python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 8 -a ' -a 4741 -l 30 ' -s datou_current_4741 -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 : 2168531 load datou : 4741 # 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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 ? 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 ? 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 ? 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 ? [ (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 ? 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 ? 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 : (5732, 'mask_refus_amiens_050123', 16384, 25088, 'mask_refus_amiens_050123', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 1, 5, 13, 23, 59), datetime.datetime(2023, 1, 5, 13, 23, 59)) FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5972, 'learn_entrant_syctomXV_111023', 16384, 25088, 'learn_entrant_syctomXV_111023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 10, 11, 15, 57, 35), datetime.datetime(2023, 10, 11, 15, 57, 35)) load thcls load THCL from format json or kwargs add thcl : 3453 in CacheModelConfig load THCL from format json or kwargs add thcl : 3783 in CacheModelConfig load pdts add pdt : 5732 in CacheModelConfig add pdt : 5972 in CacheModelConfig Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec no input labels no input values updating current state to 1 list_input_json: {} Current got : datou_id : 4741, datou_cur_ids : ['3735887'] with mtr_portfolio_ids : ['26943498'] and first list_photo_ids : [] new path : /proc/2168531/ 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 ! 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, mask_detect, blur_detection, brightness, rle_unique_nms_with_priority, crop_condition, ventilate_hashtags_in_portfolio, final, velours_tree, send_mail_cod, split_time_score list_input_json : {} origin We have 1 , BFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 7 ; length of list_pids : 7 ; length of list_args : 7 time to download the photos : 1.8075027465820312 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 : 11 step1:mask_detect Wed Sep 17 14:41:31 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 : 10589 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-17 14:41:34.242316: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-17 14:41:34.268617: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-17 14:41:34.270886: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2958000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-17 14:41:34.270938: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-17 14:41:34.275056: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-17 14:41:34.464260: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1aa58090 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-17 14:41:34.464308: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-17 14:41:34.465794: 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 2025-09-17 14:41:34.466615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:41:34.471136: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:41:34.473765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:41:34.475239: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:41:34.477356: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:41:34.478406: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:41:34.482512: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:41:34.483874: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:41:34.483938: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:41:34.484682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-17 14:41:34.484698: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-17 14:41:34.484707: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-17 14:41:34.486081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9808 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. 2025-09-17 14:41:34.776224: 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 2025-09-17 14:41:34.776337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:41:34.776384: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:41:34.776407: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:41:34.776428: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:41:34.776449: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:41:34.776469: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:41:34.776489: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:41:34.778018: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:41:34.779475: 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 2025-09-17 14:41:34.779579: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:41:34.779601: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:41:34.779621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:41:34.779654: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:41:34.779675: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:41:34.779695: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:41:34.779716: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:41:34.781283: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:41:34.781323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-17 14:41:34.781335: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-17 14:41:34.781344: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-17 14:41:34.782937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9808 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 : thcl3783 thcls : [{'id': 3783, 'mtr_user_id': 31, 'name': 'learn_entrant_syctomXV_111023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 4853, 'photo_desc_type': 5972, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 3783, 'mtr_user_id': 31, 'name': 'learn_entrant_syctomXV_111023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 4853, 'photo_desc_type': 5972, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5972 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5972, 'learn_entrant_syctomXV_111023', 16384, 25088, 'learn_entrant_syctomXV_111023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 10, 11, 15, 57, 35), datetime.datetime(2023, 10, 11, 15, 57, 35)) {'thcl': {'id': 3783, 'mtr_user_id': 31, 'name': 'learn_entrant_syctomXV_111023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 4853, 'photo_desc_type': 5972, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'sac', 'textiles', 'verre', 'organique', 'dasri', 'masque', 'encombrant', 'autre_emballage', 'autre_non_emballage', 'environnement'], 'list_hashtags_csv': 'background,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 4853, 'svm_hashtag_type_desc': 5972, 'photo_desc_type': 5972, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'sac', 'textiles', 'verre', 'organique', 'dasri', 'masque', 'encombrant', 'autre_emballage', 'autre_non_emballage', '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_entrant_syctomXV_111023 NUM_CLASSES 11 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_entrant_syctomXV_111023 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-09-17 14:41:45.524754: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:41:45.710262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_entrant_syctomXV_111023 /data/models_weight/learn_entrant_syctomXV_111023/mask_model.h5 size_local : 256052544 size in s3 : 256052544 create time local : 2023-10-26 16:36:43 create time in s3 : 2023-10-11 13:57:17 mask_model.h5 already exist and didn't need to update list_images length : 7 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.26641 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3120.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.61797 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3120.00000 nb d'objets trouves : 24 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3120.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.38750 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3120.00000 nb d'objets trouves : 19 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.58281 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3120.00000 nb d'objets trouves : 21 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3120.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 19) min: 0.00000 max: 3120.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 2168818 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5297 tf kernel not reseted sub process len(results) : 7 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 7 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 : 10589 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl3783 Catched exception ! Connect or reconnect ! thcls : [{'id': 3783, 'mtr_user_id': 31, 'name': 'learn_entrant_syctomXV_111023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 4853, 'photo_desc_type': 5972, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 3783, 'mtr_user_id': 31, 'name': 'learn_entrant_syctomXV_111023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 4853, 'photo_desc_type': 5972, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5972 ['background', 'sac', 'textiles', 'verre', 'organique', 'dasri', 'masque', 'encombrant', 'autre_emballage', 'autre_non_emballage', 'environnement'] time for calcul the mask position with numpy : 0.0008134841918945312 nb_pixel_total : 31444 time to create 1 rle with old method : 0.03426837921142578 length of segment : 307 time for calcul the mask position with numpy : 0.00034046173095703125 nb_pixel_total : 15979 time to create 1 rle with old method : 0.018099308013916016 length of segment : 253 time for calcul the mask position with numpy : 0.0004723072052001953 nb_pixel_total : 28585 time to create 1 rle with old method : 0.03281402587890625 length of segment : 292 time for calcul the mask position with numpy : 0.0006296634674072266 nb_pixel_total : 41904 time to create 1 rle with old method : 0.049199819564819336 length of segment : 232 time for calcul the mask position with numpy : 0.027968645095825195 nb_pixel_total : 725335 time to create 1 rle with new method : 0.05155467987060547 length of segment : 1125 time for calcul the mask position with numpy : 0.0002987384796142578 nb_pixel_total : 13688 time to create 1 rle with old method : 0.015198469161987305 length of segment : 205 time for calcul the mask position with numpy : 0.00030422210693359375 nb_pixel_total : 20265 time to create 1 rle with old method : 0.023777484893798828 length of segment : 145 time for calcul the mask position with numpy : 0.00029349327087402344 nb_pixel_total : 20069 time to create 1 rle with old method : 0.02228689193725586 length of segment : 220 time for calcul the mask position with numpy : 0.00095367431640625 nb_pixel_total : 82157 time to create 1 rle with old method : 0.09238815307617188 length of segment : 312 time for calcul the mask position with numpy : 0.0008270740509033203 nb_pixel_total : 40121 time to create 1 rle with old method : 0.0446162223815918 length of segment : 261 time for calcul the mask position with numpy : 0.00025844573974609375 nb_pixel_total : 8506 time to create 1 rle with old method : 0.010116100311279297 length of segment : 181 time for calcul the mask position with numpy : 0.002314329147338867 nb_pixel_total : 150715 time to create 1 rle with new method : 0.006285667419433594 length of segment : 474 time for calcul the mask position with numpy : 0.0018069744110107422 nb_pixel_total : 94466 time to create 1 rle with old method : 0.13296175003051758 length of segment : 264 time for calcul the mask position with numpy : 0.0010204315185546875 nb_pixel_total : 70339 time to create 1 rle with old method : 0.07793211936950684 length of segment : 251 time for calcul the mask position with numpy : 0.00029397010803222656 nb_pixel_total : 15833 time to create 1 rle with old method : 0.018646240234375 length of segment : 152 time for calcul the mask position with numpy : 0.0018048286437988281 nb_pixel_total : 122853 time to create 1 rle with old method : 0.13806605339050293 length of segment : 569 time for calcul the mask position with numpy : 0.001646280288696289 nb_pixel_total : 97727 time to create 1 rle with old method : 0.11310887336730957 length of segment : 286 time for calcul the mask position with numpy : 0.0032126903533935547 nb_pixel_total : 164825 time to create 1 rle with new method : 0.008012771606445312 length of segment : 507 time for calcul the mask position with numpy : 0.0018055438995361328 nb_pixel_total : 112801 time to create 1 rle with old method : 0.1286616325378418 length of segment : 316 time for calcul the mask position with numpy : 0.0012583732604980469 nb_pixel_total : 68946 time to create 1 rle with old method : 0.08003568649291992 length of segment : 469 time for calcul the mask position with numpy : 0.0003871917724609375 nb_pixel_total : 20159 time to create 1 rle with old method : 0.0243222713470459 length of segment : 164 time for calcul the mask position with numpy : 0.000530242919921875 nb_pixel_total : 12122 time to create 1 rle with old method : 0.015276193618774414 length of segment : 264 time for calcul the mask position with numpy : 0.0022373199462890625 nb_pixel_total : 127699 time to create 1 rle with old method : 0.14380526542663574 length of segment : 809 time for calcul the mask position with numpy : 0.0003764629364013672 nb_pixel_total : 13037 time to create 1 rle with old method : 0.014989137649536133 length of segment : 162 time for calcul the mask position with numpy : 0.00033211708068847656 nb_pixel_total : 19287 time to create 1 rle with old method : 0.022164344787597656 length of segment : 155 time for calcul the mask position with numpy : 0.00014400482177734375 nb_pixel_total : 7635 time to create 1 rle with old method : 0.009139537811279297 length of segment : 77 time for calcul the mask position with numpy : 0.0003376007080078125 nb_pixel_total : 20852 time to create 1 rle with old method : 0.023717403411865234 length of segment : 179 time for calcul the mask position with numpy : 0.00024509429931640625 nb_pixel_total : 10913 time to create 1 rle with old method : 0.012850284576416016 length of segment : 131 time for calcul the mask position with numpy : 0.00025391578674316406 nb_pixel_total : 14438 time to create 1 rle with old method : 0.01771712303161621 length of segment : 92 time for calcul the mask position with numpy : 0.0040323734283447266 nb_pixel_total : 266886 time to create 1 rle with new method : 0.020848751068115234 length of segment : 666 time for calcul the mask position with numpy : 0.0018665790557861328 nb_pixel_total : 102946 time to create 1 rle with old method : 0.11566972732543945 length of segment : 302 time for calcul the mask position with numpy : 0.0007789134979248047 nb_pixel_total : 33409 time to create 1 rle with old method : 0.038515329360961914 length of segment : 274 time for calcul the mask position with numpy : 0.0004737377166748047 nb_pixel_total : 23770 time to create 1 rle with old method : 0.027501344680786133 length of segment : 214 time for calcul the mask position with numpy : 0.0005583763122558594 nb_pixel_total : 37011 time to create 1 rle with old method : 0.0424802303314209 length of segment : 199 time for calcul the mask position with numpy : 0.0005600452423095703 nb_pixel_total : 19128 time to create 1 rle with old method : 0.022261857986450195 length of segment : 187 time for calcul the mask position with numpy : 0.0006136894226074219 nb_pixel_total : 34505 time to create 1 rle with old method : 0.04054117202758789 length of segment : 283 time for calcul the mask position with numpy : 0.0008158683776855469 nb_pixel_total : 37128 time to create 1 rle with old method : 0.04340314865112305 length of segment : 185 time for calcul the mask position with numpy : 0.00047588348388671875 nb_pixel_total : 21918 time to create 1 rle with old method : 0.025014162063598633 length of segment : 233 time for calcul the mask position with numpy : 0.0006341934204101562 nb_pixel_total : 29141 time to create 1 rle with old method : 0.034305572509765625 length of segment : 213 time for calcul the mask position with numpy : 0.0014538764953613281 nb_pixel_total : 64335 time to create 1 rle with old method : 0.07271981239318848 length of segment : 582 time for calcul the mask position with numpy : 0.0004971027374267578 nb_pixel_total : 24931 time to create 1 rle with old method : 0.029106616973876953 length of segment : 235 time for calcul the mask position with numpy : 0.0008614063262939453 nb_pixel_total : 23961 time to create 1 rle with old method : 0.027678728103637695 length of segment : 354 time for calcul the mask position with numpy : 0.0004723072052001953 nb_pixel_total : 22925 time to create 1 rle with old method : 0.02707815170288086 length of segment : 287 time for calcul the mask position with numpy : 0.0017476081848144531 nb_pixel_total : 82357 time to create 1 rle with old method : 0.10038256645202637 length of segment : 332 time for calcul the mask position with numpy : 0.001049041748046875 nb_pixel_total : 31582 time to create 1 rle with old method : 0.037222862243652344 length of segment : 136 time for calcul the mask position with numpy : 0.0009968280792236328 nb_pixel_total : 26096 time to create 1 rle with old method : 0.03115534782409668 length of segment : 248 time for calcul the mask position with numpy : 0.0009572505950927734 nb_pixel_total : 20817 time to create 1 rle with old method : 0.029000282287597656 length of segment : 189 time for calcul the mask position with numpy : 0.00027680397033691406 nb_pixel_total : 5031 time to create 1 rle with old method : 0.006398677825927734 length of segment : 56 time for calcul the mask position with numpy : 0.0019147396087646484 nb_pixel_total : 37824 time to create 1 rle with old method : 0.04333186149597168 length of segment : 406 time for calcul the mask position with numpy : 0.0018799304962158203 nb_pixel_total : 63461 time to create 1 rle with old method : 0.07100796699523926 length of segment : 359 time for calcul the mask position with numpy : 0.0007970333099365234 nb_pixel_total : 15679 time to create 1 rle with old method : 0.01778864860534668 length of segment : 165 time for calcul the mask position with numpy : 0.00047779083251953125 nb_pixel_total : 8911 time to create 1 rle with old method : 0.010147571563720703 length of segment : 114 time for calcul the mask position with numpy : 0.0011069774627685547 nb_pixel_total : 30862 time to create 1 rle with old method : 0.042420148849487305 length of segment : 151 time for calcul the mask position with numpy : 0.0005505084991455078 nb_pixel_total : 11946 time to create 1 rle with old method : 0.01354360580444336 length of segment : 129 time for calcul the mask position with numpy : 0.013170480728149414 nb_pixel_total : 281451 time to create 1 rle with new method : 0.03707623481750488 length of segment : 943 time for calcul the mask position with numpy : 0.0019767284393310547 nb_pixel_total : 14046 time to create 1 rle with old method : 0.016634225845336914 length of segment : 415 time for calcul the mask position with numpy : 0.002393484115600586 nb_pixel_total : 89511 time to create 1 rle with old method : 0.11088395118713379 length of segment : 331 time for calcul the mask position with numpy : 0.00020742416381835938 nb_pixel_total : 8371 time to create 1 rle with old method : 0.010080337524414062 length of segment : 104 time for calcul the mask position with numpy : 0.003565549850463867 nb_pixel_total : 71400 time to create 1 rle with old method : 0.08306694030761719 length of segment : 416 time for calcul the mask position with numpy : 0.037809133529663086 nb_pixel_total : 877158 time to create 1 rle with new method : 1.039217233657837 length of segment : 1024 time for calcul the mask position with numpy : 0.0020177364349365234 nb_pixel_total : 84017 time to create 1 rle with old method : 0.09214520454406738 length of segment : 267 time for calcul the mask position with numpy : 0.005631208419799805 nb_pixel_total : 178149 time to create 1 rle with new method : 0.007779359817504883 length of segment : 564 time for calcul the mask position with numpy : 0.0031499862670898438 nb_pixel_total : 102044 time to create 1 rle with old method : 0.13439726829528809 length of segment : 613 time for calcul the mask position with numpy : 0.0006952285766601562 nb_pixel_total : 14445 time to create 1 rle with old method : 0.017319917678833008 length of segment : 184 time for calcul the mask position with numpy : 0.0008604526519775391 nb_pixel_total : 16862 time to create 1 rle with old method : 0.02078080177307129 length of segment : 170 time for calcul the mask position with numpy : 0.0050165653228759766 nb_pixel_total : 146194 time to create 1 rle with old method : 0.1904613971710205 length of segment : 358 time for calcul the mask position with numpy : 0.0006592273712158203 nb_pixel_total : 11002 time to create 1 rle with old method : 0.016047954559326172 length of segment : 142 time for calcul the mask position with numpy : 0.0030601024627685547 nb_pixel_total : 170185 time to create 1 rle with new method : 0.008125543594360352 length of segment : 581 time for calcul the mask position with numpy : 0.0011420249938964844 nb_pixel_total : 27543 time to create 1 rle with old method : 0.032209157943725586 length of segment : 212 time for calcul the mask position with numpy : 0.005635499954223633 nb_pixel_total : 182428 time to create 1 rle with new method : 0.008895397186279297 length of segment : 662 time for calcul the mask position with numpy : 0.00044274330139160156 nb_pixel_total : 8355 time to create 1 rle with old method : 0.01060032844543457 length of segment : 148 time for calcul the mask position with numpy : 0.00025725364685058594 nb_pixel_total : 8504 time to create 1 rle with old method : 0.010260820388793945 length of segment : 81 time for calcul the mask position with numpy : 0.09879040718078613 nb_pixel_total : 1390265 time to create 1 rle with new method : 0.4781982898712158 length of segment : 1737 time for calcul the mask position with numpy : 0.0006632804870605469 nb_pixel_total : 35995 time to create 1 rle with old method : 0.04345107078552246 length of segment : 173 time for calcul the mask position with numpy : 0.002242565155029297 nb_pixel_total : 83960 time to create 1 rle with old method : 0.09965062141418457 length of segment : 437 time for calcul the mask position with numpy : 0.0031931400299072266 nb_pixel_total : 182710 time to create 1 rle with new method : 0.008078336715698242 length of segment : 656 time for calcul the mask position with numpy : 0.002500772476196289 nb_pixel_total : 97684 time to create 1 rle with old method : 0.11443901062011719 length of segment : 592 time for calcul the mask position with numpy : 0.04044032096862793 nb_pixel_total : 88710 time to create 1 rle with old method : 0.11029291152954102 length of segment : 239 time for calcul the mask position with numpy : 0.18695282936096191 nb_pixel_total : 211792 time to create 1 rle with new method : 0.015273809432983398 length of segment : 698 time for calcul the mask position with numpy : 0.005066871643066406 nb_pixel_total : 90066 time to create 1 rle with old method : 0.10807132720947266 length of segment : 297 time for calcul the mask position with numpy : 0.009496688842773438 nb_pixel_total : 36963 time to create 1 rle with old method : 0.0511627197265625 length of segment : 258 time for calcul the mask position with numpy : 0.005114555358886719 nb_pixel_total : 6559 time to create 1 rle with old method : 0.008085966110229492 length of segment : 117 time for calcul the mask position with numpy : 0.02016472816467285 nb_pixel_total : 103914 time to create 1 rle with old method : 0.13088107109069824 length of segment : 325 time for calcul the mask position with numpy : 0.04300355911254883 nb_pixel_total : 47863 time to create 1 rle with old method : 0.06205272674560547 length of segment : 170 time for calcul the mask position with numpy : 0.11997699737548828 nb_pixel_total : 50416 time to create 1 rle with old method : 0.06398296356201172 length of segment : 285 time for calcul the mask position with numpy : 0.1547553539276123 nb_pixel_total : 203680 time to create 1 rle with new method : 0.01201009750366211 length of segment : 604 time for calcul the mask position with numpy : 0.01127314567565918 nb_pixel_total : 7517 time to create 1 rle with old method : 0.06962060928344727 length of segment : 57 time for calcul the mask position with numpy : 0.3078770637512207 nb_pixel_total : 183718 time to create 1 rle with new method : 0.007108211517333984 length of segment : 701 time for calcul the mask position with numpy : 0.030966758728027344 nb_pixel_total : 50194 time to create 1 rle with old method : 0.08239579200744629 length of segment : 274 time for calcul the mask position with numpy : 0.008397817611694336 nb_pixel_total : 32326 time to create 1 rle with old method : 0.047083377838134766 length of segment : 497 time for calcul the mask position with numpy : 0.03562331199645996 nb_pixel_total : 12502 time to create 1 rle with old method : 0.021969318389892578 length of segment : 124 time for calcul the mask position with numpy : 0.055153846740722656 nb_pixel_total : 26602 time to create 1 rle with old method : 0.03517889976501465 length of segment : 278 time for calcul the mask position with numpy : 0.004037380218505859 nb_pixel_total : 2687 time to create 1 rle with old method : 0.00603485107421875 length of segment : 188 time for calcul the mask position with numpy : 0.01722431182861328 nb_pixel_total : 102679 time to create 1 rle with old method : 0.12952589988708496 length of segment : 520 time for calcul the mask position with numpy : 0.10235953330993652 nb_pixel_total : 87624 time to create 1 rle with old method : 0.11615204811096191 length of segment : 333 time for calcul the mask position with numpy : 0.0014772415161132812 nb_pixel_total : 60953 time to create 1 rle with old method : 0.07186079025268555 length of segment : 258 time for calcul the mask position with numpy : 0.0005593299865722656 nb_pixel_total : 39645 time to create 1 rle with old method : 0.04598569869995117 length of segment : 194 time for calcul the mask position with numpy : 0.06454730033874512 nb_pixel_total : 27579 time to create 1 rle with old method : 0.051264047622680664 length of segment : 281 time for calcul the mask position with numpy : 0.0033881664276123047 nb_pixel_total : 39925 time to create 1 rle with old method : 0.05753827095031738 length of segment : 345 time for calcul the mask position with numpy : 0.010459184646606445 nb_pixel_total : 35965 time to create 1 rle with old method : 0.05657696723937988 length of segment : 323 time for calcul the mask position with numpy : 0.06929659843444824 nb_pixel_total : 84335 time to create 1 rle with old method : 0.10297966003417969 length of segment : 720 time for calcul the mask position with numpy : 0.07217860221862793 nb_pixel_total : 48459 time to create 1 rle with old method : 0.07966184616088867 length of segment : 498 time for calcul the mask position with numpy : 0.07052206993103027 nb_pixel_total : 11379 time to create 1 rle with old method : 0.0339663028717041 length of segment : 150 time for calcul the mask position with numpy : 0.005038022994995117 nb_pixel_total : 11953 time to create 1 rle with old method : 0.021379709243774414 length of segment : 113 time for calcul the mask position with numpy : 0.05433154106140137 nb_pixel_total : 213110 time to create 1 rle with new method : 0.010319709777832031 length of segment : 471 time for calcul the mask position with numpy : 0.002891063690185547 nb_pixel_total : 29284 time to create 1 rle with old method : 0.034683942794799805 length of segment : 170 time for calcul the mask position with numpy : 0.016747236251831055 nb_pixel_total : 55902 time to create 1 rle with old method : 0.09863400459289551 length of segment : 327 time for calcul the mask position with numpy : 0.4525582790374756 nb_pixel_total : 1654070 time to create 1 rle with new method : 1.170691728591919 length of segment : 1195 time for calcul the mask position with numpy : 2.0863845348358154 nb_pixel_total : 3748041 time to create 1 rle with new method : 2.2875325679779053 length of segment : 3226 time for calcul the mask position with numpy : 0.002925395965576172 nb_pixel_total : 184801 time to create 1 rle with new method : 0.003737926483154297 length of segment : 325 time for calcul the mask position with numpy : 2.757361888885498 nb_pixel_total : 2786061 time to create 1 rle with new method : 1.9796879291534424 length of segment : 1827 time for calcul the mask position with numpy : 0.8283298015594482 nb_pixel_total : 3498205 time to create 1 rle with new method : 2.1325066089630127 length of segment : 1716 time spent for convertir_results : 33.58297348022461 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 112 chid ids of type : 4854 Number RLEs to save : 0 save missing photos in datou_result : time spend for datou_step_exec : 74.76059579849243 time spend to save output : 0.2744569778442383 total time spend for step 1 : 75.03505277633667 step2:mask_detect Wed Sep 17 14:42:46 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10589 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-17 14:42:53.036054: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-17 14:42:53.092599: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-17 14:42:53.094392: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2958000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-17 14:42:53.094443: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-17 14:42:53.103678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-17 14:42:53.498375: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1c222f90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-17 14:42:53.498429: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-17 14:42:53.499968: 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 2025-09-17 14:42:53.501941: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:42:53.541271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:42:53.562869: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:42:53.568460: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:42:53.624935: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:42:53.742954: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:42:53.809056: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:42:53.811131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:42:53.811657: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:42:53.813561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-17 14:42:53.813583: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-17 14:42:53.813596: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-17 14:42:53.816071: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9808 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. 