python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 20 -a ' -a 3318 ' -s datou_3318 -M 0 -S 0 -U 95,95,120 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/caffe_cuda8_python3/python', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 624923 load datou : 3318 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? [(photo_id, hashtag_id, hashtag_type, x0, x1, y0, y1, score, seg_temp, polygons), ...] was removed should we ? chemin de la photo was removed should we ? [ (photo_id_loc, hashtag_id, hashtag_type, x0, x1, y0, y1, score, None), ...] was removed should we ? chemin de la photo was removed should we ? id de la photo (peut être local ou global) was removed should we ? chemin de la photo was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? donnée sous forme de texte was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? id de la photo (peut être local ou global) was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? None was removed should we ? donnée sous forme de nombre was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? donnée sous forme de texte was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) load thcls load THCL from format json or kwargs add thcl : 2847 in CacheModelConfig load pdts add pdt : 5275 in CacheModelConfig Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 3318, datou_cur_ids : ['2733653'] with mtr_portfolio_ids : ['22153573'] and first list_photo_ids : [] new path : /proc/624923/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 9 ; length of list_pids : 9 ; length of list_args : 9 time to download the photos : 2.4091012477874756 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Wed Apr 9 11:00:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10372 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-09 11:00:36.630725: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-09 11:00:36.663090: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-09 11:00:36.665939: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5f88000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-09 11:00:36.665999: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-09 11:00:36.672054: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-09 11:00:36.915153: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x11057b90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-09 11:00:36.915224: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-09 11:00:36.916781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 11:00:36.917424: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 11:00:36.921493: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 11:00:36.943192: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 11:00:36.944780: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 11:00:36.976822: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 11:00:36.981747: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 11:00:37.040496: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 11:00:37.042294: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 11:00:37.042760: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 11:00:37.043640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 11:00:37.043668: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 11:00:37.043678: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 11:00:37.045793: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9607 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-09 11:00:37.448874: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 11:00:37.448959: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 11:00:37.448979: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 11:00:37.448998: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 11:00:37.449016: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 11:00:37.449034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 11:00:37.449052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 11:00:37.449070: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 11:00:37.450637: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 11:00:37.452135: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 11:00:37.452174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 11:00:37.452193: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 11:00:37.452210: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 11:00:37.452227: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 11:00:37.452244: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 11:00:37.452261: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 11:00:37.452278: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 11:00:37.453575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 11:00:37.453609: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 11:00:37.453617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 11:00:37.453625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 11:00:37.454989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9607 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-09 11:00:49.788922: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 11:00:50.063997: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 9 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 74 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 73 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 61 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 54 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 67 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 80 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 91 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 76 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 87 Detection mask done ! Trying to reset tf kernel 625829 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5083 tf kernel not reseted sub process len(results) : 9 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 9 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 : 10151 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.03207516670227051 nb_pixel_total : 65977 time to create 1 rle with old method : 0.07812356948852539 length of segment : 323 time for calcul the mask position with numpy : 0.015054464340209961 nb_pixel_total : 16605 time to create 1 rle with old method : 0.03263521194458008 length of segment : 157 time for calcul the mask position with numpy : 0.0415499210357666 nb_pixel_total : 33575 time to create 1 rle with old method : 0.05478525161743164 length of segment : 290 time for calcul the mask position with numpy : 0.005780935287475586 nb_pixel_total : 9213 time to create 1 rle with old method : 0.012357473373413086 length of segment : 132 time for calcul the mask position with numpy : 0.011620044708251953 nb_pixel_total : 18648 time to create 1 rle with old method : 0.025330781936645508 length of segment : 259 time for calcul the mask position with numpy : 0.018855571746826172 nb_pixel_total : 24093 time to create 1 rle with old method : 0.04089999198913574 length of segment : 151 time for calcul the mask position with numpy : 0.06807780265808105 nb_pixel_total : 96901 time to create 1 rle with old method : 0.11198639869689941 length of segment : 484 time for calcul the mask position with numpy : 0.004815101623535156 nb_pixel_total : 47688 time to create 1 rle with old method : 0.07139039039611816 length of segment : 326 time for calcul the mask position with numpy : 0.06277823448181152 nb_pixel_total : 66860 time to create 1 rle with old method : 0.08872103691101074 length of segment : 215 time for calcul the mask position with numpy : 0.008126497268676758 nb_pixel_total : 8175 time to create 1 rle with old method : 0.011622428894042969 length of segment : 66 time for calcul the mask position with numpy : 0.06335735321044922 nb_pixel_total : 52816 time to create 1 rle with old method : 0.06998705863952637 length of segment : 541 time for calcul the mask position with numpy : 0.003845691680908203 nb_pixel_total : 16437 time to create 1 rle with old method : 0.025301694869995117 length of segment : 214 time for calcul the mask position with numpy : 0.010441064834594727 nb_pixel_total : 34124 time to create 1 rle with old method : 0.044667959213256836 length of segment : 305 time for calcul the mask position with numpy : 0.01741647720336914 nb_pixel_total : 8326 time to create 1 rle with old method : 