 152741 time to create 1 rle with new method : 0.4003007411956787 time for calcul the mask position with numpy : 0.03258085250854492 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004470348358154297 time for calcul the mask position with numpy : 0.034013986587524414 nb_pixel_total : 39285 time to create 1 rle with old method : 0.04269576072692871 time for calcul the mask position with numpy : 0.03305673599243164 nb_pixel_total : 50570 time to create 1 rle with old method : 0.05355262756347656 time for calcul the mask position with numpy : 0.03361630439758301 nb_pixel_total : 319512 time to create 1 rle with new method : 0.5413098335266113 time for calcul the mask position with numpy : 0.02948760986328125 nb_pixel_total : 57622 time to create 1 rle with old method : 0.06401562690734863 time for calcul the mask position with numpy : 0.027810335159301758 nb_pixel_total : 25735 time to create 1 rle with old method : 0.029367446899414062 time for calcul the mask position with numpy : 0.030913352966308594 nb_pixel_total : 25468 time to create 1 rle with old method : 0.0402987003326416 time for calcul the mask position with numpy : 0.02472400665283203 nb_pixel_total : 47916 time to create 1 rle with old method : 0.06514263153076172 time for calcul the mask position with numpy : 0.021488428115844727 nb_pixel_total : 19661 time to create 1 rle with old method : 0.026180028915405273 time for calcul the mask position with numpy : 0.024288654327392578 nb_pixel_total : 384200 time to create 1 rle with new method : 0.5381441116333008 time for calcul the mask position with numpy : 0.023244142532348633 nb_pixel_total : 90373 time to create 1 rle with old method : 0.10003256797790527 time for calcul the mask position with numpy : 0.02053523063659668 nb_pixel_total : 39580 time to create 1 rle with old method : 0.042410850524902344 time for calcul the mask position with numpy : 0.023746490478515625 nb_pixel_total : 581520 time to create 1 rle with new method : 0.5699315071105957 time for calcul the mask position with numpy : 0.027966022491455078 nb_pixel_total : 437832 time to create 1 rle with new method : 0.4108541011810303 time for calcul the mask position with numpy : 0.021792888641357422 nb_pixel_total : 15696 time to create 1 rle with old method : 0.017877578735351562 time for calcul the mask position with numpy : 0.02125239372253418 nb_pixel_total : 30023 time to create 1 rle with old method : 0.0323939323425293 time for calcul the mask position with numpy : 0.02219414710998535 nb_pixel_total : 37540 time to create 1 rle with old method : 0.04141879081726074 create new chi : 4.501587629318237 time to delete rle : 0.004283428192138672 batch 1 Loaded 37 chid ids of type : 3594 +++++++++++++++++++++++++Number RLEs to save : 18544 TO DO : save crop sub photo not yet done ! save time : 4.6808271408081055 nb_obj : 38 nb_hashtags : 4 time to prepare the origin masks : 4.599873781204224 time for calcul the mask position with numpy : 0.5780928134918213 nb_pixel_total : 4944442 time to create 1 rle with new method : 0.468029260635376 time for calcul the mask position with numpy : 0.029358863830566406 nb_pixel_total : 48796 time to create 1 rle with old method : 0.05472898483276367 time for calcul the mask position with numpy : 0.029446125030517578 nb_pixel_total : 60622 time to create 1 rle with old method : 0.06680512428283691 time for calcul the mask position with numpy : 0.028701305389404297 nb_pixel_total : 147561 time to create 1 rle with old method : 0.16088461875915527 time for calcul the mask position with numpy : 0.028540849685668945 nb_pixel_total : 144791 time to create 1 rle with old method : 0.15700769424438477 time for calcul the mask position with numpy : 0.02837347984313965 nb_pixel_total : 71314 time to create 1 rle with old method : 0.10370922088623047 time for calcul the mask position with numpy : 0.028514623641967773 nb_pixel_total : 7690 time to create 1 rle with old method : 0.00834512710571289 time for calcul the mask position with numpy : 0.02779412269592285 nb_pixel_total : 61034 time to create 1 rle with old method : 0.06581616401672363 time for calcul the mask position with numpy : 0.02805495262145996 nb_pixel_total : 7076 time to create 1 rle with old method : 0.007822990417480469 time for calcul the mask position with numpy : 0.028616905212402344 nb_pixel_total : 23279 time to create 1 rle with old method : 0.02587890625 time for calcul the mask position with numpy : 0.028911828994750977 nb_pixel_total : 17026 time to create 1 rle with old method : 0.019334077835083008 time for calcul the mask position with numpy : 0.02979755401611328 nb_pixel_total : 95819 time to create 1 rle with old method : 0.10499691963195801 time for calcul the mask position with numpy : 0.028577566146850586 nb_pixel_total : 45518 time to create 1 rle with old method : 0.05033993721008301 time for calcul the mask position with numpy : 0.02811288833618164 nb_pixel_total : 14650 time to create 1 rle with old method : 0.015946149826049805 time for calcul the mask position with numpy : 0.028570175170898438 nb_pixel_total : 101545 time to create 1 rle with old method : 0.11382842063903809 time for calcul the mask position with numpy : 0.028810977935791016 nb_pixel_total : 6886 time to create 1 rle with old method : 0.007807016372680664 time for calcul the mask position with numpy : 0.028824567794799805 nb_pixel_total : 25481 time to create 1 rle with old method : 0.038556575775146484 time for calcul the mask position with numpy : 0.03619217872619629 nb_pixel_total : 292802 time to create 1 rle with new method : 0.5619208812713623 time for calcul the mask position with numpy : 0.028316736221313477 nb_pixel_total : 3578 time to create 1 rle with old method : 0.0041997432708740234 time for calcul the mask position with numpy : 0.029682397842407227 nb_pixel_total : 212921 time to create 1 rle with new method : 0.44316744804382324 time for calcul the mask position with numpy : 0.02886676788330078 nb_pixel_total : 73048 time to create 1 rle with old method : 0.08116841316223145 time for calcul the mask position with numpy : 0.0334932804107666 nb_pixel_total : 132906 time to create 1 rle with old method : 0.1787090301513672 time for calcul the mask position with numpy : 0.029415369033813477 nb_pixel_total : 34510 time to create 1 rle with old method : 0.0404660701751709 time for calcul the mask position with numpy : 0.031514883041381836 nb_pixel_total : 21780 time to create 1 rle with old method : 0.030504703521728516 time for calcul the mask position with numpy : 0.03288578987121582 nb_pixel_total : 10744 time to create 1 rle with old method : 0.017733097076416016 time for calcul the mask position with numpy : 0.030574321746826172 nb_pixel_total : 39924 time to create 1 rle with old method : 0.04516148567199707 time for calcul the mask position with numpy : 0.029222488403320312 nb_pixel_total : 96593 time to create 1 rle with old method : 0.10771656036376953 time for calcul the mask position with numpy : 0.028989791870117188 nb_pixel_total : 51620 time to create 1 rle with old method : 0.06067490577697754 time for calcul the mask position with numpy : 0.030475378036499023 nb_pixel_total : 43026 time to create 1 rle with old method : 0.04764389991760254 time for calcul the mask position with numpy : 0.028921127319335938 nb_pixel_total : 7344 time to create 1 rle with old method : 0.008412361145019531 time for calcul the mask position with numpy : 0.02931070327758789 nb_pixel_total : 90865 time to create 1 rle with old method : 0.09946584701538086 time for calcul the mask position with numpy : 0.034177303314208984 nb_pixel_total : 16147 time to create 1 rle with old method : 0.01803731918334961 time for calcul the mask position with numpy : 0.02947402000427246 nb_pixel_total : 1760 time to create 1 rle with old method : 0.0020592212677001953 time for calcul the mask position with numpy : 0.03046393394470215 nb_pixel_total : 41870 time to create 1 rle with old method : 0.04815506935119629 time for calcul the mask position with numpy : 0.029259204864501953 nb_pixel_total : 15991 time to create 1 rle with old method : 0.018016815185546875 time for calcul the mask position with numpy : 0.02959895133972168 nb_pixel_total : 12080 time to create 1 rle with old method : 0.014159440994262695 time for calcul the mask position with numpy : 0.029891252517700195 nb_pixel_total : 11735 time to create 1 rle with old method : 0.013315439224243164 time for calcul the mask position with numpy : 0.028844118118286133 nb_pixel_total : 8181 time to create 1 rle with old method : 0.009392261505126953 time for calcul the mask position with numpy : 0.028894901275634766 nb_pixel_total : 7285 time to create 1 rle with old method : 0.008312463760375977 create new chi : 5.118156671524048 time to delete rle : 0.003513813018798828 batch 1 Loaded 77 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22609 TO DO : save crop sub photo not yet done ! save time : 3.2940406799316406 nb_obj : 29 nb_hashtags : 4 time to prepare the origin masks : 4.617600917816162 time for calcul the mask position with numpy : 0.38905930519104004 nb_pixel_total : 4782755 time to create 1 rle with new method : 0.5870285034179688 time for calcul the mask position with numpy : 0.030805587768554688 nb_pixel_total : 261758 time to create 1 rle with new method : 0.6120877265930176 time for calcul the mask position with numpy : 0.029526948928833008 nb_pixel_total : 76628 time to create 1 rle with old method : 0.09720134735107422 time for calcul