2025-09-17 14:42:54.036499: 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 2025-09-17 14:42:54.036591: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:42:54.036613: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:42:54.036634: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:42:54.036653: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:42:54.036672: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:42:54.036701: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:42:54.036721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:42:54.038028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:42:54.039188: 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 2025-09-17 14:42:54.039238: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-17 14:42:54.039256: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:42:54.039273: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-17 14:42:54.039289: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-17 14:42:54.039305: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-17 14:42:54.039322: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-17 14:42:54.039338: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-17 14:42:54.040660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-17 14:42:54.040692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-17 14:42:54.040701: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-17 14:42:54.040709: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-17 14:42:54.042167: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9808 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 : thcl3453 thcls : [{'id': 3453, 'mtr_user_id': 31, 'name': 'mask_refus_amiens_050123', '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': 4485, 'photo_desc_type': 5732, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 3453, 'mtr_user_id': 31, 'name': 'mask_refus_amiens_050123', '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': 4485, 'photo_desc_type': 5732, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5732 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5732, 'mask_refus_amiens_050123', 16384, 25088, 'mask_refus_amiens_050123', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 1, 5, 13, 23, 59), datetime.datetime(2023, 1, 5, 13, 23, 59)) {'thcl': {'id': 3453, 'mtr_user_id': 31, 'name': 'mask_refus_amiens_050123', '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': 4485, 'photo_desc_type': 5732, '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': 4485, 'svm_hashtag_type_desc': 5732, 'photo_desc_type': 5732, '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 mask_refus_amiens_050123 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 : mask_refus_amiens_050123 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-09-17 14:42:59.750157: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 51380224 exceeds 10% of free system memory. 2025-09-17 14:43:08.181704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-17 14:43:08.877373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_refus_amiens_050123 /data/models_weight/mask_refus_amiens_050123/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2023-01-05 13:44:07 create time in s3 : 2023-01-05 12:23:52 mask_model.h5 already exist and didn't need to update list_images length : 7 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.26641 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3120.00000 nb d'objets trouves : 75 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.61797 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3120.00000 nb d'objets trouves : 69 NEW PHOTO Processing 1 images image shape: (3120, 3120, 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: 3120.00000 nb d'objets trouves : 63 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.38750 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3120.00000 nb d'objets trouves : 65 NEW PHOTO Processing 1 images image shape: (3120, 3120, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.58281 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3120.00000 nb d'objets trouves : 59 NEW PHOTO Processing 1 images image shape: (3120, 3120, 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: 3120.00000 nb d'objets trouves : 74 NEW PHOTO Processing 1 images image shape: (3120, 3120, 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: 3120.00000 nb d'objets trouves : 3 Detection mask done ! Trying to reset tf kernel 2170706 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5297 tf kernel not reseted sub process len(results) : 7 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 7 len(list_Values) 0 process is alive process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10589 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl3453 Catched exception ! Connect or reconnect ! thcls : [{'id': 3453, 'mtr_user_id': 31, 'name': 'mask_refus_amiens_050123', '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': 4485, 'photo_desc_type': 5732, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 3453, 'mtr_user_id': 31, 'name': 'mask_refus_amiens_050123', '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': 4485, 'photo_desc_type': 5732, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5732 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.9579572677612305 nb_pixel_total : 401162 time to create 1 rle with new method : 0.04390525817871094 length of segment : 619 time for calcul the mask position with numpy : 0.5518019199371338 nb_pixel_total : 143703 time to create 1 rle with old method : 0.19714879989624023 length of segment : 544 time for calcul the mask position with numpy : 0.7513554096221924 nb_pixel_total : 198661 time to create 1 rle with new method : 0.01084136962890625 length of segment : 388 time for calcul the mask position with numpy : 0.3499021530151367 nb_pixel_total : 66764 time to create 1 rle with old method : 0.08746528625488281 length of segment : 350 time for calcul the mask position with numpy : 0.2321147918701172 nb_pixel_total : 36307 time to create 1 rle with old method : 0.05298113822937012 length of segment : 159 time for calcul the mask position with numpy : 0.46710729598999023 nb_pixel_total : 62060 time to create 1 rle with old method : 0.09934425354003906 length of segment : 379 time for calcul the mask position with numpy : 0.282672643661499 nb_pixel_total : 120141 time to create 1 rle with old method : 0.1700599193572998 length of segment : 391 time for calcul the mask position with numpy : 0.08817243576049805 nb_pixel_total : 13847 time to create 1 rle with old method : 0.021046161651611328 length of segment : 99 time for calcul the mask position with numpy : 0.19352507591247559 nb_pixel_total : 38097 time to create 1 rle with old method : 0.058710575103759766 length of segment : 170 time for calcul the mask position with numpy : 0.19404149055480957 nb_pixel_total : 26650 time to create 1 rle with old method : 0.037577152252197266 length of segment : 188 time for calcul the mask position with numpy : 0.2546811103820801 nb_pixel_total : 72171 time to create 1 rle with old method : 0.11280369758605957 length of segment : 293 time for calcul the mask position with numpy : 0.16450214385986328 nb_pixel_total : 32419 time to create 1 rle with old method : 0.047745704650878906 length of segment : 225 time for calcul the mask position with numpy : 0.06764531135559082 nb_pixel_total : 7511 time to create 1 rle with old method : 0.013463973999023438 length of segment : 102 time for calcul the mask position with numpy : 0.05516338348388672 nb_pixel_total : 9166 time to create 1 rle with old method : 0.015265941619873047 length of segment : 117 time for calcul the mask position with numpy : 0.12684345245361328 nb_pixel_total : 36639 time to create 1 rle with old method : 0.04839920997619629 length of segment : 294 time for calcul the mask position with numpy : 0.34940195083618164 nb_pixel_total : 58473 time to create 1 rle with old method : 0.08203864097595215 length of segment : 301 time for calcul the mask position with numpy : 0.24773383140563965 nb_pixel_total : 53583 time to create 1 rle with old method : 0.07206130027770996 length of segment : 300 time for calcul the mask position with numpy : 0.3233795166015625 nb_pixel_total : 44294 time to create 1 rle with old method : 0.06313753128051758 length of segment : 331 time for calcul the mask position with numpy : 0.022849321365356445 nb_pixel_total : 12536 time to create 1 rle with old method : 0.01824331283569336 length of segment : 230 time for calcul the mask position with numpy : 0.19558191299438477 nb_pixel_total : 48300 time to create 1 rle with old method : 0.11110138893127441 length of segment : 174 time for calcul the mask position with numpy : 0.036234140396118164 nb_pixel_total : 10715 time to create 1 rle with old method : 0.015117645263671875 length of segment : 108 time for calcul the mask position with numpy : 0.1150059700012207 nb_pixel_total : 17949 time to create 1 rle with old method : 0.027539730072021484 length of segment : 291 time for calcul the mask position with numpy : 0.6860365867614746 nb_pixel_total : 131678 time to create 1 rle with old method : 0.17674827575683594 length of segment : 516 time for calcul the mask position with numpy : 0.5307788848876953 nb_pixel_total : 158186 time to create 1 rle with new method : 0.014605522155761719 length of segment : 448 time for calcul the mask position with numpy : 0.29396843910217285 nb_pixel_total : 83744 time to create 1 rle with old method : 0.11113739013671875 length of segment : 345 time for calcul the mask position with numpy : 0.006529331207275391 nb_pixel_total : 39047 time to create 1 rle with old method : 0.04825019836425781 length of segment : 171 time for calcul the mask position with numpy : 1.1805310249328613 nb_pixel_total : 736521 time to create 1 rle with new method : 0.07224035263061523 length of segment : 1079 time for calcul the mask position with numpy : 0.18447542190551758 nb_pixel_total : 89974 time to create 1 rle with old method : 0.11934375762939453 length of segment : 447 time for calcul the mask position with numpy : 0.12315034866333008 nb_pixel_total : 27547 time to create 1 rle with old method : 0.045200347900390625 length of segment : 235 time for calcul the mask position with numpy : 0.22056055068969727 nb_pixel_total : 95669 time to create 1 rle with old method : 0.11585545539855957 length of segment : 339 time for calcul the mask position with numpy : 0.026060104370117188 nb_pixel_total : 25505 time to create 1 rle with old method : 0.05245614051818848 length of segment : 320 time for calcul the mask position with numpy : 0.33867931365966797 nb_pixel_total : 50810 time to create 1 rle with old method : 0.06472516059875488 length of segment : 282 time for calcul the mask position with numpy : 0.6921257972717285 nb_pixel_total : 137478 time to create 1 rle with old method : 0.16079163551330566 length of segment : 444 time for calcul the mask position with numpy : 0.006346940994262695 nb_pixel_total : 41442 time to create 1 rle with old method : 0.04944753646850586 length of segment : 299 time for calcul the mask position with numpy : 0.015885591506958008 nb_pixel_total : 9994 time to create 1 rle with old method : 0.01610565185546875 length of segment : 87 time for calcul the mask position with numpy : 0.19075703620910645 nb_pixel_total : 63297 time to create 1 rle with old method : 0.08574533462524414 length of segment : 234 time for calcul the mask position with numpy : 0.18234729766845703 nb_pixel_total : 26084 time to create 1 rle with old method : 0.03635430335998535 length of segment : 270 time for calcul the mask position with numpy : 0.49029016494750977 nb_pixel_total : 399877 time to create 1 rle with new method : 0.03503751754760742 length of segment : 825 time for calcul the mask position with numpy : 0.2683749198913574 nb_pixel_total : 84912 time to create 1 rle with old method : 0.10374927520751953 length of segment : 416 time for calcul the mask position with numpy : 0.1752629280090332 nb_pixel_total : 100359 time to create 1 rle with old method : 0.11619281768798828 length of segment : 376 time for calcul the mask position with numpy : 0.20537567138671875 nb_pixel_total : 100217 time to create 1 rle with old method : 0.15634560585021973 length of segment : 324 time for calcul the mask position with numpy : 0.03532576560974121 nb_pixel_total : 13928 time to create 1 rle with old method : 0.2644045352935791 length of segment : 125 time for calcul the mask position with numpy : 1.4999446868896484 nb_pixel_total : 822589 time to create 1 rle with new method : 0.08518123626708984 length of segment : 2149 time for calcul the mask position with numpy : 0.005266427993774414 nb_pixel_total : 20338 time to create 1 rle with old method : 0.027460098266601562 length of segment : 253 time for calcul the mask position with numpy : 0.06070280075073242 nb_pixel_total : 39825 time to create 1 rle with old method : 0.05304765701293945 length of segment : 142 time for calcul the mask position with numpy : 0.0006823539733886719 nb_pixel_total : 21939 time to create 1 rle with old method : 0.02644491195678711 length of segment : 187 time for calcul the mask position with numpy : 0.05503511428833008 nb_pixel_total : 48621 time to create 1 rle with old method : 0.07201194763183594 length of segment : 222 time for calcul the mask position with numpy : 0.05656623840332031 nb_pixel_total : 19999 time to create 1 rle with old method : 0.0327143669128418 length of segment : 353 time for calcul the mask position with numpy : 0.10388493537902832 nb_pixel_total : 65631 time to create 1 rle with old method : 0.09324312210083008 length of segment : 240 time for calcul the mask position with numpy : 0.005504131317138672 nb_pixel_total : 157691 time to create 1 rle with new method : 0.01771235466003418 length of segment : 922 time for calcul the mask position with numpy : 0.15051794052124023 nb_pixel_total : 222119 time to create 1 rle with new method : 0.15584683418273926 length of segment : 747 time for calcul the mask position with numpy : 0.5895562171936035 nb_pixel_total : 467975 time to create 1 rle with new method : 0.08556842803955078 length of segment : 767 time for calcul the mask position with numpy : 0.009135961532592773 nb_pixel_total : 15923 time to create 1 rle with old method : 0.03064131736755371 length of segment : 122 time for calcul the mask position with numpy : 0.0010004043579101562 nb_pixel_total : 39671 time to create 1 rle with old method : 0.047080039978027344 length of segment : 193 time for calcul the mask position with numpy : 0.0019309520721435547 nb_pixel_total : 45700 time to create 1 rle with old method : 0.06405282020568848 length of segment : 381 time for calcul the mask position with numpy : 0.0004010200500488281 nb_pixel_total : 17015 time to create 1 rle with old method : 0.020791292190551758 length of segment : 170 time for calcul the mask position with numpy : 0.006613492965698242 nb_pixel_total : 192914 time to create 1 rle with new method : 0.01503300666809082 length of segment : 402 time for calcul the mask position with numpy : 0.0005030632019042969 nb_pixel_total : 17002 time to create 1 rle with old method : 0.02055072784423828 length of segment : 190 time for calcul the mask position with numpy : 0.21427702903747559 nb_pixel_total : 128652 time to create 1 rle with old method : 0.1693117618560791 length of segment : 458 time for calcul the mask position with numpy : 0.19214940071105957 nb_pixel_total : 88467 time to create 1 rle with old method : 0.10987353324890137 length of segment : 721 time for calcul the mask position with numpy : 0.0005505084991455078 nb_pixel_total : 9658 time to create 1 rle with old method : 0.013807296752929688 length of segment : 154 time for calcul the mask position with numpy : 0.0013430118560791016 nb_pixel_total : 39998 time to create 1 rle with old method : 0.04828023910522461 length of segment : 213 time for calcul the mask position with numpy : 0.002427816390991211 nb_pixel_total : 37636 time to create 1 rle with old method : 0.04663991928100586 length of segment : 250 time for calcul the mask position with numpy : 0.07103633880615234 nb_pixel_total : 62101 time to create 1 rle with old method : 0.07855725288391113 length of segment : 225 time for calcul the mask position with numpy : 0.08758425712585449 nb_pixel_total : 34884 time to create 1 rle with old method : 0.04506707191467285 length of segment : 222 time for calcul the mask position with numpy : 0.005164384841918945 nb_pixel_total : 60014 time to create 1 rle with old method : 0.0776529312133789 length of segment : 353 time for calcul the mask position with numpy : 0.0025229454040527344 nb_pixel_total : 46711 time to create 1 rle with old method : 0.05517888069152832 length of segment : 430 time for calcul the mask position with numpy : 0.003971576690673828 nb_pixel_total : 93503 time to create 1 rle with old method : 0.11192178726196289 length of segment : 253 time for calcul the mask position with numpy : 0.0015208721160888672 nb_pixel_total : 66903 time to create 1 rle with old method : 0.0803382396697998 length of segment : 260 time for calcul the mask position with numpy : 0.021004676818847656 nb_pixel_total : 484362 time to create 1 rle with new method : 0.029443025588989258 length of segment : 854 time for calcul the mask position with numpy : 0.0017006397247314453 nb_pixel_total : 28324 time to create 1 rle with old method : 0.046617984771728516 length of segment : 253 time for calcul the mask position with numpy : 0.002040863037109375 nb_pixel_total : 104302 time to create 1 rle with old method : 0.12241935729980469 length of segment : 297 time for calcul the mask position with numpy : 0.0024056434631347656 nb_pixel_total : 60658 time to create 1 rle with old method : 0.0841372013092041 length of segment : 296 time for calcul the mask position with numpy : 0.0006890296936035156 nb_pixel_total : 25492 time to create 1 rle with old method : 0.03597426414489746 length of segment : 284 time for calcul the mask position with numpy : 0.002826213836669922 nb_pixel_total : 61258 time to create 1 rle with old method : 0.07789301872253418 length of segment : 233 time for calcul the mask position with numpy : 0.11113977432250977 nb_pixel_total : 21102 time to create 1 rle with old method : 0.028536319732666016 length of segment : 205 time for calcul the mask position with numpy : 0.5293455123901367 nb_pixel_total : 134696 time to create 1 rle with old method : 0.16170501708984375 length of segment : 435 time for calcul the mask position with numpy : 0.0883491039276123 nb_pixel_total : 27167 time to create 1 rle with old method : 0.040183067321777344 length of segment : 181 time for calcul the mask position with numpy : 0.17534852027893066 nb_pixel_total : 64412 time to create 1 rle with old method : 0.0818636417388916 length of segment : 237 time for calcul the mask position with numpy : 0.16394352912902832 nb_pixel_total : 55378 time to create 1 rle with old method : 0.07907676696777344 length of segment : 232 time for calcul the mask position with numpy : 0.13687562942504883 nb_pixel_total : 15856 time to create 1 rle with old method : 0.02856159210205078 length of segment : 172 time for calcul the mask position with numpy : 0.10801124572753906 nb_pixel_total : 18655 time to create 1 rle with old method : 0.0302889347076416 length of segment : 196 time for calcul the mask position with numpy : 3.862290620803833 nb_pixel_total : 1112064 time to create 1 rle with new method : 0.3276097774505615 length of segment : 2260 time for calcul the mask position with numpy : 0.184098482131958 nb_pixel_total : 75446 time to create 1 rle with old method : 0.09033441543579102 length of segment : 505 time for calcul the mask position with numpy : 0.4934227466583252 nb_pixel_total : 130733 time to create 1 rle with old method : 0.1516563892364502 length of segment : 459 time for calcul the mask position with numpy : 0.08247494697570801 nb_pixel_total : 34002 time to create 1 rle with old method : 0.044608116149902344 length of segment : 195 time for calcul the mask position with numpy : 0.937964916229248 nb_pixel_total : 464902 time to create 1 rle with new method : 0.035559892654418945 length of segment : 726 time for calcul the mask position with numpy : 0.037276268005371094 nb_pixel_total : 24668 time to create 1 rle with old method : 0.04269695281982422 length of segment : 234 time for calcul the mask position with numpy : 0.002123117446899414 nb_pixel_total : 28809 time to create 1 rle with old method : 0.04828643798828125 length of segment : 452 time for calcul the mask position with numpy : 0.2624199390411377 nb_pixel_total : 105197 time to create 1 rle with old method : 0.12588024139404297 length of segment : 513 time for calcul the mask position with numpy : 0.888498067855835 nb_pixel_total : 325175 time to create 1 rle with new method : 0.025596141815185547 length of segment : 812 time for calcul the mask position with numpy : 0.14268112182617188 nb_pixel_total : 37426 time to create 1 rle with old method : 0.05012822151184082 length of segment : 252 time for calcul the mask position with numpy : 0.15207982063293457 nb_pixel_total : 173032 time to create 1 rle with new method : 0.008900642395019531 length of segment : 521 time for calcul the mask position with numpy : 0.027790307998657227 nb_pixel_total : 14599 time to create 1 rle with old method : 0.018071413040161133 length of segment : 156 time for calcul the mask position with numpy : 0.8315596580505371 nb_pixel_total : 211245 time to create 1 rle with new method : 0.016460180282592773 length of segment : 650 time for calcul the mask position with numpy : 0.48349595069885254 nb_pixel_total : 152177 time to create 1 rle with new method : 0.012796640396118164 length of segment : 381 time for calcul the mask position with numpy : 1.2898380756378174 nb_pixel_total : 714039 time to create 1 rle with new method : 0.06511259078979492 length of segment : 916 time for calcul the mask position with numpy : 0.00099945068359375 nb_pixel_total : 23934 time to create 1 rle with old method : 0.030634641647338867 length of segment : 142 time for calcul the mask position with numpy : 0.1707780361175537 nb_pixel_total : 162778 time to create 1 rle with new method : 0.017647504806518555 length of segment : 701 time for calcul the mask position with numpy : 0.07685518264770508 nb_pixel_total : 42730 time to create 1 rle with old method : 0.061936140060424805 length of segment : 193 time for calcul the mask position with numpy : 0.06455206871032715 nb_pixel_total : 81300 time to create 1 rle with old method : 0.1121058464050293 length of segment : 684 time for calcul the mask position with numpy : 0.0644369125366211 nb_pixel_total : 86197 time to create 1 rle with old method : 0.11256742477416992 length of segment : 223 time for calcul the mask position with numpy : 0.008766651153564453 nb_pixel_total : 10362 time to create 1 rle with old method : 0.02088451385498047 length of segment : 109 time for calcul the mask position with numpy : 0.06681370735168457 nb_pixel_total : 3592 time