0.013685226440429688 length of segment : 252 time for calcul the mask position with numpy : 0.007112264633178711 nb_pixel_total : 47322 time to create 1 rle with old method : 0.058770179748535156 length of segment : 228 time for calcul the mask position with numpy : 0.004937410354614258 nb_pixel_total : 28879 time to create 1 rle with old method : 0.0385134220123291 length of segment : 139 time for calcul the mask position with numpy : 0.05176734924316406 nb_pixel_total : 76809 time to create 1 rle with old method : 0.0937645435333252 length of segment : 460 time for calcul the mask position with numpy : 0.00507354736328125 nb_pixel_total : 7000 time to create 1 rle with old method : 0.011097908020019531 length of segment : 134 time for calcul the mask position with numpy : 0.00026488304138183594 nb_pixel_total : 6016 time to create 1 rle with old method : 0.007894277572631836 length of segment : 79 time for calcul the mask position with numpy : 0.02104783058166504 nb_pixel_total : 25187 time to create 1 rle with old method : 0.03662991523742676 length of segment : 218 time for calcul the mask position with numpy : 0.2314162254333496 nb_pixel_total : 551687 time to create 1 rle with new method : 0.07094287872314453 length of segment : 1235 time for calcul the mask position with numpy : 0.077911376953125 nb_pixel_total : 293545 time to create 1 rle with new method : 0.03142714500427246 length of segment : 536 time for calcul the mask position with numpy : 0.0054912567138671875 nb_pixel_total : 45379 time to create 1 rle with old method : 0.05289602279663086 length of segment : 323 time for calcul the mask position with numpy : 0.020112276077270508 nb_pixel_total : 119055 time to create 1 rle with old method : 0.14260029792785645 length of segment : 596 time for calcul the mask position with numpy : 0.00032019615173339844 nb_pixel_total : 5229 time to create 1 rle with old method : 0.006288290023803711 length of segment : 93 time for calcul the mask position with numpy : 0.0007448196411132812 nb_pixel_total : 25957 time to create 1 rle with old method : 0.03150463104248047 length of segment : 226 time for calcul the mask position with numpy : 0.0009436607360839844 nb_pixel_total : 16691 time to create 1 rle with old method : 0.020121335983276367 length of segment : 152 time for calcul the mask position with numpy : 0.011594772338867188 nb_pixel_total : 262230 time to create 1 rle with new method : 0.015630245208740234 length of segment : 754 time for calcul the mask position with numpy : 0.014932632446289062 nb_pixel_total : 13182 time to create 1 rle with old method : 0.015303611755371094 length of segment : 191 time for calcul the mask position with numpy : 0.009644746780395508 nb_pixel_total : 14462 time to create 1 rle with old method : 0.021961688995361328 length of segment : 144 time for calcul the mask position with numpy : 0.001455545425415039 nb_pixel_total : 29189 time to create 1 rle with old method : 0.033082008361816406 length of segment : 229 time for calcul the mask position with numpy : 0.004147768020629883 nb_pixel_total : 26283 time to create 1 rle with old method : 0.030645132064819336 length of segment : 237 time for calcul the mask position with numpy : 0.0010340213775634766 nb_pixel_total : 19230 time to create 1 rle with old method : 0.022742271423339844 length of segment : 132 time for calcul the mask position with numpy : 0.0020706653594970703 nb_pixel_total : 58371 time to create 1 rle with old method : 0.08127069473266602 length of segment : 258 time for calcul the mask position with numpy : 0.0057179927825927734 nb_pixel_total : 18691 time to create 1 rle with old method : 0.02168107032775879 length of segment : 184 time for calcul the mask position with numpy : 0.003434896469116211 nb_pixel_total : 13552 time to create 1 rle with old method : 0.01604747772216797 length of segment : 168 time for calcul the mask position with numpy : 0.0038237571716308594 nb_pixel_total : 28053 time to create 1 rle with old method : 0.0330202579498291 length of segment : 332 time for calcul the mask position with numpy : 0.004193305969238281 nb_pixel_total : 52980 time to create 1 rle with old method : 0.061589717864990234 length of segment : 230 time for calcul the mask position with numpy : 0.0012671947479248047 nb_pixel_total : 15746 time to create 1 rle with old method : 0.01806330680847168 length of segment : 184 time for calcul the mask position with numpy : 0.006360292434692383 nb_pixel_total : 44296 time to create 1 rle with old method : 0.051416873931884766 length of segment : 335 time for calcul the mask position with numpy : 0.0048694610595703125 nb_pixel_total : 14505 time to create 1 rle with old method : 0.01913619041442871 length of segment : 154 time for calcul the mask position with numpy : 0.011237859725952148 nb_pixel_total : 31452 time to create 1 rle with old method : 0.0431971549987793 length of segment : 208 time for calcul the mask position with numpy : 0.0026438236236572266 nb_pixel_total : 25776 time to create 1 rle with old method : 0.030488252639770508 length of segment : 388 time for calcul the mask position with numpy : 0.0037369728088378906 nb_pixel_total : 43970 time to create 1 rle with old method : 0.049592018127441406 length of segment : 343 time for calcul the mask position with numpy : 0.01854562759399414 nb_pixel_total : 123462 time to create 1 rle with old method : 0.1481173038482666 length of segment : 485 time for calcul the mask position with numpy : 0.0011670589447021484 nb_pixel_total : 16041 time to create 1 rle with old method : 0.018862009048461914 length of segment : 327 time for calcul the mask position with numpy : 0.007210731506347656 nb_pixel_total : 38941 time to create 1 rle with old method : 0.04598045349121094 length of segment : 276 time for calcul the mask position with numpy : 0.004215717315673828 nb_pixel_total : 78795 time to create 1 rle with old method : 0.0876455307006836 length of segment : 322 time for calcul the mask position with numpy : 0.0020437240600585938 nb_pixel_total : 22875 time to create 1 rle with old method : 0.026325225830078125 length of segment : 207 time for calcul the mask position with numpy : 0.006375312805175781 nb_pixel_total : 66930 time to create 1 rle with old method : 0.09844446182250977 length of segment : 554 time for calcul the mask position with numpy : 0.005815029144287109 nb_pixel_total : 54746 time to create 1 rle with old method : 0.06152629852294922 length of segment : 314 time for calcul the mask position with numpy : 0.002290964126586914 nb_pixel_total : 16366 time to create 1 rle with old method : 0.021298885345458984 length of segment : 206 time for calcul the mask position with numpy : 0.007760047912597656 nb_pixel_total : 41713 time to create 1 rle with old method : 0.0498204231262207 length of segment : 240 time for calcul the mask position with numpy : 0.0012891292572021484 nb_pixel_total : 19536 time to create 1 rle with old method : 0.022821664810180664 length of segment : 148 time for calcul the mask position with numpy : 0.007886886596679688 nb_pixel_total : 122464 time to create 1 rle with old method : 0.1400449275970459 length of segment : 589 time for calcul the mask position with numpy : 0.02336263656616211 nb_pixel_total : 31414 time to create 1 rle with old method : 0.03982686996459961 length of segment : 230 time for calcul the mask position with numpy : 0.000274658203125 nb_pixel_total : 5102 time to create 1 rle with old method : 0.006109476089477539 length of segment : 94 time for calcul the mask position with numpy : 0.0004467964172363281 nb_pixel_total : 19183 time to create 1 rle with old method : 0.02252054214477539 length of segment : 165 time for calcul the mask position with numpy : 0.008390188217163086 nb_pixel_total : 90681 time to create 1 rle with old method : 0.10862398147583008 length of segment : 647 time for calcul the mask position with numpy : 0.0015811920166015625 nb_pixel_total : 17340 time to create 1 rle with old method : 0.02194046974182129 length of segment : 179 time for calcul the mask position with numpy : 0.0060198307037353516 nb_pixel_total : 9969 time to create 1 rle with old method : 0.013000965118408203 length of segment : 85 time for calcul the mask position with numpy : 0.003101348876953125 nb_pixel_total : 30148 time to create 1 rle with old method : 0.03645205497741699 length of segment : 212 time for calcul the mask position with numpy : 0.005611896514892578 nb_pixel_total : 21897 time to create 1 rle with old method : 0.03912711143493652 length of segment : 252 time for calcul the mask position with numpy : 0.00099945068359375 nb_pixel_total : 6910 time to create 1 rle with old method : 0.011399269104003906 length of segment : 157 time for calcul the mask position with numpy : 0.05670595169067383 nb_pixel_total : 112592 time to create 1 rle with old method : 0.14987754821777344 length of segment : 473 time for calcul the mask position with numpy : 0.0018460750579833984 nb_pixel_total : 20114 time to create 1 rle with old method : 0.02382683753967285 length of segment : 191 time for calcul the mask position with numpy : 0.031768798828125 nb_pixel_total : 66403 time to create 1 rle with old method : 0.07804417610168457 length of segment : 467 time for calcul the mask position with numpy : 0.0032813549041748047 nb_pixel_total : 33413 time to create 1 rle with old method : 0.0401458740234375 length of segment : 395 time for calcul the mask position with numpy : 0.0082855224609375 nb_pixel_total : 50068 time to create 1 rle with old method : 0.0613555908203125 length of segment : 368 time for calcul the mask position with numpy : 0.0012440681457519531 nb_pixel_total : 14056 time to create 1 rle with old method : 0.016779661178588867 length of segment : 133 time for calcul the mask position with numpy : 0.0038924217224121094 nb_pixel_total : 19510 time to create 1 rle with old method : 0.023270606994628906 length of segment : 141 time for calcul the mask position with numpy : 0.015312910079956055 nb_pixel_total : 155339 time to create 1 rle with new method : 0.01327824592590332 length of segment : 742 time for calcul the mask position with numpy : 0.0012595653533935547 nb_pixel_total : 14772 time to create 1 rle with old method : 0.01791977882385254 length of segment : 169 time for calcul the mask position with numpy : 0.004130125045776367 nb_pixel_total : 13017 time to create 1 rle with old method : 0.015520811080932617 length of segment : 220 time for calcul the mask position with numpy : 0.0018067359924316406 nb_pixel_total : 25177 time to create 1 rle with old method : 0.039403676986694336 length of segment : 280 time for calcul the mask position with numpy : 0.0032689571380615234 nb_pixel_total : 56018 time to create 1 rle with old method : 0.08447933197021484 length of segment : 287 time for calcul the mask position with numpy : 0.007321596145629883 nb_pixel_total : 28137 time to create 1 rle with old method : 0.0437772274017334 length of segment : 141 time for calcul the mask position with numpy : 0.0037717819213867188 nb_pixel_total : 25302 time to create 1 rle with old method : 0.04588031768798828 length of segment : 199 time for calcul the mask position with numpy : 0.002153635025024414 nb_pixel_total : 26652 time to create 1 rle with old method : 0.030839204788208008 length of segment : 240 time for calcul the mask position with numpy : 0.002153158187866211 nb_pixel_total : 32600 time to create 1 rle with old method : 0.03768014907836914 length of segment : 324 time for calcul the mask position with numpy : 0.006856441497802734 nb_pixel_total : 53209 time to create 1 rle with old method : 0.060820579528808594 length of segment : 342 time for calcul the mask position with numpy : 0.0014050006866455078 nb_pixel_total : 11536 time to create 1 rle with old method : 0.01356959342956543 length of segment : 139 time for calcul the mask position with numpy : 0.003626585006713867 nb_pixel_total : 56095 time to create 1 rle with old method : 0.08934426307678223 length of segment : 261 time for calcul the mask position with numpy : 0.002895355224609375 nb_pixel_total : 41733 time to create 1 rle with old method : 0.04741668701171875 length of segment : 339 time for calcul the mask position with numpy : 0.0050542354583740234 nb_pixel_total : 32533 time to create 1 rle with old method : 0.05388212203979492 length of segment : 313 time for calcul the mask position with numpy : 0.02684640884399414 nb_pixel_total : 75302 time to create 1 rle with old method : 0.08946585655212402 length of segment : 481 time for calcul the mask position with numpy : 0.0013763904571533203 nb_pixel_total : 21730 time to create 1 rle with old method : 0.02497410774230957 length of segment : 229 time for calcul the mask position with numpy : 0.0010895729064941406 nb_pixel_total : 10271 time to create 1 rle with old method : 0.01207113265991211 length of segment : 165 time for calcul the mask position with numpy : 0.0033457279205322266 nb_pixel_total : 39969 time to create 1 rle with old method : 0.0462796688079834 length of segment : 298 time for calcul the mask position with numpy : 0.004315376281738281 nb_pixel_total : 46890 time to create 1 rle with old method : 0.056075334548950195 length of segment : 479 time for calcul the mask position with numpy : 0.025864124298095703 nb_pixel_total : 178225 time to create 1 rle with new method : 0.014338016510009766 length of segment : 631 time for calcul the mask position with numpy : 0.01631021499633789 nb_pixel_total : 158618 time to create 1 rle with new method : 0.011524200439453125 length of segment : 844 time for calcul the mask position with numpy : 0.0013117790222167969 nb_pixel_total : 13089 time to create 1 rle with old method : 0.015238285064697266 length of segment : 265 time for calcul the mask position with numpy : 0.0007722377777099609 nb_pixel_total : 13438 time to create 1 rle with old method : 0.019361257553100586 length of segment : 96 time for calcul the mask position with numpy : 0.002863168716430664 nb_pixel_total : 48501 time to create 1 rle with old method : 0.056497812271118164 length of segment : 189 time for calcul the mask position with numpy : 0.0009131431579589844 nb_pixel_total : 19688 time to create 1 rle with old method : 0.030103683471679688 length of segment : 141 time for calcul the mask position with numpy : 0.0017549991607666016 nb_pixel_total : 21630 time to create 1 rle with old method : 0.02450251579284668 length of segment : 254 time for calcul the mask position with numpy : 0.0067331790924072266 nb_pixel_total : 85437 time to create 1 rle with old method : 0.09665107727050781 length of segment : 308 time for calcul the mask position with numpy : 0.011807918548583984 nb_pixel_total : 118666 time to create 1 rle with old method : 0.15261435508728027 length of segment : 612 time for calcul the mask position with numpy : 0.007018327713012695 nb_pixel_total : 88712 time to create 1 rle with old method : 0.10178279876708984 length of segment : 394 time for calcul the mask position with numpy : 0.0011200904846191406 nb_pixel_total : 20363 time to create 1 rle with old method : 0.023893356323242188 length of segment : 95 time for calcul the mask position with numpy : 0.0015575885772705078 nb_pixel_total : 14341 time to create 1 rle with old method : 0.016725778579711914 length of segment : 214 time for calcul the mask position with numpy : 0.02955770492553711 nb_pixel_total : 190573 time to create 1 rle with new method : 0.057248592376708984 length of segment : 403 time for calcul the mask position with numpy : 0.0017628669738769531 nb_pixel_total : 15013 time to create 1 rle with old method : 0.01751995086669922 length of segment : 218 time for calcul the mask position with numpy : 0.003270864486694336 nb_pixel_total : 35078 time to create 1 rle with old method : 0.04025769233703613 length of segment : 252 time for calcul the mask position with numpy : 0.003565073013305664 nb_pixel_total : 78578 time to create 1 rle with old method : 0.08913922309875488 length of segment : 417 time for calcul the mask position with numpy : 0.006236553192138672 nb_pixel_total : 94291 time to create 1 rle with old method : 0.10906457901000977 length of segment : 669 time for calcul the mask position with numpy : 0.0005784034729003906 nb_pixel_total : 7641 time to create 1 rle with old method : 0.009008646011352539 length of segment : 138 time for calcul the mask position with numpy : 0.0032536983489990234 nb_pixel_total : 38350 time to create 1 rle with old method : 0.04352998733520508 length of segment : 237 time for calcul the mask position with numpy : 0.008316516876220703 nb_pixel_total : 78967 time to create 1 rle with old method : 0.08857250213623047 length of segment : 459 time for calcul