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 : 406788 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 : ['2733693'] with mtr_portfolio_ids : ['22153647'] and first list_photo_ids : [] new path : /proc/406788/ 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 : 2.30667781829834 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 10:10:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10440 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-09 10:10:34.863971: 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 10:10:34.891147: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-09 10:10:34.893342: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9b28000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-09 10:10:34.893403: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-09 10:10:34.897416: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-09 10:10:35.027810: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x33e32fa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-09 10:10:35.027864: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-09 10:10:35.029305: 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 10:10:35.029725: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:10:35.032813: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:10:35.035847: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:10:35.036268: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:10:35.038918: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:10:35.040216: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:10:35.045298: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:10:35.046750: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:10:35.046826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:10:35.047601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 10:10:35.047619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 10:10:35.047628: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 10:10:35.052705: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9671 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-09 10:10:35.357781: 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 10:10:35.357958: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:10:35.357988: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:10:35.358014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:10:35.358040: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:10:35.358063: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:10:35.358087: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:10:35.358114: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:10:35.360148: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:10:35.362128: 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 10:10:35.362287: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 10:10:35.362322: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:10:35.362350: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 10:10:35.362377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 10:10:35.362403: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 10:10:35.362429: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 10:10:35.362455: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 10:10:35.364550: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 10:10:35.364626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 10:10:35.364641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 10:10:35.364654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 10:10:35.366693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9671 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. 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 10:10:47.779262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 10:10:47.983695: 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 : 82 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 : 86 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 : 79 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 93 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 29 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 : 41 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 : 33 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 : 42 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 : 33 Detection mask done ! Trying to reset tf kernel 407528 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1748 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 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 : 0.004459857940673828 nb_pixel_total : 81936 time to create 1 rle with old method : 0.11970067024230957 length of segment : 442 time for calcul the mask position with numpy : 0.0006122589111328125 nb_pixel_total : 9612 time to create 1 rle with old method : 0.014786481857299805 length of segment : 206 time for calcul the mask position with numpy : 0.007387638092041016 nb_pixel_total : 158542 time to create 1 rle with new method : 0.021147489547729492 length of segment : 496 time for calcul the mask position with numpy : 0.0006396770477294922 nb_pixel_total : 21179 time to create 1 rle with old method : 0.03350710868835449 length of segment : 178 time for calcul the mask position with numpy : 0.006247758865356445 nb_pixel_total : 40402 time to create 1 rle with old method : 0.06645679473876953 length of segment : 287 time for calcul the mask position with numpy : 0.001608133316040039 nb_pixel_total : 45341 time to create 1 rle with old method : 0.06298017501831055 length of segment : 475 time for calcul the mask position with numpy : 0.005065202713012695 nb_pixel_total : 144447 time to create 1 rle with old method : 0.16974616050720215 length of segment : 312 time for calcul the mask position with numpy : 0.0169222354888916 nb_pixel_total : 47209 time to create 1 rle with old method : 0.057425737380981445 length of segment : 338 time for calcul the mask position with numpy : 0.005277395248413086 nb_pixel_total : 37158 time to create 1 rle with old method : 0.0419619083404541 length of segment : 258 time for calcul the mask position with numpy : 0.0009074211120605469 nb_pixel_total : 38920 time to create 1 rle with old method : 0.04360032081604004 length of segment : 337 time for calcul the mask position with numpy : 0.0007762908935546875 nb_pixel_total : 17417 time to create 1 rle with old method : 0.019459962844848633 length of segment : 228 time for calcul the mask position with numpy : 0.001220703125 nb_pixel_total : 24473 time to create 1 rle with old method : 0.03469085693359375 length of segment : 278 time for calcul the mask position with numpy : 0.021892309188842773 nb_pixel_total : 75210 time to create 1 rle with old method : 0.09696245193481445 length of segment : 390 time for calcul the mask position with numpy : 0.009787559509277344 nb_pixel_total : 15644 time to create 1 rle with old method : 0.021788835525512695 length of segment : 203 time for calcul the mask position with numpy : 0.002711772918701172 nb_pixel_total : 99577 time to create 1 rle with old method : 0.13112950325012207 length of segment : 504 time for calcul the mask position with numpy : 0.0004322528839111328 nb_pixel_total : 16370 time to create 1 rle with old method : 0.027860641479492188 length of segment : 145 time for calcul the mask position with numpy : 0.0009644031524658203 nb_pixel_total : 24012 time to create 1 rle with old method : 0.03162670135498047 length of segment : 290 time for calcul the mask position with numpy : 0.0001621246337890625 nb_pixel_total : 4542 time to create 1 rle with old method : 0.005438089370727539 length of segment : 64 time for calcul the mask position with numpy : 0.0015606880187988281 nb_pixel_total : 45405 time to create 1 rle with old method : 0.052652597427368164 length of segment : 375 time for calcul the mask position with numpy : 0.0010228157043457031 nb_pixel_total : 35646 time to create 1 rle with old method : 0.0444180965423584 length of segment : 308 time for calcul the mask position with numpy : 0.0005757808685302734 nb_pixel_total : 19749 time to create 1 rle with old method : 0.03580117225646973 length of segment : 181 time for calcul the mask position with numpy : 0.00010395050048828125 nb_pixel_total : 1747 time to create 1 rle with old method : 0.0021219253540039062 length of segment : 69 time for calcul the mask position with numpy : 0.0006558895111083984 nb_pixel_total : 19406 time to create 1 rle with old method : 0.022769927978515625 length of segment : 146 time for calcul the mask position with numpy : 0.0008394718170166016 nb_pixel_total : 19669 time to create 1 rle with old method : 0.0225374698638916 length of segment : 258 time for calcul the mask position with numpy : 0.0022270679473876953 nb_pixel_total : 82284 time to create 1 rle with old method : 0.10101199150085449 length of segment : 301 time for calcul the mask position with numpy : 0.014881134033203125 nb_pixel_total : 68593 time to create 1 rle with old method : 0.0791158676147461 length of segment : 280 time for calcul the mask position with numpy : 0.0002949237823486328 nb_pixel_total : 9155 time to create 1 rle with old method : 0.010622262954711914 length of segment : 189 time for calcul the mask position with numpy : 0.0016324520111083984 nb_pixel_total : 17870 time to create 1 rle with old method : 0.020503520965576172 length of segment : 106 time for calcul the mask position with numpy : 0.004243135452270508 nb_pixel_total : 40505 time to create 1 rle with old method : 0.05045795440673828 length of segment : 285 time for calcul the mask position with numpy : 0.0002586841583251953 nb_pixel_total : 6903 time to create 1 rle with old method : 0.008093833923339844 length of segment : 142 time for calcul the mask position with numpy : 0.0014796257019042969 nb_pixel_total : 24217 time to create 1 rle with old method : 0.02810502052307129 length of segment : 110 time for calcul the mask position with numpy : 0.00042176246643066406 nb_pixel_total : 14014 time to create 1 rle with old method : 0.016437768936157227 length of segment : 175 time for calcul the mask position with numpy : 0.0005846023559570312 nb_pixel_total : 20677 time to create 1 rle with old method : 0.023947954177856445 length of segment : 248 time for calcul the mask position with numpy : 0.0004024505615234375 nb_pixel_total : 19624 time to create 1 rle with old method : 0.022347688674926758 length of segment : 143 time for calcul the mask position with numpy : 0.0017426013946533203 nb_pixel_total : 89647 time to create 1 rle with old method : 0.10693168640136719 length of segment : 391 time for calcul the mask position with numpy : 0.0021004676818847656 nb_pixel_total : 119056 time to create 1 rle with old method : 0.1582486629486084 length of segment : 473 time for calcul the mask position with numpy : 0.00021004676818847656 nb_pixel_total : 8096 time to create 1 rle with old method : 0.009572267532348633 length of segment : 91 time for calcul the mask position with numpy : 0.00046563148498535156 nb_pixel_total : 18274 time to create 1 rle with old method : 0.02131342887878418 length of segment : 205 time for calcul the mask position with numpy : 0.0011644363403320312 nb_pixel_total : 69468 time to create 1 rle with old method : 0.07758283615112305 length of segment : 365 time for calcul the mask position with numpy : 0.003089427947998047 nb_pixel_total : 48655 time to create 1 rle with old method : 0.056030988693237305 length of segment : 218 time for calcul the mask position with numpy : 0.0005869865417480469 nb_pixel_total : 17764 time to create 1 rle with old method : 0.020348310470581055 length of segment : 207 time for calcul the mask position with numpy : 0.0035223960876464844 nb_pixel_total : 159623 time to create 1 rle with new method : 0.014213323593139648 length of segment : 544 time for calcul the mask position with numpy : 0.006480693817138672 nb_pixel_total : 86895 time to create 1 rle with old method : 0.10001230239868164 length of segment : 451 time for calcul the mask position with numpy : 0.0016870498657226562 nb_pixel_total : 22008 time to create 1 rle with old method : 0.02792835235595703 length of segment : 272 time for calcul the mask position with numpy : 0.0009648799896240234 nb_pixel_total : 13772 time to create 1 rle with old method : 0.0163419246673584 length of segment : 146 time for calcul the mask position with numpy : 0.0035872459411621094 nb_pixel_total : 60480 time to create 1 rle with old method : 0.07450485229492188 length of segment : 264 time for calcul the mask position with numpy : 0.007690906524658203 nb_pixel_total : 66101 time to create 1 rle with old method : 0.07698941230773926 length of segment : 316 time for calcul the mask position with numpy : 0.0010297298431396484 nb_pixel_total : 19975 time to create 1 rle with old method : 0.027765750885009766 length of segment : 226 time for calcul the mask position with numpy : 0.0011801719665527344 nb_pixel_total : 16960 time to create 1 rle with old method : 0.0201570987701416 length of segment : 153 time for calcul the mask position with numpy : 0.0009918212890625 nb_pixel_total : 26186 time to create 1 rle with old method : 0.030194520950317383 length of segment : 225 time for calcul the mask position with numpy : 0.0005681514739990234 nb_pixel_total : 8149 time to create 1 rle with old method : 0.009503602981567383 length of segment : 118 time for calcul the mask position with numpy : 0.005141735076904297 nb_pixel_total : 71530 time to create 1 rle with old method : 0.0846109390258789 length of segment : 389 time for calcul the mask position with numpy : 0.01000833511352539 nb_pixel_total : 85568 time to create 1 rle with old method : 0.1015312671661377 length of segment : 482 time for calcul the mask position with numpy : 0.004307985305786133 nb_pixel_total : 81149 time to create 1 rle with old method : 0.09372186660766602 length of segment : 652 time for calcul the mask position with numpy : 0.0005362033843994141 nb_pixel_total : 13568 time to create 1 rle with old method : 0.016528606414794922 length of segment : 223 time for calcul the mask position with numpy : 0.0006935596466064453 nb_pixel_total : 15792 time to create 1 rle with old method : 0.01832294464111328 length of segment : 218 time for calcul the mask position with numpy : 0.0024328231811523438 nb_pixel_total : 33395 time to create 1 rle with old method : 0.0393679141998291 length of segment : 328 time for calcul the mask position with numpy : 0.0018076896667480469 nb_pixel_total : 30667 time to create 1 rle with old method : 0.03591442108154297 length of segment : 193 time for calcul the mask position with numpy : 0.0005488395690917969 nb_pixel_total : 19394 time to create 1 rle with old method : 0.022708654403686523 length of segment : 162 time for calcul the mask position with numpy : 0.0018906593322753906 nb_pixel_total : 36353 time to create 1 rle with old method : 0.042105674743652344 length of segment : 204 time for calcul the mask position with numpy : 0.0025162696838378906 nb_pixel_total : 38272 time to create 1 rle with old method : 0.04366421699523926 length of segment : 245 time for calcul the mask position with numpy : 0.0006840229034423828 nb_pixel_total : 8168 time to create 1 rle with old method : 0.009665966033935547 length of segment : 115 time for calcul the mask position with numpy : 0.0007903575897216797 nb_pixel_total : 11861 time to create 1 rle with old method : 0.014430046081542969 length of segment : 117 time for calcul the mask position with numpy : 0.0034096240997314453 nb_pixel_total : 46287 time to create 1 rle with old method : 0.05360221862792969 length of segment : 344 time for calcul the mask position with numpy : 0.005232334136962891 nb_pixel_total : 72576 time to create 1 rle with old method : 0.08257484436035156 length of segment : 557 time for calcul the mask position with numpy : 0.0015454292297363281 nb_pixel_total : 17008 time to create 1 rle with old method : 0.02138352394104004 length of segment : 162 time for calcul the mask position with numpy : 0.0005328655242919922 nb_pixel_total : 7911 time to create 1 rle with old method : 0.009304046630859375 length of segment : 109 time for calcul the mask position with numpy : 0.0016837120056152344 nb_pixel_total : 16371 time to create 1 rle with old method : 0.019835233688354492 length of segment : 152 time for calcul the mask position with numpy : 0.0009777545928955078 nb_pixel_total : 12456 time to create 1 rle with old method : 0.014745235443115234 length of segment : 137 time for calcul the mask position with numpy : 0.003907442092895508 nb_pixel_total : 36780 time to create 1 rle with old method : 0.04392719268798828 length of segment : 297 time for calcul the mask position with numpy : 0.0013339519500732422 nb_pixel_total : 18842 time to create 1 rle with old method : 0.022130489349365234 length of segment : 211 time for calcul the mask position with numpy : 0.003629446029663086 nb_pixel_total : 47137 time to create 1 rle with old method : 0.05656719207763672 length of segment : 287 time for calcul the mask position with numpy : 0.0012483596801757812 nb_pixel_total : 21649 time to create 1 rle with old method : 0.025580406188964844 length of segment : 149 time for calcul the mask position with numpy : 0.0017333030700683594 nb_pixel_total : 29811 time to create 1 rle with old method : 0.03405928611755371 length of segment : 251 time for calcul the mask position with numpy : 0.0011010169982910156 nb_pixel_total : 13287 time to create 1 rle with old method : 0.01556396484375 length of segment : 152 time for calcul the mask position with numpy : 0.0006389617919921875 nb_pixel_total : 9021 time to create 1 rle with old method : 0.0108642578125 length of segment : 87 time for calcul the mask position with numpy : 0.0012314319610595703 nb_pixel_total : 16854 time to create 1 rle with old method : 0.0196230411529541 length of segment : 149 time for calcul the mask position with numpy : 0.0021753311157226562 nb_pixel_total : 31459 time to create 1 rle with old method : 0.036597490310668945 length of segment : 393 time for calcul the mask position with numpy : 0.0032525062561035156 nb_pixel_total : 40702 time to create 1 rle with old method : 0.04771590232849121 length of segment : 204 time for calcul the mask position with numpy : 0.0019617080688476562 nb_pixel_total : 32715 time to create 1 rle with old method : 0.038056135177612305 length of segment : 295 time for calcul the mask position with numpy : 0.003100872039794922 nb_pixel_total : 42688 time to create 1 rle with old method : 0.0491030216217041 length of segment : 194 time for calcul the mask position with numpy : 0.0019392967224121094 nb_pixel_total : 37728 time to create 1 rle with old method : 0.04374194145202637 length of segment : 253 time for calcul the mask position with numpy : 0.0015132427215576172 nb_pixel_total : 23822 time to create 1 rle with old method : 0.02754831314086914 length of segment : 254 time for calcul the mask position with numpy : 0.005259037017822266 nb_pixel_total : 77370 time to create 1 rle with old method : 0.08948373794555664 length of segment : 442 time for calcul the mask position with numpy : 0.0016067028045654297 nb_pixel_total : 23158 time to create 1 rle with old method : 0.026798486709594727 length of segment : 179 time for calcul the mask position with numpy : 0.0017232894897460938 nb_pixel_total : 29345 time to create 1 rle with old method : 0.03476095199584961 length of segment : 207 time for calcul the mask position with numpy : 0.0019659996032714844 nb_pixel_total : 24871 time to create 1 rle with old method : 0.028566360473632812 length of segment : 204 time for calcul the mask position with numpy : 0.004629850387573242 nb_pixel_total : 64265 time to create 1 rle with old method : 0.07314276695251465 length of segment : 292 time for calcul the mask position with numpy : 0.0006420612335205078 nb_pixel_total : 7684 time to create 1 rle with old method : 0.009169578552246094 length of segment : 106 time for calcul the mask position with numpy : 0.002798795700073242 nb_pixel_total : 35528 time to create 1 rle with old method : 0.041059255599975586 length of segment : 362 time for calcul the mask position with numpy : 0.0009279251098632812 nb_pixel_total : 11448 time to create 1 rle with old method : 0.013496160507202148 length of segment : 140 time for calcul the mask position with numpy : 0.0008895397186279297 nb_pixel_total : 4103 time to create 1 rle with old method : 0.005106687545776367 length of segment : 76 time for calcul the mask position with numpy : 0.004517078399658203 nb_pixel_total : 34863 time to create 1 rle with old method : 0.04033041000366211 length of segment : 229 time for calcul the mask position with numpy : 0.0013194084167480469 nb_pixel_total : 15055 time to create 1 rle with old method : 0.017560482025146484 length of segment : 204 time for calcul the mask position with numpy : 0.0023202896118164062 nb_pixel_total : 25873 time to create 1 rle with old method : 0.032048940658569336 length of segment : 193 time for calcul the mask position with numpy : 0.0019178390502929688 nb_pixel_total : 22050 time to create 1 rle with old method : 0.028539180755615234 length of segment : 240 time for calcul the mask position with numpy : 0.0018584728240966797 nb_pixel_total : 24382 time to create 1 rle with old method : 0.027494192123413086 length of segment : 350 time for calcul the mask position with numpy : 0.0006225109100341797 nb_pixel_total : 9602 time to create 1 rle with old method : 0.011200666427612305 length of segment : 114 time for calcul the mask position with numpy : 0.0035905838012695312 nb_pixel_total : 22318 time to create 1 rle with old method : 0.025422334671020508 length of segment : 293 time for calcul the mask position with numpy : 0.002325773239135742 nb_pixel_total : 16387 time to create 1 rle with old method : 0.019364595413208008 length of segment : 285 time for calcul the mask position with numpy : 0.0009620189666748047 nb_pixel_total : 27970 time to create 1 rle with old method : 0.0325322151184082 length of segment : 235 time for calcul the mask position with numpy : 0.01166534423828125 nb_pixel_total : 165287 time to create 1 rle with new method : 0.020159244537353516 length of segment : 718 time for calcul the mask position with numpy : 0.0007085800170898438 nb_pixel_total : 13533 time to create 1 rle with old method : 0.016390085220336914 length of segment : 92 time for calcul the mask position with numpy : 0.0005161762237548828 nb_pixel_total : 8126 time to create 1 rle with old method : 0.009695291519165039 length of segment : 112 time for calcul the mask position with numpy : 0.0006644725799560547 nb_pixel_total : 16112 time to create 1 rle with old method : 0.01867508888244629 length of segment : 153 time for calcul the mask position with numpy : 0.005213737487792969 nb_pixel_total : 68611 time to create 1 rle with old method : 0.07781672477722168 length of segment : 422 time for calcul the mask position with numpy : 0.0004832744598388672 nb_pixel_total : 9239 time to create 1 rle with old method : 0.01483917236328125 length of segment : 97 time for calcul the mask position with numpy : 0.0010116100311279297 nb_pixel_total : 13558 time to create 1 rle with old method : 0.015810251235961914 length of segment : 227 time for calcul the mask position with numpy : 0.0010869503021240234 nb_pixel_total : 52805 time to create 1 rle with old method : 0.06295275688171387 length of segment : 278 time for calcul the mask position with numpy : 0.0021042823791503906 nb_pixel_total : 13149 time to create 1 rle with old method : 0.015491008758544922 length of segment : 264 time for calcul the mask position with numpy : 0.0022695064544677734 nb_pixel_total : 29519 time to create 1 rle with old method : 0.03505229949951172 length of segment : 175 time for calcul the mask position with numpy : 0.004035234451293945 nb_pixel_total : 73132 time to create 1 rle with old method : 0.08659529685974121 length of segment : 261 time for calcul the mask position with numpy : 0.006007194519042969 nb_pixel_total : 92176 time to create 1 rle with old method : 0.10634255409240723 length of segment : 348 time for calcul the mask position with numpy : 0.0032911300659179688 nb_pixel_total : 50842 time to create 1 rle with old method : 0.05864906311035156 length of segment : 292 time for calcul the mask position with numpy : 0.000705718994140625 nb_pixel_total : 11053 time to create 1 rle with old method : 0.012891292572021484 length of segment : 124 time for calcul the mask position with numpy : 0.002247333526611328 nb_pixel_total : 20870 time to create 1 rle with old method : 0.02448248863220215 length of segment : 356 time for calcul the mask position with numpy : 0.0029506683349609375 nb_pixel_total : 45313 time to create 1 rle with old method : 0.051651954650878906 length of segment : 276 time for calcul the mask position with numpy : 0.00235748291015625 nb_pixel_total : 27402 time to create 1 rle with old method : 0.03226208686828613 length of segment : 270 time for calcul the mask position with numpy : 0.003200531005859375 nb_pixel_total : 53127 time to create 1 rle with old method : 0.0609889030456543 length of segment : 280 time for calcul the mask position with numpy : 0.008109807968139648 nb_pixel_total : 154732 time to create 1 rle with new method : 0.010788679122924805 length of segment : 356 time for calcul the mask position with numpy : 0.008919477462768555 nb_pixel_total : 156666 time to create 1 rle with new method : 0.013334274291992188 length of segment : 682 time for calcul the mask position with numpy : 0.0012061595916748047 nb_pixel_total : 15771 time to create 1 rle with old method : 0.01862025260925293 length of segment : 136 time for calcul the mask position with numpy : 0.0016427040100097656 nb_pixel_total : 15270 time to create 1 rle with old method : 0.018745899200439453 length of segment : 157 time for calcul the mask position with numpy : 0.0015676021575927734 nb_pixel_total : 34521 time to create 1 rle with old method : 0.040253400802612305 length of segment : 202 time for calcul the mask position with numpy : 0.007025003433227539 nb_pixel_total : 97377 time to create 1 rle with old method : 0.11092185974121094 length of segment : 397 time for calcul the mask position with numpy : 0.004131317138671875 nb_pixel_total : 58430 time to create 1 rle with old method : 0.0672919750213623 length of segment : 453 time for calcul the mask position with numpy : 0.0032417774200439453 nb_pixel_total : 48504 time to create 1 rle with old method : 0.0563504695892334 length of segment : 302 time for calcul the mask position with numpy : 0.0018889904022216797 nb_pixel_total : 26480 time to create 1 rle with old method : 0.031087875366210938 length of segment : 175 time for calcul the mask position with numpy : 0.0009505748748779297 nb_pixel_total : 13878 time to create 1 rle with old method : 0.016467809677124023 length of segment : 161 time for calcul the mask position with numpy : 0.0002894401550292969 nb_pixel_total : 9967 time to create 1 rle with old method : 0.012052059173583984 length of segment : 77 time for calcul the mask position with numpy : 0.0016963481903076172 nb_pixel_total : 22082 time to create 1 rle with old method : 0.025555849075317383 length of segment : 281 time for calcul the mask position with numpy : 0.00702667236328125 nb_pixel_total : 111409 time to create 1 rle with old method : 0.12710070610046387 length of segment : 334 time for calcul the mask position with numpy : 0.0029277801513671875 nb_pixel_total : 36127 time to create 1 rle with old method : 0.045667409896850586 length of segment : 235 time for calcul the mask position with numpy : 0.0012509822845458984 nb_pixel_total : 16663 time to create 1 rle with old method : 0.019600868225097656 length of segment : 176 time for calcul the mask position with numpy : 0.0008511543273925781 nb_pixel_total : 15816 time to create 1 rle with old method : 0.019163846969604492 length of segment : 97 time for calcul the mask position with numpy : 0.001367330551147461 nb_pixel_total : 26331 time to create 1 rle with old method : 0.030994176864624023 length of segment : 156 time for calcul the mask position with numpy : 0.0011534690856933594 nb_pixel_total : 24300 time to create 1 rle with old method : 0.028208255767822266 length of segment : 151 time for calcul the mask position with numpy : 0.0007677078247070312 nb_pixel_total : 13372 time to create 1 rle with old method : 0.015915632247924805 length of segment : 112 time for calcul the mask position with numpy : 0.0010576248168945312 nb_pixel_total : 17165 time to create 1 rle with old method : 0.020368099212646484 length of segment : 94 time for calcul the mask position with numpy : 0.0032205581665039062 nb_pixel_total : 47147 time to create 1 rle with old method : 0.054650306701660156 length of segment : 300 time for calcul the mask position with numpy : 0.002148866653442383 nb_pixel_total : 30918 time to create 1 rle with old method : 0.035886526107788086 length of segment : 238 time for calcul the mask position with numpy : 0.0005776882171630859 nb_pixel_total : 8530 time to create 1 rle with old method : 0.009968757629394531 length of segment : 92 time for calcul the mask position with numpy : 0.00567173957824707 nb_pixel_total : 76320 time to create 1 rle with old method : 0.08627820014953613 length of segment : 638 time for calcul the mask position with numpy : 0.0005335807800292969 nb_pixel_total : 12885 time to create 1 rle with old method : 0.014981746673583984 length of segment : 121 time for calcul the mask position with numpy : 0.0010066032409667969 nb_pixel_total : 251 time to create 1 rle with old method : 0.0005376338958740234 length of segment : 41 time for calcul the mask position with numpy : 0.006017208099365234 nb_pixel_total : 97717 time to create 1 rle with old method : 0.11362338066101074 length of segment : 483 time for calcul the mask position with numpy : 0.0077245235443115234 nb_pixel_total : 82985 time to create 1 rle with old method : 0.09351778030395508 length of segment : 607 time for calcul the mask position with numpy : 0.0013675689697265625 nb_pixel_total : 18034 time to create 1 rle with old method : 0.02069878578186035 length of segment : 150 time for calcul the mask position with numpy : 0.008270978927612305 nb_pixel_total : 108607 time to create 1 rle with old method : 0.13243389129638672 length of segment : 533 time for calcul the mask position with numpy : 0.0011174678802490234 nb_pixel_total : 9718 time to create 1 rle with old method : 0.011378049850463867 length of segment : 258 time for calcul the mask position with numpy : 0.006636381149291992 nb_pixel_total : 92879 time to create 1 rle with old method : 0.1092381477355957 length of segment : 389 time for calcul the mask position with numpy : 0.001874685287475586 nb_pixel_total : 29444 time to create 1 rle with old method : 0.033946990966796875 length of segment : 214 time for calcul the mask position with numpy : 0.0023157596588134766 nb_pixel_total : 26493 time to create 1 rle with old method : 0.03041362762451172 length of segment : 276 time for calcul the mask position with numpy : 0.0046384334564208984 nb_pixel_total : 71534 time to create 1 rle with old method : 0.08046674728393555 length of segment : 421 time for calcul the mask position with numpy : 0.001634836196899414 nb_pixel_total : 22255 time to create 1 rle with old method : 0.025553226470947266 length of segment : 113 time for calcul the mask position with numpy : 0.0010826587677001953 nb_pixel_total : 12621 time to create 1 rle with old method : 0.014652013778686523 length of segment : 129 time for calcul the mask position with numpy : 0.0013489723205566406 nb_pixel_total : 16439 time to create 1 rle with old method : 0.019221782684326172 length of segment : 157 time for calcul the mask position with numpy : 0.0035119056701660156 nb_pixel_total : 80913 time to create 1 rle with old method : 0.09906148910522461 length of segment : 700 time for calcul the mask position with numpy : 0.001371145248413086 nb_pixel_total : 25822 time to create 1 rle with old method : 0.02956843376159668 length of segment : 177 time for calcul the mask position with numpy : 0.0032732486724853516 nb_pixel_total : 46186 time to create 1 rle with old method : 0.05242729187011719 length of segment : 361 time for calcul the mask position with numpy : 0.002415180206298828 nb_pixel_total : 25734 time to create 1 rle with old method : 0.029711484909057617 length of segment : 208 time for calcul the mask position with numpy : 0.011330127716064453 nb_pixel_total : 120139 time to create 1 rle with old method : 0.14545679092407227 length of segment : 652 time for calcul the mask position with numpy : 0.0023822784423828125 nb_pixel_total : 34140 time to create 1 rle with old method : 0.053369760513305664 length of segment : 283 time for calcul the mask position with numpy : 0.0019397735595703125 nb_pixel_total : 23791 time to create 1 rle with old method : 0.0273895263671875 length of segment : 285 time for calcul the mask position with numpy : 0.0014338493347167969 nb_pixel_total : 17419 time to create 1 rle with old method : 0.0209197998046875 length of segment : 139 time for calcul the mask position with numpy : 0.0042459964752197266 nb_pixel_total : 28491 time to create 1 rle with old method : 0.03446078300476074 length of segment : 238 time for calcul the mask position with numpy : 0.001373291015625 nb_pixel_total : 12063 time to create 1 rle with old method : 0.01422739028930664 length of segment : 186 time for calcul the mask position with numpy : 0.004519939422607422 nb_pixel_total : 40295 time to create 1 rle with old method : 0.049709320068359375 length of segment : 286 time for calcul the mask position with numpy : 0.0022478103637695312 nb_pixel_total : 35874 time to create 1 rle with old method : 0.04607033729553223 length of segment : 406 time for calcul the mask position with numpy : 0.002726316452026367 nb_pixel_total : 44585 time to create 1 rle with old method : 0.052115440368652344 length of segment : 293 time for calcul the mask position with numpy : 0.0024225711822509766 nb_pixel_total : 50227 time to create 1 rle with old method : 0.05880093574523926 length of segment : 315 time for calcul the mask position with numpy : 0.002925395965576172 nb_pixel_total : 38124 time to create 1 rle with old method : 0.04487156867980957 length of segment : 263 time for calcul the mask position with numpy : 0.012010812759399414 nb_pixel_total : 176442 time to create 1 rle with new method : 0.023287534713745117 length of segment : 593 time for calcul the mask position with numpy : 0.0029768943786621094 nb_pixel_total : 32824 time to create 1 rle with old method : 0.03968405723571777 length of segment : 193 time for calcul the mask position with numpy : 0.0026149749755859375 nb_pixel_total : 32573 time to create 1 rle with old method : 0.03863668441772461 length of segment : 177 time for calcul the mask position with numpy : 0.0006070137023925781 nb_pixel_total : 6287 time to create 1 rle with old method : 0.007668256759643555 length of segment : 115 time for calcul the mask position with numpy : 0.0013408660888671875 nb_pixel_total : 18305 time to create 1 rle with old method : 0.02131175994873047 length of segment : 139 time for calcul the mask position with numpy : 0.0015447139739990234 nb_pixel_total : 15493 time to create 1 rle with old method : 0.01767110824584961 length of segment : 227 time for calcul the mask position with numpy : 0.0009429454803466797 nb_pixel_total : 11121 time to create 1 rle with old method : 0.013314008712768555 length of segment : 192 time for calcul the mask position with numpy : 0.0008528232574462891 nb_pixel_total : 17277 time to create 1 rle with old method : 0.019999980926513672 length of segment : 122 time for calcul the mask position with numpy : 0.0011162757873535156 nb_pixel_total : 19134 time to create 1 rle with old method : 0.02187323570251465 length of segment : 273 time for calcul the mask position with numpy : 0.0010700225830078125 nb_pixel_total : 16717 time to create 1 rle with old method : 0.019308805465698242 length of segment : 213 time for calcul the mask position with numpy : 0.0006611347198486328 nb_pixel_total : 7507 time to create 1 rle with old method : 0.00885772705078125 length of segment : 117 time for calcul the mask position with numpy : 0.007829904556274414 nb_pixel_total : 72773 time to create 1 rle with old method : 0.10340666770935059 length of segment : 396 time for calcul the mask position with numpy : 0.007439374923706055 nb_pixel_total : 105218 time to create 1 rle with old method : 0.11995267868041992 length of segment : 461 time for calcul the mask position with numpy : 0.0010612010955810547 nb_pixel_total : 30343 time to create 1 rle with old method : 0.03872799873352051 length of segment : 368 time for calcul the mask position with numpy : 0.0015201568603515625 nb_pixel_total : 17777 time to create 1 rle with old method : 0.02979111671447754 length of segment : 145 time for calcul the mask position with numpy : 0.0008769035339355469 nb_pixel_total : 8103 time to create 1 rle with old method : 0.010843515396118164 length of segment : 92 time for calcul the mask position with numpy : 0.0016083717346191406 nb_pixel_total : 19723 time to create 1 rle with old method : 0.028023242950439453 length of segment : 201 time for calcul the mask position with numpy : 0.0006585121154785156 nb_pixel_total : 6101 time to create 1 rle with old method : 0.007426738739013672 length of segment : 129 time for calcul the mask position with numpy : 0.0035223960876464844 nb_pixel_total : 31837 time to create 1 rle with old method : 0.03797292709350586 length of segment : 348 time for calcul the mask position with numpy : 0.0017991065979003906 nb_pixel_total : 23773 time to create 1 rle with old method : 0.02739119529724121 length of segment : 181 time for calcul the mask position with numpy : 0.0013909339904785156 nb_pixel_total : 14698 time to create 1 rle with old method : 0.017035961151123047 length of segment : 179 time for calcul the mask position with numpy : 0.0007863044738769531 nb_pixel_total : 8524 time to create 1 rle with old method : 0.010088682174682617 length of segment : 123 time for calcul the mask position with numpy : 0.0016589164733886719 nb_pixel_total : 23184 time to create 1 rle with old method : 0.031517982482910156 length of segment : 180 time for calcul the mask position with numpy : 0.001260995864868164 nb_pixel_total : 14594 time to create 1 rle with old method : 0.017285585403442383 length of segment : 180 time for calcul the mask position with numpy : 0.0008630752563476562 nb_pixel_total : 8163 time to create 1 rle with old method : 0.011141061782836914 length of segment : 149 time for calcul the mask position with numpy : 0.0005512237548828125 nb_pixel_total : 5328 time to create 1 rle with old method : 0.007154226303100586 length of segment : 81 time for calcul the mask position with numpy : 0.0011851787567138672 nb_pixel_total : 14738 time to create 1 rle with old method : 0.017358779907226562 length of segment : 164 time for calcul the mask position with numpy : 0.002330303192138672 nb_pixel_total : 60425 time to create 1 rle with old method : 0.07553505897521973 length of segment : 291 time for calcul the mask position with numpy : 0.0007259845733642578 nb_pixel_total : 17136 time to create 1 rle with old method : 0.02039504051208496 length of segment : 158 time for calcul the mask position with numpy : 0.0012118816375732422 nb_pixel_total : 29657 time to create 1 rle with old method : 0.04262948036193848 length of segment : 299 time for calcul the mask position with numpy : 0.0006277561187744141 nb_pixel_total : 12127 time to create 1 rle with old method : 0.014326095581054688 length of segment : 163 time for calcul the mask position with numpy : 0.0015435218811035156 nb_pixel_total : 26538 time to create 1 rle with old method : 0.03301286697387695 length of segment : 243 time for calcul the mask position with numpy : 0.0018534660339355469 nb_pixel_total : 53196 time to create 1 rle with old method : 0.06466388702392578 length of segment : 184 time for calcul the mask position with numpy : 0.0018892288208007812 nb_pixel_total : 41795 time to create 1 rle with old method : 0.05546259880065918 length of segment : 469 time for calcul the mask position with numpy : 0.001039743423461914 nb_pixel_total : 20789 time to create 1 rle with old method : 0.024227380752563477 length of segment : 210 time for calcul the mask position with numpy : 0.00593256950378418 nb_pixel_total : 166472 time to create 1 rle with new method : 0.009885787963867188 length of segment : 401 time for calcul the mask position with numpy : 0.0006682872772216797 nb_pixel_total : 16111 time to create 1 rle with old method : 0.028699159622192383 length of segment : 129 time for calcul the mask position with numpy : 0.002932310104370117 nb_pixel_total : 64927 time to create 1 rle with old method : 0.07960653305053711 length of segment : 331 time for calcul the mask position with numpy : 0.008955717086791992 nb_pixel_total : 266967 time to create 1 rle with new method : 0.01733708381652832 length of segment : 662 time for calcul the mask position with numpy : 0.0007529258728027344 nb_pixel_total : 15360 time to create 1 rle with old method : 0.01944756507873535 length of segment : 131 time for calcul the mask position with numpy : 0.0007290840148925781 nb_pixel_total : 17270 time to create 1 rle with old method : 0.020350217819213867 length of segment : 140 time for calcul the mask position with numpy : 0.0005042552947998047 nb_pixel_total : 11019 time to create 1 rle with old method : 0.013110876083374023 length of segment : 104 time for calcul the mask position with numpy : 0.0010707378387451172 nb_pixel_total : 37268 time to create 1 rle with old method : 0.045722007751464844 length of segment : 220 time for calcul the mask position with numpy : 0.0005066394805908203 nb_pixel_total : 12451 time to create 1 rle with old method : 0.014760494232177734 length of segment : 105 time for calcul the mask position with numpy : 0.0016579627990722656 nb_pixel_total : 24923 time to create 1 rle with old method : 0.02955317497253418 length of segment : 247 time for calcul the mask position with numpy : 0.0045320987701416016 nb_pixel_total : 90932 time to create 1 rle with old method : 0.10641956329345703 length of segment : 464 time for calcul the mask position with numpy : 0.0009100437164306641 nb_pixel_total : 16679 time to create 1 rle with old method : 0.020702362060546875 length of segment : 192 time for calcul the mask position with numpy : 0.007520198822021484 nb_pixel_total : 180690 time to create 1 rle with new method : 0.014097213745117188 length of segment : 463 time for calcul the mask position with numpy : 0.00043892860412597656 nb_pixel_total : 8048 time to create 1 rle with old method : 0.011828184127807617 length of segment : 85 time for calcul the mask position with numpy : 0.0015425682067871094 nb_pixel_total : 28126 time to create 1 rle with old method : 0.0326075553894043 length of segment : 461 time for calcul the mask position with numpy : 0.0005338191986083984 nb_pixel_total : 10944 time to create 1 rle with old method : 0.012776851654052734 length of segment : 139 time for calcul the mask position with numpy : 0.004717826843261719 nb_pixel_total : 88595 time to create 1 rle with old method : 0.1316690444946289 length of segment : 542 time for calcul the mask position with numpy : 0.0024209022521972656 nb_pixel_total : 69892 time to create 1 rle with old method : 0.08608293533325195 length of segment : 951 time for calcul the mask position with numpy : 0.0022439956665039062 nb_pixel_total : 31459 time to create 1 rle with old method : 0.036878108978271484 length of segment : 294 time for calcul the mask position with numpy : 0.000926971435546875 nb_pixel_total : 19570 time to create 1 rle with old method : 0.022493839263916016 length of segment : 182 time for calcul the mask position with numpy : 0.005802154541015625 nb_pixel_total : 174254 time to create 1 rle with new method : 0.008798599243164062 length of segment : 530 time for calcul the mask position with numpy : 0.0036673545837402344 nb_pixel_total : 89092 time to create 1 rle with old method : 0.10166215896606445 length of segment : 295 time for calcul the mask position with numpy : 0.0006890296936035156 nb_pixel_total : 12806 time to create 1 rle with old method : 0.016366243362426758 length of segment : 223 time for calcul the mask position with numpy : 0.0006351470947265625 nb_pixel_total : 12513 time to create 1 rle with old method : 0.014525175094604492 length of segment : 122 time for calcul the mask position with numpy : 0.0010714530944824219 nb_pixel_total : 22828 time to create 1 rle with old method : 0.028737545013427734 length of segment : 157 time for calcul the mask position with numpy : 0.003873586654663086 nb_pixel_total : 102245 time to create 1 rle with old method : 0.12015676498413086 length of segment : 535 time for calcul the mask position with numpy : 0.008233308792114258 nb_pixel_total : 77548 time to create 1 rle with old method : 0.09910726547241211 length of segment : 1043 time for calcul the mask position with numpy : 0.0008909702301025391 nb_pixel_total : 18178 time to create 1 rle with old method : 0.020910263061523438 length of segment : 161 time for calcul the mask position with numpy : 0.0007359981536865234 nb_pixel_total : 12472 time to create 1 rle with old method : 0.014819145202636719 length of segment : 174 time for calcul the mask position with numpy : 0.0006725788116455078 nb_pixel_total : 15614 time to create 1 rle with old method : 0.02144002914428711 length of segment : 108 time for calcul the mask position with numpy : 0.0003490447998046875 nb_pixel_total : 4638 time to create 1 rle with old method : 0.0056078433990478516 length of segment : 79 time for calcul the mask position with numpy : 0.008071184158325195 nb_pixel_total : 155222 time to create 1 rle with new method : 0.013960599899291992 length of segment : 566 time for calcul the mask position with numpy : 0.004128217697143555 nb_pixel_total : 122901 time to create 1 rle with old method : 0.14452099800109863 length of segment : 450 time for calcul the mask position with numpy : 0.004486083984375 nb_pixel_total : 89847 time to create 1 rle with old method : 0.11346673965454102 length of segment : 461 time for calcul the mask position with numpy : 0.0031251907348632812 nb_pixel_total : 95532 time to create 1 rle with old method : 0.12362551689147949 length of segment : 293 time for calcul the mask position with numpy : 0.0011813640594482422 nb_pixel_total : 23069 time to create 1 rle with old method : 0.0273897647857666 length of segment : 320 time for calcul the mask position with numpy : 0.0009663105010986328 nb_pixel_total : 18806 time to create 1 rle with old method : 0.02574443817138672 length of segment : 242 time for calcul the mask position with numpy : 0.0005934238433837891 nb_pixel_total : 14542 time to create 1 rle with old method : 0.02168560028076172 length of segment : 124 time for calcul the mask position with numpy : 0.0027663707733154297 nb_pixel_total : 35715 time to create 1 rle with old method : 0.05421757698059082 length of segment : 364 time for calcul the mask position with numpy : 0.008025169372558594 nb_pixel_total : 263612 time to create 1 rle with new method : 0.015303611755371094 length of segment : 572 time for calcul the mask position with numpy : 0.007460832595825195 nb_pixel_total : 212980 time to create 1 rle with new method : 0.013641834259033203 length of segment : 451 time for calcul the mask position with numpy : 0.006770610809326172 nb_pixel_total : 202782 time to create 1 rle with new method : 0.012902498245239258 length of segment : 470 time for calcul the mask position with numpy : 0.00042724609375 nb_pixel_total : 2535 time to create 1 rle with old method : 0.0034482479095458984 length of segment : 199 time for calcul the mask position with numpy : 0.002118825912475586 nb_pixel_total : 65107 time to create 1 rle with old method : 0.07756781578063965 length of segment : 307 time for calcul the mask position with numpy : 0.0007822513580322266 nb_pixel_total : 22466 time to create 1 rle with old method : 0.02807307243347168 length of segment : 171 time for calcul the mask position with numpy : 0.0030574798583984375 nb_pixel_total : 97351 time to create 1 rle with old method : 0.11644315719604492 length of segment : 311 time for calcul the mask position with numpy : 0.0010478496551513672 nb_pixel_total : 19308 time to create 1 rle with old method : 0.026705026626586914 length of segment : 285 time for calcul the mask position with numpy : 0.0006382465362548828 nb_pixel_total : 15097 time to create 1 rle with old method : 0.020720243453979492 length of segment : 162 time for calcul the mask position with numpy : 0.0010304450988769531 nb_pixel_total : 27066 time to create 1 rle with old method : 0.03233194351196289 length of segment : 180 time for calcul the mask position with numpy : 0.0012753009796142578 nb_pixel_total : 31284 time to create 1 rle with old method : 0.05069708824157715 length of segment : 201 time for calcul the mask position with numpy : 0.0001850128173828125 nb_pixel_total : 2302 time to create 1 rle with old method : 0.002986431121826172 length of segment : 37 time for calcul the mask position with numpy : 0.0016589164733886719 nb_pixel_total : 39947 time to create 1 rle with old method : 0.046441078186035156 length of segment : 113 time for calcul the mask position with numpy : 0.001477956771850586 nb_pixel_total : 29963 time to create 1 rle with old method : 0.03452110290527344 length of segment : 183 time for calcul the mask position with numpy : 0.0032672882080078125 nb_pixel_total : 57677 time to create 1 rle with old method : 0.067352294921875 length of segment : 352 time for calcul the mask position with numpy : 0.007414102554321289 nb_pixel_total : 100004 time to create 1 rle with old method : 0.1227869987487793 length of segment : 425 time for calcul the mask position with numpy : 0.0031976699829101562 nb_pixel_total : 73383 time to create 1 rle with old method : 0.09302377700805664 length of segment : 262 time for calcul the mask position with numpy : 0.004807233810424805 nb_pixel_total : 94271 time to create 1 rle with old method : 0.14085125923156738 length of segment : 404 time for calcul the mask position with numpy : 0.006003618240356445 nb_pixel_total : 124317 time to create 1 rle with old method : 0.15377044677734375 length of segment : 531 time for calcul the mask position with numpy : 0.002730131149291992 nb_pixel_total : 32488 time to create 1 rle with old method : 0.046009063720703125 length of segment : 252 time for calcul the mask position with numpy : 0.005226612091064453 nb_pixel_total : 118054 time to create 1 rle with old method : 0.15729045867919922 length of segment : 405 time for calcul the mask position with numpy : 0.0027189254760742188 nb_pixel_total : 71128 time to create 1 rle with old method : 0.08142709732055664 length of segment : 323 time for calcul the mask position with numpy : 0.005408525466918945 nb_pixel_total : 114916 time to create 1 rle with old method : 0.13063526153564453 length of segment : 341 time for calcul the mask position with numpy : 0.0015676021575927734 nb_pixel_total : 24243 time to create 1 rle with old method : 0.029696226119995117 length of segment : 185 time for calcul the mask position with numpy : 0.0006184577941894531 nb_pixel_total : 27581 time to create 1 rle with old method : 0.03206968307495117 length of segment : 178 time for calcul the mask position with numpy : 0.0012378692626953125 nb_pixel_total : 28166 time to create 1 rle with old method : 0.032712459564208984 length of segment : 131 time for calcul the mask position with numpy : 0.007048368453979492 nb_pixel_total : 169955 time to create 1 rle with new method : 0.015789508819580078 length of segment : 415 time for calcul the mask position with numpy : 0.000995635986328125 nb_pixel_total : 16501 time to create 1 rle with old method : 0.02116107940673828 length of segment : 139 time for calcul the mask position with numpy : 0.0060045719146728516 nb_pixel_total : 115518 time to create 1 rle with old method : 0.12958741188049316 length of segment : 337 time for calcul the mask position with numpy : 0.0009236335754394531 nb_pixel_total : 10679 time to create 1 rle with old method : 0.01418757438659668 length of segment : 145 time for calcul the mask position with numpy : 0.0020966529846191406 nb_pixel_total : 20776 time to create 1 rle with old method : 0.03101968765258789 length of segment : 178 time for calcul the mask position with numpy : 0.005692243576049805 nb_pixel_total : 81338 time to create 1 rle with old method : 0.09978365898132324 length of segment : 416 time for calcul the mask position with numpy : 0.0066950321197509766 nb_pixel_total : 99010 time to create 1 rle with old method : 0.11861467361450195 length of segment : 444 time for calcul the mask position with numpy : 0.005930185317993164 nb_pixel_total : 140751 time to create 1 rle with old method : 0.159745454788208 length of segment : 731 time for calcul the mask position with numpy : 0.0008745193481445312 nb_pixel_total : 31450 time to create 1 rle with old method : 0.042728424072265625 length of segment : 342 time for calcul the mask position with numpy : 0.0016429424285888672 nb_pixel_total : 35766 time to create 1 rle with old method : 0.04673457145690918 length of segment : 168 time for calcul the mask position with numpy : 0.0007793903350830078 nb_pixel_total : 17660 time to create 1 rle with old method : 0.020729541778564453 length of segment : 174 time for calcul the mask position with numpy : 0.001956462860107422 nb_pixel_total : 40004 time to create 1 rle with old method : 0.046392202377319336 length of segment : 276 time for calcul the mask position with numpy : 0.0024874210357666016 nb_pixel_total : 41565 time to create 1 rle with old method : 0.04814028739929199 length of segment : 384 time for calcul the mask position with numpy : 0.003567934036254883 nb_pixel_total : 77174 time to create 1 rle with old method : 0.08640909194946289 length of segment : 779 time for calcul the mask position with numpy : 0.002032756805419922 nb_pixel_total : 59468 time to create 1 rle with old method : 0.0861048698425293 length of segment : 367 time for calcul the mask position with numpy : 0.0008909702301025391 nb_pixel_total : 21358 time to create 1 rle with old method : 0.024791240692138672 length of segment : 177 time for calcul the mask position with numpy : 0.026993989944458008 nb_pixel_total : 648389 time to create 1 rle with new method : 0.05584001541137695 length of segment : 1638 time for calcul the mask position with numpy : 0.0008912086486816406 nb_pixel_total : 37127 time to create 1 rle with old method : 0.04340052604675293 length of segment : 233 time for calcul the mask position with numpy : 0.007910966873168945 nb_pixel_total : 195312 time to create 1 rle with new method : 0.010348081588745117 length of segment : 1258 time for calcul the mask position with numpy : 0.005976676940917969 nb_pixel_total : 180621 time to create 1 rle with new method : 0.012012243270874023 length of segment : 375 time for calcul the mask position with numpy : 0.002687692642211914 nb_pixel_total : 120993 time to create 1 rle with old method : 0.13767361640930176 length of segment : 494 time for calcul the mask position with numpy : 0.0037665367126464844 nb_pixel_total : 90012 time to create 1 rle with old method : 0.12383222579956055 length of segment : 484 time for calcul the mask position with numpy : 0.0010099411010742188 nb_pixel_total : 18923 time to create 1 rle with old method : 0.022114276885986328 length of segment : 199 time for calcul the mask position with numpy : 0.0007123947143554688 nb_pixel_total : 28640 time to create 1 rle with old method : 0.03542661666870117 length of segment : 84 time for calcul the mask position with numpy : 0.008862018585205078 nb_pixel_total : 104873 time to create 1 rle with old method : 0.1479480266571045 length of segment : 487 time for calcul the mask position with numpy : 0.0008730888366699219 nb_pixel_total : 35394 time to create 1 rle with old method : 0.039659976959228516 length of segment : 442 time for calcul the mask position with numpy : 0.0014445781707763672 nb_pixel_total : 41126 time to create 1 rle with old method : 0.047415971755981445 length