to create 1 rle with old method : 0.008032560348510742 length of segment : 86 time for calcul the mask position with numpy : 0.13876748085021973 nb_pixel_total : 110592 time to create 1 rle with old method : 0.14835190773010254 length of segment : 403 time for calcul the mask position with numpy : 0.07345795631408691 nb_pixel_total : 77807 time to create 1 rle with old method : 0.09897422790527344 length of segment : 642 time for calcul the mask position with numpy : 0.7818057537078857 nb_pixel_total : 471401 time to create 1 rle with new method : 0.02755260467529297 length of segment : 851 time for calcul the mask position with numpy : 0.35887718200683594 nb_pixel_total : 132375 time to create 1 rle with old method : 0.15763211250305176 length of segment : 359 time for calcul the mask position with numpy : 0.002428293228149414 nb_pixel_total : 117325 time to create 1 rle with old method : 0.16566228866577148 length of segment : 302 time for calcul the mask position with numpy : 0.03498530387878418 nb_pixel_total : 82288 time to create 1 rle with old method : 0.11069250106811523 length of segment : 879 time for calcul the mask position with numpy : 0.060840606689453125 nb_pixel_total : 153171 time to create 1 rle with new method : 0.01949167251586914 length of segment : 779 time for calcul the mask position with numpy : 0.016620635986328125 nb_pixel_total : 12001 time to create 1 rle with old method : 0.020540714263916016 length of segment : 150 time for calcul the mask position with numpy : 0.00045180320739746094 nb_pixel_total : 22042 time to create 1 rle with old method : 0.030157089233398438 length of segment : 147 time for calcul the mask position with numpy : 0.00785064697265625 nb_pixel_total : 38154 time to create 1 rle with old method : 0.055390119552612305 length of segment : 193 time for calcul the mask position with numpy : 0.002919912338256836 nb_pixel_total : 106603 time to create 1 rle with old method : 0.1294078826904297 length of segment : 322 time for calcul the mask position with numpy : 0.00026679039001464844 nb_pixel_total : 5887 time to create 1 rle with old method : 0.0072329044342041016 length of segment : 136 time for calcul the mask position with numpy : 0.03210878372192383 nb_pixel_total : 65556 time to create 1 rle with old method : 0.11513948440551758 length of segment : 311 time for calcul the mask position with numpy : 0.0116119384765625 nb_pixel_total : 28276 time to create 1 rle with old method : 0.03890275955200195 length of segment : 186 time for calcul the mask position with numpy : 0.055675506591796875 nb_pixel_total : 12129 time to create 1 rle with old method : 0.018293142318725586 length of segment : 99 time for calcul the mask position with numpy : 0.0035359859466552734 nb_pixel_total : 95210 time to create 1 rle with old method : 0.11166572570800781 length of segment : 467 time for calcul the mask position with numpy : 0.008702754974365234 nb_pixel_total : 6914 time to create 1 rle with old method : 0.008568286895751953 length of segment : 74 time for calcul the mask position with numpy : 0.40706896781921387 nb_pixel_total : 151394 time to create 1 rle with new method : 0.011194467544555664 length of segment : 654 time for calcul the mask position with numpy : 0.2352747917175293 nb_pixel_total : 211902 time to create 1 rle with new method : 0.012772560119628906 length of segment : 529 time for calcul the mask position with numpy : 0.0007977485656738281 nb_pixel_total : 25059 time to create 1 rle with old method : 0.03442716598510742 length of segment : 226 time for calcul the mask position with numpy : 0.10611605644226074 nb_pixel_total : 10162 time to create 1 rle with old method : 0.01688098907470703 length of segment : 334 time for calcul the mask position with numpy : 0.0009243488311767578 nb_pixel_total : 31228 time to create 1 rle with old method : 0.04443073272705078 length of segment : 266 time for calcul the mask position with numpy : 0.0007193088531494141 nb_pixel_total : 32110 time to create 1 rle with old method : 0.04664921760559082 length of segment : 206 time for calcul the mask position with numpy : 0.04807233810424805 nb_pixel_total : 53925 time to create 1 rle with old method : 0.06911754608154297 length of segment : 213 time for calcul the mask position with numpy : 0.001802682876586914 nb_pixel_total : 92401 time to create 1 rle with old method : 0.1135859489440918 length of segment : 283 time for calcul the mask position with numpy : 0.0025784969329833984 nb_pixel_total : 17192 time to create 1 rle with old method : 0.021783113479614258 length of segment : 148 time for calcul the mask position with numpy : 0.0067822933197021484 nb_pixel_total : 20972 time to create 1 rle with old method : 0.028842926025390625 length of segment : 137 time for calcul the mask position with numpy : 0.0013518333435058594 nb_pixel_total : 54657 time to create 1 rle with old method : 0.06942081451416016 length of segment : 259 time for calcul the mask position with numpy : 0.0013606548309326172 nb_pixel_total : 51113 time to create 1 rle with old method : 0.06526017189025879 length of segment : 346 time for calcul the mask position with numpy : 0.025979042053222656 nb_pixel_total : 331219 time to create 1 rle with new method : 0.02258014678955078 length of segment : 683 time for calcul the mask position with numpy : 0.0012812614440917969 nb_pixel_total : 55824 time to create 1 rle with old method : 0.07355523109436035 length of segment : 263 time for calcul the mask position with numpy : 0.0009539127349853516 nb_pixel_total : 29190 time to create 1 rle with old method : 0.04140877723693848 length of segment : 222 time for calcul the mask position with numpy : 0.11026906967163086 nb_pixel_total : 262987 time to create 1 rle with new method : 0.01613140106201172 length of segment : 758 time for calcul the mask position with numpy : 0.0058095455169677734 nb_pixel_total : 150868 time to create 1 rle with new method : 0.009897232055664062 length of segment : 641 time for calcul the mask position with numpy : 0.0008115768432617188 nb_pixel_total : 30255 time to create 1 rle with old method : 0.03633236885070801 length of segment : 251 time for calcul the mask position with numpy : 0.08390259742736816 nb_pixel_total : 156710 time to create 1 rle with new method : 0.0098876953125 length of segment : 392 time for calcul the mask position with numpy : 0.023820877075195312 nb_pixel_total : 67308 time to create 1 rle with old method : 0.0843515396118164 length of segment : 333 time for calcul the mask position with numpy : 0.0017428398132324219 nb_pixel_total : 70826 time to create 1 rle with old method : 0.08383822441101074 length of segment : 369 time for calcul the mask position with numpy : 0.052086830139160156 nb_pixel_total : 44308 time to create 1 rle with old method : 0.05671429634094238 length of segment : 238 time for calcul the mask position with numpy : 0.0011799335479736328 nb_pixel_total : 45242 time to create 1 rle with old method : 0.07275748252868652 length of segment : 311 time for calcul the mask position with numpy : 0.2780475616455078 nb_pixel_total : 82947 time to create 1 rle with old method : 0.10997986793518066 length of segment : 284 time for calcul the mask position with numpy : 0.05463695526123047 nb_pixel_total : 9576 time to create 1 rle with old method : 0.01617264747619629 length of segment : 119 time for calcul the mask position with numpy : 2.5200955867767334 nb_pixel_total : 797616 time to create 1 rle with new method : 0.053597450256347656 length of segment : 1256 time for calcul the mask position with numpy : 0.24068522453308105 nb_pixel_total : 77663 time to create 1 rle with old method : 0.0960531234741211 length of segment : 300 time for calcul the mask position with numpy : 0.05955934524536133 nb_pixel_total : 16867 time to create 1 rle with old method : 0.044608354568481445 length of segment : 130 time for calcul the mask position with numpy : 0.06586694717407227 nb_pixel_total : 18846 time to create 1 rle with old method : 0.026320934295654297 length of segment : 161 time for calcul the mask position with numpy : 1.4276189804077148 nb_pixel_total : 675401 time to create 1 rle with new method : 0.09142565727233887 length of segment : 695 time for calcul the mask position with numpy : 0.036673545837402344 nb_pixel_total : 14840 time to create 1 rle with old method : 0.022588253021240234 length of segment : 142 time for calcul the mask position with numpy : 0.2160332202911377 nb_pixel_total : 28264 time to create 1 rle with old method : 0.0427861213684082 length of segment : 224 time for calcul the mask position with numpy : 2.5674827098846436 nb_pixel_total : 812468 time to create 1 rle with new method : 0.05754828453063965 length of segment : 1891 time for calcul the mask position with numpy : 0.32019710540771484 nb_pixel_total : 275766 time to create 1 rle with new method : 0.014443397521972656 length of segment : 535 time for calcul the mask position with numpy : 0.4111368656158447 nb_pixel_total : 132145 time to create 1 rle with old method : 0.16873955726623535 length of segment : 352 time for calcul the mask position with numpy : 0.12400150299072266 nb_pixel_total : 12654 time to create 1 rle with old method : 0.024704933166503906 length of segment : 164 time for calcul the mask position with numpy : 0.1452012062072754 nb_pixel_total : 34644 time to create 1 rle with old method : 0.04968738555908203 length of segment : 290 time for calcul the mask position with numpy : 1.0299100875854492 nb_pixel_total : 174137 time to create 1 rle with new method : 0.016170501708984375 length of segment : 703 time for calcul the mask position with numpy : 0.05915331840515137 nb_pixel_total : 61209 time to create 1 rle with old method : 0.09648799896240234 length of segment : 345 time for calcul the mask position with numpy : 1.0905296802520752 nb_pixel_total : 831022 time to create 1 rle with new method : 0.19479918479919434 length of segment : 1750 time for calcul the mask position with numpy : 0.054541826248168945 nb_pixel_total : 45081 time to create 1 rle with old method : 0.05964946746826172 length of segment : 232 time for calcul the mask position with numpy : 0.11859321594238281 nb_pixel_total : 56085 time to create 1 rle with old method : 0.07003355026245117 length of segment : 184 time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 15040 time to create 1 rle with old method : 0.01919245719909668 length of segment : 179 time for calcul the mask position with numpy : 0.05109119415283203 nb_pixel_total : 11673 time to create 1 rle with old method : 0.020014286041259766 length of segment : 127 time for calcul the mask position with numpy : 0.002179384231567383 nb_pixel_total : 88197 time to create 1 rle with old method : 0.11115574836730957 length of segment : 429 time for calcul the mask position with numpy : 1.1903400421142578 nb_pixel_total : 569150 time to create 1 rle with new method : 0.2550809383392334 length of segment : 751 time for calcul the mask position with numpy : 0.02377462387084961 nb_pixel_total : 15178 time to create 1 rle with old method : 0.024767160415649414 length of segment : 187 time for calcul the mask position with numpy : 0.1816234588623047 nb_pixel_total : 38656 time to create 1 rle with old method : 0.049863338470458984 length of segment : 408 time for calcul the mask position with numpy : 0.0018024444580078125 nb_pixel_total : 74930 time to create 1 rle with old method : 0.08984231948852539 length of segment : 325 time for calcul the mask position with numpy : 0.0007956027984619141 nb_pixel_total : 29161 time to create 1 rle with old method : 0.03529191017150879 length of segment : 247 time for calcul the mask position with numpy : 0.0020971298217773438 nb_pixel_total : 55403 time to create 1 rle with old method : 0.06586027145385742 length of segment : 351 time for calcul the mask position with numpy : 0.32488536834716797 nb_pixel_total : 154815 time to create 1 rle with new method : 0.009162425994873047 length of segment : 388 time for calcul the mask position with numpy : 0.083343505859375 nb_pixel_total : 45324 time to create 1 rle with old method : 0.05923318862915039 length of segment : 285 time for calcul the mask position with numpy : 0.0006477832794189453 nb_pixel_total : 31269 time to create 1 rle with old method : 0.04003477096557617 length of segment : 161 time for calcul the mask position with numpy : 0.13697528839111328 nb_pixel_total : 387729 time to create 1 rle with new method : 0.025477170944213867 length of segment : 861 time for calcul the mask position with numpy : 0.00844430923461914 nb_pixel_total : 40101 time to create 1 rle with old method : 0.05070686340332031 length of segment : 274 time for calcul the mask position with numpy : 0.0004336833953857422 nb_pixel_total : 17116 time to create 1 rle with old method : 0.021048545837402344 length of segment : 188 time for calcul the mask position with numpy : 0.004029989242553711 nb_pixel_total : 215633 time to create 1 rle with new method : 0.011335134506225586 length of segment : 777 time for calcul the mask position with numpy : 0.00029468536376953125 nb_pixel_total : 8391 time to create 1 rle with old method : 0.010327816009521484 length of segment : 158 time for calcul the mask position with numpy : 0.0013682842254638672 nb_pixel_total : 61745 time to create 1 rle with old method : 0.08703827857971191 length of segment : 307 time for calcul the mask position with numpy : 0.0013811588287353516 nb_pixel_total : 52954 time to create 1 rle with old method : 0.08072757720947266 length of segment : 222 time for calcul the mask position with numpy : 0.007418632507324219 nb_pixel_total : 379399 time to create 1 rle with new method : 0.019451141357421875 length of segment : 944 time for calcul the mask position with numpy : 0.13468360900878906 nb_pixel_total : 79852 time to create 1 rle with old method : 0.11528491973876953 length of segment : 363 time for calcul the mask position with numpy : 0.005644321441650391 nb_pixel_total : 31157 time to create 1 rle with old method : 0.03938865661621094 length of segment : 210 time for calcul the mask position with numpy : 0.1437678337097168 nb_pixel_total : 48955 time to create 1 rle with old method : 0.06352472305297852 length of segment : 327 time for calcul the mask position with numpy : 0.0006499290466308594 nb_pixel_total : 19532 time to create 1 rle with old method : 0.033033132553100586 length of segment : 162 time for calcul the mask position with numpy : 0.009006738662719727 nb_pixel_total : 10869 time to create 1 rle with old method : 0.013719558715820312 length of segment : 364 time for calcul the mask position with numpy : 0.12421464920043945 nb_pixel_total : 80366 time to create 1 rle with old method : 0.09670114517211914 length of segment : 493 time for calcul the mask position with numpy : 0.3088967800140381 nb_pixel_total : 283189 time to create 1 rle with new method : 0.03178572654724121 length of segment : 810 time for calcul the mask position with numpy : 0.0011227130889892578 nb_pixel_total : 48585 time to create 1 rle with old method : 0.057691097259521484 length of segment : 334 time for calcul the mask position with numpy : 0.20278644561767578 nb_pixel_total : 449329 time to create 1 rle with new method : 0.025401830673217773 length of segment : 1283 time for calcul the mask position with numpy : 0.007211446762084961 nb_pixel_total : 290374 time to create 1 rle with new method : 0.0213773250579834 length of segment : 690 time for calcul the mask position with numpy : 0.0012013912200927734 nb_pixel_total : 54737 time to create 1 rle with old method : 0.06463193893432617 length of segment : 289 time for calcul the mask position with numpy : 0.007456779479980469 nb_pixel_total : 62089 time to create 1 rle with old method : 0.07700848579406738 length of segment : 207 time for calcul the mask position with numpy : 0.0006389617919921875 nb_pixel_total : 23462 time to create 1 rle with old method : 0.028258562088012695 length of segment : 162 time for calcul the mask position with numpy : 0.0005755424499511719 nb_pixel_total : 28584 time to create 1 rle with old method : 0.03482460975646973 length of segment : 135 time for calcul the mask position with numpy : 0.026454925537109375 nb_pixel_total : 49536 time to create 1 rle with old method : 0.06298995018005371 length of segment : 212 time for calcul the mask position with numpy : 0.002284526824951172 nb_pixel_total : 119068 time to create 1 rle with old method : 0.1562507152557373 length of segment : 509 time for calcul the mask position with numpy : 0.0018475055694580078 nb_pixel_total : 74007 time to create 1 rle with old method : 0.08585429191589355 length of segment : 338 time for calcul the mask position with numpy : 0.000865936279296875 nb_pixel_total : 36843 time to create 1 rle with old method : 0.04396319389343262 length of segment : 239 time for calcul the mask position with numpy : 0.002592802047729492 nb_pixel_total : 49166 time to create 1 rle with old method : 0.05909252166748047 length of segment : 218 time for calcul the mask position with numpy : 0.00975489616394043 nb_pixel_total : 481302 time to create 1 rle with new method : 0.026889562606811523 length of segment : 1427 time for calcul the mask position with numpy : 0.013483285903930664 nb_pixel_total : 504740 time to create 1 rle with new method : 0.03290414810180664 length of segment : 1361 time for calcul the mask position with numpy : 0.03990030288696289 nb_pixel_total : 81426 time to create 1 rle with old method : 0.09791207313537598 length of segment : 243 time for calcul the mask position with numpy : 0.0008816719055175781 nb_pixel_total : 38885 time to create 1 rle with old method : 0.04650568962097168 length of segment : 239 time for calcul the mask position with numpy : 0.008286714553833008 nb_pixel_total : 288890 time to create 1 rle with new method : 0.025934934616088867 length of segment : 674 time for calcul the mask position with numpy : 0.0842142105102539 nb_pixel_total : 20850 time to create 1 rle with old method : 0.02951955795288086 length of segment : 157 time for calcul the mask position with numpy : 2.7836782932281494 nb_pixel_total : 1017554 time to create 1 rle with new method : 0.13942837715148926 length of segment : 1187 time for calcul the mask position with numpy : 1.1805698871612549 nb_pixel_total : 330260 time to create 1 rle with new method : 0.031984567642211914 length of segment : 923 time for calcul the mask position with numpy : 0.9199137687683105 nb_pixel_total : 325123 time to create 1 rle with new method : 0.019103527069091797 length of segment : 558 time for calcul the mask position with numpy : 0.10625457763671875 nb_pixel_total : 31984 time to create 1 rle with old method : 0.04312276840209961 length of segment : 161 time for calcul the mask position with numpy : 0.0005037784576416016 nb_pixel_total : 20695 time to create 1 rle with old method : 0.024924278259277344 length of segment : 226 time for calcul the mask position with numpy : 0.13131213188171387 nb_pixel_total : 50074 time to create 1 rle with old method : 0.07446169853210449 length of segment : 348 time for calcul the mask position with numpy : 0.11865735054016113 nb_pixel_total : 15839 time to create 1 rle with old method : 0.027507305145263672 length of segment : 131 time for calcul the mask position with numpy : 0.08390545845031738 nb_pixel_total : 13131 time to create 1 rle with old method : 0.02154088020324707 length of segment : 173 time for calcul the mask position with numpy : 0.07492828369140625 nb_pixel_total : 14246 time to create 1 rle with old method : 0.02105879783630371 length of segment : 193 time for calcul the mask position with numpy : 2.5646607875823975 nb_pixel_total : 749253 time to create 1 rle with new method : 0.17154216766357422 length of segment : 1455 time for calcul the mask position with numpy : 0.0027451515197753906 nb_pixel_total : 22041 time to create 1 rle with old method : 0.028401851654052734 length of segment : 168 time for calcul the mask position with numpy : 0.1025238037109375 nb_pixel_total : 19447 time to create 1 rle with old method : 0.027497529983520508 length of segment : 188 time for calcul the mask position with numpy : 0.7376492023468018 nb_pixel_total : 303428 time to create 1 rle with new method : 0.014661788940429688 length of segment : 457 time for calcul the mask position with numpy : 0.2769348621368408 nb_pixel_total : 88849 time to create 1 rle with old method : 0.11179041862487793 length of segment : 281 time for calcul the mask position with numpy : 1.3696863651275635 nb_pixel_total : 837515 time to create 1 rle with new method : 0.05193448066711426 length of segment : 1922 time for calcul the mask position with numpy : 0.1100771427154541 nb_pixel_total : 125130 time to create 1 rle with old method : 0.19580316543579102 length of segment : 512 time for calcul the mask position with numpy : 0.0433506965637207 nb_pixel_total : 28713 time to create 1 rle with old method : 0.037665367126464844 length of segment : 204 time for calcul the mask position with numpy : 0.03495311737060547 nb_pixel_total : 86414 time to create 1 rle with old method : 0.10920286178588867 length of segment : 270 time for calcul the mask position with numpy : 0.27850890159606934 nb_pixel_total : 146746 time to create 1 rle with old method : 0.17627501487731934 length of segment : 697 time for calcul the mask position with numpy : 0.0015881061553955078 nb_pixel_total : 68454 time to create 1 rle with old method : 0.08348631858825684 length of segment : 295 time for calcul the mask position with numpy : 0.005940675735473633 nb_pixel_total : 12317 time to create 1 rle with old method : 0.016796112060546875 length of segment : 66 time for calcul the mask position with numpy : 0.22510719299316406 nb_pixel_total : 33244 time to create 1 rle with old method : 0.04335474967956543 length of segment : 190 time for calcul the mask position with numpy : 0.00319671630859375 nb_pixel_total : 214886 time to create 1 rle with new method : 0.00912928581237793 length of segment : 473 time for calcul the mask position with numpy : 0.19870209693908691 nb_pixel_total : 137479 time to create 1 rle with old method : 0.16465544700622559 length of segment : 331 time for calcul the mask position with numpy : 0.045508384704589844 nb_pixel_total : 258153 time to create 1 rle with new method : 0.013472557067871094 length of segment : 468 time for calcul the mask position with numpy : 0.002358675003051758 nb_pixel_total : 123481 time to create 1 rle with old method : 0.14724397659301758 length of segment : 302 time for calcul the mask position with numpy : 0.16054129600524902 nb_pixel_total : 167616 time to create 1 rle with new method : 0.010153532028198242 length of segment : 442 time for calcul the mask position with numpy : 0.12149715423583984 nb_pixel_total : 180124 time to create 1 rle with new method : 0.05698680877685547 length of segment : 590 time for calcul the mask position with numpy : 0.11736154556274414 nb_pixel_total : 17095 time to create 1 rle with old method : 0.0263824462890625 length of segment : 339 time for calcul the mask position with numpy : 0.0012345314025878906 nb_pixel_total : 46768 time to create 1 rle with old method : 0.05611419677734375 length of segment : 314 time for calcul the mask position with numpy : 0.029421329498291016 nb_pixel_total : 19520 time to create 1 rle with old method : 0.02927708625793457 length of segment : 137 time for calcul the mask position with numpy : 0.6296722888946533 nb_pixel_total : 465902 time to create 1 rle with new method : 0.05392718315124512 length of segment : 1383 time for calcul the mask position with numpy : 0.0028777122497558594 nb_pixel_total : 136179 time to create 1 rle with old method : 0.1659080982208252 length of segment : 362 time for calcul the mask position with numpy : 0.11859989166259766 nb_pixel_total : 60753 time to create 1 rle with old method : 0.07558488845825195 length of segment : 327 time for calcul the mask position with numpy : 0.9150738716125488 nb_pixel_total : 279836 time to create 1 rle with new method : 0.07332992553710938 length of segment : 840 time for calcul the mask position with numpy : 0.26167917251586914 nb_pixel_total : 220845 time to create 1 rle with new method : 0.02830958366394043 length of segment : 976 time for calcul the mask position with numpy : 0.0012066364288330078 nb_pixel_total : 60192 time to create 1 rle with old method : 0.07114768028259277 length of segment : 379 time for calcul the mask position with numpy : 0.03242349624633789 nb_pixel_total : 50014 time to create 1 rle with old method : 0.06324148178100586 length of segment : 205 time for calcul the mask position with numpy : 0.030965566635131836 nb_pixel_total : 248970 time to create 1 rle with new method : 0.013518810272216797 length of segment : 545 time for calcul the mask position with numpy : 0.07589077949523926 nb_pixel_total : 211559 time to create 1 rle with new method : 0.01423954963684082 length of segment : 694 time for calcul the mask position with numpy : 0.009626626968383789 nb_pixel_total : 6877 time to create 1 rle with old method : 0.010811805725097656 length of segment : 89 time for calcul the mask position with numpy : 0.0072460174560546875 nb_pixel_total : 278157 time to create 1 rle with new method : 0.025510072708129883 length of segment : 822 time for calcul the mask position with numpy : 0.005747079849243164 nb_pixel_total : 46740 time to create 1 rle with old method : 0.0576474666595459 length of segment : 254 time for calcul the mask position with numpy : 0.003138303756713867 nb_pixel_total : 168264 time to create 1 rle with new method : 0.008538484573364258 length of segment : 410 time for calcul the mask position with numpy : 0.01758718490600586 nb_pixel_total : 87733 time to create 1 rle with old method : 0.12183785438537598 length of segment : 496 time for calcul the mask position with numpy : 0.09638810157775879 nb_pixel_total : 50611 time to create 1 rle with old method : 0.061478614807128906 length of segment : 408 time for calcul the mask position with numpy : 0.0048978328704833984 nb_pixel_total : 39844 time to create 1 rle with old method : 0.05307340621948242 length of segment : 178 time for calcul the mask position with numpy : 0.005611896514892578 nb_pixel_total : 116033 time to create 1 rle with old method : 0.14136242866516113 length of segment : 370 time for calcul the mask position with numpy : 0.0009737014770507812 nb_pixel_total : 17878 time to create 1 rle with old method : 0.021143674850463867 length of segment : 176 time for calcul the mask position with numpy : 0.01570868492126465 nb_pixel_total : 82871 time to create 1 rle with old method : 0.11002850532531738 length of segment : 189 time for calcul the mask position with numpy : 0.0009238719940185547 nb_pixel_total : 46091 time to create 1 rle with old method : 0.05957961082458496 length of segment : 194 time for calcul the mask position with numpy : 0.04288434982299805 nb_pixel_total : 175588 time to create 