the mask position with numpy : 0.03303360939025879 nb_pixel_total : 70743 time to create 1 rle with old method : 0.07997417449951172 time for calcul the mask position with numpy : 0.029313087463378906 nb_pixel_total : 62154 time to create 1 rle with old method : 0.06809759140014648 time for calcul the mask position with numpy : 0.028572797775268555 nb_pixel_total : 34147 time to create 1 rle with old method : 0.037164926528930664 time for calcul the mask position with numpy : 0.028215646743774414 nb_pixel_total : 15589 time to create 1 rle with old method : 0.017043352127075195 time for calcul the mask position with numpy : 0.028749942779541016 nb_pixel_total : 63948 time to create 1 rle with old method : 0.06926107406616211 time for calcul the mask position with numpy : 0.028254032135009766 nb_pixel_total : 85491 time to create 1 rle with old method : 0.09027791023254395 time for calcul the mask position with numpy : 0.028280258178710938 nb_pixel_total : 28539 time to create 1 rle with old method : 0.03064584732055664 time for calcul the mask position with numpy : 0.029636859893798828 nb_pixel_total : 386624 time to create 1 rle with new method : 0.632568359375 time for calcul the mask position with numpy : 0.030142545700073242 nb_pixel_total : 55813 time to create 1 rle with old method : 0.06127572059631348 time for calcul the mask position with numpy : 0.028577804565429688 nb_pixel_total : 55759 time to create 1 rle with old method : 0.0610499382019043 time for calcul the mask position with numpy : 0.02832818031311035 nb_pixel_total : 74923 time to create 1 rle with old method : 0.08053445816040039 time for calcul the mask position with numpy : 0.027618408203125 nb_pixel_total : 36581 time to create 1 rle with old method : 0.03882408142089844 time for calcul the mask position with numpy : 0.0279691219329834 nb_pixel_total : 36973 time to create 1 rle with old method : 0.03848624229431152 time for calcul the mask position with numpy : 0.02621293067932129 nb_pixel_total : 49944 time to create 1 rle with old method : 0.05164742469787598 time for calcul the mask position with numpy : 0.026304244995117188 nb_pixel_total : 32676 time to create 1 rle with old method : 0.03260326385498047 time for calcul the mask position with numpy : 0.02624368667602539 nb_pixel_total : 12288 time to create 1 rle with old method : 0.012911319732666016 time for calcul the mask position with numpy : 0.026997804641723633 nb_pixel_total : 111219 time to create 1 rle with old method : 0.11600518226623535 time for calcul the mask position with numpy : 0.029149770736694336 nb_pixel_total : 63524 time to create 1 rle with old method : 0.06624388694763184 time for calcul the mask position with numpy : 0.02866673469543457 nb_pixel_total : 126248 time to create 1 rle with old method : 0.13236594200134277 time for calcul the mask position with numpy : 0.027867794036865234 nb_pixel_total : 42875 time to create 1 rle with old method : 0.04502511024475098 time for calcul the mask position with numpy : 0.02694082260131836 nb_pixel_total : 26154 time to create 1 rle with old method : 0.0269930362701416 time for calcul the mask position with numpy : 0.027176618576049805 nb_pixel_total : 44487 time to create 1 rle with old method : 0.04586601257324219 time for calcul the mask position with numpy : 0.026692867279052734 nb_pixel_total : 10680 time to create 1 rle with old method : 0.010933876037597656 time for calcul the mask position with numpy : 0.027045249938964844 nb_pixel_total : 50302 time to create 1 rle with old method : 0.053288936614990234 time for calcul the mask position with numpy : 0.02896595001220703 nb_pixel_total : 230758 time to create 1 rle with new method : 0.4231247901916504 time for calcul the mask position with numpy : 0.029288291931152344 nb_pixel_total : 86379 time to create 1 rle with old method : 0.0929102897644043 time for calcul the mask position with numpy : 0.028369903564453125 nb_pixel_total : 34281 time to create 1 rle with old method : 0.036882877349853516 create new chi : 5.059162616729736 time to delete rle : 0.0029146671295166016 batch 1 Loaded 59 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20825 TO DO : save crop sub photo not yet done ! save time : 2.0536868572235107 nb_obj : 25 nb_hashtags : 4 time to prepare the origin masks : 6.526538610458374 time for calcul the mask position with numpy : 0.44032859802246094 nb_pixel_total : 5630612 time to create 1 rle with new method : 0.7892775535583496 time for calcul the mask position with numpy : 0.022225618362426758 nb_pixel_total : 12734 time to create 1 rle with old method : 0.014236688613891602 time for calcul the mask position with numpy : 0.020730972290039062 nb_pixel_total : 6029 time to create 1 rle with old method : 0.006545543670654297 time for calcul the mask position with numpy : 0.020560026168823242 nb_pixel_total : 66943 time to create 1 rle with old method : 0.0717930793762207 time for calcul the mask position with numpy : 0.020644664764404297 nb_pixel_total : 4501 time to create 1 rle with old method : 0.005280971527099609 time for calcul the mask position with numpy : 0.020346879959106445 nb_pixel_total : 46495 time to create 1 rle with old method : 0.05044746398925781 time for calcul the mask position with numpy : 0.023391246795654297 nb_pixel_total : 134395 time to create 1 rle with old method : 0.14589953422546387 time for calcul the mask position with numpy : 0.02108597755432129 nb_pixel_total : 36829 time to create 1 rle with old method : 0.039724111557006836 time for calcul the mask position with numpy : 0.021837949752807617 nb_pixel_total : 74524 time to create 1 rle with old method : 0.0816640853881836 time for calcul the mask position with numpy : 0.020861387252807617 nb_pixel_total : 131576 time to create 1 rle with old method : 0.14352679252624512 time for calcul the mask position with numpy : 0.020991086959838867 nb_pixel_total : 23162 time to create 1 rle with old method : 0.02549266815185547 time for calcul the mask position with numpy : 0.021699190139770508 nb_pixel_total : 29370 time to create 1 rle with old method : 0.03228306770324707 time for calcul the mask position with numpy : 0.021715164184570312 nb_pixel_total : 16687 time to create 1 rle with old method : 0.018497228622436523 time for calcul the mask position with numpy : 0.022877216339111328 nb_pixel_total : 64692 time to create 1 rle with old method : 0.07152819633483887 time for calcul the mask position with numpy : 0.02180933952331543 nb_pixel_total : 11562 time to create 1 rle with old method : 0.012987852096557617 time for calcul the mask position with numpy : 0.021157503128051758 nb_pixel_total : 30979 time to create 1 rle with old method : 0.03388786315917969 time for calcul the mask position with numpy : 0.02212071418762207 nb_pixel_total : 28943 time to create 1 rle with old method : 0.03176140785217285 time for calcul the mask position with numpy : 0.02306842803955078 nb_pixel_total : 195972 time to create 1 rle with new method : 0.463545560836792 time for calcul the mask position with numpy : 0.022472620010375977 nb_pixel_total : 64433 time to create 1 rle with old method : 0.0714406967163086 time for calcul the mask position with numpy : 0.02208399772644043 nb_pixel_total : 33574 time to create 1 rle with old method : 0.0372309684753418 time for calcul the mask position with numpy : 0.022820472717285156 nb_pixel_total : 106611 time to create 1 rle with old method : 0.11837315559387207 time for calcul the mask position with numpy : 0.030153512954711914 nb_pixel_total : 17149 time to create 1 rle with old method : 0.018950223922729492 time for calcul the mask position with numpy : 0.033125877380371094 nb_pixel_total : 222069 time to create 1 rle with new method : 0.46273183822631836 time for calcul the mask position with numpy : 0.029554367065429688 nb_pixel_total : 16149 time to create 1 rle with old method : 0.01797342300415039 time for calcul the mask position with numpy : 0.029439687728881836 nb_pixel_total : 30279 time to create 1 rle with old method : 0.037336111068725586 time for calcul the mask position with numpy : 0.023879289627075195 nb_pixel_total : 13971 time to create 1 rle with old method : 0.01561284065246582 create new chi : 3.916611433029175 time to delete rle : 0.0023746490478515625 batch 1 Loaded 51 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16250 TO DO : save crop sub photo not yet done ! save time : 1.069244623184204 nb_obj : 26 nb_hashtags : 4 time to prepare the origin masks : 5.10333776473999 time for calcul the mask position with numpy : 0.7996459007263184 nb_pixel_total : 4230962 time to create 1 rle with new method : 0.9393551349639893 time for calcul the mask position with numpy : 0.027834653854370117 nb_pixel_total : 34694 time to create 1 rle with old method : 0.037305355072021484 time for calcul the mask position with numpy : 0.027044296264648438 nb_pixel_total : 11371 time to create 1 rle with old method : 0.01219940185546875 time for calcul the mask position with numpy : 0.027129650115966797 nb_pixel_total : 14941 time to create 1 rle with old method : 0.016138792037963867 time for calcul the mask position with numpy : 0.027378082275390625 nb_pixel_total : 59959 time to create 1 rle with old method : 0.06271195411682129 time for calcul the mask position with numpy : 0.026980876922607422 nb_pixel_total : 50738 time to create 1 rle with old method : 0.052546024322509766 time for calcul the mask position with numpy : 0.0280306339263916 nb_pixel_total : 19325 time