of segment : 357 time for calcul the mask position with numpy : 0.002316713333129883 nb_pixel_total : 46594 time to create 1 rle with old method : 0.05358266830444336 length of segment : 274 time for calcul the mask position with numpy : 0.003654003143310547 nb_pixel_total : 92875 time to create 1 rle with old method : 0.10477423667907715 length of segment : 445 time for calcul the mask position with numpy : 0.0012848377227783203 nb_pixel_total : 14148 time to create 1 rle with old method : 0.01688551902770996 length of segment : 294 time for calcul the mask position with numpy : 0.0011098384857177734 nb_pixel_total : 29303 time to create 1 rle with old method : 0.03325366973876953 length of segment : 200 time for calcul the mask position with numpy : 0.002092599868774414 nb_pixel_total : 72614 time to create 1 rle with old method : 0.08179473876953125 length of segment : 324 time for calcul the mask position with numpy : 0.011663436889648438 nb_pixel_total : 107537 time to create 1 rle with old method : 0.12285494804382324 length of segment : 458 time for calcul the mask position with numpy : 0.02036762237548828 nb_pixel_total : 622788 time to create 1 rle with new method : 0.03393816947937012 length of segment : 1499 time for calcul the mask position with numpy : 0.003694295883178711 nb_pixel_total : 106509 time to create 1 rle with old method : 0.12094306945800781 length of segment : 334 time for calcul the mask position with numpy : 0.0007939338684082031 nb_pixel_total : 13516 time to create 1 rle with old method : 0.015736103057861328 length of segment : 165 time for calcul the mask position with numpy : 0.0022814273834228516 nb_pixel_total : 51643 time to create 1 rle with old method : 0.05949282646179199 length of segment : 244 time for calcul the mask position with numpy : 0.0026209354400634766 nb_pixel_total : 66215 time to create 1 rle with old method : 0.0758202075958252 length of segment : 273 time for calcul the mask position with numpy : 0.0052471160888671875 nb_pixel_total : 137548 time to create 1 rle with old method : 0.1575031280517578 length of segment : 474 time for calcul the mask position with numpy : 0.0016522407531738281 nb_pixel_total : 41603 time to create 1 rle with old method : 0.04725337028503418 length of segment : 274 time for calcul the mask position with numpy : 0.0007269382476806641 nb_pixel_total : 15432 time to create 1 rle with old method : 0.01881551742553711 length of segment : 93 time spent for convertir_results : 29.44651198387146 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 313 chid ids of type : 3594 Number RLEs to save : 89509 save missing photos in datou_result : time spend for datou_step_exec : 151.82989740371704 time spend to save output : 10.62373661994934 total time spend for step 1 : 162.45363402366638 step2:crop_condition Wed Apr 9 10:13:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 313 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 ! map_result returned by crop_photo_return_map_crop : length : 228 About to insert : list_path_to_insert length 228 new photo from crops ! About to upload 228 photos upload in portfolio : 3736932 init cache_photo without model_param we have 228 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186445_406788 we have uploaded 228 photos in the portfolio 3736932 time of upload the photos Elapsed time : 63.25853109359741 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 ! map_result returned by crop_photo_return_map_crop : length : 47 About to insert : list_path_to_insert length 47 new photo from crops ! About to upload 47 photos upload in portfolio : 3736932 init cache_photo without model_param we have 47 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186522_406788 we have uploaded 47 photos in the portfolio 3736932 time of upload the photos Elapsed time : 16.774805545806885 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186540_406788 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.7739167213439941 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 ! map_result returned by crop_photo_return_map_crop : length : 22 About to insert : list_path_to_insert length 22 new photo from crops ! About to upload 22 photos upload in portfolio : 3736932 init cache_photo without model_param we have 22 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186556_406788 we have uploaded 22 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.614787817001343 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 ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186569_406788 we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.109855890274048 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 ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186573_406788 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6988766193389893 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 3736932 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744186576_406788 we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.5905194282531738 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350418422, 1350418421, 1350418419, 1350418415, 1350394804, 1350394798, 1350394731, 1350394725, 1350394723, 1350394534, 1350394518] Looping around the photos to save general results len do output : 313 /1350705064Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705070Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705076Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705078Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705083Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705085Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705087Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705091Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705093Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705097Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705098Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705099Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705104Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705110Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705117Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705118Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705121Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705123Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705124Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705125Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705129Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705131Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705135Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705141Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705143Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705153Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705159Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705161Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705163Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705170Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705177Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705178Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705184Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705187Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705189Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705190Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705192Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705198Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705202Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705207Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705219Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705221Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705222Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705224Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705226Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705228Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705230Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705232Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705234Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705235Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705238Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705239Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705241Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705243Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705245Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705248Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705251Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705253Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705254Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705256Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705258Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705260Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705262Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705264Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705266Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705267Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705277Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705311Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705325Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705326Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705327Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705332Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705334Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705385Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705418Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705419Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705420Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705425Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705427Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705428Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705433Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705436Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705437Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705444Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705445Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705449Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705452Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705453Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705454Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705458Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705461Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705462Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705485Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705532Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705883Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705903Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705906Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350705923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706152Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706157Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706159Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706163Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706164Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706167Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706168Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706175Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706177Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706178Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706182Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706186Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706187Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706214Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350706300Didn'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, '2733693') ('3318', '22153647', '1350418422', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418421', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418419', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418415', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394804', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394798', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394731', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394725', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394723', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394534', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394518', None, None, None, None, None, '2733693') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 950 time used for this insertion : 1.772348165512085 save_final save missing photos in datou_result : time spend for datou_step_exec : 183.07559752464294 time spend to save output : 1.7834103107452393 total time spend for step 2 : 184.85900783538818 step3:rle_unique_nms_with_priority Wed Apr 9 10:16:19 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 313 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 32 nb_hashtags : 4 time to prepare the origin masks : 4.172365188598633 time for calcul the mask position with numpy : 0.6639041900634766 nb_pixel_total : 5743507 time to create 1 rle with new method : 0.8684935569763184 time for calcul the mask position with numpy : 0.030942916870117188 nb_pixel_total : 24473 time to create 1 rle with old method : 0.027471065521240234 time for calcul the mask position with numpy : 0.03482937812805176 nb_pixel_total : 17870 time to create 1 rle with old method : 0.028037071228027344 time for calcul the mask position with numpy : 0.030631065368652344 nb_pixel_total : 81936 time to create 1 rle with old method : 0.0928952693939209 time for calcul the mask position with numpy : 0.029251813888549805 nb_pixel_total : 19406 time to create 1 rle with old method : 0.02210092544555664 time for calcul the mask position with numpy : 0.029497385025024414 nb_pixel_total : 45341 time to create 1 rle with old method : 0.051614999771118164 time for calcul the mask position with numpy : 0.02939128875732422 nb_pixel_total : 24012 time to create 1 rle with old method : 0.02736186981201172 time for calcul the mask position with numpy : 0.02929067611694336 nb_pixel_total : 19669 time to create 1 rle with old method : 0.022298574447631836 time for calcul the mask position with numpy : 0.029160261154174805 nb_pixel_total : 38920 time to create 1 rle with old method : 0.04406285285949707 time for calcul the mask position with numpy : 0.029726028442382812 nb_pixel_total : 35646 time to create 1 rle with old method : 0.1328754425048828 time for calcul the mask position with numpy : 0.09856867790222168 nb_pixel_total : 9113 time to create 1 rle with old method : 0.01636791229248047 time for calcul the mask position with numpy : 0.03742027282714844 nb_pixel_total : 20800 time to create 1 rle with old method : 0.038248300552368164 time for calcul the mask position with numpy : 0.03787541389465332 nb_pixel_total : 75210 time to create 1 rle with old method : 0.1001272201538086 time for calcul the mask position with numpy : 0.029463768005371094 nb_pixel_total : 9612 time to create 1 rle with old method : 0.011199951171875 time for calcul the mask position with numpy : 0.031067609786987305 nb_pixel_total : 47209 time to create 1 rle with old method : 0.054764747619628906 time for calcul the mask position with numpy : 0.029274463653564453 nb_pixel_total : 14014 time to create 1 rle with old method : 0.01608872413635254 time for calcul the mask position with numpy : 0.029555082321166992 nb_pixel_total : 68593 time to create 1 rle with old method : 0.0773932933807373 time for calcul the mask position with numpy : 0.029137134552001953 nb_pixel_total : 40505 time to create 1 rle with old method : 0.04549813270568848 time for calcul the mask position with numpy : 0.029181241989135742 nb_pixel_total : 37158 time to create 1 rle with old method : 0.04204249382019043 time for calcul the mask position with numpy : 0.02923893928527832 nb_pixel_total : 40402 time to create 1 rle with old method : 0.05109095573425293 time for calcul the mask position with numpy : 0.036940574645996094 nb_pixel_total : 15644 time to create 1 rle with old method : 0.029225587844848633 time for calcul the mask position with numpy : 0.037683725357055664 nb_pixel_total : 17417 time to create 1 rle with old method : 0.031011104583740234 time for calcul the mask position with numpy : 0.0329892635345459 nb_pixel_total : 19749 time to create 1 rle with old method : 0.022401094436645508 time for calcul the mask position with numpy : 0.029689311981201172 nb_pixel_total : 24217 time to create 1 rle with old method : 0.027080297470092773 time for calcul the mask position with numpy : 0.03019404411315918 nb_pixel_total : 158542 time to create 1 rle with new method : 0.8073282241821289 time for calcul the mask position with numpy : 0.03677964210510254 nb_pixel_total : 99577 time to create 1 rle with old method : 0.1111595630645752 time for calcul the mask position with numpy : 0.028936386108398438 nb_pixel_total : 82284 time to create 1 rle with old method : 0.09138107299804688 time for calcul the mask position with numpy : 0.02881646156311035 nb_pixel_total : 45405 time to create 1 rle with old method : 0.06921029090881348 time for calcul the mask position with numpy : 0.030055522918701172 nb_pixel_total : 144447 time to create 1 rle with old method : 0.1641371250152588 time for calcul the mask position with numpy : 0.029440879821777344 nb_pixel_total : 1747 time to create 1 rle with old method : 0.0034003257751464844 time for calcul the mask position with numpy : 0.02958369255065918 nb_pixel_total : 6903 time to create 1 rle with old method : 0.008020401000976562 time for calcul the mask position with numpy : 0.02930617332458496 nb_pixel_total : 16370 time to create 1 rle with old method : 0.0185544490814209 time for calcul the mask position with numpy : 0.029442310333251953 nb_pixel_total : 4542 time to create 1 rle with old method : 0.005424976348876953 create new chi : 4.946658372879028 time to delete rle : 0.017443180084228516 batch 1 Loaded 65 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18825 TO DO : save crop sub photo not yet done ! save time : 8.432438850402832 nb_obj : 27 nb_hashtags : 4 time to prepare the origin masks : 4.037520408630371 time for calcul the mask position with numpy : 0.37761354446411133 nb_pixel_total : 5826738 time to create 1 rle with new method : 0.609443187713623 time for calcul the mask position with numpy : 0.02640366554260254 nb_pixel_total : 8096 time to create 1 rle with old method : 0.00827169418334961 time for calcul the mask position with numpy : 0.026430368423461914 nb_pixel_total : 33395 time to create 1 rle with old method : 0.0343012809753418 time for calcul the mask