1 rle with new method : 0.014162540435791016 length of segment : 669 time for calcul the mask position with numpy : 0.011854410171508789 nb_pixel_total : 40087 time to create 1 rle with old method : 0.04709887504577637 length of segment : 231 time for calcul the mask position with numpy : 0.010622024536132812 nb_pixel_total : 28604 time to create 1 rle with old method : 0.03728294372558594 length of segment : 249 time for calcul the mask position with numpy : 0.0010058879852294922 nb_pixel_total : 41731 time to create 1 rle with old method : 0.04953932762145996 length of segment : 231 time for calcul the mask position with numpy : 0.0007970333099365234 nb_pixel_total : 27937 time to create 1 rle with old method : 0.03339433670043945 length of segment : 200 time for calcul the mask position with numpy : 0.0007102489471435547 nb_pixel_total : 38322 time to create 1 rle with old method : 0.04592728614807129 length of segment : 216 time for calcul the mask position with numpy : 0.0010776519775390625 nb_pixel_total : 46756 time to create 1 rle with old method : 0.055838584899902344 length of segment : 253 time for calcul the mask position with numpy : 0.0010445117950439453 nb_pixel_total : 36402 time to create 1 rle with old method : 0.043215036392211914 length of segment : 267 time for calcul the mask position with numpy : 0.0017027854919433594 nb_pixel_total : 92945 time to create 1 rle with old method : 0.1121218204498291 length of segment : 343 time for calcul the mask position with numpy : 0.0007240772247314453 nb_pixel_total : 34381 time to create 1 rle with old method : 0.04125833511352539 length of segment : 242 time for calcul the mask position with numpy : 0.0009679794311523438 nb_pixel_total : 34363 time to create 1 rle with old method : 0.042020320892333984 length of segment : 258 time for calcul the mask position with numpy : 0.004268169403076172 nb_pixel_total : 206405 time to create 1 rle with new method : 0.013042449951171875 length of segment : 654 time for calcul the mask position with numpy : 0.005940914154052734 nb_pixel_total : 113349 time to create 1 rle with old method : 0.13292980194091797 length of segment : 510 time for calcul the mask position with numpy : 1.5486564636230469 nb_pixel_total : 469746 time to create 1 rle with new method : 0.030319690704345703 length of segment : 872 time for calcul the mask position with numpy : 0.08254837989807129 nb_pixel_total : 15593 time to create 1 rle with old method : 0.023332834243774414 length of segment : 253 time for calcul the mask position with numpy : 0.0006127357482910156 nb_pixel_total : 25842 time to create 1 rle with old method : 0.515892505645752 length of segment : 213 time for calcul the mask position with numpy : 0.1876842975616455 nb_pixel_total : 28507 time to create 1 rle with old method : 0.03857707977294922 length of segment : 152 time for calcul the mask position with numpy : 3.186951160430908 nb_pixel_total : 556823 time to create 1 rle with new method : 0.03732013702392578 length of segment : 1424 time for calcul the mask position with numpy : 0.8596560955047607 nb_pixel_total : 288256 time to create 1 rle with new method : 0.014205694198608398 length of segment : 746 time for calcul the mask position with numpy : 0.0339963436126709 nb_pixel_total : 32785 time to create 1 rle with old method : 0.04455375671386719 length of segment : 191 time for calcul the mask position with numpy : 1.8756077289581299 nb_pixel_total : 776208 time to create 1 rle with new method : 0.1131751537322998 length of segment : 1004 time for calcul the mask position with numpy : 0.5989406108856201 nb_pixel_total : 111217 time to create 1 rle with old method : 0.15417242050170898 length of segment : 468 time for calcul the mask position with numpy : 0.15883922576904297 nb_pixel_total : 191287 time to create 1 rle with new method : 0.012945413589477539 length of segment : 612 time for calcul the mask position with numpy : 0.3137071132659912 nb_pixel_total : 110914 time to create 1 rle with old method : 0.15122199058532715 length of segment : 261 time for calcul the mask position with numpy : 0.3741030693054199 nb_pixel_total : 143391 time to create 1 rle with old method : 0.20151710510253906 length of segment : 576 time for calcul the mask position with numpy : 0.2490859031677246 nb_pixel_total : 62609 time to create 1 rle with old method : 0.0789175033569336 length of segment : 232 time for calcul the mask position with numpy : 0.0454106330871582 nb_pixel_total : 26896 time to create 1 rle with old method : 0.03728842735290527 length of segment : 201 time for calcul the mask position with numpy : 1.4584367275238037 nb_pixel_total : 750170 time to create 1 rle with new method : 0.07105398178100586 length of segment : 1220 time for calcul the mask position with numpy : 0.0035479068756103516 nb_pixel_total : 42767 time to create 1 rle with old method : 0.05137920379638672 length of segment : 214 time for calcul the mask position with numpy : 0.002130270004272461 nb_pixel_total : 86562 time to create 1 rle with old method : 0.10402989387512207 length of segment : 376 time for calcul the mask position with numpy : 0.4323902130126953 nb_pixel_total : 248054 time to create 1 rle with new method : 0.014148950576782227 length of segment : 613 time for calcul the mask position with numpy : 0.009446859359741211 nb_pixel_total : 31376 time to create 1 rle with old method : 0.04171609878540039 length of segment : 285 time for calcul the mask position with numpy : 0.05726361274719238 nb_pixel_total : 58936 time to create 1 rle with old method : 0.07381153106689453 length of segment : 260 time for calcul the mask position with numpy : 0.0006964206695556641 nb_pixel_total : 17882 time to create 1 rle with old method : 0.022194862365722656 length of segment : 162 time for calcul the mask position with numpy : 0.0009396076202392578 nb_pixel_total : 24459 time to create 1 rle with old method : 0.04050469398498535 length of segment : 335 time for calcul the mask position with numpy : 0.02505016326904297 nb_pixel_total : 78633 time to create 1 rle with old method : 0.11233663558959961 length of segment : 380 time for calcul the mask position with numpy : 0.4026217460632324 nb_pixel_total : 569047 time to create 1 rle with new method : 0.04839134216308594 length of segment : 1182 time for calcul the mask position with numpy : 0.008794069290161133 nb_pixel_total : 67445 time to create 1 rle with old method : 0.08236265182495117 length of segment : 275 time for calcul the mask position with numpy : 0.32384729385375977 nb_pixel_total : 119568 time to create 1 rle with old method : 0.14407610893249512 length of segment : 310 time for calcul the mask position with numpy : 0.21073222160339355 nb_pixel_total : 291278 time to create 1 rle with new method : 0.05218911170959473 length of segment : 623 time for calcul the mask position with numpy : 0.0006804466247558594 nb_pixel_total : 34240 time to create 1 rle with old method : 0.04206442832946777 length of segment : 255 time for calcul the mask position with numpy : 0.33313632011413574 nb_pixel_total : 492392 time to create 1 rle with new method : 0.02562427520751953 length of segment : 764 time for calcul the mask position with numpy : 0.039586782455444336 nb_pixel_total : 693554 time to create 1 rle with new method : 0.15035533905029297 length of segment : 1570 time for calcul the mask position with numpy : 0.03568220138549805 nb_pixel_total : 501739 time to create 1 rle with new method : 0.01933908462524414 length of segment : 788 time for calcul the mask position with numpy : 0.0051670074462890625 nb_pixel_total : 263242 time to create 1 rle with new method : 0.01476907730102539 length of segment : 694 time for calcul the mask position with numpy : 0.00044798851013183594 nb_pixel_total : 12610 time to create 1 rle with old method : 0.015083789825439453 length of segment : 256 time for calcul the mask position with numpy : 0.003899097442626953 nb_pixel_total : 177188 time to create 1 rle with new method : 0.01237630844116211 length of segment : 586 time for calcul the mask position with numpy : 0.0248110294342041 nb_pixel_total : 40039 time to create 1 rle with old method : 0.047240257263183594 length of segment : 346 time for calcul the mask position with numpy : 0.003793001174926758 nb_pixel_total : 201841 time to create 1 rle with new method : 0.010820627212524414 length of segment : 696 time for calcul the mask position with numpy : 0.00040340423583984375 nb_pixel_total : 15975 time to create 1 rle with old method : 0.01937413215637207 length of segment : 105 time for calcul the mask position with numpy : 0.04350900650024414 nb_pixel_total : 103428 time to create 1 rle with old method : 0.1274261474609375 length of segment : 355 time for calcul the mask position with numpy : 0.3919224739074707 nb_pixel_total : 141049 time to create 1 rle with old method : 0.17823481559753418 length of segment : 541 time for calcul the mask position with numpy : 0.04360485076904297 nb_pixel_total : 116583 time to create 1 rle with old method : 0.14736485481262207 length of segment : 411 time for calcul the mask position with numpy : 0.485975980758667 nb_pixel_total : 409092 time to create 1 rle with new method : 0.02881026268005371 length of segment : 596 time for calcul the mask position with numpy : 0.0022983551025390625 nb_pixel_total : 98139 time to create 1 rle with old method : 0.11649441719055176 length of segment : 425 time for calcul the mask position with numpy : 0.009541988372802734 nb_pixel_total : 24034 time to create 1 rle with old method : 0.02884960174560547 length of segment : 144 time for calcul the mask position with numpy : 0.007666110992431641 nb_pixel_total : 77329 time to create 1 rle with old method : 0.09248113632202148 length of segment : 254 time for calcul the mask position with numpy : 0.002868175506591797 nb_pixel_total : 143985 time to create 1 rle with old method : 0.16756105422973633 length of segment : 542 time for calcul the mask position with numpy : 0.005169391632080078 nb_pixel_total : 157295 time to create 1 rle with new method : 0.011015176773071289 length of segment : 1319 time for calcul the mask position with numpy : 0.0007388591766357422 nb_pixel_total : 33456 time to create 1 rle with old method : 0.04114079475402832 length of segment : 234 time for calcul the mask position with numpy : 0.08391976356506348 nb_pixel_total : 47931 time to create 1 rle with old method : 0.05908703804016113 length of segment : 304 time for calcul the mask position with numpy : 0.000904083251953125 nb_pixel_total : 44966 time to create 1 rle with old method : 0.06201934814453125 length of segment : 172 time for calcul the mask position with numpy : 0.00074005126953125 nb_pixel_total : 32485 time to create 1 rle with old method : 0.038143157958984375 length of segment : 297 time for calcul the mask position with numpy : 0.004209280014038086 nb_pixel_total : 198313 time to create 1 rle with new method : 0.012673139572143555 length of segment : 618 time for calcul the mask position with numpy : 0.0029053688049316406 nb_pixel_total : 101494 time to create 1 rle with old method : 0.11873459815979004 length of segment : 357 time for calcul the mask position with numpy : 0.0011777877807617188 nb_pixel_total : 58477 time to create 1 rle with old method : 0.0707852840423584 length of segment : 207 time for calcul the mask position with numpy : 0.004624843597412109 nb_pixel_total : 212042 time to create 1 rle with new method : 0.01230311393737793 length of segment : 538 time for calcul the mask position with numpy : 0.006697416305541992 nb_pixel_total : 29955 time to create 1 rle with old method : 0.03589153289794922 length of segment : 228 time for calcul the mask position with numpy : 0.0031473636627197266 nb_pixel_total : 84591 time to create 1 rle with old method : 0.09956717491149902 length of segment : 741 time for calcul the mask position with numpy : 0.0013566017150878906 nb_pixel_total : 53449 time to create 1 rle with old method : 0.06538915634155273 length of segment : 330 time for calcul the mask position with numpy : 0.0021905899047851562 nb_pixel_total : 110350 time to create 1 rle with old method : 0.15371155738830566 length of segment : 401 time for calcul the mask position with numpy : 0.0017688274383544922 nb_pixel_total : 97904 time to create 1 rle with old method : 0.115692138671875 length of segment : 394 time for calcul the mask position with numpy : 2.2527692317962646 nb_pixel_total : 557010 time to create 1 rle with new method : 0.03087639808654785 length of segment : 867 time for calcul the mask position with numpy : 1.2887287139892578 nb_pixel_total : 435545 time to create 1 rle with new method : 0.026271343231201172 length of segment : 611 time for calcul the mask position with numpy : 2.0614192485809326 nb_pixel_total : 694734 time to create 1 rle with new method : 0.053403615951538086 length of segment : 1079 time for calcul the mask position with numpy : 0.31465935707092285 nb_pixel_total : 58202 time to create 1 rle with old method : 0.07414460182189941 length of segment : 317 time for calcul the mask position with numpy : 0.25616955757141113 nb_pixel_total : 92934 time to create 1 rle with old method : 0.14133262634277344 length of segment : 559 time for calcul the mask position with numpy : 0.16916728019714355 nb_pixel_total : 36190 time to create 1 rle with old method : 0.042463064193725586 length of segment : 239 time for calcul the mask position with numpy : 1.787426471710205 nb_pixel_total : 641351 time to create 1 rle with new method : 0.03556418418884277 length of segment : 983 time for calcul the mask position with numpy : 0.1591339111328125 nb_pixel_total : 14030 time to create 1 rle with old method : 0.02086806297302246 length of segment : 221 time for calcul the mask position with numpy : 0.12656450271606445 nb_pixel_total : 20375 time to create 1 rle with old method : 0.030404329299926758 length of segment : 218 time for calcul the mask position with numpy : 0.0680854320526123 nb_pixel_total : 14502 time to create 1 rle with old method : 0.020706653594970703 length of segment : 161 time for calcul the mask position with numpy : 0.09252452850341797 nb_pixel_total : 10523 time to create 1 rle with old method : 0.01676154136657715 length of segment : 231 time for calcul the mask position with numpy : 0.03990888595581055 nb_pixel_total : 279685 time to create 1 rle with new method : 0.019199371337890625 length of segment : 678 time for calcul the mask position with numpy : 0.4920809268951416 nb_pixel_total : 75722 time to create 1 rle with old method : 0.0935666561126709 length of segment : 296 time for calcul the mask position with numpy : 0.061758995056152344 nb_pixel_total : 13898 time to create 1 rle with old method : 0.018062829971313477 length of segment : 651 time for calcul the mask position with numpy : 0.1622447967529297 nb_pixel_total : 74897 time to create 1 rle with old method : 0.11577773094177246 length of segment : 419 time for calcul the mask position with numpy : 0.0012807846069335938 nb_pixel_total : 44308 time to create 1 rle with old method : 0.05832982063293457 length of segment : 294 time for calcul the mask position with numpy : 0.050333261489868164 nb_pixel_total : 223744 time to create 1 rle with new method : 0.014737606048583984 length of segment : 490 time for calcul the mask position with numpy : 0.3537774085998535 nb_pixel_total : 407416 time to create 1 rle with new method : 0.031755924224853516 length of segment : 1189 time for calcul the mask position with numpy : 0.0670170783996582 nb_pixel_total : 32678 time to create 1 rle with old method : 0.056301116943359375 length of segment : 180 time for calcul the mask position with numpy : 0.025650978088378906 nb_pixel_total : 26696 time to create 1 rle with old method : 0.032501220703125 length of segment : 143 time for calcul the mask position with numpy : 0.892646074295044 nb_pixel_total : 287113 time to create 1 rle with new method : 0.016872167587280273 length of segment : 519 time for calcul the mask position with numpy : 0.6658473014831543 nb_pixel_total : 133739 time to create 1 rle with old method : 0.1685042381286621 length of segment : 429 time for calcul the mask position with numpy : 0.0004661083221435547 nb_pixel_total : 14767 time to create 1 rle with old method : 0.018054962158203125 length of segment : 165 time for calcul the mask position with numpy : 0.0010938644409179688 nb_pixel_total : 53575 time to create 1 rle with old method : 0.07493829727172852 length of segment : 310 time for calcul the mask position with numpy : 0.0022513866424560547 nb_pixel_total : 8151 time to create 1 rle with old method : 0.009942293167114258 length of segment : 106 time for calcul the mask position with numpy : 0.0006704330444335938 nb_pixel_total : 26356 time to create 1 rle with old method : 0.03189659118652344 length of segment : 294 time for calcul the mask position with numpy : 1.5952582359313965 nb_pixel_total : 345422 time to create 1 rle with new method : 0.10956621170043945 length of segment : 1056 time for calcul the mask position with numpy : 0.002299070358276367 nb_pixel_total : 83238 time to create 1 rle with old method : 0.09979414939880371 length of segment : 317 time for calcul the mask position with numpy : 0.6900186538696289 nb_pixel_total : 197386 time to create 1 rle with new method : 0.012956380844116211 length of segment : 651 time for calcul the mask position with numpy : 0.002054452896118164 nb_pixel_total : 22566 time to create 1 rle with old method : 0.02767014503479004 length of segment : 205 time for calcul the mask position with numpy : 0.09344887733459473 nb_pixel_total : 267344 time to create 1 rle with new method : 0.018233776092529297 length of segment : 561 time for calcul the mask position with numpy : 0.09675478935241699 nb_pixel_total : 25444 time to create 1 rle with old method : 0.0348362922668457 length of segment : 177 time for calcul the mask position with numpy : 0.021928071975708008 nb_pixel_total : 54662 time to create 1 rle with old method : 0.0702812671661377 length of segment : 200 time for calcul the mask position with numpy : 0.13914132118225098 nb_pixel_total : 33268 time to create 1 rle with old method : 0.05458426475524902 length of segment : 200 time for calcul the mask position with numpy : 0.02038264274597168 nb_pixel_total : 38119 time to create 1 rle with old method : 0.05135703086853027 length of segment : 227 time for calcul the mask position with numpy : 0.003596782684326172 nb_pixel_total : 29182 time to create 1 rle with old method : 0.03841423988342285 length of segment : 177 time for calcul the mask position with numpy : 0.0026285648345947266 nb_pixel_total : 101393 time to create 1 rle with old method : 0.30169153213500977 length of segment : 838 time for calcul the mask position with numpy : 0.0019211769104003906 nb_pixel_total : 80887 time to create 1 rle with old method : 0.1044611930847168 length of segment : 423 time for calcul the mask position with numpy : 0.7489471435546875 nb_pixel_total : 321440 time to create 1 rle with new method : 0.03054666519165039 length of segment : 736 time for calcul the mask position with numpy : 0.001390695571899414 nb_pixel_total : 57160 time to create 1 rle with old method : 0.06822443008422852 length of segment : 122 time for calcul the mask position with numpy : 0.0738825798034668 nb_pixel_total : 32703 time to create 1 rle with old method : 0.05333876609802246 length of segment : 169 time for calcul the mask position with numpy : 0.270535945892334 nb_pixel_total : 37196 time to create 1 rle with old method : 0.04807329177856445 length of segment : 144 time for calcul the mask position with numpy : 0.04030942916870117 nb_pixel_total : 11074 time to create 1 rle with old method : 0.017820358276367188 length of segment : 107 time for calcul the mask position with numpy : 0.21962738037109375 nb_pixel_total : 129250 time to create 1 rle with old method : 0.15559601783752441 length of segment : 423 time for calcul the mask position with numpy : 0.0018720626831054688 nb_pixel_total : 74894 time to create 1 rle with old method : 0.09050798416137695 length of segment : 352 time for calcul the mask position with numpy : 0.0026628971099853516 nb_pixel_total : 115628 time to create 1 rle with old method : 0.1389777660369873 length of segment : 314 time for calcul the mask position with numpy : 0.0013103485107421875 nb_pixel_total : 62317 time to create 1 rle with old method : 0.0748741626739502 length of segment : 214 time for calcul the mask position with numpy : 0.04036450386047363 nb_pixel_total : 64837 time to create 1 rle with old method : 0.07962322235107422 length of segment : 355 time for calcul the mask position with numpy : 0.0004277229309082031 nb_pixel_total : 19983 time to create 1 rle with old method : 0.024367570877075195 length of segment : 156 time for calcul the mask position with numpy : 0.020258188247680664 nb_pixel_total : 44716 time to create 1 rle with old method : 0.05304408073425293 length of segment : 303 time for calcul the mask position with numpy : 0.001336812973022461 nb_pixel_total : 10877 time to create 1 rle with old method : 0.013271093368530273 length of segment : 109 time for calcul the mask position with numpy : 0.02159738540649414 nb_pixel_total : 81744 time to create 1 rle with old method : 0.0978233814239502 length of segment : 350 time for calcul the mask position with numpy : 0.0015616416931152344 nb_pixel_total : 72541 time to create 1 rle with old method : 0.08490252494812012 length of segment : 366 time for calcul the mask position with numpy : 0.002545595169067383 nb_pixel_total : 36623 time to create 1 rle with old method : 0.04414987564086914 length of segment : 197 time for calcul the mask position with numpy : 0.14058685302734375 nb_pixel_total : 66179 time to create 1 rle with old method : 0.08861374855041504 length of segment : 255 time for calcul the mask position with numpy : 0.0016820430755615234 nb_pixel_total : 83689 time to create 1 rle with old method : 0.10081005096435547 length of segment : 298 time for calcul the mask position with numpy : 0.05122661590576172 nb_pixel_total : 46866 time to create 1 rle with old method : 0.08086252212524414 length of segment : 204 time for calcul the mask position with numpy : 0.030492544174194336 nb_pixel_total : 67711 time to create 1 rle with old method : 0.10922932624816895 length of segment : 653 time for calcul the mask position with numpy : 0.005959272384643555 nb_pixel_total : 75901 time to create 1 rle with old method : 0.08865904808044434 length of segment : 379 time for calcul the mask position with numpy : 0.009958267211914062 nb_pixel_total : 8780 time to create 1 rle with old method : 0.016323089599609375 length of segment : 206 time for calcul the mask position with numpy : 0.0016617774963378906 nb_pixel_total : 75608 time to create 1 rle with old method : 0.09238195419311523 length of segment : 259 time for calcul the mask position with numpy : 0.0061037540435791016 nb_pixel_total : 12281 time to create 1 rle with old method : 0.015082597732543945 length of segment : 381 time for calcul the mask position with numpy : 0.0013823509216308594 nb_pixel_total : 46561 time to create 1 rle with old method : 0.05504775047302246 length of segment : 281 time for calcul the mask position with numpy : 0.06735420227050781 nb_pixel_total : 103101 time to create 1 rle with old method : 0.15575909614562988 length of segment : 491 time for calcul the mask position with numpy : 0.03233456611633301 nb_pixel_total : 162575 time to create 1 rle with new method : 0.010597467422485352 length of segment : 522 time for calcul the mask position with numpy : 0.07745885848999023 nb_pixel_total : 191446 time to create 1 rle with new method : 0.012409448623657227 length of segment : 595 time for calcul the mask position with numpy : 0.0009441375732421875 nb_pixel_total : 28753 time to create 1 rle with old method : 0.034188032150268555 length of segment : 176 time for calcul the mask position with numpy : 0.0422666072845459 nb_pixel_total : 35099 time to create 1 rle with old method : 0.04594731330871582 length of segment : 128 time for calcul the mask position with numpy : 0.003701448440551758 nb_pixel_total : 150114 time to create 1 rle with new method : 0.010836124420166016 length of segment : 474 time for calcul the mask position with numpy : 0.0008127689361572266 nb_pixel_total : 44309 time to create 1 rle with old method : 0.053542375564575195 length of segment : 306 time for calcul the mask position with numpy : 0.021171092987060547 nb_pixel_total : 46425 time to create 1 rle with old method : 0.05966806411743164 length of segment : 186 time for calcul the mask position with numpy : 0.0033571720123291016 nb_pixel_total : 177264 time to create 1 rle with new method : 0.009376764297485352 length of segment : 591 time for calcul the mask position with numpy : 0.00046753883361816406 nb_pixel_total : 20963 time to create 1 rle with old method : 0.02508401870727539 length of segment : 132 time for calcul the mask position with numpy : 0.0026230812072753906 nb_pixel_total : 113349 time to create 1 rle with old method : 0.1338975429534912 length of segment : 571 time for calcul the mask position with numpy : 1.2120866775512695 nb_pixel_total : 7072182 time to create 1 rle with new method : 1.0403809547424316 length of segment : 3148 time for calcul the mask position with numpy : 0.021295785903930664 nb_pixel_total : 1844843 time to create 1 rle with new method : 0.11200475692749023 length of segment : 1352 time for calcul the mask position with numpy : 0.000148773193359375 nb_pixel_total : 7055 time to create 1 rle with old method : 0.008495807647705078 length of segment : 90 time spent for convertir_results : 155.62393021583557 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 520 chid ids of type : 4854 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 save missing photos in datou_result : time spend for datou_step_exec : 369.07902455329895 time spend to save output : 0.2796475887298584 total time spend for step 2 : 369.3586721420288 step3:blur_detection Wed Sep 17 14:48:55 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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: variance laplacian (3120, 3120) 1407.8044556324444 (3120, 3120) 826.3568154750284 (3120, 3120) 1291.586942406081 (3120, 3120) 1031.5909147963284 (3120, 3120) 516.6679264481787 (3120, 3120) 1426.6414802911725 (3120, 3120) 183.7956999714713 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 : 7 time used for this insertion : 0.0122833251953125 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 7 time used for this insertion : 0.016306161880493164 save missing photos in datou_result : time spend for datou_step_exec : 1.8302812576293945 time spend to save output : 0.03338479995727539 total time spend for step 3 : 1.86366605758667 step4:brightness Wed Sep 17 14:48:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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/1758112889_2168531_1384152197_14da91cf36f1a3a563c72d43503cee46.jpg