the mask position with numpy : 0.00923466682434082 nb_pixel_total : 113698 time to create 1 rle with old method : 0.13877058029174805 length of segment : 315 time for calcul the mask position with numpy : 0.0007338523864746094 nb_pixel_total : 17555 time to create 1 rle with old method : 0.02083134651184082 length of segment : 72 time for calcul the mask position with numpy : 0.005283355712890625 nb_pixel_total : 53069 time to create 1 rle with old method : 0.061820268630981445 length of segment : 295 time for calcul the mask position with numpy : 0.005231380462646484 nb_pixel_total : 66678 time to create 1 rle with old method : 0.07501339912414551 length of segment : 470 time for calcul the mask position with numpy : 0.0011234283447265625 nb_pixel_total : 18030 time to create 1 rle with old method : 0.022785425186157227 length of segment : 168 time for calcul the mask position with numpy : 0.008239030838012695 nb_pixel_total : 12229 time to create 1 rle with old method : 0.02058887481689453 length of segment : 126 time for calcul the mask position with numpy : 0.021561145782470703 nb_pixel_total : 75819 time to create 1 rle with old method : 0.09495878219604492 length of segment : 488 time for calcul the mask position with numpy : 0.1575467586517334 nb_pixel_total : 219747 time to create 1 rle with new method : 0.018780231475830078 length of segment : 417 time for calcul the mask position with numpy : 0.07361793518066406 nb_pixel_total : 156560 time to create 1 rle with new method : 0.009033679962158203 length of segment : 347 time for calcul the mask position with numpy : 0.05767178535461426 nb_pixel_total : 208516 time to create 1 rle with new method : 0.01284337043762207 length of segment : 500 time for calcul the mask position with numpy : 0.0029435157775878906 nb_pixel_total : 16406 time to create 1 rle with old method : 0.020185232162475586 length of segment : 157 time for calcul the mask position with numpy : 0.006081342697143555 nb_pixel_total : 38504 time to create 1 rle with old method : 0.043215274810791016 length of segment : 322 time for calcul the mask position with numpy : 0.0018346309661865234 nb_pixel_total : 9557 time to create 1 rle with old method : 0.010778188705444336 length of segment : 246 time for calcul the mask position with numpy : 0.03497672080993652 nb_pixel_total : 82717 time to create 1 rle with old method : 0.09354257583618164 length of segment : 343 time for calcul the mask position with numpy : 0.005995035171508789 nb_pixel_total : 18352 time to create 1 rle with old method : 0.021933794021606445 length of segment : 208 time for calcul the mask position with numpy : 0.0005478858947753906 nb_pixel_total : 7117 time to create 1 rle with old method : 0.007868766784667969 length of segment : 199 time for calcul the mask position with numpy : 0.014843940734863281 nb_pixel_total : 26085 time to create 1 rle with old method : 0.033985137939453125 length of segment : 216 time for calcul the mask position with numpy : 0.001222372055053711 nb_pixel_total : 10015 time to create 1 rle with old method : 0.011501789093017578 length of segment : 118 time for calcul the mask position with numpy : 0.0047299861907958984 nb_pixel_total : 66718 time to create 1 rle with old method : 0.07493948936462402 length of segment : 323 time for calcul the mask position with numpy : 0.007431745529174805 nb_pixel_total : 161341 time to create 1 rle with new method : 0.008513689041137695 length of segment : 425 time for calcul the mask position with numpy : 0.0056688785552978516 nb_pixel_total : 88023 time to create 1 rle with old method : 0.10059165954589844 length of segment : 284 time for calcul the mask position with numpy : 0.0014450550079345703 nb_pixel_total : 26272 time to create 1 rle with old method : 0.03182053565979004 length of segment : 174 time for calcul the mask position with numpy : 0.0062139034271240234 nb_pixel_total : 103664 time to create 1 rle with old method : 0.11744403839111328 length of segment : 385 time for calcul the mask position with numpy : 0.0011608600616455078 nb_pixel_total : 18540 time to create 1 rle with old method : 0.023366212844848633 length of segment : 224 time for calcul the mask position with numpy : 0.0012657642364501953 nb_pixel_total : 19660 time to create 1 rle with old method : 0.02215743064880371 length of segment : 263 time for calcul the mask position with numpy : 0.001085042953491211 nb_pixel_total : 19394 time to create 1 rle with old method : 0.02247166633605957 length of segment : 205 time for calcul the mask position with numpy : 0.005330324172973633 nb_pixel_total : 97917 time to create 1 rle with old method : 0.10788702964782715 length of segment : 342 time for calcul the mask position with numpy : 0.0010025501251220703 nb_pixel_total : 15162 time to create 1 rle with old method : 0.01728081703186035 length of segment : 223 time for calcul the mask position with numpy : 0.0005784034729003906 nb_pixel_total : 6184 time to create 1 rle with old method : 0.007123470306396484 length of segment : 178 time for calcul the mask position with numpy : 0.003795623779296875 nb_pixel_total : 73603 time to create 1 rle with old method : 0.08167386054992676 length of segment : 376 time for calcul the mask position with numpy : 0.0022864341735839844 nb_pixel_total : 52562 time to create 1 rle with old method : 0.06042909622192383 length of segment : 380 time for calcul the mask position with numpy : 0.0009119510650634766 nb_pixel_total : 17739 time to create 1 rle with old method : 0.0202481746673584 length of segment : 120 time for calcul the mask position with numpy : 0.002705812454223633 nb_pixel_total : 65268 time to create 1 rle with old method : 0.07254457473754883 length of segment : 480 time for calcul the mask position with numpy : 0.006340503692626953 nb_pixel_total : 155515 time to create 1 rle with new method : 0.006408214569091797 length of segment : 376 time for calcul the mask position with numpy : 0.0006656646728515625 nb_pixel_total : 12671 time to create 1 rle with old method : 0.014502286911010742 length of segment : 205 time for calcul the mask position with numpy : 0.0007035732269287109 nb_pixel_total : 16755 time to create 1 rle with old method : 0.019566774368286133 length of segment : 93 time for calcul the mask position with numpy : 0.0006070137023925781 nb_pixel_total : 10462 time to create 1 rle with old method : 0.01248788833618164 length of segment : 96 time for calcul the mask position with numpy : 0.00033211708068847656 nb_pixel_total : 4935 time to create 1 rle with old method : 0.005948305130004883 length of segment : 103 time for calcul the mask position with numpy : 0.00046753883361816406 nb_pixel_total : 10560 time to create 1 rle with old method : 0.012239456176757812 length of segment : 128 time for calcul the mask position with numpy : 0.0010738372802734375 nb_pixel_total : 26029 time to create 1 rle with old method : 0.029231786727905273 length of segment : 250 time for calcul the mask position with numpy : 0.01135706901550293 nb_pixel_total : 114614 time to create 1 rle with old method : 0.12859749794006348 length of segment : 405 time for calcul the mask position with numpy : 0.0005712509155273438 nb_pixel_total : 18827 time to create 1 rle with old method : 0.021425962448120117 length of segment : 188 time for calcul the mask position with numpy : 0.0009167194366455078 nb_pixel_total : 13197 time to create 1 rle with old method : 0.0154571533203125 length of segment : 313 time for calcul the mask position with numpy : 0.0030808448791503906 nb_pixel_total : 53449 time to create 1 rle with old method : 0.05996561050415039 length of segment : 400 time for calcul the mask position with numpy : 0.006158113479614258 nb_pixel_total : 112406 time to create 1 rle with old method : 0.1285724639892578 length of segment : 448 time for calcul the mask position with numpy : 0.007739067077636719 nb_pixel_total : 65945 time to create 1 rle with old method : 0.08373308181762695 length of segment : 476 time for calcul the mask position with numpy : 0.0010523796081542969 nb_pixel_total : 14961 time to create 1 rle with old method : 0.01772284507751465 length of segment : 116 time for calcul the mask position with numpy : 0.003072977066040039 nb_pixel_total : 45845 time to create 1 rle with old method : 0.052436113357543945 length of segment : 252 time for calcul the mask