position with numpy : 0.027211666107177734 nb_pixel_total : 69468 time to create 1 rle with old method : 0.07302451133728027 time for calcul the mask position with numpy : 0.027373313903808594 nb_pixel_total : 48655 time to create 1 rle with old method : 0.05228757858276367 time for calcul the mask position with numpy : 0.026325464248657227 nb_pixel_total : 13772 time to create 1 rle with old method : 0.014050483703613281 time for calcul the mask position with numpy : 0.026828527450561523 nb_pixel_total : 13568 time to create 1 rle with old method : 0.014750957489013672 time for calcul the mask position with numpy : 0.027831554412841797 nb_pixel_total : 66101 time to create 1 rle with old method : 0.06803274154663086 time for calcul the mask position with numpy : 0.027495622634887695 nb_pixel_total : 15792 time to create 1 rle with old method : 0.016347408294677734 time for calcul the mask position with numpy : 0.02682638168334961 nb_pixel_total : 71530 time to create 1 rle with old method : 0.0724937915802002 time for calcul the mask position with numpy : 0.02611064910888672 nb_pixel_total : 22008 time to create 1 rle with old method : 0.022615432739257812 time for calcul the mask position with numpy : 0.026229143142700195 nb_pixel_total : 19975 time to create 1 rle with old method : 0.02046942710876465 time for calcul the mask position with numpy : 0.026482820510864258 nb_pixel_total : 20677 time to create 1 rle with old method : 0.022320985794067383 time for calcul the mask position with numpy : 0.028361082077026367 nb_pixel_total : 119056 time to create 1 rle with old method : 0.12392735481262207 time for calcul the mask position with numpy : 0.027051210403442383 nb_pixel_total : 19394 time to create 1 rle with old method : 0.020109176635742188 time for calcul the mask position with numpy : 0.028183937072753906 nb_pixel_total : 159623 time to create 1 rle with new method : 0.4892563819885254 time for calcul the mask position with numpy : 0.02727508544921875 nb_pixel_total : 60480 time to create 1 rle with old method : 0.06294727325439453 time for calcul the mask position with numpy : 0.027219295501708984 nb_pixel_total : 81149 time to create 1 rle with old method : 0.08441352844238281 time for calcul the mask position with numpy : 0.02668023109436035 nb_pixel_total : 26047 time to create 1 rle with old method : 0.027486562728881836 time for calcul the mask position with numpy : 0.02695322036743164 nb_pixel_total : 17764 time to create 1 rle with old method : 0.019362211227416992 time for calcul the mask position with numpy : 0.0286405086517334 nb_pixel_total : 86895 time to create 1 rle with old method : 0.09373736381530762 time for calcul the mask position with numpy : 0.026895523071289062 nb_pixel_total : 30667 time to create 1 rle with old method : 0.031950950622558594 time for calcul the mask position with numpy : 0.027884244918823242 nb_pixel_total : 8149 time to create 1 rle with old method : 0.009069442749023438 time for calcul the mask position with numpy : 0.027954578399658203 nb_pixel_total : 85568 time to create 1 rle with old method : 0.09223747253417969 time for calcul the mask position with numpy : 0.027577638626098633 nb_pixel_total : 19624 time to create 1 rle with old method : 0.02081298828125 time for calcul the mask position with numpy : 0.027707338333129883 nb_pixel_total : 4961 time to create 1 rle with old method : 0.005524158477783203 time for calcul the mask position with numpy : 0.026580333709716797 nb_pixel_total : 89647 time to create 1 rle with old method : 0.09338760375976562 time for calcul the mask position with numpy : 0.02745842933654785 nb_pixel_total : 11441 time to create 1 rle with old method : 0.01220560073852539 create new chi : 3.379044771194458 time to delete rle : 0.0022780895233154297 batch 1 Loaded 55 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17331 TO DO : save crop sub photo not yet done ! save time : 3.464627742767334 nb_obj : 51 nb_hashtags : 3 time to prepare the origin masks : 4.386209726333618 time for calcul the mask position with numpy : 0.24958419799804688 nb_pixel_total : 5676380 time to create 1 rle with new method : 0.9718258380889893 time for calcul the mask position with numpy : 0.03224587440490723 nb_pixel_total : 4100 time to create 1 rle with old method : 0.006388425827026367 time for calcul the mask position with numpy : 0.030349016189575195 nb_pixel_total : 16371 time to create 1 rle with old method : 0.018564701080322266 time for calcul the mask position with numpy : 0.0293121337890625 nb_pixel_total : 36780 time to create 1 rle with old method : 0.042265892028808594 time for calcul the mask position with numpy : 0.028832674026489258 nb_pixel_total : 31459 time to create 1 rle with old method : 0.03591728210449219 time for calcul the mask position with numpy : 0.03241539001464844 nb_pixel_total : 32715 time to create 1 rle with old method : 0.05258440971374512 time for calcul the mask position with numpy : 0.028989791870117188 nb_pixel_total : 14125 time to create 1 rle with old method : 0.01602315902709961 time for calcul the mask position with numpy : 0.028847694396972656 nb_pixel_total : 17008 time to create 1 rle with old method : 0.019017457962036133 time for calcul the mask position with numpy : 0.028870820999145508 nb_pixel_total : 16854 time to create 1 rle with old method : 0.01889348030090332 time for calcul the mask position with numpy : 0.028874874114990234 nb_pixel_total : 7911 time to create 1 rle with old method : 0.009033441543579102 time for calcul the mask position with numpy : 0.028751850128173828 nb_pixel_total : 37728 time to create 1 rle with old method : 0.04218888282775879 time for calcul the mask position with numpy : 0.028800249099731445 nb_pixel_total : 15978 time to create 1 rle with old method : 0.01818251609802246 time for calcul the mask position with numpy : 0.028865814208984375 nb_pixel_total : 38272 time to create 1 rle with old method : 0.043222665786743164 time for calcul the mask position with numpy : 0.03474879264831543 nb_pixel_total : 72576 time to create 1 rle with old method : 0.08341145515441895 time for calcul the mask position with numpy : 0.029332637786865234 nb_pixel_total : 23158 time to create 1 rle with old method : 0.026021242141723633 time for calcul the mask position with numpy : 0.029738426208496094 nb_pixel_total : 34863 time to create 1 rle with old method : 0.03972125053405762 time for calcul the mask position with numpy : 0.028908729553222656 nb_pixel_total : 22318 time to create 1 rle with old method : 0.025041818618774414 time for calcul the mask position with numpy : 0.03058171272277832 nb_pixel_total : 13558 time to create 1 rle with old method : 0.01542520523071289 time for calcul the mask position with numpy : 0.028907299041748047 nb_pixel_total : 24382 time to create 1 rle with old method : 0.027469635009765625 time for calcul the mask position with numpy : 0.028635740280151367 nb_pixel_total : 11861 time to create 1 rle with old method : 0.013487577438354492 time for calcul the mask position with numpy : 0.02888798713684082 nb_pixel_total : 15055 time to create 1 rle with old method : 0.017354726791381836 time for calcul the mask position with numpy : 0.02982950210571289 nb_pixel_total : 119853 time to create 1 rle with old method : 0.13429570198059082 time for calcul the mask position with numpy : 0.028932809829711914 nb_pixel_total : 19747 time to create 1 rle with old method : 0.022392749786376953 time for calcul the mask position with numpy : 0.02918839454650879 nb_pixel_total : 29811 time to create 1 rle with old method : 0.033477783203125 time for calcul the mask position with numpy : 0.02924633026123047 nb_pixel_total : 46287 time to create 1 rle with old method : 0.05170702934265137 time for calcul the mask position with numpy : 0.029230356216430664 nb_pixel_total : 3445 time to create 1 rle with old method : 0.00426173210144043 time for calcul the mask position with numpy : 0.029364824295043945 nb_pixel_total : 25873 time to create 1 rle with old method : 0.029431819915771484 time for calcul the mask position with numpy : 0.029387712478637695 nb_pixel_total : 42688 time to create 1 rle with old method : 0.04924321174621582 time for calcul the mask position with numpy : 0.029415369033813477 nb_pixel_total : 47137 time to create 1 rle with old method : 0.05796003341674805 time for calcul the mask position with numpy : 0.02936530113220215 nb_pixel_total : 13149 time to create 1 rle with old method : 0.015125274658203125 time for calcul the mask position with numpy : 0.029512405395507812 nb_pixel_total : 11448 time to create 1 rle with old method : 0.014271736145019531 time for calcul the mask position with numpy : 0.029229402542114258 nb_pixel_total : 64265 time to create 1 rle with old method : 0.07268309593200684 time for calcul the mask position with numpy : 0.028918027877807617 nb_pixel_total : 29345 time to create 1 rle with old method : 0.03756451606750488 time for calcul the mask position with numpy : 0.03287386894226074 nb_pixel_total : 16054 time to create 1 rle with old method : 0.018123865127563477 time for calcul the mask position with numpy : 0.0289919376373291 nb_pixel_total : 9602 time to create 1 rle with old method : 0.010970830917358398 time for calcul the mask position with numpy : 0.02922224998474121 nb_pixel_total : 16387 time to create 1 rle with old method : 0.018938064575195312 time for calcul the mask position with numpy : 0.02916097640991211 nb_pixel_total : 8126 time to create 1 rle with old method : 0.009330272674560547 time for calcul the mask position with numpy : 0.029078006744384766 nb_pixel_total : 13533 time to create 1 rle with old method : 0.015764713287353516 time for calcul the mask position with numpy : 0.031126976013183594 nb_pixel_total : 40702 time to create 1 rle with old method : 0.04614424705505371 time for calcul the mask position with numpy : 0.029165983200073242 nb_pixel_total : 13287 time to create 1 rle with old method : 0.015271663665771484 time for calcul the mask position with numpy : 0.029638290405273438 nb_pixel_total : 36353 time to create 1 rle with old method : 0.04203534126281738 time for calcul the mask position with numpy : 0.029513120651245117 nb_pixel_total : 21649 time to create 1 rle with old method : 0.02699756622314453 time for calcul the mask position with numpy : 0.029605627059936523 nb_pixel_total : 23822 time to create 1 rle with old method : 0.027127981185913086 time for calcul the mask position with numpy : 0.030025005340576172 nb_pixel_total : 77370 time to create 1 rle with old method : 0.08833098411560059 time for calcul the mask position with numpy : 0.030595064163208008 nb_pixel_total : 66797 time to create 1 rle with old method : 0.10681891441345215 time for calcul the mask position with numpy : 0.03732585906982422 nb_pixel_total : 7684 time to create 1 rle with old method : 0.014142036437988281 time for calcul the mask position with numpy : 0.03241682052612305 nb_pixel_total : 12456 time to create 1 rle with old method : 0.014368295669555664 time for calcul the mask position with numpy : 0.030292272567749023 nb_pixel_total : 9017 time to create 1 rle with old method : 0.011058807373046875 time for calcul the mask position with numpy : 0.030352354049682617 nb_pixel_total : 9021 time to create 1 rle with old method : 0.012827634811401367 time for calcul the mask position with numpy : 0.030885934829711914 nb_pixel_total : 18841 time to create 1 rle with old method : 0.024153470993041992 time for calcul the mask position with numpy : 0.034410953521728516 nb_pixel_total : 8168 time to create 1 rle with old method : 0.013321399688720703 time for calcul the mask position with numpy : 0.03334522247314453 nb_pixel_total : 24871 time to create 1 rle with old method : 0.030337810516357422 create new chi : 4.437838315963745 time to delete rle : 0.003967761993408203 batch 1 Loaded 103 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24385 TO DO : save crop sub photo not yet done ! save time : 1.7192962169647217 nb_obj : 35 nb_hashtags : 4 time to prepare the origin masks : 4.317059278488159 time for calcul the mask position with numpy : 0.6269674301147461 nb_pixel_total : 5571542 time to create 1 rle with new method : 0.774970293045044 time for calcul the mask position with numpy : 0.030411720275878906 nb_pixel_total : 156666 time to create 1 rle with new method : 0.459317684173584 time for calcul the mask position with numpy : 0.028837919235229492 nb_pixel_total : 251 time to create 1 rle with old method : 0.0003571510314941406 time for calcul the mask position with numpy : 0.02893543243408203 nb_pixel_total : 9544 time to create 1 rle with old method : 0.010795354843139648 time for calcul the mask position with numpy : 0.029072999954223633 nb_pixel_total : 20870 time to create 1 rle with old method : 0.023607254028320312 time for calcul the mask position with numpy : 0.028911113739013672 nb_pixel_total : 8530 time to create 1 rle with old method : 0.009779930114746094 time for calcul the mask position with numpy : 0.0291290283203125 nb_pixel_total : 27402 time to create 1 rle with old method : 0.03326892852783203 time for calcul the mask position with numpy : 0.02944779396057129 nb_pixel_total : 29519 time to create 1 rle with old method : 0.03336596488952637 time for calcul the mask position with numpy : 0.029602766036987305 nb_pixel_total : 58430 time to create 1 rle with old method : 0.06575179100036621 time for calcul the mask position with numpy : 0.029143571853637695 nb_pixel_total : 13878 time to create 1 rle with old method : 0.01582956314086914 time for calcul the mask position with numpy : 0.028600215911865234 nb_pixel_total : 15816 time to create 1 rle with old method : 0.01757502555847168 time for calcul the mask position with numpy : 0.02797985076904297 nb_pixel_total : 26331 time to create 1 rle with old method : 0.02850794792175293 time for calcul the mask position with numpy : 0.02837371826171875 nb_pixel_total : 34519 time to create 1 rle with old method : 0.03814959526062012 time for calcul the mask position with numpy : 0.029500484466552734 nb_pixel_total : 154732 time to create 1 rle with new method : 0.5275030136108398 time for calcul the mask position with numpy : 0.029619693756103516 nb_pixel_total : 48504 time to create 1 rle with old method : 0.05768752098083496 time for calcul the mask position with numpy : 0.029107332229614258 nb_pixel_total : 22082 time to create 1 rle with old method : 0.02449941635131836 time for calcul the mask position with numpy : 0.02970123291015625 nb_pixel_total : 111409 time to create 1 rle with old method : 0.12479734420776367 time for calcul the mask position with numpy : 0.0291750431060791 nb_pixel_total : 15771 time to create 1 rle with old method : 0.01767563819885254 time for calcul the mask position with numpy : 0.029488086700439453 nb_pixel_total : 97377 time to create 1 rle with old method : 0.1090085506439209 time for calcul the mask position with numpy : 0.029189586639404297 nb_pixel_total : 30918 time to create 1 rle with old method : 0.0350801944732666 time for calcul the mask position with numpy : 0.0317535400390625 nb_pixel_total : 24300 time to create 1 rle with old method : 0.03965878486633301 time for calcul the mask position with numpy : 0.030910253524780273 nb_pixel_total : 26480 time to create 1 rle with old method : 0.029814481735229492 time for calcul the mask position with numpy : 0.029406309127807617 nb_pixel_total : 16663 time to create 1 rle with old method : 0.02028346061706543 time for calcul the mask position with numpy : 0.029184818267822266 nb_pixel_total : 36127 time to create 1 rle with old method : 0.044205427169799805 time for calcul the mask position with numpy : 0.03452491760253906 nb_pixel_total : 76320 time to create 1 rle with old method : 0.09436368942260742 time for calcul the mask position with numpy : 0.029483556747436523 nb_pixel_total : 92176 time to create 1 rle with old method : 0.10747241973876953 time for calcul the mask position with numpy : 0.03364372253417969 nb_pixel_total : 31924 time to create 1 rle with old method : 0.03912162780761719 time for calcul the mask position with numpy : 0.031107425689697266 nb_pixel_total : 11053 time to create 1 rle with old method : 0.012524604797363281 time for calcul the mask position with numpy : 0.03006911277770996 nb_pixel_total : 17165 time to create 1 rle with old method : 0.019901752471923828 time for calcul the mask position with numpy : 0.030813932418823242 nb_pixel_total : 50842 time to create 1 rle with old method : 0.056575775146484375 time for calcul the mask position with numpy : 0.02899765968322754 nb_pixel_total : 53127 time to create 1 rle with old method : 0.05916643142700195 time for calcul the mask position with numpy : 0.029190778732299805 nb_pixel_total : 73132 time to create 1 rle with old method : 0.0814371109008789 time for calcul the mask position with numpy : 0.02903294563293457 nb_pixel_total : 45313 time to create 1 rle with old method : 0.05073237419128418 time for calcul the mask position with numpy : 0.0290985107421875 nb_pixel_total : 15270 time to create 1 rle with old method : 0.02210259437561035 time for calcul the mask position with numpy : 0.032863616943359375 nb_pixel_total : 12885 time to create 1 rle with old method : 0.021003007888793945 time for calcul the mask position with numpy : 0.03024768829345703 nb_pixel_total : 13372 time to create 1 rle with old method : 0.014935016632080078 create new chi : 4.876882076263428 time to delete rle : 0.004407405853271484 batch 1 Loaded 71 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19046 TO DO : save crop sub photo not yet done ! save time : 2.997549057006836 nb_obj : 54 nb_hashtags : 2 time to prepare the origin masks : 4.581156015396118 time for calcul the mask position with numpy : 0.4730954170227051 nb_pixel_total : 5094148 time to create 1 rle with new method : 0.6930162906646729 time for calcul the mask position with numpy : 0.02859187126159668 nb_pixel_total : 23791 time to create 1 rle with old method : 0.030516862869262695 time for calcul the mask position with numpy : 0.02930283546447754 nb_pixel_total : 1059 time to create 1 rle with old method : 0.0013332366943359375 time for calcul the mask position with numpy : 0.02877974510192871 nb_pixel_total : 7507 time to create 1 rle with old method : 0.008450984954833984 time for calcul the mask position with numpy : 0.02874755859375 nb_pixel_total : 18305 time to create 1 rle with old method : 0.020079612731933594 time for calcul the mask position with numpy : 0.028502702713012695 nb_pixel_total : 16439 time to create 1 rle with old method : 0.01848316192626953 time for calcul the mask position with numpy : 0.02842545509338379 nb_pixel_total : 9718 time to create 1 rle with old method : 0.01083827018737793 time for calcul the mask position with numpy : 0.02857494354248047 