treat image : temp/1758112889_2168531_1384151343_08691b1de63b1910b0f33604180b6e22.jpg treat image : temp/1758112889_2168531_1384151335_551be593faac71cdc6f12b567e8fc8af.jpg treat image : temp/1758112889_2168531_1384151318_f3349472158c0d90e5b9086b12531e82.jpg treat image : temp/1758112889_2168531_1384151295_93982564ddfbc03fe24c0f8f4e9ba804.jpg treat image : temp/1758112889_2168531_1384151259_3c2a24f0caa869d77b2d2766d8d5b312.jpg treat image : temp/1758112889_2168531_1384151225_aea59a95bf9b317cfcfeee92e2d44119.jpg 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 : 7 time used for this insertion : 0.015745162963867188 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 7 time used for this insertion : 0.014714717864990234 save missing photos in datou_result : time spend for datou_step_exec : 9.095310688018799 time spend to save output : 0.1858205795288086 total time spend for step 4 : 9.281131267547607 step5:rle_unique_nms_with_priority Wed Sep 17 14:49:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 520 chid ids of type : 4854 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 84 nb_hashtags : 8 time to prepare the origin masks : 44.723336696624756 time for calcul the mask position with numpy : 0.35154128074645996 nb_pixel_total : 3524701 time to create 1 rle with new method : 1.2197353839874268 time for calcul the mask position with numpy : 0.040549516677856445 nb_pixel_total : 722 time to create 1 rle with old method : 0.0011031627655029297 time for calcul the mask position with numpy : 0.0404970645904541 nb_pixel_total : 37951 time to create 1 rle with old method : 0.04427599906921387 time for calcul the mask position with numpy : 0.04056096076965332 nb_pixel_total : 40870 time to create 1 rle with old method : 0.04765796661376953 time for calcul the mask position with numpy : 0.04137301445007324 nb_pixel_total : 143362 time to create 1 rle with old method : 0.17283296585083008 time for calcul the mask position with numpy : 0.040538787841796875 nb_pixel_total : 67335 time to create 1 rle with old method : 0.07834577560424805 time for calcul the mask position with numpy : 0.04032182693481445 nb_pixel_total : 31310 time to create 1 rle with old method : 0.036468505859375 time for calcul the mask position with numpy : 0.04269266128540039 nb_pixel_total : 380624 time to create 1 rle with new method : 0.6837236881256104 time for calcul the mask position with numpy : 0.0455327033996582 nb_pixel_total : 770062 time to create 1 rle with new method : 0.6952428817749023 time for calcul the mask position with numpy : 0.040557861328125 nb_pixel_total : 27501 time to create 1 rle with old method : 0.03384971618652344 time for calcul the mask position with numpy : 0.04061698913574219 nb_pixel_total : 13892 time to create 1 rle with old method : 0.01644158363342285 time for calcul the mask position with numpy : 0.040885210037231445 nb_pixel_total : 35760 time to create 1 rle with old method : 0.041629791259765625 time for calcul the mask position with numpy : 0.0431981086730957 nb_pixel_total : 137034 time to create 1 rle with old method : 0.1628873348236084 time for calcul the mask position with numpy : 0.04200601577758789 nb_pixel_total : 48487 time to create 1 rle with old method : 0.057524919509887695 time for calcul the mask position with numpy : 0.041448354721069336 nb_pixel_total : 9109 time to create 1 rle with old method : 0.010891914367675781 time for calcul the mask position with numpy : 0.041579246520996094 nb_pixel_total : 20314 time to create 1 rle with old method : 0.025384187698364258 time for calcul the mask position with numpy : 0.04126334190368652 nb_pixel_total : 22154 time to create 1 rle with old method : 0.026200294494628906 time for calcul the mask position with numpy : 0.040688276290893555 nb_pixel_total : 17818 time to create 1 rle with old method : 0.02074599266052246 time for calcul the mask position with numpy : 0.04075932502746582 nb_pixel_total : 31370 time to create 1 rle with old method : 0.03668832778930664 time for calcul the mask position with numpy : 0.04123854637145996 nb_pixel_total : 89887 time to create 1 rle with old method : 0.10775375366210938 time for calcul the mask position with numpy : 0.040682315826416016 nb_pixel_total : 19316 time to create 1 rle with old method : 0.022629261016845703 time for calcul the mask position with numpy : 0.040738582611083984 nb_pixel_total : 37326 time to create 1 rle with old method : 0.043625831604003906 time for calcul the mask position with numpy : 0.04045248031616211 nb_pixel_total : 4818 time to create 1 rle with old method : 0.005715847015380859 time for calcul the mask position with numpy : 0.04091763496398926 nb_pixel_total : 48209 time to create 1 rle with old method : 0.05621647834777832 time for calcul the mask position with numpy : 0.043273210525512695 nb_pixel_total : 21524 time to create 1 rle with old method : 0.02526712417602539 time for calcul the mask position with numpy : 0.0403594970703125 nb_pixel_total : 2023 time to create 1 rle with old method : 0.002451181411743164 time for calcul the mask position with numpy : 0.040860891342163086 nb_pixel_total : 58368 time to create 1 rle with old method : 0.06884050369262695 time for calcul the mask position with numpy : 0.039478302001953125 nb_pixel_total : 26819 time to create 1 rle with old method : 0.03174781799316406 time for calcul the mask position with numpy : 0.043845176696777344 nb_pixel_total : 687989 time to create 1 rle with new method : 1.3614921569824219 time for calcul the mask position with numpy : 0.04044938087463379 nb_pixel_total : 26001 time to create 1 rle with old method : 0.029581785202026367 time for calcul the mask position with numpy : 0.040168046951293945 nb_pixel_total : 44335 time to create 1 rle with old method : 0.05118083953857422 time for calcul the mask position with numpy : 0.04061293601989746 nb_pixel_total : 34744 time to create 1 rle with old method : 0.03961658477783203 time for calcul the mask position with numpy : 0.03975677490234375 nb_pixel_total : 13646 time to create 1 rle with old method : 0.016016244888305664 time for calcul the mask position with numpy : 0.04059004783630371 nb_pixel_total : 100160 time to create 1 rle with old method : 0.11713123321533203 time for calcul the mask position with numpy : 0.04058980941772461 nb_pixel_total : 914 time to create 1 rle with old method : 0.0011322498321533203 time for calcul the mask position with numpy : 0.04036879539489746 nb_pixel_total : 15827 time to create 1 rle with old method : 0.018428564071655273 time for calcul the mask position with numpy : 0.040979623794555664 nb_pixel_total : 50570 time to create 1 rle with old method : 0.2865605354309082 time for calcul the mask position with numpy : 0.039925575256347656 nb_pixel_total : 32243 time to create 1 rle with old method : 0.03702855110168457 time for calcul the mask position with numpy : 0.03964376449584961 nb_pixel_total : 31461 time to create 1 rle with old method : 0.036183834075927734 time for calcul the mask position with numpy : 0.040099143981933594 nb_pixel_total : 39507 time to create 1 rle with old method : 0.045209646224975586 time for calcul the mask position with numpy : 0.04079604148864746 nb_pixel_total : 28565 time to create 1 rle with old method : 0.032608747482299805 time for calcul the mask position with numpy : 0.03975248336791992 nb_pixel_total : 17906 time to create 1 rle with old method : 0.02138376235961914 time for calcul the mask position with numpy : 0.04215717315673828 nb_pixel_total : 463569 time to create 1 rle with new method : 1.304826021194458 time for calcul the mask position with numpy : 0.0491025447845459 nb_pixel_total : 61814 time to create 1 rle with old method : 0.07773375511169434 time for calcul the mask position with numpy : 0.04218864440917969 nb_pixel_total : 65501 time to create 1 rle with old method : 0.09453582763671875 time for calcul the mask position with numpy : 0.05413031578063965 nb_pixel_total : 99908 time to create 1 rle with old method : 0.14853191375732422 time for calcul the mask position with numpy : 0.047080278396606445 nb_pixel_total : 769 time to create 1 rle with old method : 0.0012335777282714844 time for calcul the mask position with numpy : 0.04072117805480957 nb_pixel_total : 36266 time to create 1 rle with old method : 0.0424504280090332 time for calcul the mask position with numpy : 0.040436506271362305 nb_pixel_total : 118271 time to create 1 rle with old method : 0.1360924243927002 time for calcul the mask position with numpy : 0.039670705795288086 nb_pixel_total : 13713 time to create 1 rle with old method : 0.015346050262451172 time for calcul the mask position with numpy : 0.04075765609741211 nb_pixel_total : 9928 time to create 1 rle with old method : 0.011935949325561523 time for calcul the mask position with numpy : 0.04124307632446289 nb_pixel_total : 83791 time to create 1 rle with old method : 0.09768939018249512 time for calcul the mask position with numpy : 0.040970563888549805 nb_pixel_total : 119749 time to create 1 rle with old method : 0.13857412338256836 time for calcul the mask position with numpy : 0.04084634780883789 nb_pixel_total : 16428 time to create 1 rle with old method : 0.018928050994873047 time for calcul the mask position with numpy : 0.04204058647155762 nb_pixel_total : 400583 time to create 1 rle with new method : 1.1618187427520752 time for calcul the mask position with numpy : 0.040593862533569336 nb_pixel_total : 45297 time to create 1 rle with old method : 0.05160260200500488 time for calcul the mask position with numpy : 0.039582252502441406 nb_pixel_total : 15550 time to create 1 rle with old method : 0.01772022247314453 time for calcul the mask position with numpy : 0.03999042510986328 nb_pixel_total : 40409 time to create 1 rle with old method : 0.04484915733337402 time for calcul the mask position with numpy : 0.039319753646850586 nb_pixel_total : 10152 time to create 1 rle with old method : 0.011937856674194336 time for calcul the mask position with numpy : 0.04013967514038086 nb_pixel_total : 20167 time to create 1 rle with old method : 0.022543907165527344 time for calcul the mask position with numpy : 0.03894186019897461 nb_pixel_total : 2288 time to create 1 rle with old method : 0.0028493404388427734 time for calcul the mask position with numpy : 0.040558815002441406 nb_pixel_total : 66538 time to create 1 rle with old method : 0.07656335830688477 time for calcul the mask position with numpy : 0.03976941108703613 nb_pixel_total : 59358 time to create 1 rle with old method : 0.0675969123840332 time for calcul the mask position with numpy : 0.040619850158691406 nb_pixel_total : 157719 time to create 1 rle with new method : 1.1877853870391846 time for calcul the mask position with numpy : 0.0402979850769043 nb_pixel_total : 10676 time to create 1 rle with old method : 0.012329578399658203 time for calcul the mask position with numpy : 0.040741920471191406 nb_pixel_total : 85431 time to create 1 rle with old method : 0.10645413398742676 time for calcul the mask position with numpy : 0.049700260162353516 nb_pixel_total : 84488 time to create 1 rle with old method : 0.10003018379211426 time for calcul the mask position with numpy : 0.040137290954589844 nb_pixel_total : 24793 time to create 1 rle with old method : 0.02882528305053711 time for calcul the mask position with numpy : 0.040660858154296875 nb_pixel_total : 41408 time to create 1 rle with old method : 0.05085134506225586 time for calcul the mask position with numpy : 0.04027080535888672 nb_pixel_total : 36587 time to create 1 rle with old method : 0.0416712760925293 time for calcul the mask position with numpy : 0.04013323783874512 nb_pixel_total : 119829 time to create 1 rle with old method : 0.1377851963043213 time for calcul the mask position with numpy : 0.04008936882019043 nb_pixel_total : 20015 time to create 1 rle with old method : 0.023264169692993164 time for calcul the mask position with numpy : 0.040250301361083984 nb_pixel_total : 112614 time to create 1 rle with old method : 0.13058114051818848 time for calcul the mask position with numpy : 0.04643607139587402 nb_pixel_total : 7542 time to create 1 rle with old method : 0.011972188949584961 time for calcul the mask position with numpy : 0.044113874435424805 nb_pixel_total : 39550 time to create 1 rle with old method : 0.046417951583862305 time for calcul the mask position with numpy : 0.04009747505187988 nb_pixel_total : 20382 time to create 1 rle with old method : 0.02396845817565918 time for calcul the mask position with numpy : 0.03989148139953613 nb_pixel_total : 7439 time to create 1 rle with old method : 0.008786201477050781 time for calcul the mask position with numpy : 0.04194927215576172 nb_pixel_total : 198089 time to create 1 rle with new method : 1.1345839500427246 time for calcul the mask position with numpy : 0.04092741012573242 nb_pixel_total : 9368 time to create 1 rle with old method : 0.010837078094482422 time for calcul the mask position with numpy : 0.0401608943939209 nb_pixel_total : 15918 time to create 1 rle with old method : 0.020334482192993164 time for calcul the mask position with numpy : 0.042668819427490234 nb_pixel_total : 95521 time to create 1 rle with old method : 0.1087949275970459 time for calcul the mask position with numpy : 0.03992795944213867 nb_pixel_total : 7625 time to create 1 rle with old method : 0.009109020233154297 time for calcul the mask position with numpy : 0.04060101509094238 nb_pixel_total : 63182 time to create 1 rle with old method : 0.07372403144836426 time for calcul the mask position with numpy : 0.04550361633300781 nb_pixel_total : 7176 time to create 1 rle with old method : 0.00873422622680664 time for calcul the mask position with numpy : 0.040476322174072266 nb_pixel_total : 26465 time to create 1 rle with old method : 0.030791521072387695 create new chi : 16.835603952407837 time to delete rle : 0.09333515167236328 batch 1 Loaded 85 chid ids of type : 4855 Number RLEs to save : 52517 TO DO : save crop sub photo not yet done ! save time : 2.97825288772583 nb_obj : 92 nb_hashtags : 11 time to prepare the origin masks : 42.16208362579346 time for calcul the mask position with numpy : 0.0683145523071289 nb_pixel_total : 2978151 time to create 1 rle with new method : 0.8543434143066406 time for calcul the mask position with numpy : 0.04141712188720703 nb_pixel_total : 94116 time to create 1 rle with old method : 0.10862994194030762 time for calcul the mask position with numpy : 0.04103231430053711 nb_pixel_total : 77402 time to create 1 rle with old method : 0.08976984024047852 time for calcul the mask position with numpy : 0.04036831855773926 nb_pixel_total : 2033 time to create 1 rle with old method : 0.002443075180053711 time for calcul the mask position with numpy : 0.04526948928833008 nb_pixel_total : 79887 time to create 1 rle with old method : 0.09607839584350586 time for calcul the mask position with numpy : 0.04083871841430664 nb_pixel_total : 37368 time to create 1 rle with old method : 0.0435338020324707 time for calcul the mask position with numpy : 0.040663957595825195 nb_pixel_total : 9333 time to create 1 rle with old method : 0.011497020721435547 time for calcul the mask position with numpy : 0.04236102104187012 nb_pixel_total : 164674 time to create 1 rle with new method : 0.8590009212493896 time for calcul the mask position with numpy : 0.039902687072753906 nb_pixel_total : 109199 time to create 1 rle with old method : 0.12358784675598145 time for calcul the mask position with numpy : 0.039975881576538086 nb_pixel_total : 162772 time to create 1 rle with new method : 0.6760642528533936 time for calcul the mask position with numpy : 0.0444490909576416 nb_pixel_total : 485052 time to create 1 rle with new method : 0.5168764591217041 time for calcul the mask position with numpy : 0.04750871658325195 nb_pixel_total : 1067813 time to create 1 rle with new method : 0.6327040195465088 time for calcul the mask position with numpy : 0.04000091552734375 nb_pixel_total : 194106 time to create 1 rle with new method : 0.7985942363739014 time for calcul the mask position with numpy : 0.039141178131103516 nb_pixel_total : 97205 time to create 1 rle with old method : 0.1091926097869873 time for calcul the mask position with numpy : 0.038160085678100586 nb_pixel_total : 50243 time to create 1 rle with old method : 0.05878901481628418 time for calcul the mask position with numpy : 0.03929853439331055 nb_pixel_total : 859 time to create 1 rle with old method : 0.001192331314086914 time for calcul the mask position with numpy : 0.03931069374084473 nb_pixel_total : 1619 time to create 1 rle with old method : 0.001961231231689453 time for calcul the mask position with numpy : 0.040930747985839844 nb_pixel_total : 169 time to create 1 rle with old method : 0.0003256797790527344 time for calcul the mask position with numpy : 0.04532885551452637 nb_pixel_total : 18774 time to create 1 rle with old method : 0.03361821174621582 time for calcul the mask position with numpy : 0.04133200645446777 nb_pixel_total : 67200 time to create 1 rle with old method : 0.07813048362731934 time for calcul the mask position with numpy : 0.04065132141113281 nb_pixel_total : 19243 time to create 1 rle with old method : 0.027936458587646484 time for calcul the mask position with numpy : 0.04518938064575195 nb_pixel_total : 211721 time to create 1 rle with new method : 0.9749507904052734 time for calcul the mask position with numpy : 0.03940629959106445 nb_pixel_total : 27063 time to create 1 rle with old method : 0.030530691146850586 time for calcul the mask position with numpy : 0.04027390480041504 nb_pixel_total : 130497 time to create 1 rle with old method : 0.14635276794433594 time for calcul the mask position with numpy : 0.0405733585357666 nb_pixel_total : 61267 time to create 1 rle with old method : 0.07189416885375977 time for calcul the mask position with numpy : 0.040891408920288086 nb_pixel_total : 2419 time to create 1 rle with old method : 0.0030803680419921875 time for calcul the mask position with numpy : 0.04134035110473633 nb_pixel_total : 21215 time to create 1 rle with old method : 0.025200366973876953 time for calcul the mask position with numpy : 0.0411529541015625 nb_pixel_total : 17120 time to create 1 rle with old method : 0.019778728485107422 time for calcul the mask position with numpy : 0.04087471961975098 nb_pixel_total : 179874 time to create 1 rle with new method : 1.013810634613037 time for calcul the mask position with numpy : 0.04417872428894043 nb_pixel_total : 105042 time to create 1 rle with old method : 0.12113809585571289 time for calcul the mask position with numpy : 0.04060935974121094 nb_pixel_total : 14315 time to create 1 rle with old method : 0.016448259353637695 time for calcul the mask position with numpy : 0.039780378341674805 nb_pixel_total : 150539 time to create 1 rle with new method : 0.7693040370941162 time for calcul the mask position with numpy : 0.040528297424316406 nb_pixel_total : 27671 time to create 1 rle with old method : 0.03190946578979492 time for calcul the mask position with numpy : 0.04025459289550781 nb_pixel_total : 6351 time to create 1 rle with old method : 0.007588863372802734 time for calcul the mask position with numpy : 0.04056191444396973 nb_pixel_total : 53135 time to create 1 rle with old method : 0.062219858169555664 time for calcul the mask position with numpy : 0.040084123611450195 nb_pixel_total : 12035 time to create 1 rle with old method : 0.01428532600402832 time for calcul the mask position with numpy : 0.04016685485839844 nb_pixel_total : 23391 time to create 1 rle with old method : 0.02757883071899414 time for calcul the mask position with numpy : 0.04050397872924805 nb_pixel_total : 42320 time to create 1 rle with old method : 0.06024479866027832 time for calcul the mask position with numpy : 0.04549741744995117 nb_pixel_total : 10867 time to create 1 rle with old method : 0.012717485427856445 time for calcul the mask position with numpy : 0.04065203666687012 nb_pixel_total : 143445 time to create 1 rle with old method : 0.16632628440856934 time for calcul the mask position with numpy : 0.040793657302856445 nb_pixel_total : 132022 time to create 1 rle with old method : 0.15291190147399902 time for calcul the mask position with numpy : 0.041434288024902344 nb_pixel_total : 13006 time to create 1 rle with old method : 0.016152143478393555 time for calcul the mask position with numpy : 0.04492902755737305 nb_pixel_total : 64093 time to create 1 rle with old method : 0.07564616203308105 time for calcul the mask position with numpy : 0.04076075553894043 nb_pixel_total : 8887 time to create 1 rle with old method : 0.01031041145324707 time for calcul the mask position with numpy : 0.040261030197143555 nb_pixel_total : 24648 time to create 1 rle with old method : 0.028977155685424805 time for calcul the mask position with numpy : 0.04040670394897461 nb_pixel_total : 20842 time to create 1 rle with old method : 0.024008750915527344 time for calcul the mask position with numpy : 0.0392603874206543 nb_pixel_total : 33817 time to create 1 rle with old method : 0.03888416290283203 time for calcul the mask position with numpy : 0.03982973098754883 nb_pixel_total : 2512 time to create 1 rle with old method : 0.002897024154663086 time for calcul the mask position with numpy : 0.045647382736206055 nb_pixel_total : 53640 time to create 1 rle with old method : 0.06148815155029297 time for calcul the mask position with numpy : 0.0385279655456543 nb_pixel_total : 75455 time to create 1 rle with old method : 0.08309340476989746 time for calcul the mask position with numpy : 0.04368257522583008 nb_pixel_total : 81444 time to create 1 rle with old method : 0.09364175796508789 time for calcul the mask position with numpy : 0.0393221378326416 nb_pixel_total : 76159 time to create 1 rle with old method : 0.09070777893066406 time for calcul the mask position with numpy : 0.04053831100463867 nb_pixel_total : 461 time to create 1 rle with old method : 0.0006420612335205078 time for calcul the mask position with numpy : 0.04258227348327637 nb_pixel_total : 29141 time to create 1 rle with old method : 0.03741455078125 time for calcul the mask position with numpy : 0.040366172790527344 nb_pixel_total : 18611 time to create 1 rle with old method : 0.021622896194458008 time for calcul the mask position with numpy : 0.04039645195007324 nb_pixel_total : 12036 time to create 1 rle with old method : 0.014048337936401367 time for calcul the mask position with numpy : 0.04043292999267578 nb_pixel_total : 85490 time to create 1 rle with old method : 0.09901285171508789 time for calcul the mask position with numpy : 0.040105342864990234 nb_pixel_total : 1634 time to create 1 rle with old method : 0.002443075180053711 time for calcul the mask position with numpy : 0.03970050811767578 nb_pixel_total : 122847 time to create 1 rle with old method : 0.14410614967346191 time for calcul the mask position with numpy : 0.04017496109008789 nb_pixel_total : 102385 time to create 1 rle with old method : 0.11828994750976562 time for calcul the mask position with numpy : 0.038739681243896484 nb_pixel_total : 49567 time to create 1 rle with old method : 0.056427717208862305 time for calcul the mask position with numpy : 0.041289567947387695 nb_pixel_total : 15771 time to create 1 rle with old method : 0.018452882766723633 time for calcul the mask position with numpy : 0.03975367546081543 nb_pixel_total : 205429 time to create 1 rle with new method : 1.0194430351257324 time for calcul the mask position with numpy : 0.04066658020019531 nb_pixel_total : 16237 time to create 1 rle with old method : 0.019400358200073242 time for calcul the mask position with numpy : 0.04250693321228027 nb_pixel_total : 266500 time to create 1 rle with new method : 0.7769069671630859 time for calcul the mask position with numpy : 0.040418386459350586 nb_pixel_total : 1152 time to create 1 rle with old method : 0.0014925003051757812 time for calcul the mask position with numpy : 0.04842686653137207 nb_pixel_total : 356270 time to create 1 rle with new method : 0.8708183765411377 time for calcul the mask position with numpy : 0.05066275596618652 nb_pixel_total : 1027 time to create 1 rle with old method : 0.0028045177459716797 time for calcul the mask position with numpy : 0.045419931411743164 nb_pixel_total : 112538 time to create 1 rle with old method : 0.12869524955749512 time for calcul the mask position with numpy : 0.04089808464050293 nb_pixel_total : 33331 time to create 1 rle with old method : 0.03865861892700195 time for calcul the mask position with numpy : 0.04050040245056152 nb_pixel_total : 2688 time to create 1 rle with old method : 0.0033903121948242188 time for calcul the mask position with numpy : 0.040454864501953125 nb_pixel_total : 2780 time to create 1 rle with old method : 0.0035440921783447266 time for calcul the mask position with numpy : 0.042575836181640625 nb_pixel_total : 243743 time to create 1 rle with new method : 1.261958360671997 time for calcul the mask position with numpy : 0.039827823638916016 nb_pixel_total : 1899 time to create 1 rle with old method : 0.0022826194763183594 time for calcul the mask position with numpy : 0.03979229927062988 nb_pixel_total : 20988 time to create 1 rle with old method : 0.02408909797668457 time for calcul the mask position with numpy : 0.03930807113647461 nb_pixel_total : 447 time to create 1 rle with old method : 0.0006661415100097656 time for calcul the mask position with numpy : 0.040123701095581055 nb_pixel_total : 15814 time to create 1 rle with old method : 0.017621755599975586 time for calcul the mask position with numpy : 0.03920173645019531 nb_pixel_total : 4382 time to create 1 rle with old method : 0.005096435546875 time for calcul the mask position with numpy : 0.03847169876098633 nb_pixel_total : 2215 time to create 1 rle with old method : 0.0025529861450195312 time for calcul the mask position with numpy : 0.03960585594177246 nb_pixel_total : 2156 time to create 1 rle with old method : 0.00257110595703125 time for calcul the mask position with numpy : 0.0391538143157959 nb_pixel_total : 4603 time to create 1 rle with old method : 0.0055048465728759766 time for calcul the mask position with numpy : 0.04078793525695801 nb_pixel_total : 17 time to create 1 rle with old method : 4.267692565917969e-05 time for calcul the mask position with numpy : 0.04100394248962402 nb_pixel_total : 134512 time to create 1 rle with old method : 0.178558349609375 time for calcul the mask position with numpy : 0.041480064392089844 nb_pixel_total : 2706 time to create 1 rle with old method : 0.003366708755493164 time for calcul the mask position with numpy : 0.040921688079833984 nb_pixel_total : 37962 time to create 1 rle with old method : 0.04410147666931152 time for calcul the mask position with numpy : 0.041921377182006836 nb_pixel_total : 151853 time to create 1 rle with new method : 0.9645602703094482 time for calcul the mask position with