position with numpy : 0.0030939579010009766 nb_pixel_total : 42142 time to create 1 rle with old method : 0.04861855506896973 length of segment : 336 time for calcul the mask position with numpy : 0.0012919902801513672 nb_pixel_total : 25411 time to create 1 rle with old method : 0.0296630859375 length of segment : 166 time for calcul the mask position with numpy : 0.0021963119506835938 nb_pixel_total : 38162 time to create 1 rle with old method : 0.04428744316101074 length of segment : 236 time for calcul the mask position with numpy : 0.017055034637451172 nb_pixel_total : 267051 time to create 1 rle with new method : 0.018895864486694336 length of segment : 962 time for calcul the mask position with numpy : 0.005041599273681641 nb_pixel_total : 71725 time to create 1 rle with old method : 0.08803248405456543 length of segment : 481 time for calcul the mask position with numpy : 0.0015380382537841797 nb_pixel_total : 27469 time to create 1 rle with old method : 0.03266477584838867 length of segment : 171 time for calcul the mask position with numpy : 0.005229949951171875 nb_pixel_total : 90978 time to create 1 rle with old method : 0.10839486122131348 length of segment : 291 time for calcul the mask position with numpy : 0.0006744861602783203 nb_pixel_total : 8686 time to create 1 rle with old method : 0.010779619216918945 length of segment : 92 time for calcul the mask position with numpy : 0.004058122634887695 nb_pixel_total : 41985 time to create 1 rle with old method : 0.04875898361206055 length of segment : 350 time for calcul the mask position with numpy : 0.0027413368225097656 nb_pixel_total : 40854 time to create 1 rle with old method : 0.04947471618652344 length of segment : 238 time for calcul the mask position with numpy : 0.007251262664794922 nb_pixel_total : 95910 time to create 1 rle with old method : 0.11199259757995605 length of segment : 479 time for calcul the mask position with numpy : 0.012776851654052734 nb_pixel_total : 116693 time to create 1 rle with old method : 0.14333295822143555 length of segment : 361 time for calcul the mask position with numpy : 0.0009453296661376953 nb_pixel_total : 13097 time to create 1 rle with old method : 0.015622854232788086 length of segment : 124 time for calcul the mask position with numpy : 0.0015006065368652344 nb_pixel_total : 25978 time to create 1 rle with old method : 0.030909061431884766 length of segment : 172 time for calcul the mask position with numpy : 0.0021419525146484375 nb_pixel_total : 21008 time to create 1 rle with old method : 0.024507522583007812 length of segment : 189 time for calcul the mask position with numpy : 0.0014543533325195312 nb_pixel_total : 21204 time to create 1 rle with old method : 0.024780988693237305 length of segment : 171 time for calcul the mask position with numpy : 0.01207113265991211 nb_pixel_total : 197890 time to create 1 rle with new method : 0.015666961669921875 length of segment : 530 time for calcul the mask position with numpy : 0.001825571060180664 nb_pixel_total : 25077 time to create 1 rle with old method : 0.029639482498168945 length of segment : 304 time for calcul the mask position with numpy : 0.00021409988403320312 nb_pixel_total : 6725 time to create 1 rle with old method : 0.008449316024780273 length of segment : 59 time for calcul the mask position with numpy : 0.0014147758483886719 nb_pixel_total : 20205 time to create 1 rle with old method : 0.02326226234436035 length of segment : 270 time for calcul the mask position with numpy : 0.003429412841796875 nb_pixel_total : 54678 time to create 1 rle with old method : 0.06667709350585938 length of segment : 315 time for calcul the mask position with numpy : 0.0035178661346435547 nb_pixel_total : 42226 time to create 1 rle with old method : 0.049036264419555664 length of segment : 415 time for calcul the mask position with numpy : 0.0014605522155761719 nb_pixel_total : 14833 time to create 1 rle with old method : 0.017402172088623047 length of segment : 145 time for calcul the mask position with numpy : 0.0010454654693603516 nb_pixel_total : 13600 time to create 1 rle with old method : 0.016000032424926758 length of segment : 177 time for calcul the mask position with numpy : 0.004531383514404297 nb_pixel_total : 63451 time to create 1 rle with old method : 0.07608509063720703 length of segment : 396 time for calcul the mask position with numpy : 0.0014150142669677734 nb_pixel_total : 17861 time to create 1 rle with old method : 0.020837783813476562 length of segment : 211 time for calcul the mask position with numpy : 0.0010280609130859375 nb_pixel_total : 17509 time to create 1 rle with old method : 0.020677566528320312 length of segment : 167 time for calcul the mask position with numpy : 0.0012803077697753906 nb_pixel_total : 15182 time to create 1 rle with old method : 0.018431901931762695 length of segment : 278 time for calcul the mask position with numpy : 0.01584482192993164 nb_pixel_total : 241534 time to create 1 rle with new method : 0.01586151123046875 length of segment : 617 time for calcul the mask position with numpy : 0.0018591880798339844 nb_pixel_total : 21243 time to create 1 rle with old method : 0.024919748306274414 length of segment : 189 time for calcul the mask position with numpy : 0.0015492439270019531 nb_pixel_total : 15394 time to create 1 rle with old method : 0.018162012100219727 length of segment : 178 time for calcul the mask position with numpy : 0.0033943653106689453 nb_pixel_total : 73133 time to create 1 rle with old method : 0.09287452697753906 length of segment : 197 time for calcul the mask position with numpy : 0.009181737899780273 nb_pixel_total : 99304 time to create 1 rle with old method : 0.1172628402709961 length of segment : 496 time for calcul the mask position with numpy : 0.0037326812744140625 nb_pixel_total : 53202 time to create 1 rle with old method : 0.0697324275970459 length of segment : 296 time for calcul the mask position with numpy : 0.0015263557434082031 nb_pixel_total : 19338 time to create 1 rle with old method : 0.029653310775756836 length of segment : 163 time for calcul the mask position with numpy : 0.0018777847290039062 nb_pixel_total : 15001 time to create 1 rle with old method : 0.02520895004272461 length of segment : 198 time for calcul the mask position with numpy : 0.00916290283203125 nb_pixel_total : 126496 time to create 1 rle with old method : 0.1464684009552002 length of segment : 789 time for calcul the mask position with numpy : 0.0024857521057128906 nb_pixel_total : 30651 time to create 1 rle with old method : 0.04054594039916992 length of segment : 205 time for calcul the mask position with numpy : 0.0021021366119384766 nb_pixel_total : 29142 time to create 1 rle with old method : 0.035058021545410156 length of segment : 201 time for calcul the mask position with numpy : 0.008606433868408203 nb_pixel_total : 55391 time to create 1 rle with old method : 0.07693099975585938 length of segment : 261 time for calcul the mask position with numpy : 0.0036079883575439453 nb_pixel_total : 48086 time to create 1 rle with old method : 0.05725502967834473 length of segment : 291 time for calcul the mask position with numpy : 0.00960850715637207 nb_pixel_total : 107372 time to create 1 rle with old method : 0.14516711235046387 length of segment : 634 time for calcul the mask position with numpy : 0.0017855167388916016 nb_pixel_total : 15191 time to create 1 rle with old method : 0.01769876480102539 length of segment : 232 time for calcul the mask position with numpy : 0.005176544189453125 nb_pixel_total : 43212 time to create 1 rle with old method : 0.05228900909423828 length of segment : 228 time for calcul the mask position with numpy : 0.0008656978607177734 nb_pixel_total : 10441 time to create 1 rle with old method : 0.012302398681640625 length of segment : 128 time for calcul the mask position with numpy : 0.00357818603515625 nb_pixel_total : 48419 time to create 1 rle with old method : 0.055606842041015625 length of segment : 366 time for calcul the mask position with numpy : 0.0033469200134277344 nb_pixel_total : 46621 time to create 1 rle with old method : 0.05512094497680664 length of segment : 267 time for calcul the mask position with numpy : 0.0024623870849609375 nb_pixel_total : 25236 time to create 1 rle with old method : 0.030439376831054688 length of segment : 188 time for calcul the mask position with