nb_pixel_total : 14594 time to create 1 rle with old method : 0.01641678810119629 time for calcul the mask position with numpy : 0.028513193130493164 nb_pixel_total : 17777 time to create 1 rle with old method : 0.019602298736572266 time for calcul the mask position with numpy : 0.028607845306396484 nb_pixel_total : 17419 time to create 1 rle with old method : 0.019526958465576172 time for calcul the mask position with numpy : 0.028473854064941406 nb_pixel_total : 34098 time to create 1 rle with old method : 0.03713631629943848 time for calcul the mask position with numpy : 0.0279691219329834 nb_pixel_total : 28491 time to create 1 rle with old method : 0.03126358985900879 time for calcul the mask position with numpy : 0.028467416763305664 nb_pixel_total : 12063 time to create 1 rle with old method : 0.013360261917114258 time for calcul the mask position with numpy : 0.027447938919067383 nb_pixel_total : 32286 time to create 1 rle with old method : 0.03523683547973633 time for calcul the mask position with numpy : 0.029615402221679688 nb_pixel_total : 6287 time to create 1 rle with old method : 0.007805585861206055 time for calcul the mask position with numpy : 0.0312955379486084 nb_pixel_total : 19134 time to create 1 rle with old method : 0.021622180938720703 time for calcul the mask position with numpy : 0.029001235961914062 nb_pixel_total : 46186 time to create 1 rle with old method : 0.05176949501037598 time for calcul the mask position with numpy : 0.02900218963623047 nb_pixel_total : 25822 time to create 1 rle with old method : 0.028968334197998047 time for calcul the mask position with numpy : 0.02890467643737793 nb_pixel_total : 6101 time to create 1 rle with old method : 0.006898403167724609 time for calcul the mask position with numpy : 0.0292203426361084 nb_pixel_total : 12621 time to create 1 rle with old method : 0.014212846755981445 time for calcul the mask position with numpy : 0.028849124908447266 nb_pixel_total : 15493 time to create 1 rle with old method : 0.017475605010986328 time for calcul the mask position with numpy : 0.029052019119262695 nb_pixel_total : 73590 time to create 1 rle with old method : 0.08690524101257324 time for calcul the mask position with numpy : 0.029028892517089844 nb_pixel_total : 31837 time to create 1 rle with old method : 0.036324501037597656 time for calcul the mask position with numpy : 0.02893352508544922 nb_pixel_total : 23773 time to create 1 rle with old method : 0.026730775833129883 time for calcul the mask position with numpy : 0.028763532638549805 nb_pixel_total : 17277 time to create 1 rle with old method : 0.019457101821899414 time for calcul the mask position with numpy : 0.030379772186279297 nb_pixel_total : 108607 time to create 1 rle with old method : 0.1217491626739502 time for calcul the mask position with numpy : 0.029270648956298828 nb_pixel_total : 72773 time to create 1 rle with old method : 0.08262896537780762 time for calcul the mask position with numpy : 0.028474092483520508 nb_pixel_total : 18034 time to create 1 rle with old method : 0.020511865615844727 time for calcul the mask position with numpy : 0.02893209457397461 nb_pixel_total : 23184 time to create 1 rle with old method : 0.026034832000732422 time for calcul the mask position with numpy : 0.028974056243896484 nb_pixel_total : 92879 time to create 1 rle with old method : 0.10221219062805176 time for calcul the mask position with numpy : 0.029396772384643555 nb_pixel_total : 82985 time to create 1 rle with old method : 0.0921630859375 time for calcul the mask position with numpy : 0.029142141342163086 nb_pixel_total : 8103 time to create 1 rle with old method : 0.009223699569702148 time for calcul the mask position with numpy : 0.029017210006713867 nb_pixel_total : 32824 time to create 1 rle with old method : 0.03726625442504883 time for calcul the mask position with numpy : 0.029323101043701172 nb_pixel_total : 16717 time to create 1 rle with old method : 0.0187532901763916 time for calcul the mask position with numpy : 0.028619766235351562 nb_pixel_total : 120139 time to create 1 rle with old method : 0.13369154930114746 time for calcul the mask position with numpy : 0.029657840728759766 nb_pixel_total : 35874 time to create 1 rle with old method : 0.0398867130279541 time for calcul the mask position with numpy : 0.028462886810302734 nb_pixel_total : 25734 time to create 1 rle with old method : 0.028196096420288086 time for calcul the mask position with numpy : 0.028551578521728516 nb_pixel_total : 50227 time to create 1 rle with old method : 0.056098222732543945 time for calcul the mask position with numpy : 0.029341697692871094 nb_pixel_total : 29444 time to create 1 rle with old method : 0.03330850601196289 time for calcul the mask position with numpy : 0.029369115829467773 nb_pixel_total : 44585 time to create 1 rle with old method : 0.050142526626586914 time for calcul the mask position with numpy : 0.028994321823120117 nb_pixel_total : 14698 time to create 1 rle with old method : 0.016727209091186523 time for calcul the mask position with numpy : 0.028883934020996094 nb_pixel_total : 38124 time to create 1 rle with old method : 0.04310894012451172 time for calcul the mask position with numpy : 0.029506683349609375 nb_pixel_total : 97717 time to create 1 rle with old method : 0.11054730415344238 time for calcul the mask position with numpy : 0.0292813777923584 nb_pixel_total : 19723 time to create 1 rle with old method : 0.025424718856811523 time for calcul the mask position with numpy : 0.029056787490844727 nb_pixel_total : 14738 time to create 1 rle with old method : 0.016525983810424805 time for calcul the mask position with numpy : 0.028797626495361328 nb_pixel_total : 71528 time to create 1 rle with old method : 0.07978391647338867 time for calcul the mask position with numpy : 0.03128409385681152 nb_pixel_total : 26493 time to create 1 rle with old method : 0.02962493896484375 time for calcul the mask position with numpy : 0.02916121482849121 nb_pixel_total : 105218 time to create 1 rle with old method : 0.12634992599487305 time for calcul the mask position with numpy : 0.031363487243652344 nb_pixel_total : 176442 time to create 1 rle with new method : 1.004206895828247 time for calcul the mask position with numpy : 0.028059720993041992 nb_pixel_total : 22255 time to create 1 rle with old method : 0.02344369888305664 time for calcul the mask position with numpy : 0.027026653289794922 nb_pixel_total : 11121 time to create 1 rle with old method : 0.011647939682006836 time for calcul the mask position with numpy : 0.02685689926147461 nb_pixel_total : 5096 time to create 1 rle with old method : 0.005444765090942383 time for calcul the mask position with numpy : 0.02724289894104004 nb_pixel_total : 40295 time to create 1 rle with old method : 0.04314088821411133 time for calcul the mask position with numpy : 0.029973506927490234 nb_pixel_total : 30343 time to create 1 rle with old method : 0.034393310546875 time for calcul the mask position with numpy : 0.028905153274536133 nb_pixel_total : 8524 time to create 1 rle with old method : 0.009642601013183594 create new chi : 5.80131721496582 time to delete rle : 0.007807016372680664 batch 1 Loaded 109 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 30058 TO DO : save crop sub photo not yet done ! save time : 3.2208142280578613 nb_obj : 17 nb_hashtags : 4 time to prepare the origin masks : 7.3083696365356445 time for calcul the mask position with numpy : 0.725489616394043 nb_pixel_total : 6180738 time to create 1 rle with new method : 0.9535133838653564 time for calcul the mask position with numpy : 0.03533196449279785 nb_pixel_total : 12451 time to create 1 rle with old method : 0.013986349105834961 time for calcul the mask position with numpy : 0.03592658042907715 nb_pixel_total : 37268 time to create 1 rle with old method : 0.04266810417175293 time for calcul the mask position with numpy : 0.04066205024719238 nb_pixel_total : 11019 time to create 1 rle with old method : 0.01244211196899414 time for calcul the mask position with numpy : 0.036112070083618164 nb_pixel_total : 17270 time to create 1 rle with old method : 0.01953721046447754 time for calcul the mask position with numpy : 0.035958051681518555 nb_pixel_total : 15360 time to create 1 rle with old method : 0.01718282699584961 time for calcul the mask position with numpy : 0.03785848617553711 nb_pixel_total : 266967 time to create 1 rle with new method : 0.5682339668273926 time for calcul the mask position with numpy : 0.03542685508728027 nb_pixel_total : 64927 time to create 1 rle with old method : 0.07239627838134766 time for calcul the mask position with numpy : 0.03403067588806152 nb_pixel_total : 16105 time to create 1 rle with old method : 0.01769232749938965 time for calcul the mask position with numpy : 0.039485931396484375 nb_pixel_total : 166472 time to create 1 rle with new method : 0.6257750988006592 time for calcul the mask position with numpy : 0.03302645683288574 nb_pixel_total : 20789 time to create 1 rle with old method : 0.02261519432067871 time for calcul the mask position with numpy : 0.034810543060302734 nb_pixel_total : 41795 time to create 1 rle with old method : 0.048671722412109375 time for calcul the mask position with numpy : 0.03340458869934082 nb_pixel_total : 53196 time to create 1 rle with old method : 0.05681920051574707 time for calcul the mask position with numpy : 0.0339045524597168 nb_pixel_total : 26538 time to create 1 rle with old method : 0.029097795486450195 time for calcul the mask position with numpy : 0.03398013114929199 nb_pixel_total : 12127 time to create 1 rle with old method : 0.013686180114746094 time for calcul the mask position with numpy : 0.03472757339477539 nb_pixel_total : 29657 time to create 1 rle with old method : 0.033324480056762695 time for calcul the mask position with numpy : 0.03518390655517578 nb_pixel_total : 17136 time to create 1 rle with old method : 0.019205331802368164 time for calcul the mask position with numpy : 0.03358817100524902 nb_pixel_total : 60425 time to create 1 rle with old method : 0.06770849227905273 create new chi : 4.0427916049957275 time to delete rle : 0.002288341522216797 batch 1 Loaded 35 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 10634 TO DO : save crop sub photo not yet done ! save time : 1.1709785461425781 nb_obj : 22 nb_hashtags : 5 time to prepare the origin masks : 9.14220404624939 time for calcul the mask position with numpy : 0.3619065284729004 nb_pixel_total : 5977072 time to create 1 rle with new method : 0.7010438442230225 time for calcul the mask position with numpy : 0.027411460876464844 nb_pixel_total : 4638 time to create 1 rle with old method : 0.005279541015625 time for calcul the mask position with numpy : 0.02154684066772461 nb_pixel_total : 15614 time to create 1 rle with old method : 0.017635107040405273 time for calcul the mask position with numpy : 0.023396730422973633 nb_pixel_total : 12472 time to create 1 rle with old method : 0.014124155044555664 time for calcul the mask position with numpy : 0.02333831787109375 nb_pixel_total : 18178 time to create 1 rle with old method : 0.02073383331298828 time for calcul the mask position with numpy : 0.022559404373168945 nb_pixel_total : 64072 time to create 1 rle with old method : 0.07200837135314941 time for calcul the mask position with numpy : 0.021875858306884766 nb_pixel_total : 85066 time to create 1 rle with old method : 0.09550094604492188 time for calcul the mask position with numpy : 0.021585464477539062 nb_pixel_total : 22683 time to create 1 rle with old method : 0.025020837783813477 time for calcul the mask position with numpy : 0.023279905319213867 nb_pixel_total : 12513 time to create 1 rle with old method : 0.014085769653320312 time for calcul the mask position with numpy : 0.02181720733642578 nb_pixel_total : 12806 time to create 1 rle with old method : 0.014335155487060547 time for calcul the mask position with numpy : 0.0298001766204834 nb_pixel_total : 89092 time to create 1 rle with old method : 0.09856438636779785 time for calcul the mask position with numpy : 0.036242008209228516 nb_pixel_total : 174254 time to create 1 rle with new method : 0.5645711421966553 time for calcul the mask position with numpy : 0.03632044792175293 nb_pixel_total : 19570 time to create 1 rle with old method : 0.02160811424255371 time for calcul the mask position with numpy : 0.03494834899902344 nb_pixel_total : 31459 time to create 1 rle with old method : 0.0413966178894043 time for calcul the mask position with numpy : 0.03842735290527344 nb_pixel_total : 61814 time to create 1 rle with old method : 0.06983518600463867 time for calcul the mask position with numpy : 0.026363134384155273 nb_pixel_total : 88595 time to create 1 rle with old method : 0.09877991676330566 time for calcul the mask position with numpy : 0.022223711013793945 nb_pixel_total : 10944 time to create 1 rle with old method : 0.012375593185424805 time for calcul the mask position with numpy : 0.022648096084594727 nb_pixel_total : 28126 time to create 1 rle with old method : 0.031244516372680664 time for calcul the mask position with numpy : 0.02156662940979004 nb_pixel_total : 8048 time to create 1 rle with old method : 0.008867263793945312 time for calcul the mask position with numpy : 0.022219181060791016 nb_pixel_total : 180690 time to create 1 rle with new method : 0.45154809951782227 time for calcul the mask position with numpy : 0.022810697555541992 nb_pixel_total : 16679 time to create 1 rle with old method : 0.018414735794067383 time for calcul the mask position with numpy : 0.02151036262512207 nb_pixel_total : 90932 time to create 1 rle with old method : 0.10003423690795898 time for calcul the mask position with numpy : 0.02368617057800293 nb_pixel_total : 24923 time to create 1 rle with old method : 0.038197994232177734 create new chi : 3.549745559692383 time to delete rle : 0.0031366348266601562 batch 1 Loaded 45 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++Number RLEs to save : 15528 TO DO : save crop sub photo not yet done ! save time : 1.314598560333252 nb_obj : 20 nb_hashtags : 5 time to prepare the origin masks : 6.575822591781616 time for calcul the mask position with numpy : 0.48038744926452637 nb_pixel_total : 5532716 time to create 1 rle with new method : 0.7525277137756348 time for calcul the mask position with numpy : 0.02146172523498535 nb_pixel_total : 2302 time to create 1 rle with old method : 0.002597808837890625 time for calcul the mask position with numpy : 0.020668506622314453 nb_pixel_total : 31284 time to create 1 rle with old method : 0.03457975387573242 time for calcul the mask position with numpy : 0.021754026412963867 nb_pixel_total : 27066 time to create 1 rle with old method : 0.029328346252441406 time for calcul the mask position with numpy : 0.02211141586303711 nb_pixel_total : 15097 time to create 1 rle with old method : 0.017184019088745117 time for calcul the mask position with numpy : 0.021179914474487305 nb_pixel_total : 19308 time to create 1 rle with old method : 0.021638870239257812 time for calcul the mask position with numpy : 0.021225929260253906 nb_pixel_total : 97351 time to create 1 rle with old method : 0.10837817192077637 time for calcul the mask position with numpy : 0.021073579788208008 nb_pixel_total : 22466 time to create 1 rle with old method : 0.024585962295532227 time for calcul the mask position with numpy : 0.021570205688476562 nb_pixel_total : 65107 time to create 1 rle with old method : 0.07088923454284668 time for calcul the mask position with numpy : 0.02126288414001465 nb_pixel_total : 2535 time to create 1 rle with old method : 0.0027506351470947266 time for calcul the mask position with numpy : 0.02260732650756836 nb_pixel_total : 202782 time to create 1 rle with new method : 0.5002400875091553 time for calcul the mask position with numpy : 0.02566814422607422 nb_pixel_total : 212980 time to create 1 rle with new method : 0.6245317459106445 time for calcul the mask position with numpy : 0.02408456802368164 nb_pixel_total : 263612 time to create 1 rle with new method : 0.6441645622253418 time for calcul the mask position with numpy : 0.02340388298034668 nb_pixel_total : 35715 time to create 1 rle with old method : 0.040818214416503906 time for calcul the mask position with numpy : 0.02443981170654297 nb_pixel_total : 14542 time to create 1 rle with old method : 0.017951250076293945 time for calcul the mask position with numpy : 0.023554563522338867 nb_pixel_total : 18806 time to create 1 rle with old method : 0.022600412368774414 time for calcul the mask position with numpy : 0.024969816207885742 nb_pixel_total : 23069 time to create 1 rle with old method : 0.028561830520629883 time for calcul the mask position with numpy : 0.025019168853759766 nb_pixel_total : 95532 time to create 1 rle with old method : 0.13091778755187988 time for calcul the mask position with numpy : 0.026513099670410156 nb_pixel_total : 89847 time to create 1 rle with old method : 0.11108613014221191 time for calcul the mask position with numpy : 0.036763906478881836 nb_pixel_total : 122901 time to create 1 rle with old method : 0.17304372787475586 time for calcul the mask position with numpy : 0.028513669967651367 nb_pixel_total : 155222 time to create 1 rle with new method : 0.4549572467803955 create new chi : 4.904499769210815 time to delete rle : 0.003225088119506836 batch 1 Loaded 41 chid ids of type : 3594 ++++++++++++++++++++++++++++Number RLEs to save : 14492 TO DO : save crop sub photo not yet done ! save time : 1.3090901374816895 nb_obj : 19 nb_hashtags : 3 time to prepare the origin masks : 9.82962441444397 time for calcul the mask position with numpy : 0.4170265197753906 nb_pixel_total : 5859792 time to create 1 rle with new method : 0.6118054389953613 time for calcul the mask position with numpy : 0.04502606391906738 nb_pixel_total : 20776 time to create 1 rle with old method : 0.023753881454467773 time for calcul the mask position with numpy : 0.03992962837219238 nb_pixel_total : 10679 time to create 1 rle with old method : 0.013618230819702148 time for calcul the mask position with numpy : 0.039028167724609375 nb_pixel_total : 115518 time to create 1 rle with old method : 0.13285112380981445 time for calcul the mask position with numpy : 0.03776741027832031 nb_pixel_total : 16501 time to create 1 rle with old method : 0.018964290618896484 time for calcul the mask position with numpy : 0.03663372993469238 nb_pixel_total : 90974 time to create 1 rle with old method : 0.11274480819702148 time for calcul the mask position with numpy : 0.03810763359069824 nb_pixel_total : 28166 time to create 1 rle with old method : 0.04602479934692383 time for calcul the mask position with numpy : 0.02737569808959961 nb_pixel_total : 27443 time to create 1 rle with old method : 0.031110763549804688 time for calcul the mask position with