to create 1 rle with old method : 0.02110886573791504 time for calcul the mask position with numpy : 0.02846670150756836 nb_pixel_total : 49569 time to create 1 rle with old method : 0.05467867851257324 time for calcul the mask position with numpy : 0.027829408645629883 nb_pixel_total : 136073 time to create 1 rle with old method : 0.17472100257873535 time for calcul the mask position with numpy : 0.03759431838989258 nb_pixel_total : 704282 time to create 1 rle with new method : 0.37044405937194824 time for calcul the mask position with numpy : 0.030903100967407227 nb_pixel_total : 397626 time to create 1 rle with new method : 0.5132710933685303 time for calcul the mask position with numpy : 0.0291900634765625 nb_pixel_total : 32298 time to create 1 rle with old method : 0.036125898361206055 time for calcul the mask position with numpy : 0.03119802474975586 nb_pixel_total : 13968 time to create 1 rle with old method : 0.01558375358581543 time for calcul the mask position with numpy : 0.033838748931884766 nb_pixel_total : 20619 time to create 1 rle with old method : 0.02482891082763672 time for calcul the mask position with numpy : 0.03025650978088379 nb_pixel_total : 203201 time to create 1 rle with new method : 0.5479857921600342 time for calcul the mask position with numpy : 0.029261112213134766 nb_pixel_total : 329287 time to create 1 rle with new method : 0.35244035720825195 time for calcul the mask position with numpy : 0.029605627059936523 nb_pixel_total : 221836 time to create 1 rle with new method : 0.3897404670715332 time for calcul the mask position with numpy : 0.028950214385986328 nb_pixel_total : 49069 time to create 1 rle with old method : 0.057811737060546875 time for calcul the mask position with numpy : 0.029354333877563477 nb_pixel_total : 22188 time to create 1 rle with old method : 0.02422618865966797 time for calcul the mask position with numpy : 0.029165267944335938 nb_pixel_total : 84522 time to create 1 rle with old method : 0.09274125099182129 time for calcul the mask position with numpy : 0.028609752655029297 nb_pixel_total : 46936 time to create 1 rle with old method : 0.05147099494934082 time for calcul the mask position with numpy : 0.029120206832885742 nb_pixel_total : 26799 time to create 1 rle with old method : 0.02949833869934082 time for calcul the mask position with numpy : 0.0289914608001709 nb_pixel_total : 34648 time to create 1 rle with old method : 0.03845524787902832 time for calcul the mask position with numpy : 0.029210567474365234 nb_pixel_total : 21531 time to create 1 rle with old method : 0.024111509323120117 time for calcul the mask position with numpy : 0.02962636947631836 nb_pixel_total : 168556 time to create 1 rle with new method : 0.4173297882080078 time for calcul the mask position with numpy : 0.028478384017944336 nb_pixel_total : 17035 time to create 1 rle with old method : 0.01852869987487793 time for calcul the mask position with numpy : 0.028870582580566406 nb_pixel_total : 48207 time to create 1 rle with old method : 0.052965641021728516 create new chi : 6.154097080230713 time to delete rle : 0.003759145736694336 batch 1 Loaded 53 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22447 TO DO : save crop sub photo not yet done ! save time : 1.6278455257415771 nb_obj : 31 nb_hashtags : 3 time to prepare the origin masks : 3.617596387863159 time for calcul the mask position with numpy : 0.3962526321411133 nb_pixel_total : 6156465 time to create 1 rle with new method : 0.6190292835235596 time for calcul the mask position with numpy : 0.029070377349853516 nb_pixel_total : 4467 time to create 1 rle with old method : 0.0049991607666015625 time for calcul the mask position with numpy : 0.02896857261657715 nb_pixel_total : 22586 time to create 1 rle with old method : 0.024875879287719727 time for calcul the mask position with numpy : 0.02897810935974121 nb_pixel_total : 29124 time to create 1 rle with old method : 0.03218269348144531 time for calcul the mask position with numpy : 0.028729677200317383 nb_pixel_total : 13275 time to create 1 rle with old method : 0.014943599700927734 time for calcul the mask position with numpy : 0.028247594833374023 nb_pixel_total : 10802 time to create 1 rle with old method : 0.01184701919555664 time for calcul the mask position with numpy : 0.02886486053466797 nb_pixel_total : 24144 time to create 1 rle with old method : 0.026801586151123047 time for calcul the mask position with numpy : 0.028720378875732422 nb_pixel_total : 9641 time to create 1 rle with old method : 0.010671615600585938 time for calcul the mask position with numpy : 0.028415203094482422 nb_pixel_total : 120488 time to create 1 rle with old method : 0.13340282440185547 time for calcul the mask position with numpy : 0.033838510513305664 nb_pixel_total : 30118 time to create 1 rle with old method : 0.053991079330444336 time for calcul the mask position with numpy : 0.02827930450439453 nb_pixel_total : 19437 time to create 1 rle with old method : 0.020920515060424805 time for calcul the mask position with numpy : 0.028345346450805664 nb_pixel_total : 69661 time to create 1 rle with old method : 0.07540035247802734 time for calcul the mask position with numpy : 0.0286562442779541 nb_pixel_total : 16406 time to create 1 rle with old method : 0.017957448959350586 time for calcul the mask position with numpy : 0.028942584991455078 nb_pixel_total : 8044 time to create 1 rle with old method : 0.008961200714111328 time for calcul the mask position with numpy : 0.028758764266967773 nb_pixel_total : 52402 time to create 1 rle with old method : 0.05836129188537598 time for calcul the mask position with numpy : 0.02913045883178711 nb_pixel_total : 27258 time to create 1 rle with old method : 0.030170440673828125 time for calcul the mask position with numpy : 0.029064416885375977 nb_pixel_total : 80190 time to create 1 rle with old method : 0.08825516700744629 time for calcul the mask position with numpy : 0.029074668884277344 nb_pixel_total : 15600 time to create 1 rle with old method : 0.017486095428466797 time for calcul the mask position with numpy : 0.028872966766357422 nb_pixel_total : 11620 time to create 1 rle with old method : 0.013090848922729492 time for calcul the mask position with numpy : 0.029026508331298828 nb_pixel_total : 55369 time to create 1 rle with old method : 0.06150531768798828 time for calcul the mask position with numpy : 0.030910253524780273 nb_pixel_total : 77768 time to create 1 rle with old method : 0.08521842956542969 time for calcul the mask position with numpy : 0.028304338455200195 nb_pixel_total : 5829 time to create 1 rle with old method : 0.0063724517822265625 time for calcul the mask position with numpy : 0.02778029441833496 nb_pixel_total : 22152 time to create 1 rle with old method : 0.024190187454223633 time for calcul the mask position with numpy : 0.02840399742126465 nb_pixel_total : 8966 time to create 1 rle with old method : 0.00994110107421875 time for calcul the mask position with numpy : 0.02847456932067871 nb_pixel_total : 7741 time to create 1 rle with old method : 0.008471250534057617 time for calcul the mask position with numpy : 0.028439760208129883 nb_pixel_total : 30469 time to create 1 rle with old method : 0.033495187759399414 time for calcul the mask position with numpy : 0.028692007064819336 nb_pixel_total : 9633 time to create 1 rle with old method : 0.010790586471557617 time for calcul the mask position with numpy : 0.02852916717529297 nb_pixel_total : 39839 time to create 1 rle with old method : 0.04377317428588867 time for calcul the mask position with numpy : 0.02803206443786621 nb_pixel_total : 17447 time to create 1 rle with old method : 0.028386831283569336 time for calcul the mask position with numpy : 0.0327458381652832 nb_pixel_total : 24864 time to create 1 rle with old method : 0.0347747802734375 time for calcul the mask position with numpy : 0.028305768966674805 nb_pixel_total : 18649 time to create 1 rle with old method : 0.020273208618164062 time for calcul the mask position with numpy : 0.02741408348083496 nb_pixel_total : 9786 time to create 1 rle with old method : 0.01043701171875 create new chi : 2.9697535037994385 time to delete rle : 0.0022194385528564453 batch 1 Loaded 63 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16097 TO DO : save crop sub photo not yet done ! save time : 5.817898511886597 nb_obj : 29 nb_hashtags : 3 time to prepare the origin masks : 4.190160512924194 time for calcul the mask position with numpy : 0.7785871028900146 nb_pixel_total : 5621219 time to create 1 rle with new method : 0.8376803398132324 time for calcul the mask position with numpy : 0.028768539428710938 nb_pixel_total : 5987 time to create 1 rle with old method : 0.006669044494628906 time for calcul the mask position with numpy : 0.028108596801757812 nb_pixel_total : 30425 time to create 1 rle with old method : 0.03274369239807129 time for calcul the mask position with numpy : 0.028534412384033203 nb_pixel_total : 17533 time to create 1 rle with old method : 0.01975703239440918 time for calcul the mask position with numpy : 0.02866506576538086 nb_pixel_total : 44004 time to create 1 rle with old method : 0.04796600341796875 time for calcul the mask position with numpy : 0.028468847274780273 nb_pixel_total : 49459 time to create 1 rle with old method : 0.05339813232421875 time for calcul the mask position with numpy : 0.02837228775024414 nb_pixel_total : 28045 time to create 1 rle with old method : 0.030135631561279297 time for calcul the mask