numpy : 0.0035686492919921875 nb_pixel_total : 33767 time to create 1 rle with old method : 0.04564690589904785 length of segment : 365 time for calcul the mask position with numpy : 0.0005424022674560547 nb_pixel_total : 5218 time to create 1 rle with old method : 0.0063130855560302734 length of segment : 104 time for calcul the mask position with numpy : 0.003175973892211914 nb_pixel_total : 32651 time to create 1 rle with old method : 0.038643598556518555 length of segment : 228 time for calcul the mask position with numpy : 0.0011906623840332031 nb_pixel_total : 10962 time to create 1 rle with old method : 0.012944698333740234 length of segment : 95 time for calcul the mask position with numpy : 0.003157377243041992 nb_pixel_total : 38142 time to create 1 rle with old method : 0.043471574783325195 length of segment : 275 time for calcul the mask position with numpy : 0.00034737586975097656 nb_pixel_total : 6667 time to create 1 rle with old method : 0.008092641830444336 length of segment : 88 time for calcul the mask position with numpy : 0.00115203857421875 nb_pixel_total : 15429 time to create 1 rle with old method : 0.020368576049804688 length of segment : 190 time for calcul the mask position with numpy : 0.0007731914520263672 nb_pixel_total : 13099 time to create 1 rle with old method : 0.015166997909545898 length of segment : 156 time for calcul the mask position with numpy : 0.0009617805480957031 nb_pixel_total : 10642 time to create 1 rle with old method : 0.012539863586425781 length of segment : 120 time for calcul the mask position with numpy : 0.0016319751739501953 nb_pixel_total : 26703 time to create 1 rle with old method : 0.030544042587280273 length of segment : 223 time for calcul the mask position with numpy : 0.0021123886108398438 nb_pixel_total : 26784 time to create 1 rle with old method : 0.03065967559814453 length of segment : 212 time for calcul the mask position with numpy : 0.0007762908935546875 nb_pixel_total : 11095 time to create 1 rle with old method : 0.012087106704711914 length of segment : 147 time for calcul the mask position with numpy : 0.0006237030029296875 nb_pixel_total : 11561 time to create 1 rle with old method : 0.01325082778930664 length of segment : 116 time for calcul the mask position with numpy : 0.0007183551788330078 nb_pixel_total : 9186 time to create 1 rle with old method : 0.010289430618286133 length of segment : 113 time for calcul the mask position with numpy : 0.0009884834289550781 nb_pixel_total : 14102 time to create 1 rle with old method : 0.015035867691040039 length of segment : 238 time for calcul the mask position with numpy : 0.0024559497833251953 nb_pixel_total : 52115 time to create 1 rle with old method : 0.05756807327270508 length of segment : 280 time for calcul the mask position with numpy : 0.003360271453857422 nb_pixel_total : 57504 time to create 1 rle with old method : 0.06406831741333008 length of segment : 402 time for calcul the mask position with numpy : 0.008269786834716797 nb_pixel_total : 116460 time to create 1 rle with old method : 0.13485240936279297 length of segment : 499 time for calcul the mask position with numpy : 0.0034914016723632812 nb_pixel_total : 39493 time to create 1 rle with old method : 0.04632902145385742 length of segment : 187 time for calcul the mask position with numpy : 0.0013036727905273438 nb_pixel_total : 13534 time to create 1 rle with old method : 0.016103029251098633 length of segment : 105 time for calcul the mask position with numpy : 0.004318952560424805 nb_pixel_total : 43273 time to create 1 rle with old method : 0.050792694091796875 length of segment : 326 time for calcul the mask position with numpy : 0.002598285675048828 nb_pixel_total : 29901 time to create 1 rle with old method : 0.0355377197265625 length of segment : 192 time for calcul the mask position with numpy : 0.0019443035125732422 nb_pixel_total : 18449 time to create 1 rle with old method : 0.022606372833251953 length of segment : 252 time for calcul the mask position with numpy : 0.004308462142944336 nb_pixel_total : 43884 time to create 1 rle with old method : 0.050817251205444336 length of segment : 266 time for calcul the mask position with numpy : 0.0025174617767333984 nb_pixel_total : 29073 time to create 1 rle with old method : 0.03444790840148926 length of segment : 156 time for calcul the mask position with numpy : 0.0008993148803710938 nb_pixel_total : 10095 time to create 1 rle with old method : 0.012618303298950195 length of segment : 140 time for calcul the mask position with numpy : 0.00648188591003418 nb_pixel_total : 70187 time to create 1 rle with old method : 0.08318638801574707 length of segment : 840 time for calcul the mask position with numpy : 0.0016548633575439453 nb_pixel_total : 22830 time to create 1 rle with old method : 0.02613067626953125 length of segment : 265 time for calcul the mask position with numpy : 0.006739616394042969 nb_pixel_total : 60672 time to create 1 rle with old method : 0.07345390319824219 length of segment : 459 time for calcul the mask position with numpy : 0.002025604248046875 nb_pixel_total : 31726 time to create 1 rle with old method : 0.03631114959716797 length of segment : 308 time for calcul the mask position with numpy : 0.0019648075103759766 nb_pixel_total : 26850 time to create 1 rle with old method : 0.03135395050048828 length of segment : 346 time for calcul the mask position with numpy : 0.0008416175842285156 nb_pixel_total : 14267 time to create 1 rle with old method : 0.017657995223999023 length of segment : 165 time for calcul the mask position with numpy : 0.0012814998626708984 nb_pixel_total : 17147 time to create 1 rle with old method : 0.022417545318603516 length of segment : 201 time for calcul the mask position with numpy : 0.0012803077697753906 nb_pixel_total : 13417 time to create 1 rle with old method : 0.01680922508239746 length of segment : 100 time for calcul the mask position with numpy : 0.004170894622802734 nb_pixel_total : 64924 time to create 1 rle with old method : 0.08722376823425293 length of segment : 357 time for calcul the mask position with numpy : 0.007042407989501953 nb_pixel_total : 102845 time to create 1 rle with old method : 0.1204068660736084 length of segment : 529 time for calcul the mask position with numpy : 0.0007898807525634766 nb_pixel_total : 9500 time to create 1 rle with old method : 0.011028528213500977 length of segment : 138 time for calcul the mask position with numpy : 0.004194021224975586 nb_pixel_total : 64430 time to create 1 rle with old method : 0.07225298881530762 length of segment : 329 time for calcul the mask position with numpy : 0.002582550048828125 nb_pixel_total : 36246 time to create 1 rle with old method : 0.0418858528137207 length of segment : 326 time for calcul the mask position with numpy : 0.0018351078033447266 nb_pixel_total : 25426 time to create 1 rle with old method : 0.030974388122558594 length of segment : 192 time for calcul the mask position with numpy : 0.0045206546783447266 nb_pixel_total : 82177 time to create 1 rle with old method : 0.09501194953918457 length of segment : 531 time for calcul the mask position with numpy : 0.006839275360107422 nb_pixel_total : 103615 time to create 1 rle with old method : 0.13904619216918945 length of segment : 354 time for calcul the mask position with numpy : 0.002500295639038086 nb_pixel_total : 34202 time to create 1 rle with old method : 0.04177141189575195 length of segment : 296 time for calcul the mask position with numpy : 0.0008158683776855469 nb_pixel_total : 9284 time to create 1 rle with old method : 0.011574029922485352 length of segment : 109 time for calcul the mask position with numpy : 0.0060498714447021484 nb_pixel_total : 120158 time to create 1 rle with old method : 0.14435863494873047 length of segment : 443 time for calcul the mask position with numpy : 0.002709627151489258 nb_pixel_total : 24456 time to create 1 rle with old method : 0.030162811279296875 length of segment : 502 time for calcul the mask position with numpy : 0.0004546642303466797 nb_pixel_total : 6579 time to create 1 rle with old method : 0.007749319076538086 length of segment : 119 time for calcul the mask position with numpy : 0.0033159255981445312 nb_pixel_total : 35592 time to create 1 rle with old method : 0.041851043701171875 length of segment : 301 time for calcul the mask position with numpy : 0.004390716552734375 nb_pixel_total : 62610 time to create 1 rle with old method : 0.07032322883605957 length of segment : 368 time for calcul the mask position with numpy : 0.0005497932434082031 nb_pixel_total : 5117 time to create 1 rle with old method : 0.006215095520019531 length of segment : 86 time for calcul the mask position with numpy : 0.0023958683013916016 nb_pixel_total : 30488 time to create 1 rle with old method : 0.034821271896362305 length of segment : 247 time for calcul the mask position with numpy : 0.0030663013458251953 nb_pixel_total : 66859 time to create 1 rle with old method : 0.07616853713989258 length of segment : 310 time for calcul the mask position with numpy : 0.0006837844848632812 nb_pixel_total : 31001 time to create 1 rle with old method : 0.053839683532714844 length of segment : 191 time for calcul the mask position with numpy : 0.0031769275665283203 nb_pixel_total : 70533 time to create 1 rle with old method : 0.0802609920501709 length of segment : 843 time for calcul the mask position with numpy : 0.003206968307495117 nb_pixel_total : 15916 time to create 1 rle with old method : 0.018732547760009766 length of segment : 432 time for calcul the mask position with numpy : 0.0026514530181884766 nb_pixel_total : 37174 time to create 1 rle with old method : 0.04263019561767578 length of segment : 323 time for calcul the mask position with numpy : 0.00047016143798828125 nb_pixel_total : 11823 time to create 1 rle with old method : 0.015175342559814453 length of segment : 246 time for calcul the mask position with numpy : 0.001589059829711914 nb_pixel_total : 15663 time to create 1 rle with old method : 0.019707679748535156 length of segment : 226 time for calcul the mask position with numpy : 0.001873016357421875 nb_pixel_total : 31942 time to create 1 rle with old method : 0.03740501403808594 length of segment : 242 time for calcul the mask position with numpy : 0.004077434539794922 nb_pixel_total : 43813 time to create 1 rle with old method : 0.051415205001831055 length of segment : 444 time for calcul the mask position with numpy : 0.0006635189056396484 nb_pixel_total : 7205 time to create 1 rle with old method : 0.008559942245483398 length of segment : 120 time for calcul the mask position with numpy : 0.0014340877532958984 nb_pixel_total : 20857 time to create 1 rle with old method : 0.024315595626831055 length of segment : 144 time for calcul the mask position with numpy : 0.0006029605865478516 nb_pixel_total : 12692 time to create 1 rle with old method : 0.01705336570739746 length of segment : 161 time for calcul the mask position with numpy : 0.0005247592926025391 nb_pixel_total : 6184 time to create 1 rle with old method : 0.008041858673095703 length of segment : 198 time for calcul the mask position with numpy : 0.001302480697631836 nb_pixel_total : 8477 time to create 1 rle with old method : 0.03154182434082031 length of segment : 147 time for calcul the mask position with numpy : 0.005111217498779297 nb_pixel_total : 47335 time to create 1 rle with old method : 0.06767153739929199 length of segment : 269 time for calcul the mask position with numpy : 0.0008206367492675781 nb_pixel_total : 15493 time to create 1 rle with old method : 0.021438121795654297 length of segment : 165 time for calcul the mask position with numpy : 0.0035419464111328125 nb_pixel_total : 51123 time to create 1 rle with old method : 0.07868742942810059 length of segment : 382 time for calcul the mask position with numpy : 0.0018661022186279297 nb_pixel_total : 5739 time to create 1 rle with old method : 0.006981372833251953 length of segment : 137 time for calcul the mask position with numpy : 0.0002684593200683594 nb_pixel_total : 6529 time to create 1 rle with old method : 0.008602142333984375 length of segment : 75 time for calcul the mask position with numpy : 0.002735614776611328 nb_pixel_total : 63205 time to create 1 rle with old method : 0.08115935325622559 length of segment : 333 time for calcul the mask position with numpy : 0.0011224746704101562 nb_pixel_total : 27907 time to create 1 rle with old method : 0.03809309005737305 length of segment : 216 time for calcul the mask position with numpy : 0.0008285045623779297 nb_pixel_total : 13481 time to create 1 rle with old method : 0.02175760269165039 length of segment : 229 time for calcul the mask position with numpy : 0.0008904933929443359 nb_pixel_total : 12465 time to create 1 rle with old method : 0.020484209060668945 length of segment : 130 time for calcul the mask position with numpy : 0.00024318695068359375 nb_pixel_total : 4079 time to create 1 rle with old method : 0.005209445953369141 length of segment : 144 time for calcul the mask position with numpy : 0.0040090084075927734 nb_pixel_total : 42243 time to create 1 rle with old method : 0.052683353424072266 length of segment : 527 time for calcul the mask position with numpy : 0.0018351078033447266 nb_pixel_total : 27699 time to create 1 rle with old method : 0.03856992721557617 length of segment : 188 time for calcul the mask position with numpy : 0.0017046928405761719 nb_pixel_total : 12261 time to create 1 rle with old method : 0.023181676864624023 length of segment : 147 time for calcul the mask position with numpy : 0.0012505054473876953 nb_pixel_total : 19803 time to create 1 rle with old method : 0.02331376075744629 length of segment : 202 time for calcul the mask position with numpy : 0.0038743019104003906 nb_pixel_total : 31668 time to create 1 rle with old method : 0.03983712196350098 length of segment : 372 time for calcul the mask position with numpy : 0.0004012584686279297 nb_pixel_total : 14373 time to create 1 rle with old method : 0.0175783634185791 length of segment : 116 time for calcul the mask position with numpy : 0.0007123947143554688 nb_pixel_total : 7266 time to create 1 rle with old method : 0.008880615234375 length of segment : 107 time for calcul the mask position with numpy : 0.004250049591064453 nb_pixel_total : 63339 time to create 1 rle with old method : 0.07823061943054199 length of segment : 310 time for calcul the mask position with numpy : 0.0007319450378417969 nb_pixel_total : 7191 time to create 1 rle with old method : 0.008289575576782227 length of segment : 189 time for calcul the mask position with numpy : 0.002763986587524414 nb_pixel_total : 45921 time to create 1 rle with old method : 0.057801008224487305 length of segment : 180 time for calcul the mask position with numpy : 0.002126455307006836 nb_pixel_total : 28772 time to create 1 rle with old method : 0.03550457954406738 length of segment : 214 time for calcul the mask position with numpy : 0.0027234554290771484 nb_pixel_total : 30375 time to create 1 rle with old method : 0.0393679141998291 length of segment : 266 time for calcul the mask position with numpy : 0.0013196468353271484 nb_pixel_total : 18911 time to create 1 rle with old method : 0.023967981338500977 length of segment : 228 time for calcul the mask position with numpy : 0.0011048316955566406 nb_pixel_total : 18874 time to create 1 rle with old method : 0.02240157127380371 length of segment : 154 time for calcul the mask position with numpy : 0.0026857852935791016 nb_pixel_total : 26266 time to create 1 rle with old method : 0.035738229751586914 length of segment : 215 time for calcul the mask position with numpy : 0.0006561279296875 nb_pixel_total : 13393 time to create 1 rle with old method : 0.016392230987548828 length of segment : 108 time for calcul the mask position with numpy : 0.0007817745208740234 nb_pixel_total : 20875 time to create 1 rle with old method : 0.026430368423461914 length of segment : 189 time for calcul the mask position with numpy : 0.00465083122253418 nb_pixel_total : 62793 time to create 1 rle with old method : 0.07736039161682129 length of segment : 305 time for calcul the mask position with numpy : 0.003907203674316406 nb_pixel_total : 66141 time to create 1 rle with old method : 0.0901041030883789 length of segment : 265 time for calcul the mask position with numpy : 0.003751516342163086 nb_pixel_total : 52706 time to create 1 rle with old method : 0.062294721603393555 length of segment : 264 time for calcul the mask position with numpy : 0.0008533000946044922 nb_pixel_total : 10334 time to create 1 rle with old method : 0.01463937759399414 length of segment : 76 time for calcul the mask position with numpy : 0.0026254653930664062 