numpy : 0.023966073989868164 nb_pixel_total : 24243 time to create 1 rle with old method : 0.027440547943115234 time for calcul the mask position with numpy : 0.023917436599731445 nb_pixel_total : 114916 time to create 1 rle with old method : 0.13034605979919434 time for calcul the mask position with numpy : 0.023171424865722656 nb_pixel_total : 71128 time to create 1 rle with old method : 0.08146071434020996 time for calcul the mask position with numpy : 0.0215914249420166 nb_pixel_total : 118054 time to create 1 rle with old method : 0.13309741020202637 time for calcul the mask position with numpy : 0.02148723602294922 nb_pixel_total : 32488 time to create 1 rle with old method : 0.04993104934692383 time for calcul the mask position with numpy : 0.02591228485107422 nb_pixel_total : 124317 time to create 1 rle with old method : 0.14252066612243652 time for calcul the mask position with numpy : 0.02190375328063965 nb_pixel_total : 94271 time to create 1 rle with old method : 0.10552597045898438 time for calcul the mask position with numpy : 0.024568796157836914 nb_pixel_total : 73383 time to create 1 rle with old method : 0.08235836029052734 time for calcul the mask position with numpy : 0.02481222152709961 nb_pixel_total : 100004 time to create 1 rle with old method : 0.11391305923461914 time for calcul the mask position with numpy : 0.02232670783996582 nb_pixel_total : 57677 time to create 1 rle with old method : 0.06711387634277344 time for calcul the mask position with numpy : 0.03749275207519531 nb_pixel_total : 29963 time to create 1 rle with old method : 0.03430533409118652 time for calcul the mask position with numpy : 0.033039093017578125 nb_pixel_total : 39947 time to create 1 rle with old method : 0.04522442817687988 create new chi : 3.0330424308776855 time to delete rle : 0.002122163772583008 batch 1 Loaded 39 chid ids of type : 3594 +++++++++++++++++++++++++Number RLEs to save : 12484 TO DO : save crop sub photo not yet done ! save time : 0.7552292346954346 nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 8.487884759902954 time for calcul the mask position with numpy : 0.2675819396972656 nb_pixel_total : 4861885 time to create 1 rle with new method : 0.8158257007598877 time for calcul the mask position with numpy : 0.024046659469604492 nb_pixel_total : 46594 time to create 1 rle with old method : 0.055855512619018555 time for calcul the mask position with numpy : 0.022719144821166992 nb_pixel_total : 35982 time to create 1 rle with old method : 0.0446622371673584 time for calcul the mask position with numpy : 0.027631282806396484 nb_pixel_total : 35394 time to create 1 rle with old method : 0.03963947296142578 time for calcul the mask position with numpy : 0.02135491371154785 nb_pixel_total : 104873 time to create 1 rle with old method : 0.11418581008911133 time for calcul the mask position with numpy : 0.022431373596191406 nb_pixel_total : 28640 time to create 1 rle with old method : 0.03347182273864746 time for calcul the mask position with numpy : 0.024402618408203125 nb_pixel_total : 18923 time to create 1 rle with old method : 0.02485489845275879 time for calcul the mask position with numpy : 0.025162220001220703 nb_pixel_total : 90012 time to create 1 rle with old method : 0.11274266242980957 time for calcul the mask position with numpy : 0.027665376663208008 nb_pixel_total : 120944 time to create 1 rle with old method : 0.14093518257141113 time for calcul the mask position with numpy : 0.02321171760559082 nb_pixel_total : 180621 time to create 1 rle with new method : 0.8905537128448486 time for calcul the mask position with numpy : 0.022893905639648438 nb_pixel_total : 195312 time to create 1 rle with new method : 0.7532429695129395 time for calcul the mask position with numpy : 0.0304567813873291 nb_pixel_total : 37127 time to create 1 rle with old method : 0.06297779083251953 time for calcul the mask position with numpy : 0.03400468826293945 nb_pixel_total : 648389 time to create 1 rle with new method : 0.46250343322753906 time for calcul the mask position with numpy : 0.022330284118652344 nb_pixel_total : 21358 time to create 1 rle with old method : 0.02401137351989746 time for calcul the mask position with numpy : 0.022306442260742188 nb_pixel_total : 59468 time to create 1 rle with old method : 0.06707620620727539 time for calcul the mask position with numpy : 0.02208733558654785 nb_pixel_total : 77174 time to create 1 rle with old method : 0.08151841163635254 time for calcul the mask position with numpy : 0.021283626556396484 nb_pixel_total : 41565 time to create 1 rle with old method : 0.044915199279785156 time for calcul the mask position with numpy : 0.02129364013671875 nb_pixel_total : 40004 time to create 1 rle with old method : 0.051322221755981445 time for calcul the mask position with numpy : 0.022225141525268555 nb_pixel_total : 17660 time to create 1 rle with old method : 0.01993560791015625 time for calcul the mask position with numpy : 0.023009300231933594 nb_pixel_total : 35766 time to create 1 rle with old method : 0.04442715644836426 time for calcul the mask position with numpy : 0.022574901580810547 nb_pixel_total : 31450 time to create 1 rle with old method : 0.03548169136047363 time for calcul the mask position with numpy : 0.023754119873046875 nb_pixel_total : 140751 time to create 1 rle with old method : 0.15759611129760742 time for calcul the mask position with numpy : 0.023131608963012695 nb_pixel_total : 99010 time to create 1 rle with old method : 0.11423516273498535 time for calcul the mask position with numpy : 0.024664640426635742 nb_pixel_total : 81338 time to create 1 rle with old method : 0.09278440475463867 create new chi : 5.224604368209839 time to delete rle : 0.0035932064056396484 batch 1 Loaded 47 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23125 TO DO : save crop sub photo not yet done ! save time : 1.7172019481658936 nb_obj : 13 nb_hashtags : 4 time to prepare the origin masks : 9.426729440689087 time for calcul the mask position with numpy : 1.3011772632598877 nb_pixel_total : 5678509 time to create 1 rle with new method : 1.1161673069000244 time for calcul the mask position with numpy : 0.03528165817260742 nb_pixel_total : 15432 time to create 1 rle with old method : 0.017375469207763672 time for calcul the mask position with numpy : 0.035309553146362305 nb_pixel_total : 41603 time to create 1 rle with old method : 0.045761823654174805 time for calcul the mask position with numpy : 0.03532099723815918 nb_pixel_total : 137548 time to create 1 rle with old method : 0.14906716346740723 time for calcul the mask position with numpy : 0.03491401672363281 nb_pixel_total : 66215 time to create 1 rle with old method : 0.07364845275878906 time for calcul the mask position with numpy : 0.03495430946350098 nb_pixel_total : 51643 time to create 1 rle with old method : 0.05805826187133789 time for calcul the mask position with numpy : 0.03553462028503418 nb_pixel_total : 13516 time to create 1 rle with old method : 0.015326738357543945 time for calcul the mask position with numpy : 0.03562307357788086 nb_pixel_total : 106509 time to create 1 rle with old method : 0.11681938171386719 time for calcul the mask position with numpy : 0.038228750228881836 nb_pixel_total : 622788 time to create 1 rle with new method : 0.8021667003631592 time for calcul the mask position with numpy : 0.03283810615539551 nb_pixel_total : 107537 time to create 1 rle with old method : 0.12338685989379883 time for calcul the mask position with numpy : 0.02987384796142578 nb_pixel_total : 72614 time to create 1 rle with old method : 0.08095526695251465 time for calcul the mask position with numpy : 0.023465871810913086 nb_pixel_total : 29303 time to create 1 rle with old method : 0.0327296257019043 time for calcul the mask position with numpy : 0.03198885917663574 nb_pixel_total : 14148 time to create 1 rle with old method : 0.016223907470703125 time for calcul the mask position with numpy : 0.035494327545166016 nb_pixel_total : 92875 time to create 1 rle with old method : 0.10352396965026855 create new chi : 4.559707164764404 time to delete rle : 0.002331256866455078 batch 1 Loaded 27 chid ids of type : 3594 ++++++++++++++++++++Number RLEs to save : 12314 TO DO : save crop sub photo not yet done ! save time : 3.0389740467071533 map_output_result : {1350418422: (0.0, 'Should be the crop_list due to order', 0), 1350418421: (0.0, 'Should be the crop_list due to order', 0), 1350418419: (0.0, 'Should be the crop_list due to order', 0), 1350418415: (0.0, 'Should be the crop_list due to order', 0), 1350394804: (0.0, 'Should be the crop_list due to order', 0), 1350394798: (0.0, 'Should be the crop_list due to order', 0), 1350394731: (0.0, 'Should be the crop_list due to order', 0), 1350394725: (0.0, 'Should be the crop_list due to order', 0), 1350394723: (0.0, 'Should be the crop_list due to order', 0), 1350394534: (0.0, 'Should be the crop_list due to order', 0), 1350394518: (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 [1350418422, 1350418421, 1350418419, 1350418415, 1350394804, 1350394798, 1350394731, 1350394725, 1350394723, 1350394534, 1350394518] Looping around the photos to save general results len do output : 11 /1350418422.Didn't retrieve data . /1350418421.Didn't retrieve data . /1350418419.Didn't retrieve data . /1350418415.Didn't retrieve data . /1350394804.Didn't retrieve data . /1350394798.Didn't retrieve data . /1350394731.Didn't retrieve data . /1350394725.Didn't retrieve data . /1350394723.Didn't retrieve data . /1350394534.Didn't retrieve data . /1350394518.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, '2733693') ('3318', '22153647', '1350418422', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418421', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418419', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418415', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394804', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394798', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394731', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394725', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394723', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394534', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394518', None, None, None, None, None, '2733693') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.05101609230041504 save_final save missing photos in datou_result : time spend for datou_step_exec : 151.7068316936493 time spend to save output : 0.05150771141052246 total time spend for step 3 : 151.75833940505981 step4:ventilate_hashtags_in_portfolio Wed Apr 9 10:18:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 22153647 get user id for portfolio 22153647 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`=22153647 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pet_fonce','papier','carton','flou','metal','pehd','mal_croppe','background','autre','environnement')) 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`=22153647 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pet_fonce','papier','carton','flou','metal','pehd','mal_croppe','background','autre','environnement')) 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`=22153647 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pet_fonce','papier','carton','flou','metal','pehd','mal_croppe','background','autre','environnement')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22155354,22155355,22155356,22155357,22155358,22155359,22155360,22155361,22155362,22155363,22155364?tags=pet_clair,pet_fonce,papier,carton,flou,metal,pehd,mal_croppe,background,autre,environnement Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350418422, 1350418421, 1350418419, 1350418415, 1350394804, 1350394798, 1350394731, 1350394725, 1350394723, 1350394534, 1350394518] Looping around the photos to save general results len do output : 1 /22153647. 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, '2733693') ('3318', '22153647', '1350418422', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418421', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418419', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418415', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394804', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394798', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394731', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394725', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394723', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394534', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394518', None, None, None, None, None, '2733693') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.1063086986541748 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.7539374828338623 time spend to save output : 0.10663008689880371 total time spend for step 4 : 1.860567569732666 step5:final Wed Apr 9 10:18:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1350418422: ('0.20050398026424382',), 1350418421: ('0.20050398026424382',), 1350418419: ('0.20050398026424382',), 1350418415: ('0.20050398026424382',), 1350394804: ('0.20050398026424382',), 1350394798: ('0.20050398026424382',), 1350394731: ('0.20050398026424382',), 1350394725: ('0.20050398026424382',), 1350394723: ('0.20050398026424382',), 1350394534: ('0.20050398026424382',), 1350394518: ('0.20050398026424382',)} new output for save of step final : {1350418422: ('0.20050398026424382',), 1350418421: ('0.20050398026424382',), 1350418419: ('0.20050398026424382',), 1350418415: ('0.20050398026424382',), 1350394804: ('0.20050398026424382',), 1350394798: ('0.20050398026424382',), 1350394731: ('0.20050398026424382',), 1350394725: ('0.20050398026424382',), 1350394723: ('0.20050398026424382',), 1350394534: ('0.20050398026424382',), 1350394518: ('0.20050398026424382',)} [1350418422, 1350418421, 1350418419, 1350418415, 1350394804, 1350394798, 1350394731, 1350394725, 1350394723, 1350394534, 1350394518] Looping around the photos to save general results len do output : 11 /1350418422.Didn't retrieve data . /1350418421.Didn't retrieve data . /1350418419.Didn't retrieve data . /1350418415.Didn't retrieve data . /1350394804.Didn't retrieve data . /1350394798.Didn't retrieve data . /1350394731.Didn't retrieve data . /1350394725.Didn't retrieve data . /1350394723.Didn't retrieve data . /1350394534.Didn't retrieve data . /1350394518.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, '2733693') ('3318', '22153647', '1350418422', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418421', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418419', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418415', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394804', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394798', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394731', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394725', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394723', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394534', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394518', None, None, None, None, None, '2733693') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.014041423797607422 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10662984848022461 time spend to save output : 0.014696836471557617 total time spend for step 5 : 0.12132668495178223 step6:blur_detection Wed Apr 9 10:18:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578.jpg resize: (2160, 3264) 1350418422 -3.636329960025763 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946.jpg resize: (2160, 3264) 1350418421 -4.247738479051066 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97.jpg resize: (2160, 3264) 1350418419 -3.604139060826624 treat image : temp/1744186229_406788_1350418415_7d998b12d6dd18a81a5a0289f9ed2782.jpg resize: (2160, 3264) 1350418415 -3.629360499117513 treat image : temp/1744186229_406788_1350394804_f1932d940ab623727dac18f0c38c5587.jpg resize: (2160, 3264) 1350394804 -1.9718436983418033 treat image : temp/1744186229_406788_1350394798_a115c4f5f3a3b2044907d0443f08d3fc.jpg resize: (2160, 3264) 1350394798 -1.6508356248895117 treat image : temp/1744186229_406788_1350394731_1ea339efcbdbe30dc91e025fd386c535.jpg resize: (2160, 3264) 1350394731 -5.474860317028827 treat image : temp/1744186229_406788_1350394725_c2f4c7a9055b5006df6a40c0118f3329.jpg resize: (2160, 3264) 1350394725 -2.39417275352331 treat image : temp/1744186229_406788_1350394723_dc97d29fae28771c705fe91671f59bdd.jpg resize: (2160, 3264) 1350394723 -3.729728159738123 treat image : temp/1744186229_406788_1350394534_9083a6195cfa7c90e480058e2c558407.jpg resize: (2160, 3264) 1350394534 -4.501790608224114 treat image : temp/1744186229_406788_1350394518_d441dbefe91007ebbc255497a4574a37.jpg resize: (2160, 3264) 1350394518 -4.304447867876845 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115689_0.png resize: (365, 371) 1350705064 -1.8966354249119055 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115693_0.png resize: (284, 212) 1350705067 -1.150142637680667 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115716_0.png resize: (106, 196) 1350705068 -0.5767129231653958 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115691_0.png resize: (464, 633) 1350705070 -1.9699887792522026 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115692_0.png resize: (176, 181) 1350705075 -0.8598789858157196 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115706_0.png resize: (57, 100) 1350705076 0.9677344388205279 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115717_0.png resize: (226, 272) 1350705078 -2.0973935389753975 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115702_0.png resize: (150, 147) 1350705083 -0.9555463214918644 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115704_0.png resize: (145, 149) 1350705084 -1.8498420636844275 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115694_0.png resize: (312, 190) 1350705085 -2.0379599048173667 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115695_0.png resize: (310, 631) 1350705087 -1.5326022730601385 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115700_0.png resize: (272, 129) 1350705091 -0.2705969426887003 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115698_0.png resize: (241, 246) 1350705092 -1.9898667754300496 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115705_0.png resize: (274, 136) 1350705093 -0.9508457696466621 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115712_0.png resize: (213, 138) 1350705097 -0.8301886134656071 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115714_0.png resize: (237, 448) 1350705098 -2.2001989104974222 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115715_0.png resize: (172, 83) 1350705099 -2.5602010961573463 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115690_0.png