numpy : 0.041405677795410156 nb_pixel_total : 70117 time to create 1 rle with old method : 0.08580756187438965 time for calcul the mask position with numpy : 0.03821730613708496 nb_pixel_total : 7630 time to create 1 rle with old method : 0.008983850479125977 time for calcul the mask position with numpy : 0.041603803634643555 nb_pixel_total : 36941 time to create 1 rle with old method : 0.041387081146240234 time for calcul the mask position with numpy : 0.03776693344116211 nb_pixel_total : 1 time to create 1 rle with old method : 2.193450927734375e-05 time for calcul the mask position with numpy : 0.03924894332885742 nb_pixel_total : 11735 time to create 1 rle with old method : 0.013294696807861328 time for calcul the mask position with numpy : 0.03983592987060547 nb_pixel_total : 4495 time to create 1 rle with old method : 0.005248308181762695 time for calcul the mask position with numpy : 0.038970947265625 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003464221954345703 create new chi : 19.76345205307007 time to delete rle : 0.009029865264892578 batch 1 Loaded 93 chid ids of type : 4855 Number RLEs to save : 54776 TO DO : save crop sub photo not yet done ! save time : 2.995551586151123 nb_obj : 86 nb_hashtags : 10 time to prepare the origin masks : 45.43683218955994 time for calcul the mask position with numpy : 0.23386049270629883 nb_pixel_total : 2906159 time to create 1 rle with new method : 2.6491661071777344 time for calcul the mask position with numpy : 0.04351091384887695 nb_pixel_total : 30597 time to create 1 rle with old method : 0.03665328025817871 time for calcul the mask position with numpy : 0.03967905044555664 nb_pixel_total : 16671 time to create 1 rle with old method : 0.019023418426513672 time for calcul the mask position with numpy : 0.03896951675415039 nb_pixel_total : 16748 time to create 1 rle with old method : 0.018300533294677734 time for calcul the mask position with numpy : 0.03957796096801758 nb_pixel_total : 36998 time to create 1 rle with old method : 0.04191422462463379 time for calcul the mask position with numpy : 0.0396270751953125 nb_pixel_total : 44757 time to create 1 rle with old method : 0.04952383041381836 time for calcul the mask position with numpy : 0.05121874809265137 nb_pixel_total : 745556 time to create 1 rle with new method : 3.4337899684906006 time for calcul the mask position with numpy : 0.039066314697265625 nb_pixel_total : 65 time to create 1 rle with old method : 0.00014281272888183594 time for calcul the mask position with numpy : 0.03948807716369629 nb_pixel_total : 14774 time to create 1 rle with old method : 0.016852855682373047 time for calcul the mask position with numpy : 0.04022574424743652 nb_pixel_total : 972 time to create 1 rle with old method : 0.0012004375457763672 time for calcul the mask position with numpy : 0.04082655906677246 nb_pixel_total : 74197 time to create 1 rle with old method : 0.08598804473876953 time for calcul the mask position with numpy : 0.052973031997680664 nb_pixel_total : 477 time to create 1 rle with old method : 0.0008952617645263672 time for calcul the mask position with numpy : 0.0485379695892334 nb_pixel_total : 29092 time to create 1 rle with old method : 0.04192948341369629 time for calcul the mask position with numpy : 0.058823585510253906 nb_pixel_total : 21702 time to create 1 rle with old method : 0.030930519104003906 time for calcul the mask position with numpy : 0.043643951416015625 nb_pixel_total : 433273 time to create 1 rle with new method : 1.5083320140838623 time for calcul the mask position with numpy : 0.03869771957397461 nb_pixel_total : 11638 time to create 1 rle with old method : 0.012691259384155273 time for calcul the mask position with numpy : 0.03846096992492676 nb_pixel_total : 9560 time to create 1 rle with old method : 0.010898590087890625 time for calcul the mask position with numpy : 0.03902173042297363 nb_pixel_total : 6 time to create 1 rle with old method : 0.00010013580322265625 time for calcul the mask position with numpy : 0.04464435577392578 nb_pixel_total : 490 time to create 1 rle with old method : 0.0006935596466064453 time for calcul the mask position with numpy : 0.04175686836242676 nb_pixel_total : 149784 time to create 1 rle with old method : 0.1665635108947754 time for calcul the mask position with numpy : 0.04458498954772949 nb_pixel_total : 14454 time to create 1 rle with old method : 0.02451038360595703 time for calcul the mask position with numpy : 0.050750017166137695 nb_pixel_total : 5310 time to create 1 rle with old method : 0.016771554946899414 time for calcul the mask position with numpy : 0.04192638397216797 nb_pixel_total : 4614 time to create 1 rle with old method : 0.005543231964111328 time for calcul the mask position with numpy : 0.0404057502746582 nb_pixel_total : 55889 time to create 1 rle with old method : 0.06108260154724121 time for calcul the mask position with numpy : 0.038115739822387695 nb_pixel_total : 22624 time to create 1 rle with old method : 0.024848461151123047 time for calcul the mask position with numpy : 0.03867816925048828 nb_pixel_total : 24915 time to create 1 rle with old method : 0.027759313583374023 time for calcul the mask position with numpy : 0.03952503204345703 nb_pixel_total : 22176 time to create 1 rle with old method : 0.02592301368713379 time for calcul the mask position with numpy : 0.043853759765625 nb_pixel_total : 11560 time to create 1 rle with old method : 0.013315916061401367 time for calcul the mask position with numpy : 0.03869342803955078 nb_pixel_total : 8291 time to create 1 rle with old method : 0.00927281379699707 time for calcul the mask position with numpy : 0.04108858108520508 nb_pixel_total : 277791 time to create 1 rle with new method : 1.2508544921875 time for calcul the mask position with numpy : 0.03955578804016113 nb_pixel_total : 45266 time to create 1 rle with old method : 0.05074286460876465 time for calcul the mask position with numpy : 0.03844308853149414 nb_pixel_total : 55337 time to create 1 rle with old method : 0.06030702590942383 time for calcul the mask position with numpy : 0.03889775276184082 nb_pixel_total : 48949 time to create 1 rle with old method : 0.05424141883850098 time for calcul the mask position with numpy : 0.04016900062561035 nb_pixel_total : 3620 time to create 1 rle with old method : 0.00426793098449707 time for calcul the mask position with numpy : 0.04172849655151367 nb_pixel_total : 18403 time to create 1 rle with old method : 0.02067875862121582 time for calcul the mask position with numpy : 0.03866887092590332 nb_pixel_total : 1002 time to create 1 rle with old method : 0.0013360977172851562 time for calcul the mask position with numpy : 0.03918051719665527 nb_pixel_total : 19353 time to create 1 rle with old method : 0.021391868591308594 time for calcul the mask position with numpy : 0.039887428283691406 nb_pixel_total : 131785 time to create 1 rle with old method : 0.14335083961486816 time for calcul the mask position with numpy : 0.03932547569274902 nb_pixel_total : 80330 time to create 1 rle with old method : 0.0905306339263916 time for calcul the mask position with numpy : 0.03938794136047363 nb_pixel_total : 2411 time to create 1 rle with old method : 0.003265380859375 time for calcul the mask position with numpy : 0.039990901947021484 nb_pixel_total : 605 time to create 1 rle with old method : 0.0009105205535888672 time for calcul the mask position with numpy : 0.03897881507873535 nb_pixel_total : 14789 time to create 1 rle with old method : 0.016285419464111328 time for calcul the mask position with numpy : 0.03942608833312988 nb_pixel_total : 28067 time to create 1 rle with old method : 0.03148293495178223 time for calcul the mask position with numpy : 0.03946423530578613 nb_pixel_total : 3659 time to create 1 rle with old method : 0.004256486892700195 time for calcul the mask position with numpy : 0.03988909721374512 nb_pixel_total : 127823 time to create 1 rle with old method : 0.14031696319580078 time for calcul the mask position with numpy : 0.04018902778625488 nb_pixel_total : 64247 time to create 1 rle with old method : 0.07029914855957031 time for calcul the mask position with numpy : 0.04036283493041992 nb_pixel_total : 1924 time to create 1 rle with old method : 0.0023500919342041016 time for calcul the mask position with numpy : 0.03899860382080078 nb_pixel_total : 34615 time to create 1 rle with old method : 0.03917813301086426 time for calcul the mask position with numpy : 0.041263580322265625 nb_pixel_total : 12631 time to create 1 rle with old method : 0.014615058898925781 time for calcul the mask position with numpy : 0.04626917839050293 nb_pixel_total : 34362 time to create 1 rle with old method : 0.038845062255859375 time for calcul the mask position with numpy : 0.04102945327758789 nb_pixel_total : 13964 time to create 1 rle with old method : 0.01605224609375 time for calcul the mask position with numpy : 0.04042816162109375 nb_pixel_total : 15085 time to create 1 rle with old method : 0.017189741134643555 time for calcul the mask position with numpy : 0.04404854774475098 nb_pixel_total : 61649 time to create 1 rle with old method : 0.07199668884277344 time for calcul the mask position with numpy : 0.04091334342956543 nb_pixel_total : 23216 time to create 1 rle with old method : 0.02781987190246582 time for calcul the mask position with numpy : 0.04622793197631836 nb_pixel_total : 13027 time to create 1 rle with old method : 0.015282392501831055 time for calcul the mask position with numpy : 0.04053187370300293 nb_pixel_total : 2809 time to create 1 rle with old method : 0.0032482147216796875 time for calcul the mask position with numpy : 0.04470062255859375 nb_pixel_total : 386914 time to create 1 rle with new method : 2.907552719116211 time for calcul the mask position with numpy : 0.04044914245605469 nb_pixel_total : 27994 time to create 1 rle with old method : 0.03172945976257324 time for calcul the mask position with numpy : 0.03937864303588867 nb_pixel_total : 54583 time to create 1 rle with old method : 0.06042599678039551 time for calcul the mask position with numpy : 0.039148569107055664 nb_pixel_total : 31178 time to create 1 rle with old method : 0.034773826599121094 time for calcul the mask position with numpy : 0.04181385040283203 nb_pixel_total : 113586 time to create 1 rle with old method : 0.1263880729675293 time for calcul the mask position with numpy : 0.040055036544799805 nb_pixel_total : 8574 time to create 1 rle with old method : 0.014701128005981445 time for calcul the mask position with numpy : 0.05792975425720215 nb_pixel_total : 549967 time to create 1 rle with new method : 1.3521711826324463 time for calcul the mask position with numpy : 0.0383143424987793 nb_pixel_total : 11432 time to create 1 rle with old method : 0.012394428253173828 time for calcul the mask position with numpy : 0.07295751571655273 nb_pixel_total : 816402 time to create 1 rle with new method : 1.0367677211761475 time for calcul the mask position with numpy : 0.051546335220336914 nb_pixel_total : 805319 time to create 1 rle with new method : 1.250159740447998 time for calcul the mask position with numpy : 0.041290283203125 nb_pixel_total : 1922 time to create 1 rle with old method : 0.0025551319122314453 time for calcul the mask position with numpy : 0.04243803024291992 nb_pixel_total : 18802 time to create 1 rle with old method : 0.030566692352294922 time for calcul the mask position with numpy : 0.04547572135925293 nb_pixel_total : 1040 time to create 1 rle with old method : 0.0012934207916259766 time for calcul the mask position with numpy : 0.04146933555603027 nb_pixel_total : 4967 time to create 1 rle with old method : 0.006476640701293945 time for calcul the mask position with numpy : 0.04126095771789551 nb_pixel_total : 19888 time to create 1 rle with old method : 0.02285027503967285 time for calcul the mask position with numpy : 0.03985428810119629 nb_pixel_total : 3732 time to create 1 rle with old method : 0.004857063293457031 time for calcul the mask position with numpy : 0.040735483169555664 nb_pixel_total : 77234 time to create 1 rle with old method : 0.11754274368286133 time for calcul the mask position with numpy : 0.039275407791137695 nb_pixel_total : 9149 time to create 1 rle with old method : 0.010840177536010742 time for calcul the mask position with numpy : 0.03879714012145996 nb_pixel_total : 52626 time to create 1 rle with old method : 0.08320951461791992 time for calcul the mask position with numpy : 0.048210859298706055 nb_pixel_total : 598945 time to create 1 rle with new method : 1.1963210105895996 time for calcul the mask position with numpy : 0.04298591613769531 nb_pixel_total : 1281 time to create 1 rle with old method : 0.0017821788787841797 time for calcul the mask position with numpy : 0.041080474853515625 nb_pixel_total : 27229 time to create 1 rle with old method : 0.031656503677368164 time for calcul the mask position with numpy : 0.04141497611999512 nb_pixel_total : 14990 time to create 1 rle with old method : 0.01753997802734375 time for calcul the mask position with numpy : 0.04078245162963867 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006802082061767578 time for calcul the mask position with numpy : 0.04091215133666992 nb_pixel_total : 14364 time to create 1 rle with old method : 0.02019524574279785 time for calcul the mask position with numpy : 0.04870891571044922 nb_pixel_total : 82778 time to create 1 rle with old method : 0.10171794891357422 time for calcul the mask position with numpy : 0.04409933090209961 nb_pixel_total : 66294 time to create 1 rle with old method : 0.07874107360839844 time for calcul the mask position with numpy : 0.04121112823486328 nb_pixel_total : 33236 time to create 1 rle with old method : 0.03857994079589844 time for calcul the mask position with numpy : 0.041352033615112305 nb_pixel_total : 2808 time to create 1 rle with old method : 0.003922939300537109 time for calcul the mask position with numpy : 0.04133296012878418 nb_pixel_total : 676 time to create 1 rle with old method : 0.0009531974792480469 time for calcul the mask position with numpy : 0.042231082916259766 nb_pixel_total : 45951 time to create 1 rle with old method : 0.05346965789794922 create new chi : 23.530615091323853 time to delete rle : 0.019207239151000977 batch 1 Loaded 87 chid ids of type : 4855 Number RLEs to save : 49861 TO DO : save crop sub photo not yet done ! save time : 2.9666829109191895 nb_obj : 81 nb_hashtags : 11 time to prepare the origin masks : 47.27191662788391 time for calcul the mask position with numpy : 0.05606198310852051 nb_pixel_total : 2378296 time to create 1 rle with new method : 2.0657527446746826 time for calcul the mask position with numpy : 0.04001617431640625 nb_pixel_total : 10301 time to create 1 rle with old method : 0.011605978012084961 time for calcul the mask position with numpy : 0.04081988334655762 nb_pixel_total : 302746 time to create 1 rle with new method : 0.8941149711608887 time for calcul the mask position with numpy : 0.040430545806884766 nb_pixel_total : 6816 time to create 1 rle with old method : 0.00801849365234375 time for calcul the mask position with numpy : 0.04068303108215332 nb_pixel_total : 58695 time to create 1 rle with old method : 0.07213306427001953 time for calcul the mask position with numpy : 0.039427757263183594 nb_pixel_total : 4494 time to create 1 rle with old method : 0.005338907241821289 time for calcul the mask position with numpy : 0.03965282440185547 nb_pixel_total : 41579 time to create 1 rle with old method : 0.04698443412780762 time for calcul the mask position with numpy : 0.03922748565673828 nb_pixel_total : 17831 time to create 1 rle with old method : 0.02074146270751953 time for calcul the mask position with numpy : 0.04051327705383301 nb_pixel_total : 142223 time to create 1 rle with old method : 0.16137266159057617 time for calcul the mask position with numpy : 0.03943276405334473 nb_pixel_total : 50875 time to create 1 rle with old method : 0.05950760841369629 time for calcul the mask position with numpy : 0.04045677185058594 nb_pixel_total : 93161 time to create 1 rle with old method : 0.10886836051940918 time for calcul the mask position with numpy : 0.04031229019165039 nb_pixel_total : 10963 time to create 1 rle with old method : 0.012891769409179688 time for calcul the mask position with numpy : 0.043747663497924805 nb_pixel_total : 504930 time to create 1 rle with new method : 0.9097962379455566 time for calcul the mask position with numpy : 0.03966975212097168 nb_pixel_total : 19414 time to create 1 rle with old method : 0.02203536033630371 time for calcul the mask position with numpy : 0.03931260108947754 nb_pixel_total : 2983 time to create 1 rle with old method : 0.003406047821044922 time for calcul the mask position with numpy : 0.040178537368774414 nb_pixel_total : 31878 time to create 1 rle with old method : 0.0372462272644043 time for calcul the mask position with numpy : 0.04013967514038086 nb_pixel_total : 3576 time to create 1 rle with old method : 0.004801034927368164 time for calcul the mask position with numpy : 0.04028058052062988 nb_pixel_total : 19391 time to create 1 rle with old method : 0.022627592086791992 time for calcul the mask position with numpy : 0.04074406623840332 nb_pixel_total : 129535 time to create 1 rle with old method : 0.1731269359588623 time for calcul the mask position with numpy : 0.04090738296508789 nb_pixel_total : 20781 time to create 1 rle with old method : 0.02334141731262207 time for calcul the mask position with numpy : 0.039882659912109375 nb_pixel_total : 82486 time to create 1 rle with old method : 0.09309649467468262 time for calcul the mask position with numpy : 0.03960466384887695 nb_pixel_total : 49804 time to create 1 rle with old method : 0.08007073402404785 time for calcul the mask position with numpy : 0.040670156478881836 nb_pixel_total : 8285 time to create 1 rle with old method : 0.009709358215332031 time for calcul the mask position with numpy : 0.03963351249694824 nb_pixel_total : 35 time to create 1 rle with old method : 0.0001938343048095703 time for calcul the mask position with numpy : 0.040329694747924805 nb_pixel_total : 101826 time to create 1 rle with old method : 0.11632657051086426 time for calcul the mask position with numpy : 0.03914594650268555 nb_pixel_total : 10334 time to create 1 rle with old method : 0.012688875198364258 time for calcul the mask position with numpy : 0.04060673713684082 nb_pixel_total : 46450 time to create 1 rle with old method : 0.053894996643066406 time for calcul the mask position with numpy : 0.04057955741882324 nb_pixel_total : 62202 time to create 1 rle with old method : 0.07214021682739258 time for calcul the mask position with numpy : 0.04117870330810547 nb_pixel_total : 172459 time to create 1 rle with new method : 1.120009422302246 time for calcul the mask position with numpy : 0.04261946678161621 nb_pixel_total : 46012 time to create 1 rle with old method : 0.05347895622253418 time for calcul the mask position with numpy : 0.04123115539550781 nb_pixel_total : 7935 time to create 1 rle with old method : 0.00949716567993164 time for calcul the mask position with numpy : 0.040640830993652344 nb_pixel_total : 13544 time to create 1 rle with old method : 0.01615595817565918 time for calcul the mask position with numpy : 0.04068946838378906 nb_pixel_total : 83800 time to create 1 rle with old method : 0.09690189361572266 time for calcul the mask position with numpy : 0.0399782657623291 nb_pixel_total : 1091 time to create 1 rle with old method : 0.0014491081237792969 time for calcul the mask position with numpy : 0.04056239128112793 nb_pixel_total : 13107 time to create 1 rle with old method : 0.01519918441772461 time for calcul the mask position with numpy : 0.04091501235961914 nb_pixel_total : 178269 time to create 1 rle with new method : 0.7701101303100586 time for calcul the mask position with numpy : 0.04063701629638672 nb_pixel_total : 33116 time to create 1 rle with old method : 0.041788339614868164 time for calcul the mask position with numpy : 0.040990352630615234 nb_pixel_total : 6853 time to create 1 rle with old method : 0.00951075553894043 time for calcul the mask position with numpy : 0.0417020320892334 nb_pixel_total : 16639 time to create 1 rle with old method : 0.01990509033203125 time for calcul the mask position with numpy : 0.041728973388671875 nb_pixel_total : 27365 time to create 1 rle with old method : 0.034609317779541016 time for calcul the mask position with numpy : 0.04199790954589844 nb_pixel_total : 1404 time to create 1 rle with old method : 0.0019121170043945312 time for calcul the mask position with numpy : 0.04190254211425781 nb_pixel_total : 159811 time to create 1 rle with new method : 1.1541872024536133 time for calcul the mask position with numpy : 0.042121171951293945 nb_pixel_total : 390216 time to create 1 rle with new method : 0.8296599388122559 time for calcul the mask position with numpy : 0.03948569297790527 nb_pixel_total : 15749 time to create 1 rle with old method : 0.018037080764770508 time for calcul the mask position with numpy : 0.04008054733276367 nb_pixel_total : 2116 time to create 1 rle with old method : 0.0026199817657470703 time for calcul the mask position with numpy : 0.039560794830322266 nb_pixel_total : 16925 time to create 1 rle with old method : 0.01929473876953125 time for calcul the mask position with numpy : 0.04020524024963379 nb_pixel_total : 39606 time to create 1 rle with old method : 0.0486454963684082 time for calcul the mask position with numpy : 0.04046487808227539 nb_pixel_total : 14222 time to create 1 rle with old method : 0.016669273376464844 time for calcul the mask position with numpy : 0.03977608680725098 nb_pixel_total : 2233 time to create 1 rle with old method : 0.0027625560760498047 time for calcul the mask position with numpy : 0.04389524459838867 nb_pixel_total : 6237 time to create 1 rle with old method : 0.007842063903808594 time for calcul the mask position with numpy : 0.04029488563537598 nb_pixel_total : 1390 time to create 1 rle with old method : 0.0021059513092041016 time for calcul the mask position with numpy : 0.04131913185119629 nb_pixel_total : 177975 time to create 1 rle with new method : 0.9534077644348145 time for calcul the mask position with numpy : 0.04079747200012207 nb_pixel_total : 2295 time to create 1 rle with old method : 0.0028319358825683594 time for calcul the mask position with numpy : 0.04065394401550293 nb_pixel_total : 113199 time to create 1 rle with old method : 0.13193035125732422 time for calcul the mask position with numpy : 0.03973031044006348 nb_pixel_total : 7853 time to create 1 rle with old method : 0.009310722351074219 time for calcul the mask position with numpy : 0.0399632453918457 nb_pixel_total : 271197 time to create 1 rle with new method : 1.0788228511810303 time for calcul the mask position with numpy : 0.041443586349487305 nb_pixel_total : 88551 time to create 1 rle with old method : 0.11290359497070312 time for calcul the mask position with numpy : 0.04309558868408203 nb_pixel_total : 324452 time to create 1 rle with new method : 1.2257139682769775 time for calcul the mask position with numpy : 0.04206585884094238 nb_pixel_total : 87552 time to create 1 rle with old method : 0.11816191673278809 time for calcul the mask position with numpy : 0.040244340896606445 nb_pixel_total : 4737 time to create 1 rle with old method : 0.006001710891723633 time for calcul the mask position with numpy : 0.040558576583862305 nb_pixel_total : 86204 time to create 1 rle with old method : 0.09954237937927246 time for calcul the mask position with numpy : 0.043627262115478516 nb_pixel_total : 68315 time to create 1 rle with old method : 0.08391594886779785 time for calcul the mask position with numpy : 0.04889845848083496 nb_pixel_total : 832646 time to create 1 rle with new method : 1.1335043907165527 time for calcul the mask position with numpy : 0.04220271110534668 nb_pixel_total : 18621 time to create 1 rle with old method : 0.024087190628051758 time for calcul the mask position with numpy : 0.042984962463378906 nb_pixel_total : 156253 time to create 1 rle with new method : 1.6863822937011719 time for calcul the mask position with numpy : 0.048592329025268555 nb_pixel_total : 876451 time to create 1 rle with new method : 0.9456393718719482 time for calcul the mask position with numpy : 0.04702115058898926 nb_pixel_total : 49951 time to create 1 rle with old method : 0.05798625946044922 time for calcul the mask position with numpy : 0.04175066947937012 nb_pixel_total : 181697 time to create 1 rle with new method : 0.8289744853973389 time for calcul the mask position with numpy : 0.04051566123962402 nb_pixel_total : 2312 time to create 1 rle with old method : 0.0030188560485839844 time for calcul the mask position with numpy : 0.040044546127319336 nb_pixel_total : 2607 time to create 1 rle with old method : 0.003662109375 time for calcul the mask position with numpy : 0.04021406173706055 nb_pixel_total : 26781 time to create 1 rle with old method : 0.030750751495361328 time for calcul the mask position with numpy : 0.040956974029541016 nb_pixel_total : 4229 time to create 1 rle with old method : 0.00529789924621582 time for calcul the mask position with numpy : 0.04321694374084473 nb_pixel_total : 329895 time to create 1 rle with new method : 0.9238002300262451 time for calcul the mask position with numpy : 0.04166817665100098 nb_pixel_total : 196460 time to create 1 rle with new method : 0.5968186855316162 time for calcul the mask position with numpy : 0.04010605812072754 nb_pixel_total : 35080 time to create 1 rle with old method : 0.03981447219848633 time for calcul the mask position with numpy : 0.039405107498168945 nb_pixel_total : 240 time to create 1 rle with old method : 0.00045180320739746094 time for calcul the mask position with numpy : 0.040628671646118164 nb_pixel_total : 71115 time to create 1 rle with old method : 0.08112812042236328 time for calcul the mask position with numpy : 0.04082012176513672 nb_pixel_total : 20591 time to create 1 rle with old method : 0.02374410629272461 time for calcul the mask position with numpy : 0.04147005081176758 nb_pixel_total : 62615 time to create 1 rle with old method : 0.07419872283935547 time for calcul the mask position with numpy : 0.04159259796142578 nb_pixel_total : 135899 time to create 1 rle with old method : 0.16289305686950684 time for calcul the mask position with numpy : 0.04102134704589844 nb_pixel_total : 8134 time to create 1 rle with old method : 0.009629011154174805 time for calcul the mask position with numpy : 0.040194034576416016 nb_pixel_total : 28504 time to create 1 rle with old method : 0.03304696083068848 create new chi : 23.782623052597046 time to delete rle : 0.00485992431640625 batch 1 Loaded 82 chid ids of type : 4855 Number RLEs to save : 55678 TO DO : save crop sub photo not yet done ! save time : 3.062626600265503 nb_obj : 78 nb_hashtags : 9 time to prepare the origin masks : 41.94213581085205 time for calcul the mask