position with numpy : 0.027683019638061523 nb_pixel_total : 15970 time to create 1 rle with old method : 0.01719975471496582 time for calcul the mask position with numpy : 0.027542591094970703 nb_pixel_total : 27858 time to create 1 rle with old method : 0.039728403091430664 time for calcul the mask position with numpy : 0.03267860412597656 nb_pixel_total : 16432 time to create 1 rle with old method : 0.026131868362426758 time for calcul the mask position with numpy : 0.027848482131958008 nb_pixel_total : 15567 time to create 1 rle with old method : 0.017080068588256836 time for calcul the mask position with numpy : 0.028216838836669922 nb_pixel_total : 9561 time to create 1 rle with old method : 0.010712623596191406 time for calcul the mask position with numpy : 0.028113365173339844 nb_pixel_total : 32699 time to create 1 rle with old method : 0.03553605079650879 time for calcul the mask position with numpy : 0.028100967407226562 nb_pixel_total : 3561 time to create 1 rle with old method : 0.00397801399230957 time for calcul the mask position with numpy : 0.028165340423583984 nb_pixel_total : 53191 time to create 1 rle with old method : 0.057639122009277344 time for calcul the mask position with numpy : 0.028240680694580078 nb_pixel_total : 22507 time to create 1 rle with old method : 0.02425408363342285 time for calcul the mask position with numpy : 0.028096675872802734 nb_pixel_total : 74074 time to create 1 rle with old method : 0.08069920539855957 time for calcul the mask position with numpy : 0.028270721435546875 nb_pixel_total : 25617 time to create 1 rle with old method : 0.027575969696044922 time for calcul the mask position with numpy : 0.027673006057739258 nb_pixel_total : 15834 time to create 1 rle with old method : 0.01672220230102539 time for calcul the mask position with numpy : 0.03118729591369629 nb_pixel_total : 422467 time to create 1 rle with new method : 0.48615312576293945 time for calcul the mask position with numpy : 0.029187917709350586 nb_pixel_total : 95578 time to create 1 rle with old method : 0.10521221160888672 time for calcul the mask position with numpy : 0.028844594955444336 nb_pixel_total : 44850 time to create 1 rle with old method : 0.048700571060180664 time for calcul the mask position with numpy : 0.029839277267456055 nb_pixel_total : 19966 time to create 1 rle with old method : 0.032534122467041016 time for calcul the mask position with numpy : 0.034110069274902344 nb_pixel_total : 101546 time to create 1 rle with old method : 0.11392498016357422 time for calcul the mask position with numpy : 0.0295870304107666 nb_pixel_total : 11559 time to create 1 rle with old method : 0.012831926345825195 time for calcul the mask position with numpy : 0.031207561492919922 nb_pixel_total : 117755 time to create 1 rle with old method : 0.1271352767944336 time for calcul the mask position with numpy : 0.02898859977722168 nb_pixel_total : 61144 time to create 1 rle with old method : 0.06655335426330566 time for calcul the mask position with numpy : 0.029522180557250977 nb_pixel_total : 30452 time to create 1 rle with old method : 0.03338885307312012 time for calcul the mask position with numpy : 0.02891683578491211 nb_pixel_total : 9938 time to create 1 rle with old method : 0.010989904403686523 time for calcul the mask position with numpy : 0.03038787841796875 nb_pixel_total : 25442 time to create 1 rle with old method : 0.029642581939697266 create new chi : 4.1285951137542725 time to delete rle : 0.0027952194213867188 batch 1 Loaded 60 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17744 TO DO : save crop sub photo not yet done ! save time : 1.718533992767334 nb_obj : 38 nb_hashtags : 3 time to prepare the origin masks : 3.878417730331421 time for calcul the mask position with numpy : 0.37397265434265137 nb_pixel_total : 5829944 time to create 1 rle with new method : 0.6725342273712158 time for calcul the mask position with numpy : 0.027538299560546875 nb_pixel_total : 5578 time to create 1 rle with old method : 0.006059169769287109 time for calcul the mask position with numpy : 0.027562856674194336 nb_pixel_total : 6382 time to create 1 rle with old method : 0.006675004959106445 time for calcul the mask position with numpy : 0.02736210823059082 nb_pixel_total : 22991 time to create 1 rle with old method : 0.024216175079345703 time for calcul the mask position with numpy : 0.026598215103149414 nb_pixel_total : 32979 time to create 1 rle with old method : 0.03317403793334961 time for calcul the mask position with numpy : 0.026309490203857422 nb_pixel_total : 39270 time to create 1 rle with old method : 0.04126119613647461 time for calcul the mask position with numpy : 0.02734684944152832 nb_pixel_total : 32763 time to create 1 rle with old method : 0.03379964828491211 time for calcul the mask position with numpy : 0.026438236236572266 nb_pixel_total : 26969 time to create 1 rle with old method : 0.027330398559570312 time for calcul the mask position with numpy : 0.026300668716430664 nb_pixel_total : 9961 time to create 1 rle with old method : 0.010338306427001953 time for calcul the mask position with numpy : 0.027581453323364258 nb_pixel_total : 12133 time to create 1 rle with old method : 0.01286172866821289 time for calcul the mask position with numpy : 0.02783489227294922 nb_pixel_total : 40888 time to create 1 rle with old method : 0.042192935943603516 time for calcul the mask position with numpy : 0.027692794799804688 nb_pixel_total : 3939 time to create 1 rle with old method : 0.004120588302612305 time for calcul the mask position with numpy : 0.02714252471923828 nb_pixel_total : 28997 time to create 1 rle with old method : 0.030795574188232422 time for calcul the mask position with numpy : 0.028854846954345703 nb_pixel_total : 44326 time to create 1 rle with old method : 0.04919886589050293 time for calcul the mask position with numpy : 0.028038501739501953 nb_pixel_total : 31405 time to create 1 rle with old method : 0.03290867805480957 time for calcul the mask position with numpy : 0.026382923126220703 nb_pixel_total : 6166 time to create 1 rle with old method : 0.006507396697998047 time for calcul the mask position with numpy : 0.02763509750366211 nb_pixel_total : 82951 time to create 1 rle with old method : 0.08648133277893066 time for calcul the mask position with numpy : 0.02842235565185547 nb_pixel_total : 38703 time to create 1 rle with old method : 0.04134368896484375 time for calcul the mask position with numpy : 0.028165340423583984 nb_pixel_total : 11431 time to create 1 rle with old method : 0.012147903442382812 time for calcul the mask position with numpy : 0.02870488166809082 nb_pixel_total : 86384 time to create 1 rle with old method : 0.09571981430053711 time for calcul the mask position with numpy : 0.02907729148864746 nb_pixel_total : 100532 time to create 1 rle with old method : 0.11182284355163574 time for calcul the mask position with numpy : 0.028811931610107422 nb_pixel_total : 21617 time to create 1 rle with old method : 0.024239301681518555 time for calcul the mask position with numpy : 0.028603076934814453 nb_pixel_total : 18458 time to create 1 rle with old method : 0.02059173583984375 time for calcul the mask position with numpy : 0.02907419204711914 nb_pixel_total : 54759 time to create 1 rle with old method : 0.06774449348449707 time for calcul the mask position with numpy : 0.02891230583190918 nb_pixel_total : 20684 time to create 1 rle with old method : 0.022714614868164062 time for calcul the mask position with numpy : 0.028064250946044922 nb_pixel_total : 15698 time to create 1 rle with old method : 0.01687908172607422 time for calcul the mask position with numpy : 0.027936220169067383 nb_pixel_total : 5848 time to create 1 rle with old method : 0.006437063217163086 time for calcul the mask position with numpy : 0.02775883674621582 nb_pixel_total : 10084 time to create 1 rle with old method : 0.010603189468383789 time for calcul the mask position with numpy : 0.027974605560302734 nb_pixel_total : 8614 time to create 1 rle with old method : 0.009357213973999023 time for calcul the mask position with numpy : 0.028344154357910156 nb_pixel_total : 24350 time to create 1 rle with old method : 0.026226282119750977 time for calcul the mask position with numpy : 0.02780771255493164 nb_pixel_total : 5850 time to create 1 rle with old method : 0.0063474178314208984 time for calcul the mask position with numpy : 0.02847743034362793 nb_pixel_total : 120220 time to create 1 rle with old method : 0.1489124298095703 time for calcul the mask position with numpy : 0.033373355865478516 nb_pixel_total : 137471 time to create 1 rle with old method : 0.14957571029663086 time for calcul the mask position with numpy : 0.028784990310668945 nb_pixel_total : 579 time to create 1 rle with old method : 0.0006864070892333984 time for calcul the mask position with numpy : 0.02795553207397461 nb_pixel_total : 4134 time to create 1 rle with old method : 0.004483938217163086 time for calcul the mask position with numpy : 0.028155803680419922 nb_pixel_total : 18260 time to create 1 rle with old method : 0.01981186866760254 time for calcul the mask position with numpy : 0.02824687957763672 nb_pixel_total : 19530 time to create 1 rle with old method : 0.0217742919921875 time for calcul the mask position with numpy : 0.028682470321655273 nb_pixel_total : 59513 time to create 1 rle with old method : 0.06496596336364746 time for calcul the mask position with numpy : 0.030948877334594727 nb_pixel_total : 9879 time to create 