resize: (206, 84) 1350705103 -1.2920950907968638 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115713_0.png resize: (284, 511) 1350705104 -2.0522092550111646 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115697_0.png resize: (256, 236) 1350705105 -2.2061632206860007 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115701_0.png resize: (346, 391) 1350705107 -2.5862875118528943 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115720_0.png resize: (163, 126) 1350705110 -3.231943543762775 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115699_0.png resize: (227, 103) 1350705111 -1.10436007950326 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115710_0.png resize: (60, 39) 1350705112 2.1970556774869365 treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115718_0.png resize: (141, 77) 1350705116 -1.9805535043980378 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115721_0.png resize: (245, 143) 1350705117 -1.98482029151577 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115747_0.png resize: (162, 167) 1350705118 -1.6461305422562742 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115740_0.png resize: (382, 351) 1350705121 -1.6689390677734872 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115733_0.png resize: (146, 167) 1350705123 -3.4154289246839915 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115741_0.png resize: (274, 557) 1350705124 -2.6660159752348127 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115736_0.png resize: (202, 150) 1350705125 -1.7181853457791518 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115732_0.png resize: (226, 188) 1350705129 -1.9978215448831989 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115739_0.png resize: (118, 95) 1350705130 -1.1966400357410685 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115744_0.png resize: (218, 88) 1350705131 -0.5507170496099343 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115735_0.png resize: (311, 403) 1350705135 -2.583416956570278 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115743_0.png resize: (167, 168) 1350705136 -3.8391803153439454 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115737_0.png resize: (148, 180) 1350705137 -3.1731949541855626 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115723_0.png resize: (251, 581) 1350705141 -2.664363455745968 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115742_0.png resize: (507, 248) 1350705142 -2.418862988643322 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115745_0.png resize: (288, 193) 1350705143 -3.963624701455765 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115731_0.png resize: (285, 555) 1350705144 -4.087128101805366 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115724_0.png resize: (473, 312) 1350705148 -3.541907469746966 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115726_0.png resize: (177, 184) 1350705149 -1.7176176593858519 treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115730_0.png resize: (521, 536) 1350705150 -2.1932938493327834 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115794_0.png resize: (348, 324) 1350705153 -2.35928905683834 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115765_0.png resize: (144, 152) 1350705155 -1.1001760691421862 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115753_0.png resize: (453, 245) 1350705156 -1.9421895109349165 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115756_0.png resize: (152, 270) 1350705159 -2.557248929724679 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115770_0.png resize: (227, 275) 1350705161 -2.336285390422132 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115767_0.png resize: (204, 321) 1350705162 -1.855256207746936 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115757_0.png resize: (135, 134) 1350705163 -1.501580321486148 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115761_0.png resize: (149, 208) 1350705170 -1.090046109671195 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115776_0.png resize: (285, 323) 1350705171 -2.2691637612884494 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115777_0.png resize: (91, 115) 1350705172 -2.1038104886657347 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115787_0.png resize: (292, 174) 1350705176 -2.001141093679151 treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115764_0.png resize: (73, 173) 1350705177 -2.1995277667554713 treat image : 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list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 324 time used for this insertion : 0.06119537353515625 save missing photos in datou_result : time spend for datou_step_exec : 46.482138872146606 time spend to save output : 0.13836359977722168 total time spend for step 6 : 46.62050247192383 step7:brightness Wed Apr 9 10:19:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578.jpg treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946.jpg treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97.jpg treat image : temp/1744186229_406788_1350418415_7d998b12d6dd18a81a5a0289f9ed2782.jpg treat image : temp/1744186229_406788_1350394804_f1932d940ab623727dac18f0c38c5587.jpg treat image : temp/1744186229_406788_1350394798_a115c4f5f3a3b2044907d0443f08d3fc.jpg treat image : temp/1744186229_406788_1350394731_1ea339efcbdbe30dc91e025fd386c535.jpg treat image : temp/1744186229_406788_1350394725_c2f4c7a9055b5006df6a40c0118f3329.jpg treat image : temp/1744186229_406788_1350394723_dc97d29fae28771c705fe91671f59bdd.jpg treat image : temp/1744186229_406788_1350394534_9083a6195cfa7c90e480058e2c558407.jpg treat image : 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temp/1744186229_406788_1350394725_c2f4c7a9055b5006df6a40c0118f3329_rle_crop_3751115936_0.png treat image : temp/1744186229_406788_1350394723_dc97d29fae28771c705fe91671f59bdd_rle_crop_3751115951_0.png treat image : temp/1744186229_406788_1350394723_dc97d29fae28771c705fe91671f59bdd_rle_crop_3751115963_0.png treat image : temp/1744186229_406788_1350394723_dc97d29fae28771c705fe91671f59bdd_rle_crop_3751115961_0.png treat image : temp/1744186229_406788_1350394723_dc97d29fae28771c705fe91671f59bdd_rle_crop_3751115957_0.png treat image : temp/1744186229_406788_1350394723_dc97d29fae28771c705fe91671f59bdd_rle_crop_3751115955_0.png treat image : temp/1744186229_406788_1350394534_9083a6195cfa7c90e480058e2c558407_rle_crop_3751115976_0.png treat image : temp/1744186229_406788_1350394534_9083a6195cfa7c90e480058e2c558407_rle_crop_3751115987_0.png treat image : temp/1744186229_406788_1350394534_9083a6195cfa7c90e480058e2c558407_rle_crop_3751115975_0.png treat image : temp/1744186229_406788_1350394534_9083a6195cfa7c90e480058e2c558407_rle_crop_3751115974_0.png treat image : temp/1744186229_406788_1350394518_d441dbefe91007ebbc255497a4574a37_rle_crop_3751116001_0.png treat image : temp/1744186229_406788_1350394518_d441dbefe91007ebbc255497a4574a37_rle_crop_3751115994_0.png treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115727_0.png treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115746_0.png treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115748_0.png treat image : temp/1744186229_406788_1350418419_1d825f1fa404fc7e117a12695709cc97_rle_crop_3751115786_0.png treat image : temp/1744186229_406788_1350394725_c2f4c7a9055b5006df6a40c0118f3329_rle_crop_3751115945_0.png treat image : temp/1744186229_406788_1350394534_9083a6195cfa7c90e480058e2c558407_rle_crop_3751115979_0.png treat image : temp/1744186229_406788_1350394518_d441dbefe91007ebbc255497a4574a37_rle_crop_3751115998_0.png treat image : temp/1744186229_406788_1350418421_c9694fb3244d6ebda294a22b44e71946_rle_crop_3751115734_0.png treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115719_0.png treat image : temp/1744186229_406788_1350418422_d823ab8840548ce6c5af7c78cbb14578_rle_crop_3751115709_0.png treat image : temp/1744186229_406788_1350418415_7d998b12d6dd18a81a5a0289f9ed2782_rle_crop_3751115829_0.png treat image : temp/1744186229_406788_1350418415_7d998b12d6dd18a81a5a0289f9ed2782_rle_crop_3751115823_0.png treat image : temp/1744186229_406788_1350394731_1ea339efcbdbe30dc91e025fd386c535_rle_crop_3751115923_0.png treat image : temp/1744186229_406788_1350394725_c2f4c7a9055b5006df6a40c0118f3329_rle_crop_3751115940_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 : 324 time used for this insertion : 0.022315502166748047 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 324 time used for this insertion : 0.06112313270568848 save missing photos in datou_result : time spend for datou_step_exec : 11.495957374572754 time spend to save output : 0.08992624282836914 total time spend for step 7 : 11.585883617401123 step8:velours_tree Wed Apr 9 10:19:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 0.1038675308227539 time spend to save output : 5.817413330078125e-05 total time spend for step 8 : 0.10392570495605469 step9:send_mail_cod Wed Apr 9 10:19:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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_P22153647_09-04-2025_10_19_51.pdf 22155354 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 .imagette221553541744186791 22155355 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221553551744186793 22155356 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 .imagette221553561744186793 22155357 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 .imagette221553571744186794 22155358 imagette221553581744186796 22155359 change filename to text .change filename to text .imagette221553591744186796 22155360 change filename to text .imagette221553601744186796 22155361 imagette221553611744186796 22155362 imagette221553621744186796 22155363 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 .imagette221553631744186796 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22153647 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22155354,22155355,22155356,22155357,22155358,22155359,22155360,22155361,22155362,22155363,22155364?tags=pet_clair,pet_fonce,papier,carton,flou,metal,pehd,mal_croppe,background,autre,environnement args[1350418422] : ((1350418422, -3.636329960025763, 492609224), (1350418422, 0.14656972730085252, 2107752395), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350418421] : ((1350418421, -4.247738479051066, 492609224), (1350418421, -0.13324604195910844, 496442774), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350418419] : ((1350418419, -3.604139060826624, 492609224), (1350418419, -0.1023414655298069, 496442774), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350418415] : ((1350418415, -3.629360499117513, 492609224), (1350418415, -0.09960437246419866, 496442774), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350394804] : ((1350394804, -1.9718436983418033, 492688767), (1350394804, -0.07492622511845176, 496442774), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350394798] : ((1350394798, -1.6508356248895117, 492688767), (1350394798, 0.04575070305689251, 2107752395), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350394731] : ((1350394731, -5.474860317028827, 492609224), (1350394731, 0.2481238514303226, 2107752395), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350394725] : ((1350394725, -2.39417275352331, 492609224), (1350394725, 0.1719390930051946, 2107752395), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350394723] : ((1350394723, -3.729728159738123, 492609224), (1350394723, -0.042730138752633755, 2107752395), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350394534] : ((1350394534, -4.501790608224114, 492609224), (1350394534, -0.08127759085606485, 496442774), '0.20050398026424382') We are sending mail with results at report@fotonower.com args[1350394518] : ((1350394518, -4.304447867876845, 492609224), (1350394518, -0.1828614542250996, 496442774), '0.20050398026424382') We are sending mail with results at report@fotonower.com refus_total : 0.20050398026424382 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=22153647 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350394518,1350394534,1350394723,1350394804,1350418415,1350418419,1350418421,1350418422,1350394725,1350394731,1350394798) Found this number of photos: 11 begin to download photo : 1350394518 begin to download photo : 1350394804 begin to download photo : 1350418421 begin to download photo : 1350394731 download finish for photo 1350418421 begin to download photo : 1350418422 download finish for photo 1350394804 begin to download photo : 1350418415 download finish for photo 1350394731 begin to download photo : 1350394798 download finish for photo 1350394518 begin to download photo : 1350394534 download finish for photo 1350394798 download finish for photo 1350394534 begin to download photo : 1350394723 download finish for photo 1350418415 begin to download photo : 1350418419 download finish for photo 1350418422 begin to download photo : 1350394725 download finish for photo 1350394723 download finish for photo 1350394725 download finish for photo 1350418419 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153647_09-04-2025_10_19_51.pdf results_Auto_P22153647_09-04-2025_10_19_51.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153647_09-04-2025_10_19_51.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','22153647','results_Auto_P22153647_09-04-2025_10_19_51.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153647_09-04-2025_10_19_51.pdf','pdf','','1.06','0.20050398026424382') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22153647

https://www.fotonower.com/image?json=false&list_photos_id=1350418422
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350418421
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350418419
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350418415
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350394804
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350394798
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350394731
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350394725
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350394723
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350394534
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350394518
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 20.05%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_clair: https://www.fotonower.com/view/22155354?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22155355?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22155356?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22155357?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22155359?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22155360?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22155363?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153647_09-04-2025_10_19_51.pdf.

Lien vers velours :https://www.fotonower.com/velours/22155354,22155355,22155356,22155357,22155358,22155359,22155360,22155361,22155362,22155363,22155364?tags=pet_clair,pet_fonce,papier,carton,flou,metal,pehd,mal_croppe,background,autre,environnement.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 08:20:00 GMT Content-Length: 0 Connection: close X-Message-Id: 2Tp69d0YRqabo3hXdbnZgg 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 [1350418422, 1350418421, 1350418419, 1350418415, 1350394804, 1350394798, 1350394731, 1350394725, 1350394723, 1350394534, 1350394518] 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, '2733693') ('3318', '22153647', '1350418422', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418421', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418419', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418415', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394804', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394798', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394731', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394725', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394723', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394534', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394518', None, None, None, None, None, '2733693') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.012078285217285156 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.461090564727783 time spend to save output : 0.012346267700195312 total time spend for step 9 : 9.473436832427979 step10:split_time_score Wed Apr 9 10:20:00 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'}] (('19', 11),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 07042025 22153647 Nombre de photos uploadées : 11 / 23040 (0%) 07042025 22153647 Nombre de photos taguées (types de déchets): 0 / 11 (0%) 07042025 22153647 Nombre de photos taguées (volume) : 0 / 11 (0%) elapsed_time : load_data_split_time_score 2.384185791015625e-06 elapsed_time : order_list_meta_photo_and_scores 5.0067901611328125e-06 ??????????? elapsed_time : fill_and_build_computed_from_old_data 0.0005795955657958984 elapsed_time : insert_dashboard_record_day_entry 0.02440023422241211 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 Qualite : 0.20050398026424382 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153647_09-04-2025_10_19_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153647 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`=22153647 AND mptpi.`type`=3594 To do 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 [1350418422, 1350418421, 1350418419, 1350418415, 1350394804, 1350394798, 1350394731, 1350394725, 1350394723, 1350394534, 1350394518] Looping around the photos to save general results len do output : 1 /22153647Didn'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, '2733693') ('3318', '22153647', '1350418422', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418421', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418419', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350418415', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394804', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394798', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394731', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394725', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394723', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394534', None, None, None, None, None, '2733693') ('3318', None, None, None, None, None, None, None, '2733693') ('3318', '22153647', '1350394518', None, None, None, None, None, '2733693') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.028248310089111328 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.474279880523682 time spend to save output : 0.028560400009155273 total time spend for step 10 : 13.502840280532837 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 272.67user 168.45system 9:49.58elapsed 74%CPU (0avgtext+0avgdata 7281200maxresident)k 1596888inputs+196944outputs (35454major+24580057minor)pagefaults 0swaps