position with numpy : 0.9745361804962158 nb_pixel_total : 2598008 time to create 1 rle with new method : 1.4173500537872314 time for calcul the mask position with numpy : 0.03998708724975586 nb_pixel_total : 22460 time to create 1 rle with old method : 0.026353836059570312 time for calcul the mask position with numpy : 0.040459632873535156 nb_pixel_total : 88357 time to create 1 rle with old method : 0.10347771644592285 time for calcul the mask position with numpy : 0.042659759521484375 nb_pixel_total : 7485 time to create 1 rle with old method : 0.009026765823364258 time for calcul the mask position with numpy : 0.04229378700256348 nb_pixel_total : 110631 time to create 1 rle with old method : 0.1268763542175293 time for calcul the mask position with numpy : 0.04252028465270996 nb_pixel_total : 18238 time to create 1 rle with old method : 0.021674394607543945 time for calcul the mask position with numpy : 0.05039501190185547 nb_pixel_total : 667603 time to create 1 rle with new method : 0.6428892612457275 time for calcul the mask position with numpy : 0.03872275352478027 nb_pixel_total : 26860 time to create 1 rle with old method : 0.030030488967895508 time for calcul the mask position with numpy : 0.039345741271972656 nb_pixel_total : 21589 time to create 1 rle with old method : 0.02391791343688965 time for calcul the mask position with numpy : 0.03884148597717285 nb_pixel_total : 16945 time to create 1 rle with old method : 0.018382549285888672 time for calcul the mask position with numpy : 0.03802490234375 nb_pixel_total : 2233 time to create 1 rle with old method : 0.002459287643432617 time for calcul the mask position with numpy : 0.041342735290527344 nb_pixel_total : 469256 time to create 1 rle with new method : 0.7091102600097656 time for calcul the mask position with numpy : 0.03772282600402832 nb_pixel_total : 319 time to create 1 rle with old method : 0.0005354881286621094 time for calcul the mask position with numpy : 0.038507699966430664 nb_pixel_total : 3093 time to create 1 rle with old method : 0.004085540771484375 time for calcul the mask position with numpy : 0.037871599197387695 nb_pixel_total : 2268 time to create 1 rle with old method : 0.002637624740600586 time for calcul the mask position with numpy : 0.04128241539001465 nb_pixel_total : 273699 time to create 1 rle with new method : 0.7473721504211426 time for calcul the mask position with numpy : 0.03873848915100098 nb_pixel_total : 140753 time to create 1 rle with old method : 0.15194463729858398 time for calcul the mask position with numpy : 0.03836655616760254 nb_pixel_total : 6548 time to create 1 rle with old method : 0.007140159606933594 time for calcul the mask position with numpy : 0.037592172622680664 nb_pixel_total : 2541 time to create 1 rle with old method : 0.003023862838745117 time for calcul the mask position with numpy : 0.037740468978881836 nb_pixel_total : 52300 time to create 1 rle with old method : 0.057177066802978516 time for calcul the mask position with numpy : 0.038521766662597656 nb_pixel_total : 2694 time to create 1 rle with old method : 0.0031366348266601562 time for calcul the mask position with numpy : 0.03925633430480957 nb_pixel_total : 47758 time to create 1 rle with old method : 0.05328035354614258 time for calcul the mask position with numpy : 0.038678646087646484 nb_pixel_total : 25699 time to create 1 rle with old method : 0.028334379196166992 time for calcul the mask position with numpy : 0.038907527923583984 nb_pixel_total : 4743 time to create 1 rle with old method : 0.005501508712768555 time for calcul the mask position with numpy : 0.039034366607666016 nb_pixel_total : 4 time to create 1 rle with old method : 0.0002884864807128906 time for calcul the mask position with numpy : 0.03888964653015137 nb_pixel_total : 32653 time to create 1 rle with old method : 0.03653144836425781 time for calcul the mask position with numpy : 0.03996133804321289 nb_pixel_total : 28421 time to create 1 rle with old method : 0.03168511390686035 time for calcul the mask position with numpy : 0.038599491119384766 nb_pixel_total : 76993 time to create 1 rle with old method : 0.0894625186920166 time for calcul the mask position with numpy : 0.04096508026123047 nb_pixel_total : 35966 time to create 1 rle with old method : 0.04056072235107422 time for calcul the mask position with numpy : 0.03872561454772949 nb_pixel_total : 74841 time to create 1 rle with old method : 0.08119535446166992 time for calcul the mask position with numpy : 0.038570404052734375 nb_pixel_total : 50762 time to create 1 rle with old method : 0.05553007125854492 time for calcul the mask position with numpy : 0.039560794830322266 nb_pixel_total : 99250 time to create 1 rle with old method : 0.10609793663024902 time for calcul the mask position with numpy : 0.03793001174926758 nb_pixel_total : 58506 time to create 1 rle with old method : 0.06231832504272461 time for calcul the mask position with numpy : 0.03753352165222168 nb_pixel_total : 800 time to create 1 rle with old method : 0.0012433528900146484 time for calcul the mask position with numpy : 0.03856182098388672 nb_pixel_total : 203407 time to create 1 rle with new method : 0.5136840343475342 time for calcul the mask position with numpy : 0.04141688346862793 nb_pixel_total : 530489 time to create 1 rle with new method : 1.1621425151824951 time for calcul the mask position with numpy : 0.03818368911743164 nb_pixel_total : 2349 time to create 1 rle with old method : 0.002624034881591797 time for calcul the mask position with numpy : 0.03756999969482422 nb_pixel_total : 3605 time to create 1 rle with old method : 0.004179954528808594 time for calcul the mask position with numpy : 0.037612199783325195 nb_pixel_total : 13734 time to create 1 rle with old method : 0.015010356903076172 time for calcul the mask position with numpy : 0.03802156448364258 nb_pixel_total : 98921 time to create 1 rle with old method : 0.10603809356689453 time for calcul the mask position with numpy : 0.04072427749633789 nb_pixel_total : 111179 time to create 1 rle with old method : 0.14327716827392578 time for calcul the mask position with numpy : 0.03919076919555664 nb_pixel_total : 605 time to create 1 rle with old method : 0.0008215904235839844 time for calcul the mask position with numpy : 0.043351173400878906 nb_pixel_total : 556197 time to create 1 rle with new method : 0.5932796001434326 time for calcul the mask position with numpy : 0.038094282150268555 nb_pixel_total : 78513 time to create 1 rle with old method : 0.08678007125854492 time for calcul the mask position with numpy : 0.039971351623535156 nb_pixel_total : 190771 time to create 1 rle with new method : 1.196784257888794 time for calcul the mask position with numpy : 0.03961181640625 nb_pixel_total : 15 time to create 1 rle with old method : 5.2928924560546875e-05 time for calcul the mask position with numpy : 0.03894639015197754 nb_pixel_total : 32233 time to create 1 rle with old method : 0.03447151184082031 time for calcul the mask position with numpy : 0.037897586822509766 nb_pixel_total : 36835 time to create 1 rle with old method : 0.03933525085449219 time for calcul the mask position with numpy : 0.03878593444824219 nb_pixel_total : 116441 time to create 1 rle with old method : 0.12367439270019531 time for calcul the mask position with numpy : 0.037699222564697266 nb_pixel_total : 700 time to create 1 rle with old method : 0.0009438991546630859 time for calcul the mask position with numpy : 0.03748750686645508 nb_pixel_total : 20863 time to create 1 rle with old method : 0.022960901260375977 time for calcul the mask position with numpy : 0.038199424743652344 nb_pixel_total : 78515 time to create 1 rle with old method : 0.08353233337402344 time for calcul the mask position with numpy : 0.04611778259277344 nb_pixel_total : 774898 time to create 1 rle with new method : 0.8569748401641846 time for calcul the mask position with numpy : 0.048314809799194336 nb_pixel_total : 211618 time to create 1 rle with new method : 0.5581662654876709 time for calcul the mask position with numpy : 0.038556814193725586 nb_pixel_total : 103684 time to create 1 rle with old method : 0.1119086742401123 time for calcul the mask position with numpy : 0.03839230537414551 nb_pixel_total : 39974 time to create 1 rle with old method : 0.04289889335632324 time for calcul the mask position with numpy : 0.03791356086730957 nb_pixel_total : 3580 time to create 1 rle with old method : 0.004132270812988281 time for calcul the mask position with numpy : 0.03910374641418457 nb_pixel_total : 50038 time to create 1 rle with old method : 0.05389809608459473 time for calcul the mask position with numpy : 0.0379331111907959 nb_pixel_total : 11070 time to create 1 rle with old method : 0.012686967849731445 time for calcul the mask position with numpy : 0.037485599517822266 nb_pixel_total : 103 time to create 1 rle with old method : 0.00020623207092285156 time for calcul the mask position with numpy : 0.03786420822143555 nb_pixel_total : 20983 time to create 1 rle with old method : 0.022740840911865234 time for calcul the mask position with numpy : 0.03766131401062012 nb_pixel_total : 15734 time to create 1 rle with old method : 0.017103195190429688 time for calcul the mask position with numpy : 0.03806257247924805 nb_pixel_total : 6290 time to create 1 rle with old method : 0.007091045379638672 time for calcul the mask position with numpy : 0.03846335411071777 nb_pixel_total : 4198 time to create 1 rle with old method : 0.005415439605712891 time for calcul the mask position with numpy : 0.039794206619262695 nb_pixel_total : 288172 time to create 1 rle with new method : 0.7083926200866699 time for calcul the mask position with numpy : 0.03756976127624512 nb_pixel_total : 14669 time to create 1 rle with old method : 0.01656794548034668 time for calcul the mask position with numpy : 0.04059171676635742 nb_pixel_total : 489876 time to create 1 rle with new method : 0.5594444274902344 time for calcul the mask position with numpy : 0.04184842109680176 nb_pixel_total : 102 time to create 1 rle with old method : 0.0002346038818359375 time for calcul the mask position with numpy : 0.047506093978881836 nb_pixel_total : 31316 time to create 1 rle with old method : 0.036423444747924805 time for calcul the mask position with numpy : 0.04219198226928711 nb_pixel_total : 143176 time to create 1 rle with old method : 0.16519474983215332 time for calcul the mask position with numpy : 0.041457176208496094 nb_pixel_total : 15574 time to create 1 rle with old method : 0.01878809928894043 time for calcul the mask position with numpy : 0.040030717849731445 nb_pixel_total : 47733 time to create 1 rle with old method : 0.05667614936828613 time for calcul the mask position with numpy : 0.0392765998840332 nb_pixel_total : 41735 time to create 1 rle with old method : 0.04599809646606445 time for calcul the mask position with numpy : 0.03867697715759277 nb_pixel_total : 89946 time to create 1 rle with old method : 0.09740948677062988 time for calcul the mask position with numpy : 0.039121389389038086 nb_pixel_total : 3011 time to create 1 rle with old method : 0.0040857791900634766 time for calcul the mask position with numpy : 0.04029202461242676 nb_pixel_total : 35325 time to create 1 rle with old method : 0.0426483154296875 time for calcul the mask position with numpy : 0.046642303466796875 nb_pixel_total : 62437 time to create 1 rle with old method : 0.0685434341430664 time for calcul the mask position with numpy : 0.038968563079833984 nb_pixel_total : 60793 time to create 1 rle with old method : 0.06656837463378906 time for calcul the mask position with numpy : 0.04058265686035156 nb_pixel_total : 22717 time to create 1 rle with old method : 0.024846553802490234 create new chi : 17.00214171409607 time to delete rle : 0.00451350212097168 batch 1 Loaded 79 chid ids of type : 4855 Number RLEs to save : 49454 TO DO : save crop sub photo not yet done ! save time : 2.62773060798645 nb_obj : 82 nb_hashtags : 10 time to prepare the origin masks : 44.846556425094604 time for calcul the mask position with numpy : 0.053850412368774414 nb_pixel_total : 2749223 time to create 1 rle with new method : 1.0867533683776855 time for calcul the mask position with numpy : 0.04047751426696777 nb_pixel_total : 46704 time to create 1 rle with old method : 0.053177595138549805 time for calcul the mask position with numpy : 0.04035687446594238 nb_pixel_total : 28039 time to create 1 rle with old method : 0.0329744815826416 time for calcul the mask position with numpy : 0.040424346923828125 nb_pixel_total : 10461 time to create 1 rle with old method : 0.01221013069152832 time for calcul the mask position with numpy : 0.04051542282104492 nb_pixel_total : 7963 time to create 1 rle with old method : 0.00931549072265625 time for calcul the mask position with numpy : 0.04068565368652344 nb_pixel_total : 10958 time to create 1 rle with old method : 0.012861490249633789 time for calcul the mask position with numpy : 0.04017233848571777 nb_pixel_total : 22597 time to create 1 rle with old method : 0.026131153106689453 time for calcul the mask position with numpy : 0.04380655288696289 nb_pixel_total : 92181 time to create 1 rle with old method : 0.10508966445922852 time for calcul the mask position with numpy : 0.039544105529785156 nb_pixel_total : 8712 time to create 1 rle with old method : 0.010274648666381836 time for calcul the mask position with numpy : 0.03969216346740723 nb_pixel_total : 67218 time to create 1 rle with old method : 0.07743287086486816 time for calcul the mask position with numpy : 0.04163718223571777 nb_pixel_total : 286689 time to create 1 rle with new method : 0.7370491027832031 time for calcul the mask position with numpy : 0.03919243812561035 nb_pixel_total : 4015 time to create 1 rle with old method : 0.004981040954589844 time for calcul the mask position with numpy : 0.03986406326293945 nb_pixel_total : 65870 time to create 1 rle with old method : 0.07560992240905762 time for calcul the mask position with numpy : 0.03962445259094238 nb_pixel_total : 11916 time to create 1 rle with old method : 0.01405477523803711 time for calcul the mask position with numpy : 0.039614200592041016 nb_pixel_total : 58140 time to create 1 rle with old method : 0.06615018844604492 time for calcul the mask position with numpy : 0.0392913818359375 nb_pixel_total : 32544 time to create 1 rle with old method : 0.03695321083068848 time for calcul the mask position with numpy : 0.0402371883392334 nb_pixel_total : 127954 time to create 1 rle with old method : 0.14533138275146484 time for calcul the mask position with numpy : 0.04322457313537598 nb_pixel_total : 44413 time to create 1 rle with old method : 0.051047325134277344 time for calcul the mask position with numpy : 0.04028749465942383 nb_pixel_total : 6593 time to create 1 rle with old method : 0.008140325546264648 time for calcul the mask position with numpy : 0.04086780548095703 nb_pixel_total : 25407 time to create 1 rle with old method : 0.028985261917114258 time for calcul the mask position with numpy : 0.039415836334228516 nb_pixel_total : 8731 time to create 1 rle with old method : 0.010256290435791016 time for calcul the mask position with numpy : 0.04008674621582031 nb_pixel_total : 14444 time to create 1 rle with old method : 0.016655683517456055 time for calcul the mask position with numpy : 0.03953742980957031 nb_pixel_total : 28620 time to create 1 rle with old method : 0.033031463623046875 time for calcul the mask position with numpy : 0.040241241455078125 nb_pixel_total : 3217 time to create 1 rle with old method : 0.003957986831665039 time for calcul the mask position with numpy : 0.040374755859375 nb_pixel_total : 20887 time to create 1 rle with old method : 0.03284621238708496 time for calcul the mask position with numpy : 0.044934749603271484 nb_pixel_total : 35916 time to create 1 rle with old method : 0.04152321815490723 time for calcul the mask position with numpy : 0.04057002067565918 nb_pixel_total : 10804 time to create 1 rle with old method : 0.012746572494506836 time for calcul the mask position with numpy : 0.04052281379699707 nb_pixel_total : 28595 time to create 1 rle with old method : 0.033342838287353516 time for calcul the mask position with numpy : 0.040599822998046875 nb_pixel_total : 66946 time to create 1 rle with old method : 0.07737421989440918 time for calcul the mask position with numpy : 0.04154038429260254 nb_pixel_total : 272161 time to create 1 rle with new method : 0.9050502777099609 time for calcul the mask position with numpy : 0.040276288986206055 nb_pixel_total : 45307 time to create 1 rle with old method : 0.054672956466674805 time for calcul the mask position with numpy : 0.039919376373291016 nb_pixel_total : 36146 time to create 1 rle with old method : 0.04204583168029785 time for calcul the mask position with numpy : 0.03966021537780762 nb_pixel_total : 35744 time to create 1 rle with old method : 0.040926218032836914 time for calcul the mask position with numpy : 0.039911746978759766 nb_pixel_total : 102358 time to create 1 rle with old method : 0.11560654640197754 time for calcul the mask position with numpy : 0.03947615623474121 nb_pixel_total : 133244 time to create 1 rle with old method : 0.1497340202331543 time for calcul the mask position with numpy : 0.03945159912109375 nb_pixel_total : 6373 time to create 1 rle with old method : 0.00754547119140625 time for calcul the mask position with numpy : 0.039376020431518555 nb_pixel_total : 47701 time to create 1 rle with old method : 0.05498957633972168 time for calcul the mask position with numpy : 0.03977513313293457 nb_pixel_total : 17384 time to create 1 rle with old method : 0.019782543182373047 time for calcul the mask position with numpy : 0.03973865509033203 nb_pixel_total : 75494 time to create 1 rle with old method : 0.08711767196655273 time for calcul the mask position with numpy : 0.04029083251953125 nb_pixel_total : 35813 time to create 1 rle with old method : 0.04563450813293457 time for calcul the mask position with numpy : 0.04137706756591797 nb_pixel_total : 196821 time to create 1 rle with new method : 0.9694404602050781 time for calcul the mask position with numpy : 0.04101920127868652 nb_pixel_total : 327479 time to create 1 rle with new method : 1.0164172649383545 time for calcul the mask position with numpy : 0.040383100509643555 nb_pixel_total : 10430 time to create 1 rle with old method : 0.012330770492553711 time for calcul the mask position with numpy : 0.039826393127441406 nb_pixel_total : 54428 time to create 1 rle with old method : 0.06203889846801758 time for calcul the mask position with numpy : 0.039458274841308594 nb_pixel_total : 1610 time to create 1 rle with old method : 0.0020940303802490234 time for calcul the mask position with numpy : 0.04625654220581055 nb_pixel_total : 1700 time to create 1 rle with old method : 0.002091646194458008 time for calcul the mask position with numpy : 0.04129838943481445 nb_pixel_total : 112261 time to create 1 rle with old method : 0.13000965118408203 time for calcul the mask position with numpy : 0.04073619842529297 nb_pixel_total : 37049 time to create 1 rle with old method : 0.046463727951049805 time for calcul the mask position with numpy : 0.04332256317138672 nb_pixel_total : 1023 time to create 1 rle with old method : 0.0016486644744873047 time for calcul the mask position with numpy : 0.04100847244262695 nb_pixel_total : 191307 time to create 1 rle with new method : 0.8572113513946533 time for calcul the mask position with numpy : 0.03998708724975586 nb_pixel_total : 13891 time to create 1 rle with old method : 0.016441822052001953 time for calcul the mask position with numpy : 0.03969693183898926 nb_pixel_total : 304 time to create 1 rle with old method : 0.0005278587341308594 time for calcul the mask position with numpy : 0.04061293601989746 nb_pixel_total : 68988 time to create 1 rle with old method : 0.08240890502929688 time for calcul the mask position with numpy : 0.04303574562072754 nb_pixel_total : 185871 time to create 1 rle with new method : 2.587838649749756 time for calcul the mask position with numpy : 0.04152822494506836 nb_pixel_total : 90153 time to create 1 rle with old method : 0.11192011833190918 time for calcul the mask position with numpy : 0.04114365577697754 nb_pixel_total : 9999 time to create 1 rle with old method : 0.011990070343017578 time for calcul the mask position with numpy : 0.04085874557495117 nb_pixel_total : 614 time to create 1 rle with old method : 0.0008890628814697266 time for calcul the mask position with numpy : 0.04077482223510742 nb_pixel_total : 39871 time to create 1 rle with old method : 0.04653477668762207 time for calcul the mask position with numpy : 0.04773855209350586 nb_pixel_total : 694758 time to create 1 rle with new method : 0.8948440551757812 time for calcul the mask position with numpy : 0.0396885871887207 nb_pixel_total : 628 time to create 1 rle with old method : 0.0012111663818359375 time for calcul the mask position with numpy : 0.039231061935424805 nb_pixel_total : 43685 time to create 1 rle with old method : 0.05170249938964844 time for calcul the mask position with numpy : 0.042707204818725586 nb_pixel_total : 433940 time to create 1 rle with new method : 0.786632776260376 time for calcul the mask position with numpy : 0.04175281524658203 nb_pixel_total : 76778 time to create 1 rle with old method : 0.08966779708862305 time for calcul the mask position with numpy : 0.040970802307128906 nb_pixel_total : 29002 time to create 1 rle with old method : 0.033876895904541016 time for calcul the mask position with numpy : 0.0409245491027832 nb_pixel_total : 606 time to create 1 rle with old method : 0.0009217262268066406 time for calcul the mask position with numpy : 0.04133343696594238 nb_pixel_total : 19651 time to create 1 rle with old method : 0.023233890533447266 time for calcul the mask position with numpy : 0.04295778274536133 nb_pixel_total : 162128 time to create 1 rle with new method : 1.2031588554382324 time for calcul the mask position with numpy : 0.045830488204956055 nb_pixel_total : 3417 time to create 1 rle with old method : 0.006102561950683594 time for calcul the mask position with numpy : 0.044486284255981445 nb_pixel_total : 44123 time to create 1 rle with old method : 0.05016732215881348 time for calcul the mask position with numpy : 0.0386199951171875 nb_pixel_total : 2527 time to create 1 rle with old method : 0.0034723281860351562 time for calcul the mask position with numpy : 0.039688825607299805 nb_pixel_total : 23346 time to create 1 rle with old method : 0.027322769165039062 time for calcul the mask position with numpy : 0.043923377990722656 nb_pixel_total : 407734 time to create 1 rle with new method : 0.9373230934143066 time for calcul the mask position with numpy : 0.04654216766357422 nb_pixel_total : 641217 time to create 1 rle with new method : 0.6084835529327393 time for calcul the mask position with numpy : 0.04772543907165527 nb_pixel_total : 555529 time to create 1 rle with new method : 0.7257342338562012 time for calcul the mask position with numpy : 0.04038667678833008 nb_pixel_total : 299 time to create 1 rle with old method : 0.0004706382751464844 time for calcul the mask position with numpy : 0.0421597957611084 nb_pixel_total : 83918 time to create 1 rle with old method : 0.09739065170288086 time for calcul the mask position with numpy : 0.040947675704956055 nb_pixel_total : 17212 time to create 1 rle with old method : 0.020931005477905273 time for calcul the mask position with numpy : 0.04314732551574707 nb_pixel_total : 269573 time to create 1 rle with new method : 0.855276346206665 time for calcul the mask position with numpy : 0.041368722915649414 nb_pixel_total : 74869 time to create 1 rle with old method : 0.0872809886932373 time for calcul the mask position with numpy : 0.04096508026123047 nb_pixel_total : 11993 time to create 1 rle with old method : 0.021622419357299805 time for calcul the mask position with numpy : 0.03997635841369629 nb_pixel_total : 25294 time to create 1 rle with old method : 0.03004598617553711 time for calcul the mask position with numpy : 0.03946089744567871 nb_pixel_total : 20325 time to create 1 rle with old method : 0.022896766662597656 time for calcul the mask position with numpy : 0.03889894485473633 nb_pixel_total : 14567 time to create 1 rle with old method : 0.016516923904418945 create new chi : 20.831090450286865 time to delete rle : 0.004662036895751953 batch 1 Loaded 83 chid ids of type : 4855 Number RLEs to save : 51036 TO DO : save crop sub photo not yet done ! save time : 2.789823293685913 nb_obj : 8 nb_hashtags : 2 time to prepare the origin masks : 8.481892347335815 time for calcul the mask position with numpy : 0.09850382804870605 nb_pixel_total : 1421208 time to create 1 rle with new method : 0.5696628093719482 time for calcul the mask position with numpy : 0.06318092346191406 nb_pixel_total : 662307 time to create 1 rle with new method : 0.4499814510345459 time for calcul the mask position with numpy : 0.03657245635986328 nb_pixel_total : 25195 time to create 1 rle with old method : 0.030517578125 time for calcul the mask position with numpy : 0.031687021255493164 nb_pixel_total : 59115 time to create 1 rle with old method : 0.06781601905822754 time for calcul the mask position with numpy : 0.03436446189880371 nb_pixel_total : 7045 time to create 1 rle with old method : 0.008289575576782227 time for calcul the mask position with numpy : 0.033873558044433594 nb_pixel_total : 3185 time to create 1 rle with old method : 0.0035483837127685547 time for calcul the mask position with numpy : 0.03537154197692871 nb_pixel_total : 273134 time to create 1 rle with new method : 0.5455770492553711 time for calcul the mask position with numpy : 0.03920602798461914 nb_pixel_total : 202979 time to create 1 rle with new method : 0.5274016857147217 time for calcul the mask position with numpy : 0.38202357292175293 nb_pixel_total : 7080232 time to create 1 rle with new method : 0.4511525630950928 create new chi : 3.6028547286987305 time to delete rle : 0.0014755725860595703 batch 1 Loaded 9 chid ids of type : 4855 Number RLEs to save : 13546 TO DO : save crop sub photo not yet done ! save time : 0.7881555557250977 map_output_result : {1384152197: (0.5482123449868288, 'Should be the crop_list due to order', 0.5043904105574972), 1384151343: (0.5482123449868288, 'Should be the crop_list due to order', 0.7485119433709498), 1384151335: (0.5482123449868288, 'Should be the crop_list due to order', 0.43489354714720657), 1384151318: (0.5482123449868288, 'Should be the crop_list due to order', 0.5354499610664096), 1384151295: (0.5482123449868288, 'Should be the crop_list due to order', 0.42335604836027046), 1384151259: (0.5482123449868288, 'Should be the crop_list due to order', 0.1908845044054675), 1384151225: (0.5482123449868288, 'Should be the crop_list due to order', 1.0)} 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 [1384152197, 1384151343, 1384151335, 1384151318, 1384151295, 1384151259, 1384151225] Looping around the photos to save general results len do output : 7 /1384152197.Didn't retrieve data . /1384151343.Didn't retrieve data . /1384151335.Didn't retrieve data . /1384151318.Didn't retrieve data . /1384151295.Didn't retrieve data . /1384151259.Didn't retrieve data . /1384151225.