1 rle with old method : 0.011004924774169922 create new chi : 3.4919469356536865 time to delete rle : 0.002837657928466797 batch 1 Loaded 77 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19329 TO DO : save crop sub photo not yet done ! save time : 1.9144470691680908 nb_obj : 31 nb_hashtags : 3 time to prepare the origin masks : 4.164340496063232 time for calcul the mask position with numpy : 1.5397844314575195 nb_pixel_total : 6134550 time to create 1 rle with new method : 0.5092718601226807 time for calcul the mask position with numpy : 0.02809929847717285 nb_pixel_total : 14009 time to create 1 rle with old method : 0.014873266220092773 time for calcul the mask position with numpy : 0.027945518493652344 nb_pixel_total : 19780 time to create 1 rle with old method : 0.021666765213012695 time for calcul the mask position with numpy : 0.02816152572631836 nb_pixel_total : 10219 time to create 1 rle with old method : 0.011277914047241211 time for calcul the mask position with numpy : 0.02840876579284668 nb_pixel_total : 14816 time to create 1 rle with old method : 0.016505002975463867 time for calcul the mask position with numpy : 0.028623580932617188 nb_pixel_total : 22353 time to create 1 rle with old method : 0.024436235427856445 time for calcul the mask position with numpy : 0.02869248390197754 nb_pixel_total : 47749 time to create 1 rle with old method : 0.05326223373413086 time for calcul the mask position with numpy : 0.027721405029296875 nb_pixel_total : 37758 time to create 1 rle with old method : 0.045476436614990234 time for calcul the mask position with numpy : 0.028038501739501953 nb_pixel_total : 20319 time to create 1 rle with old method : 0.021863698959350586 time for calcul the mask position with numpy : 0.027962923049926758 nb_pixel_total : 45091 time to create 1 rle with old method : 0.04807925224304199 time for calcul the mask position with numpy : 0.028069734573364258 nb_pixel_total : 18135 time to create 1 rle with old method : 0.019628286361694336 time for calcul the mask position with numpy : 0.028439998626708984 nb_pixel_total : 9966 time to create 1 rle with old method : 0.010838031768798828 time for calcul the mask position with numpy : 0.027957916259765625 nb_pixel_total : 43940 time to create 1 rle with old method : 0.046976566314697266 time for calcul the mask position with numpy : 0.028449296951293945 nb_pixel_total : 47498 time to create 1 rle with old method : 0.05244874954223633 time for calcul the mask position with numpy : 0.0280759334564209 nb_pixel_total : 62830 time to create 1 rle with old method : 0.06585478782653809 time for calcul the mask position with numpy : 0.0278170108795166 nb_pixel_total : 37706 time to create 1 rle with old method : 0.04111814498901367 time for calcul the mask position with numpy : 0.029011249542236328 nb_pixel_total : 11676 time to create 1 rle with old method : 0.013152360916137695 time for calcul the mask position with numpy : 0.029879093170166016 nb_pixel_total : 20479 time to create 1 rle with old method : 0.022828340530395508 time for calcul the mask position with numpy : 0.028391361236572266 nb_pixel_total : 22856 time to create 1 rle with old method : 0.024845600128173828 time for calcul the mask position with numpy : 0.027910232543945312 nb_pixel_total : 39384 time to create 1 rle with old method : 0.043251991271972656 time for calcul the mask position with numpy : 0.02910161018371582 nb_pixel_total : 60602 time to create 1 rle with old method : 0.06602740287780762 time for calcul the mask position with numpy : 0.029181480407714844 nb_pixel_total : 37059 time to create 1 rle with old method : 0.040250539779663086 time for calcul the mask position with numpy : 0.02952408790588379 nb_pixel_total : 98909 time to create 1 rle with old method : 0.1067345142364502 time for calcul the mask position with numpy : 0.029121875762939453 nb_pixel_total : 64635 time to create 1 rle with old method : 0.06959176063537598 time for calcul the mask position with numpy : 0.02910923957824707 nb_pixel_total : 16026 time to create 1 rle with old method : 0.01794147491455078 time for calcul the mask position with numpy : 0.029204130172729492 nb_pixel_total : 26327 time to create 1 rle with old method : 0.029366254806518555 time for calcul the mask position with numpy : 0.028306007385253906 nb_pixel_total : 12055 time to create 1 rle with old method : 0.013216018676757812 time for calcul the mask position with numpy : 0.027825117111206055 nb_pixel_total : 11612 time to create 1 rle with old method : 0.012598514556884766 time for calcul the mask position with numpy : 0.02790522575378418 nb_pixel_total : 12110 time to create 1 rle with old method : 0.013099908828735352 time for calcul the mask position with numpy : 0.028200387954711914 nb_pixel_total : 6335 time to create 1 rle with old method : 0.0069828033447265625 time for calcul the mask position with numpy : 0.028579235076904297 nb_pixel_total : 11833 time to create 1 rle with old method : 0.013069629669189453 time for calcul the mask position with numpy : 0.028057575225830078 nb_pixel_total : 11623 time to create 1 rle with old method : 0.012722492218017578 create new chi : 3.967681884765625 time to delete rle : 0.0037031173706054688 batch 1 Loaded 63 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16828 TO DO : save crop sub photo not yet done ! save time : 1.6980700492858887 map_output_result : {1350433591: (0.0, 'Should be the crop_list due to order', 0), 1350433508: (0.0, 'Should be the crop_list due to order', 0), 1350433246: (0.0, 'Should be the crop_list due to order', 0), 1350433188: (0.0, 'Should be the crop_list due to order', 0), 1350382174: (0.0, 'Should be the crop_list due to order', 0), 1350382169: (0.0, 'Should be the crop_list due to order', 0), 1350382164: (0.0, 'Should be the crop_list due to order', 0), 1350382052: (0.0, 'Should be the crop_list due to order', 0), 1350382045: (0.0, 'Should be the crop_list due to order', 0), 1350382041: (0.0, 'Should be the crop_list due to order', 0), 1350382029: (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 [1350433591, 1350433508, 1350433246, 1350433188, 1350382174, 1350382169, 1350382164, 1350382052, 1350382045, 1350382041, 1350382029] Looping around the photos to save general results len do output : 11 /1350433591.Didn't retrieve data . /1350433508.Didn't retrieve data . /1350433246.Didn't retrieve data . /1350433188.Didn't retrieve data . /1350382174.Didn't retrieve data . /1350382169.Didn't retrieve data . /1350382164.Didn't retrieve data . /1350382052.Didn't retrieve data . /1350382045.Didn't retrieve data . /1350382041.Didn't retrieve data . /1350382029.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, '2733691') ('3318', '22153644', '1350433591', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433508', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433246', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433188', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382174', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382169', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382164', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382052', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382045', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382041', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382029', None, None, None, None, None, '2733691') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.01354360580444336 save_final save missing photos in datou_result : time spend for datou_step_exec : 135.64881777763367 time spend to save output : 0.014206171035766602 total time spend for step 3 : 135.66302394866943 step4:ventilate_hashtags_in_portfolio Wed Apr 9 10:10:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 22153644 get user id for portfolio 22153644 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`=22153644 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pet_clair','metal','papier','pet_fonce','mal_croppe','flou','background','pehd','environnement','carton')) 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`=22153644 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pet_clair','metal','papier','pet_fonce','mal_croppe','flou','background','pehd','environnement','carton')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") 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`=22153644 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','pet_clair','metal','papier','pet_fonce','mal_croppe','flou','background','pehd','environnement','carton')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22154850,22154851,22154852,22154853,22154854,22154855,22154856,22154857,22154858,22154859,22154860?tags=autre,pet_clair,metal,papier,pet_fonce,mal_croppe,flou,background,pehd,environnement,carton Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350433591, 1350433508, 1350433246, 1350433188, 1350382174, 1350382169, 1350382164, 1350382052, 1350382045, 1350382041, 1350382029] Looping around the photos to save general results len do output : 1 /22153644. 