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 ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384152197', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151343', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151335', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151318', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151295', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151259', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151225', None, None, None, None, None, '3735887') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.014104366302490234 save_final save missing photos in datou_result : time spend for datou_step_exec : 421.0317795276642 time spend to save output : 0.03783297538757324 total time spend for step 5 : 421.06961250305176 step6:crop_condition Wed Sep 17 14:56:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 some photos are not treated, begin crop_condition Loading chi in step crop with photo_hashtag_type : 4855 Loading chi in step crop for list_pids : 7 ! batch 1 Loaded 518 chid ids of type : 4855 begin to crop the class : papier param for this class : {'min_score': 0.1} filtre for class : papier hashtag_id of this class : 492668766 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 61 About to insert : list_path_to_insert length 61 new photo from crops ! About to upload 61 photos upload in portfolio : 3015255 init cache_photo without model_param we have 61 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113831_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 61 photos in the portfolio 3015255 time of upload the photos Elapsed time : 17.06682562828064 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.1} filtre for class : carton hashtag_id of this class : 492774966 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 62 About to insert : list_path_to_insert length 62 new photo from crops ! About to upload 62 photos upload in portfolio : 3015255 init cache_photo without model_param we have 62 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113873_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 62 photos in the portfolio 3015255 time of upload the photos Elapsed time : 16.51801085472107 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.1} filtre for class : metal hashtag_id of this class : 492628673 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 12 About to insert : list_path_to_insert length 12 new photo from crops ! About to upload 12 photos upload in portfolio : 3015255 init cache_photo without model_param we have 12 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113893_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 12 photos in the portfolio 3015255 time of upload the photos Elapsed time : 4.286133766174316 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.1} filtre for class : pet_clair hashtag_id of this class : 2107755846 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 25 About to insert : list_path_to_insert length 25 new photo from crops ! About to upload 25 photos upload in portfolio : 3015255 init cache_photo without model_param we have 25 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113907_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 25 photos in the portfolio 3015255 time of upload the photos Elapsed time : 7.462764024734497 we have finished the crop for the class : pet_clair begin to crop the class : pehd param for this class : {'min_score': 0.1} filtre for class : pehd hashtag_id of this class : 628944319 Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3015255 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113917_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 3 photos in the portfolio 3015255 time of upload the photos Elapsed time : 1.0118026733398438 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.1} filtre for class : pet_fonce hashtag_id of this class : 2107755900 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 : 3015255 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113921_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3015255 time of upload the photos Elapsed time : 0.903162956237793 we have finished the crop for the class : pet_fonce begin to crop the class : sac param for this class : {'min_score': 0.1} filtre for class : sac hashtag_id of this class : 492618350 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 27 About to insert : list_path_to_insert length 27 new photo from crops ! About to upload 27 photos upload in portfolio : 3015255 init cache_photo without model_param we have 27 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113933_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 27 photos in the portfolio 3015255 time of upload the photos Elapsed time : 7.110447883605957 we have finished the crop for the class : sac begin to crop the class : textiles param for this class : {'min_score': 0.1} filtre for class : textiles hashtag_id of this class : 494789423 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 8 About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos upload in portfolio : 3015255 init cache_photo without model_param we have 8 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113944_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 8 photos in the portfolio 3015255 time of upload the photos Elapsed time : 2.213848352432251 we have finished the crop for the class : textiles begin to crop the class : verre param for this class : {'min_score': 0.1} filtre for class : verre hashtag_id of this class : 492631919 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 : 3015255 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758113948_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3015255 time of upload the photos Elapsed time : 0.5288755893707275 we have finished the crop for the class : verre begin to crop the class : organique param for this class : {'min_score': 0.1} filtre for class : organique hashtag_id of this class : 530726390 begin to crop the class : dasri param for this class : {'min_score': 0.1} filtre for class : dasri hashtag_id of this class : 1608424396 begin to crop the class : masque param for this class : {'min_score': 0.1} filtre for class : masque hashtag_id of this class : 492626781 begin to crop the class : encombrant param for this class : {'min_score': 0.1} filtre for class : encombrant hashtag_id of this class : 605975549 begin to crop the class : autre param for this class : {'min_score': 0.1} filtre for class : autre hashtag_id of this class : 494826614 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 67 About to insert : list_path_to_insert length 67 new photo from crops ! About to upload 67 photos upload in portfolio : 3015255 init cache_photo without model_param we have 67 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758114000_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 67 photos in the portfolio 3015255 time of upload the photos Elapsed time : 20.120458364486694 we have finished the crop for the class : autre begin to crop the class : autre_emballage param for this class : {'min_score': 0.1} filtre for class : autre_emballage hashtag_id of this class : 2107760328 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : autre_emballage begin to crop the class : autre_non_emballage param for this class : {'min_score': 0.1} filtre for class : autre_non_emballage hashtag_id of this class : 2107760329 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 26 About to insert : list_path_to_insert length 26 new photo from crops ! About to upload 26 photos upload in portfolio : 3015255 init cache_photo without model_param we have 26 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758114037_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 26 photos in the portfolio 3015255 time of upload the photos Elapsed time : 7.863797187805176 we have finished the crop for the class : autre_non_emballage begin to crop the class : environnement param for this class : {'min_score': 0.1} filtre for class : environnement hashtag_id of this class : 493012381 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 72 About to insert : list_path_to_insert length 72 new photo from crops ! About to upload 72 photos upload in portfolio : 3015255 init cache_photo without model_param we have 72 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758114123_2168531 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 72 photos in the portfolio 3015255 time of upload the photos Elapsed time : 20.567814111709595 we have finished the crop for the class : environnement delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1384152197, 1384151343, 1384151335, 1384151318, 1384151295, 1384151259, 1384151225] Looping around the photos to save general results len do output : 365 /1384232655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384232718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384233839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234160Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234165Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234167Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234169Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234174Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234186Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234190Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234193Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234197Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234212Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234268Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234485Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384234992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1384235028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235057Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235059Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235065Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235081Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235094Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235098Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235134Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235153Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1384235704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384235720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238265Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238279Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238305Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238306Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238412Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238417Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238418Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238421Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238423Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238424Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238426Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1384238429Didn'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 ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384152197', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151343', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151335', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151318', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151295', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151259', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151225', None, None, None, None, None, '3735887') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1102 time used for this insertion : 0.09077715873718262 save_final save missing photos in datou_result : time spend for datou_step_exec : 382.3663160800934 time spend to save output : 0.17485594749450684 total time spend for step 6 : 382.5411720275879 step7:ventilate_hashtags_in_portfolio Wed Sep 17 15:02:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 26943498 get user id for portfolio 26943498 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`=26943498 AND mptpi.`type`=4855 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','autre','flux_dev','mal_croppe','flou','film_dev_souple','sac_om_plein')) AND mptpi.`min_score`=0.1 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`=26943498 AND mptpi.`type`=4855 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','autre','flux_dev','mal_croppe','flou','film_dev_souple','sac_om_plein')) AND mptpi.`min_score`=0.1 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") 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") 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`=26943498 AND mptpi.`type`=4856 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','autre','flux_dev','mal_croppe','flou','film_dev_souple','sac_om_plein')) AND mptpi.`min_score`=0.1 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`=26943498 AND mptpi.`type`=4856 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','autre','flux_dev','mal_croppe','flou','film_dev_souple','sac_om_plein')) AND mptpi.`min_score`=0.1 To do lien utilise dans velours : https://marlene.fotonower.com/velours/26950767,26950768,26950770,26950771,26950772,26950778,26950781,26950782,26950783,26950784,26950785,26950786,26950788,26950789,26950790,26950791,26950792,26950793,26950794,26950795?tags=papier,carton,metal,pet_clair,pehd,ela,textiles,verre,organique,dasri,masque,encombrant,autre_non_emballage,environnement,autre,flux_dev,mal_croppe,flou,film_dev_souple,sac_om_plein&datou_id_consolidate=4742&port_consolidate=26943498 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1384152197, 1384151343, 1384151335, 1384151318, 1384151295, 1384151259, 1384151225] Looping around the photos to save general results len do output : 1 /26943498. 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 ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384152197', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151343', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151335', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151318', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151295', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151259', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151225', None, None, None, None, None, '3735887') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.015710830688476562 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.75193190574646 time spend to save output : 0.015981435775756836 total time spend for step 7 : 4.767913341522217 step8:final Wed Sep 17 15:02:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1384152197: ('0.4807904677631897',), 1384151343: ('0.4807904677631897',), 1384151335: ('0.4807904677631897',), 1384151318: ('0.4807904677631897',), 1384151295: ('0.4807904677631897',), 1384151259: ('0.4807904677631897',), 1384151225: ('0.4807904677631897',)} new output for save of step final : {1384152197: ('0.4807904677631897',), 1384151343: ('0.4807904677631897',), 1384151335: ('0.4807904677631897',), 1384151318: ('0.4807904677631897',), 1384151295: ('0.4807904677631897',), 1384151259: ('0.4807904677631897',), 1384151225: ('0.4807904677631897',)} [1384152197, 1384151343, 1384151335, 1384151318, 1384151295, 1384151259, 1384151225] Looping around the photos to save general results len do output : 7 /1384152197.Didn't retrieve data . /1384151343.Didn't retrieve data . /1384151335.Didn't retrieve data . /1384151318.Didn't retrieve data . /1384151295.Didn't retrieve data . /1384151259.Didn't retrieve data . /1384151225.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 ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384152197', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151343', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151335', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151318', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151295', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151259', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151225', None, None, None, None, None, '3735887') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.013349056243896484 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.16931605339050293 time spend to save output : 0.014598608016967773 total time spend for step 8 : 0.1839146614074707 step9:velours_tree Wed Sep 17 15:02:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 4.480534553527832 time spend to save output : 0.00033545494079589844 total time spend for step 9 : 4.480870008468628 step10:send_mail_cod Wed Sep 17 15:02:40 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 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_P26943498_17-09-2025_15_02_40.pdf 26950725 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 .imagette269507251758114160 26950726 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 .imagette269507261758114162 26950727 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 .imagette269507271758114163 26950728 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 .imagette269507281758114164 26950729 change filename to text .change filename to text .change filename to text .imagette269507291758114166 26950730 change filename to text .imagette269507301758114167 26950731 imagette269507311758114167 26950732 imagette269507321758114167 26950733 imagette269507331758114167 26950734 imagette269507341758114167 26950735 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 .imagette269507351758114167 26950736 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 .imagette269507361758114169 26950737 change filename to text .imagette269507371758114169 26950738 imagette269507381758114169 26950739 imagette269507391758114169 26950740 imagette269507401758114169 26950741 imagette269507411758114169 26950742 imagette269507421758114169 26950743 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 .imagette269507431758114169 26950745 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 .imagette269507451758114171 26950746 imagette269507461758114173 26950747 imagette269507471758114173 26950748 imagette269507481758114173 26950749 imagette269507491758114173 26950750 imagette269507501758114173 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=26943498 and hashtag_type = 4855 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/26950767,26950768,26950770,26950771,26950772,26950778,26950781,26950782,26950783,26950784,26950785,26950786,26950788,26950789,26950790,26950791,26950792,26950793,26950794,26950795?tags=papier,carton,metal,pet_clair,pehd,ela,textiles,verre,organique,dasri,masque,encombrant,autre_non_emballage,environnement,autre,flux_dev,mal_croppe,flou,film_dev_souple,sac_om_plein&datou_id_consolidate=4742&port_consolidate=26943498 your option no_mail is active, we will not send the real mail to your client args[1384152197] : ((1384152197, 1407.8044556324444, 2107751945), (1384152197, -0.24504598457096832, 496442774), '0.4807904677631897') We are sending mail with results at report@fotonower.com,cod@fotonower.com args[1384151343] : ((1384151343, 826.3568154750284, 2107751945), (1384151343, -0.36007017166282196, 496442774), '0.4807904677631897') We are sending mail with results at report@fotonower.com,cod@fotonower.com args[1384151335] : ((1384151335, 1291.586942406081, 2107751945), (1384151335, -0.13575375123043207, 496442774), '0.4807904677631897') We are sending mail with results at report@fotonower.com,cod@fotonower.com args[1384151318] : ((1384151318, 1031.5909147963284, 2107751945), (1384151318, -0.19459505241507896, 496442774), '0.4807904677631897') We are sending mail with results at report@fotonower.com,cod@fotonower.com args[1384151295] : ((1384151295, 516.6679264481787, 492688767), (1384151295, -0.09574280400369606, 496442774), '0.4807904677631897') We are sending mail with results at report@fotonower.com,cod@fotonower.com args[1384151259] : ((1384151259, 1426.6414802911725, 2107751945), (1384151259, -0.11668735898508042, 496442774), '0.4807904677631897') We are sending mail with results at report@fotonower.com,cod@fotonower.com args[1384151225] : ((1384151225, 183.7956999714713, 492688767), (1384151225, -1.0300994488712756, 501862349), '0.4807904677631897') We are sending mail with results at report@fotonower.com,cod@fotonower.com refus_total : 0.4807904677631897 2022-04-13 10:29:59 0 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26943498_17-09-2025_15_02_40.pdf results_Auto_P26943498_17-09-2025_15_02_40.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26943498_17-09-2025_15_02_40.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('4741','26943498','results_Auto_P26943498_17-09-2025_15_02_40.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26943498_17-09-2025_15_02_40.pdf','pdf','','2.51','0.4807904677631897') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1384152197, 1384151343, 1384151335, 1384151318, 1384151295, 1384151259, 1384151225] 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 ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384152197', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151343', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151335', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151318', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151295', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151259', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151225', None, None, None, None, None, '3735887') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 7 time used for this insertion : 0.01326894760131836 save_final save missing photos in datou_result : time spend for datou_step_exec : 17.864023447036743 time spend to save output : 0.013530492782592773 total time spend for step 10 : 17.877553939819336 step11:split_time_score Wed Sep 17 15:02:57 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 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 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'}] (('41', 1), ('42', 3), ('43', 3)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 17092025 26943498 Nombre de photos uploadées : 7 / 23040 (0%) 17092025 26943498 Nombre de photos taguées (types de déchets): 0 / 7 (0%) 17092025 26943498 Nombre de photos taguées (volume) : 0 / 7 (0%) elapsed_time : load_data_split_time_score 1.6689300537109375e-06 elapsed_time : order_list_meta_photo_and_scores 5.4836273193359375e-06 ??????? elapsed_time : fill_and_build_computed_from_old_data 0.0008294582366943359 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.2147831916809082 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.71442544970509 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26936374_17-09-2025_10_23_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26936374 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26936374 AND mptpi.`type`=4855 To do Qualite : 0.5292626295683981 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26936381_17-09-2025_10_28_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26936381 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26936381 AND mptpi.`type`=4855 To do Qualite : 0.4807904677631897 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P26943498_17-09-2025_15_02_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 26943498 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 13156 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 13160 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 13160 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 13158 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 13157 crop_condition is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 13157 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 13161 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 13159 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 13159 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 13156 doesn't seem to be define in the database( WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 13161 doesn't seem to be define in the database( WARNING : type of input 3 of step 13159 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13163 have datatype=6 whereas input 2 of step 13157 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 0 of step 13162 have datatype=6 whereas input 2 of step 13157 have datatype=None We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 13161 have datatype=10 whereas input 3 of step 13164 have datatype=6 WARNING : output 0 of step 13161 have datatype=10 whereas input 0 of step 13166 have datatype=18 WARNING : type of input 5 of step 13164 doesn't seem to be define in the database( WARNING : output 0 of step 13166 have datatype=11 whereas input 5 of step 13164 have datatype=None WARNING : type of input 2 of step 13157 doesn't seem to be define in the database( WARNING : output 1 of step 13158 have datatype=7 whereas input 2 of step 13157 have datatype=None WARNING : type of output 3 of step 13157 doesn't seem to be define in the database( WARNING : type of input 3 of step 13161 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13163 doesn't seem to be define in the database( WARNING : type of output 2 of step 13160 doesn't seem to be define in the database( WARNING : type of input 1 of step 13162 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`=26943498 AND mptpi.`type`=4855 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'17092025': {'nb_upload': 7, '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 [1384152197, 1384151343, 1384151335, 1384151318, 1384151295, 1384151259, 1384151225] Looping around the photos to save general results len do output : 1 /26943498Didn'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 ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384152197', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151343', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151335', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151318', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151295', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151259', None, None, None, None, None, '3735887') ('4741', None, None, None, None, None, None, None, '3735887') ('4741', '26943498', '1384151225', None, None, None, None, None, '3735887') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.012618303298950195 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.6339290142059326 time spend to save output : 0.012815237045288086 total time spend for step 11 : 0.6467442512512207 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 1 len(input) + len(total_photo_id_missing) : 7 set_done_treatment 685.84user 335.42system 21:36.82elapsed 78%CPU (0avgtext+0avgdata 4226192maxresident)k 25862968inputs+655208outputs (756040major+50098653minor)pagefaults 0swaps