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, '2733691') ('3318', '22153644', '1350433591', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433508', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433246', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433188', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382174', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382169', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382164', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382052', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382045', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382041', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382029', None, None, None, None, None, '2733691') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.013744115829467773 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.6847848892211914 time spend to save output : 0.01401972770690918 total time spend for step 4 : 1.6988046169281006 step5:final Wed Apr 9 10:10:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : {1350433591: ('0.2387647538497724',), 1350433508: ('0.2387647538497724',), 1350433246: ('0.2387647538497724',), 1350433188: ('0.2387647538497724',), 1350382174: ('0.2387647538497724',), 1350382169: ('0.2387647538497724',), 1350382164: ('0.2387647538497724',), 1350382052: ('0.2387647538497724',), 1350382045: ('0.2387647538497724',), 1350382041: ('0.2387647538497724',), 1350382029: ('0.2387647538497724',)} new output for save of step final : {1350433591: ('0.2387647538497724',), 1350433508: ('0.2387647538497724',), 1350433246: ('0.2387647538497724',), 1350433188: ('0.2387647538497724',), 1350382174: ('0.2387647538497724',), 1350382169: ('0.2387647538497724',), 1350382164: ('0.2387647538497724',), 1350382052: ('0.2387647538497724',), 1350382045: ('0.2387647538497724',), 1350382041: ('0.2387647538497724',), 1350382029: ('0.2387647538497724',)} [1350433591, 1350433508, 1350433246, 1350433188, 1350382174, 1350382169, 1350382164, 1350382052, 1350382045, 1350382041, 1350382029] Looping around the photos to save general results len do output : 11 /1350433591.Didn't retrieve data . /1350433508.Didn't retrieve data . /1350433246.Didn't retrieve data . /1350433188.Didn't retrieve data . /1350382174.Didn't retrieve data . /1350382169.Didn't retrieve data . /1350382164.Didn't retrieve data . /1350382052.Didn't retrieve data . /1350382045.Didn't retrieve data . /1350382041.Didn't retrieve data . /1350382029.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, '2733691') ('3318', '22153644', '1350433591', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433508', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433246', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433188', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382174', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382169', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382164', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382052', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382045', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382041', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382029', None, None, None, None, None, '2733691') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.016314268112182617 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10731816291809082 time spend to save output : 0.019536256790161133 total time spend for step 5 : 0.12685441970825195 step6:blur_detection Wed Apr 9 10:10:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487.jpg resize: (2160, 3264) 1350433591 -4.844280165707691 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e.jpg resize: (2160, 3264) 1350433508 -4.966982020566459 treat image : temp/1744185631_306356_1350433246_2a007ce28bfe288039b9147baf15cb07.jpg resize: (2160, 3264) 1350433246 -3.1145404306280566 treat image : temp/1744185631_306356_1350433188_3a1c5b878b7c979dcc29e04914cc5a8b.jpg resize: (2160, 3264) 1350433188 -4.268111354660713 treat image : temp/1744185631_306356_1350382174_6a18b29d0f3ac33ff651c74aac0bc9ef.jpg resize: (2160, 3264) 1350382174 -3.996316249646768 treat image : temp/1744185631_306356_1350382169_d92727f49ece61a0e5450ac563b0d259.jpg resize: (2160, 3264) 1350382169 -2.5944032981232907 treat image : temp/1744185631_306356_1350382164_8615cd786ff912c15e0e1cc669d8529e.jpg resize: (2160, 3264) 1350382164 -3.1232038793553967 treat image : temp/1744185631_306356_1350382052_c5001694ffbeed9f1ded23e1f5853df9.jpg resize: (2160, 3264) 1350382052 -1.1978578153088195 treat image : temp/1744185631_306356_1350382045_9787be6f517954ea90e840a98f3cb3d8.jpg resize: (2160, 3264) 1350382045 -4.49528347279198 treat image : temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955.jpg resize: (2160, 3264) 1350382041 -3.8534472352171125 treat image : temp/1744185631_306356_1350382029_909c0465671438a3695de1e59246409b.jpg resize: (2160, 3264) 1350382029 -4.9926103401242266 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075005_0.png resize: (220, 130) 1350694668 -1.9382781287638877 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075039_0.png resize: (180, 144) 1350694670 -1.2531472630860052 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751074992_0.png resize: (393, 429) 1350694672 -3.0093456582374367 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075008_0.png resize: (236, 241) 1350694673 -2.361033668574041 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075024_0.png resize: (114, 141) 1350694674 -1.8082093676686246 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075033_0.png resize: (214, 360) 1350694677 -2.628636583204171 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075010_0.png resize: (191, 214) 1350694678 -1.8716343906464163 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075021_0.png resize: (228, 322) 1350694679 -2.8799069326202154 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075031_0.png resize: (59, 128) 1350694681 6.207635065039613 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751074999_0.png resize: (372, 339) 1350694683 -3.5002569343458707 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075022_0.png resize: (85, 170) 1350694684 -1.4865466084522574 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075034_0.png resize: (168, 118) 1350694687 -0.4121851562105876 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751074993_0.png resize: (178, 114) 1350694689 0.5186723036276171 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075026_0.png resize: (167, 119) 1350694690 -2.741956698490888 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075025_0.png resize: (198, 166) 1350694692 -2.44809295701526 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075007_0.png resize: (152, 169) 1350694694 -2.3785305436029116 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075037_0.png resize: (421, 378) 1350694695 -3.2834504555107964 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075028_0.png resize: (202, 534) 1350694697 -3.4138644243182124 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075035_0.png resize: (168, 168) 1350694699 -1.8284874415546808 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075000_0.png resize: (103, 218) 1350694702 -1.8284961591069417 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075030_0.png resize: (174, 186) 1350694704 -1.6335153237090887 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075006_0.png resize: (164, 169) 1350694706 -0.3595271649137979 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075001_0.png resize: (135, 262) 1350694709 -3.575606032175169 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075038_0.png resize: (161, 259) 1350694710 -4.131394510186555 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751074998_0.png resize: (242, 283) 1350694713 -2.706283001737405 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075032_0.png resize: (166, 190) 1350694714 -3.9940268717177254 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075009_0.png resize: (204, 288) 1350694715 -1.805464173640589 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751074991_0.png resize: (397, 331) 1350694718 -2.3376343900455328 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075003_0.png resize: (169, 163) 1350694719 -3.4724188446175153 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075036_0.png resize: (138, 175) 1350694721 -3.5225744187446515 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751074997_0.png resize: (110, 182) 1350694722 -2.508096286194025 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075019_0.png resize: (266, 262) 1350694725 -2.465680253054707 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075018_0.png resize: (325, 248) 1350694726 -3.1679427368314133 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075004_0.png resize: (194, 186) 1350694727 -2.8367700452465976 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075016_0.png resize: (235, 277) 1350694730 -2.1814152076450104 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075015_0.png resize: (281, 363) 1350694731 -2.9248076165248236 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751074990_0.png resize: (188, 185) 1350694732 -2.1207497264850006 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751074994_0.png resize: (144, 116) 1350694733 -3.842521455868789 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075029_0.png resize: (107, 78) 1350694736 -2.611761200543998 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075012_0.png resize: (125, 114) 1350694737 -1.8799345696706717 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075023_0.png resize: (204, 186) 1350694738 -3.1434950910304553 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075020_0.png resize: (296, 236) 1350694741 -2.106815994270656 treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075014_0.png resize: (113, 114) 1350694742 -3.4029795666993983 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075089_0.png resize: (275, 111) 1350694743 -2.6811311964884754 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075054_0.png resize: (139, 122) 1350694746 -2.529159235239775 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075051_0.png resize: (382, 312) 1350694747 -2.65768100857647 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075060_0.png resize: (259, 158) 1350694748 -2.5105534050201914 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075062_0.png resize: (253, 188) 1350694751 -2.379227854516176 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075048_0.png resize: (146, 110) 1350694753 -1.734605000079457 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075064_0.png resize: (286, 212) 1350694754 -2.176051035749958 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075042_0.png resize: (129, 294) 1350694756 -3.1830820924394754 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075088_0.png resize: (191, 172) 1350694758 -3.9891108024740913 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075079_0.png resize: (195, 116) 1350694759 -2.6130577981715537 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075052_0.png resize: (159, 214) 1350694760 -2.2255817758443 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075069_0.png resize: (321, 234) 1350694763 -2.6342026540544397 treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075063_0.png resize: (211, 166) 1350694764 -3.3673806888666182 treat image : 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temp/1744185631_306356_1350433246_2a007ce28bfe288039b9147baf15cb07_rle_crop_3751075107_0.png resize: (274, 340) 1350696383 -2.9842788605255377 treat image : temp/1744185631_306356_1350382164_8615cd786ff912c15e0e1cc669d8529e_rle_crop_3751075224_0.png resize: (231, 239) 1350696385 -3.598672967394343 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 : 381 time used for this insertion : 0.03010416030883789 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 381 time used for this insertion : 0.06948208808898926 save missing photos in datou_result : time spend for datou_step_exec : 53.75899696350098 time spend to save output : 0.1073000431060791 total time spend for step 6 : 53.866297006607056 step7:brightness Wed Apr 9 10:11:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 calcul brightness treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487.jpg treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e.jpg treat image : temp/1744185631_306356_1350433246_2a007ce28bfe288039b9147baf15cb07.jpg treat image : temp/1744185631_306356_1350433188_3a1c5b878b7c979dcc29e04914cc5a8b.jpg treat image : temp/1744185631_306356_1350382174_6a18b29d0f3ac33ff651c74aac0bc9ef.jpg treat image : temp/1744185631_306356_1350382169_d92727f49ece61a0e5450ac563b0d259.jpg treat image : temp/1744185631_306356_1350382164_8615cd786ff912c15e0e1cc669d8529e.jpg treat image : temp/1744185631_306356_1350382052_c5001694ffbeed9f1ded23e1f5853df9.jpg treat image : temp/1744185631_306356_1350382045_9787be6f517954ea90e840a98f3cb3d8.jpg treat image : temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955.jpg treat image : 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temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955_rle_crop_3751075327_0.png treat image : temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955_rle_crop_3751075294_0.png treat image : temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955_rle_crop_3751075319_0.png treat image : temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955_rle_crop_3751075311_0.png treat image : temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955_rle_crop_3751075301_0.png treat image : temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955_rle_crop_3751075325_0.png treat image : temp/1744185631_306356_1350382029_909c0465671438a3695de1e59246409b_rle_crop_3751075347_0.png treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075077_0.png treat image : temp/1744185631_306356_1350382174_6a18b29d0f3ac33ff651c74aac0bc9ef_rle_crop_3751075177_0.png treat image : temp/1744185631_306356_1350382169_d92727f49ece61a0e5450ac563b0d259_rle_crop_3751075199_0.png treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075027_0.png treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075002_0.png treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075055_0.png treat image : temp/1744185631_306356_1350433246_2a007ce28bfe288039b9147baf15cb07_rle_crop_3751075106_0.png treat image : temp/1744185631_306356_1350433246_2a007ce28bfe288039b9147baf15cb07_rle_crop_3751075105_0.png treat image : temp/1744185631_306356_1350433188_3a1c5b878b7c979dcc29e04914cc5a8b_rle_crop_3751075132_0.png treat image : temp/1744185631_306356_1350433188_3a1c5b878b7c979dcc29e04914cc5a8b_rle_crop_3751075118_0.png treat image : temp/1744185631_306356_1350433188_3a1c5b878b7c979dcc29e04914cc5a8b_rle_crop_3751075144_0.png treat image : 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temp/1744185631_306356_1350382169_d92727f49ece61a0e5450ac563b0d259_rle_crop_3751075196_0.png treat image : temp/1744185631_306356_1350382164_8615cd786ff912c15e0e1cc669d8529e_rle_crop_3751075221_0.png treat image : temp/1744185631_306356_1350382052_c5001694ffbeed9f1ded23e1f5853df9_rle_crop_3751075247_0.png treat image : temp/1744185631_306356_1350382052_c5001694ffbeed9f1ded23e1f5853df9_rle_crop_3751075258_0.png treat image : temp/1744185631_306356_1350382052_c5001694ffbeed9f1ded23e1f5853df9_rle_crop_3751075232_0.png treat image : temp/1744185631_306356_1350382045_9787be6f517954ea90e840a98f3cb3d8_rle_crop_3751075285_0.png treat image : temp/1744185631_306356_1350382041_568ff51d81f50a2d583d2fa28a5b1955_rle_crop_3751075316_0.png treat image : temp/1744185631_306356_1350382029_909c0465671438a3695de1e59246409b_rle_crop_3751075352_0.png treat image : temp/1744185631_306356_1350382029_909c0465671438a3695de1e59246409b_rle_crop_3751075350_0.png treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075044_0.png treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075046_0.png treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075061_0.png treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075091_0.png treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075041_0.png treat image : temp/1744185631_306356_1350433508_2df3fcced3bb83d8c534a470ee0bfd0e_rle_crop_3751075076_0.png treat image : temp/1744185631_306356_1350433188_3a1c5b878b7c979dcc29e04914cc5a8b_rle_crop_3751075148_0.png treat image : temp/1744185631_306356_1350433188_3a1c5b878b7c979dcc29e04914cc5a8b_rle_crop_3751075138_0.png treat image : temp/1744185631_306356_1350433591_65dff8dcdc12c56f2dae9a93a2afc487_rle_crop_3751075040_0.png treat image : temp/1744185631_306356_1350433246_2a007ce28bfe288039b9147baf15cb07_rle_crop_3751075107_0.png treat image : temp/1744185631_306356_1350382164_8615cd786ff912c15e0e1cc669d8529e_rle_crop_3751075224_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 : 381 time used for this insertion : 0.2539091110229492 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 381 time used for this insertion : 0.3362140655517578 save missing photos in datou_result : time spend for datou_step_exec : 12.914617538452148 time spend to save output : 0.5974147319793701 total time spend for step 7 : 13.512032270431519 step8:velours_tree Wed Apr 9 10:11:42 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 : 0.24273109436035156 time spend to save output : 6.341934204101562e-05 total time spend for step 8 : 0.24279451370239258 step9:send_mail_cod Wed Apr 9 10:11:42 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_P22153644_09-04-2025_10_11_42.pdf 22154850 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 .imagette221548501744186302 22154851 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 .imagette221548511744186303 22154852 change filename to text .change filename to text .change filename to text .imagette221548521744186305 22154853 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 .imagette221548531744186305 22154854 imagette221548541744186306 22154855 imagette221548551744186306 22154856 imagette221548561744186306 22154857 imagette221548571744186306 22154858 change filename to text .change filename to text .change filename to text .imagette221548581744186306 22154860 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 .imagette221548601744186306 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22153644 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22154850,22154851,22154852,22154853,22154854,22154855,22154856,22154857,22154858,22154859,22154860?tags=autre,pet_clair,metal,papier,pet_fonce,mal_croppe,flou,background,pehd,environnement,carton args[1350433591] : ((1350433591, -4.844280165707691, 492609224), (1350433591, 0.038171976163200415, 2107752395), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350433508] : ((1350433508, -4.966982020566459, 492609224), (1350433508, -0.1592401342891808, 496442774), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350433246] : ((1350433246, -3.1145404306280566, 492609224), (1350433246, -0.2224292695716978, 496442774), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350433188] : ((1350433188, -4.268111354660713, 492609224), (1350433188, -0.12730981329515595, 496442774), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350382174] : ((1350382174, -3.996316249646768, 492609224), (1350382174, -0.26339879985438125, 496442774), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350382169] : ((1350382169, -2.5944032981232907, 492609224), (1350382169, 0.2068152928122102, 2107752395), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350382164] : ((1350382164, -3.1232038793553967, 492609224), (1350382164, 0.020725587295294876, 2107752395), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350382052] : ((1350382052, -1.1978578153088195, 492688767), (1350382052, -0.03645791602227607, 2107752395), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350382045] : ((1350382045, -4.49528347279198, 492609224), (1350382045, 0.07528127571180254, 2107752395), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350382041] : ((1350382041, -3.8534472352171125, 492609224), (1350382041, -0.0678171147028053, 2107752395), '0.2387647538497724') We are sending mail with results at report@fotonower.com args[1350382029] : ((1350382029, -4.9926103401242266, 492609224), (1350382029, -0.049553570905081494, 2107752395), '0.2387647538497724') We are sending mail with results at report@fotonower.com refus_total : 0.2387647538497724 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=22153644 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350382164,1350382174,1350433246,1350382029,1350382045,1350433188,1350433508,1350382041,1350382052,1350382169,1350433591) Found this number of photos: 11 begin to download photo : 1350382164 begin to download photo : 1350382029 begin to download photo : 1350433508 begin to download photo : 1350382169 download finish for photo 1350382164 begin to download photo : 1350382174 download finish for photo 1350382169 begin to download photo : 1350433591 download finish for photo 1350382029 begin to download photo : 1350382045 download finish for photo 1350433508 begin to download photo : 1350382041 download finish for photo 1350382174 begin to download photo : 1350433246 download finish for photo 1350433591 download finish for photo 1350382041 begin to download photo : 1350382052 download finish for photo 1350382045 begin to download photo : 1350433188 download finish for photo 1350433246 download finish for photo 1350382052 download finish for photo 1350433188 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153644_09-04-2025_10_11_42.pdf results_Auto_P22153644_09-04-2025_10_11_42.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153644_09-04-2025_10_11_42.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','22153644','results_Auto_P22153644_09-04-2025_10_11_42.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153644_09-04-2025_10_11_42.pdf','pdf','','0.93','0.2387647538497724') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22153644

https://www.fotonower.com/image?json=false&list_photos_id=1350433591
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350433508
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350433246
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350433188
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350382174
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350382169
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350382164
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350382052
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350382045
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350382041
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350382029
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 23.88%
Veuillez trouver les photos des contaminants.

exemples de contaminants: autre: https://www.fotonower.com/view/22154850?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22154851?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22154852?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22154853?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22154858?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22154860?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153644_09-04-2025_10_11_42.pdf.

Lien vers velours :https://www.fotonower.com/velours/22154850,22154851,22154852,22154853,22154854,22154855,22154856,22154857,22154858,22154859,22154860?tags=autre,pet_clair,metal,papier,pet_fonce,mal_croppe,flou,background,pehd,environnement,carton.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 08:11:52 GMT Content-Length: 0 Connection: close X-Message-Id: Qtjki2rkT0CYtGNTMqMtRg 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 [1350433591, 1350433508, 1350433246, 1350433188, 1350382174, 1350382169, 1350382164, 1350382052, 1350382045, 1350382041, 1350382029] 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, '2733691') ('3318', '22153644', '1350433591', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433508', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433246', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433188', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382174', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382169', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382164', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382052', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382045', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382041', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382029', None, None, None, None, None, '2733691') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.47495484352111816 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.50545597076416 time spend to save output : 0.4752037525177002 total time spend for step 9 : 9.98065972328186 step10:split_time_score Wed Apr 9 10:11:52 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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'}] (('18', 11),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 07042025 22153644 Nombre de photos uploadées : 11 / 23040 (0%) 07042025 22153644 Nombre de photos taguées (types de déchets): 0 / 11 (0%) 07042025 22153644 Nombre de photos taguées (volume) : 0 / 11 (0%) elapsed_time : load_data_split_time_score 3.337860107421875e-06 elapsed_time : order_list_meta_photo_and_scores 2.4557113647460938e-05 ??????????? elapsed_time : fill_and_build_computed_from_old_data 0.0006482601165771484 elapsed_time : insert_dashboard_record_day_entry 0.024787425994873047 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153527 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153533 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153536 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153537 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153567 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153572 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153573 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153575 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153579 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153585 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153590 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153594 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153599 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153631 order by id desc limit 1 Qualite : 0.2387647538497724 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153644_09-04-2025_10_11_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153644 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`=22153644 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153647 order by id desc limit 1 Qualite : 0.16284528966389794 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153651_09-04-2025_09_56_05.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153651 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`=22153651 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153655 order by id desc limit 1 Qualite : 0.13395730297527286 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153656_09-04-2025_09_56_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153656 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`=22153656 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'07042025': {'nb_upload': 11, '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 [1350433591, 1350433508, 1350433246, 1350433188, 1350382174, 1350382169, 1350382164, 1350382052, 1350382045, 1350382041, 1350382029] Looping around the photos to save general results len do output : 1 /22153644Didn'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, '2733691') ('3318', '22153644', '1350433591', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433508', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433246', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350433188', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382174', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382169', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382164', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382052', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382045', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382041', None, None, None, None, None, '2733691') ('3318', None, None, None, None, None, None, None, '2733691') ('3318', '22153644', '1350382029', None, None, None, None, None, '2733691') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.013926267623901367 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.893268346786499 time spend to save output : 0.014172077178955078 total time spend for step 10 : 13.907440423965454 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 11 set_done_treatment 317.55user 152.34system 11:41.47elapsed 66%CPU (0avgtext+0avgdata 6411548maxresident)k 12733704inputs+219760outputs (343741major+27927445minor)pagefaults 0swaps