nb_pixel_total : 30988 time to create 1 rle with old method : 0.03644990921020508 length of segment : 237 time for calcul the mask position with numpy : 0.004062175750732422 nb_pixel_total : 49737 time to create 1 rle with old method : 0.08411502838134766 length of segment : 363 time for calcul the mask position with numpy : 0.0018291473388671875 nb_pixel_total : 33643 time to create 1 rle with old method : 0.04052114486694336 length of segment : 425 time for calcul the mask position with numpy : 0.0010886192321777344 nb_pixel_total : 16932 time to create 1 rle with old method : 0.01965951919555664 length of segment : 190 time for calcul the mask position with numpy : 0.0019910335540771484 nb_pixel_total : 22987 time to create 1 rle with old method : 0.03315019607543945 length of segment : 184 time for calcul the mask position with numpy : 0.0006434917449951172 nb_pixel_total : 5777 time to create 1 rle with old method : 0.006888151168823242 length of segment : 92 time for calcul the mask position with numpy : 0.0009548664093017578 nb_pixel_total : 17121 time to create 1 rle with old method : 0.02020263671875 length of segment : 125 time for calcul the mask position with numpy : 0.00045013427734375 nb_pixel_total : 8635 time to create 1 rle with old method : 0.010279178619384766 length of segment : 107 time for calcul the mask position with numpy : 0.0004382133483886719 nb_pixel_total : 6095 time to create 1 rle with old method : 0.009534835815429688 length of segment : 80 time for calcul the mask position with numpy : 0.0023691654205322266 nb_pixel_total : 33692 time to create 1 rle with old method : 0.03890419006347656 length of segment : 233 time spent for convertir_results : 29.456241130828857 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 312 chid ids of type : 3594 Number RLEs to save : 86545 save missing photos in datou_result : time spend for datou_step_exec : 161.43615102767944 time spend to save output : 18.389535903930664 total time spend for step 1 : 179.8256869316101 step2:crop_condition Wed Apr 9 11:03:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 9 ! batch 1 Loaded 312 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 240 About to insert : list_path_to_insert length 240 new photo from crops ! About to upload 240 photos upload in portfolio : 3736932 init cache_photo without model_param we have 240 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744189463_624923 we have uploaded 240 photos in the portfolio 3736932 time of upload the photos Elapsed time : 90.1792414188385 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 40 About to insert : list_path_to_insert length 40 new photo from crops ! About to upload 40 photos upload in portfolio : 3736932 init cache_photo without model_param we have 40 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744189565_624923 we have uploaded 40 photos in the portfolio 3736932 time of upload the photos Elapsed time : 10.015050888061523 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744189577_624923 we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.1057484149932861 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 21 About to insert : list_path_to_insert length 21 new photo from crops ! About to upload 21 photos upload in portfolio : 3736932 init cache_photo without model_param we have 21 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744189589_624923 we have uploaded 21 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.390850067138672 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744189597_624923 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.1630425453186035 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744189599_624923 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.303598403930664 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744189603_624923 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.5215909481048584 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350336436, 1350336421, 1350336284, 1350336226, 1350287350, 1350287140, 1350287110, 1350287098, 1350287073] Looping around the photos to save general results len do output : 312 /1350737582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737907Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737909Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737930Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737946Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737963Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350737999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738005Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738007Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738031Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738048Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738190Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738191Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738192Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738198Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738199Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738202Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738214Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738217Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738219Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738220Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738222Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738224Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738225Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738226Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738228Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738229Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738231Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738234Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738235Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738236Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738238Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738239Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738240Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738242Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738243Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738245Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738267Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738391Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738432Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738433Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738435Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738456Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350738469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336436', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336421', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336284', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336226', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287350', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287140', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287110', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287098', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287073', None, None, None, None, None, '2733653') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 945 time used for this insertion : 0.20106196403503418 save_final save missing photos in datou_result : time spend for datou_step_exec : 192.53176140785217 time spend to save output : 0.20957446098327637 total time spend for step 2 : 192.74133586883545 step3:rle_unique_nms_with_priority Wed Apr 9 11:06:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 312 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 35 nb_hashtags : 4 time to prepare the origin masks : 4.742932319641113 time for calcul the mask position with numpy : 0.29921770095825195 nb_pixel_total : 5021796 time to create 1 rle with new method : 0.4710845947265625 time for calcul the mask position with numpy : 0.03523397445678711 nb_pixel_total : 14462 time to create 1 rle with old method : 0.016379117965698242 time for calcul the mask position with numpy : 0.029757261276245117 nb_pixel_total : 25187 time to create 1 rle with old method : 0.02870035171508789 time for calcul the mask position with numpy : 0.029811620712280273 nb_pixel_total : 66860 time to create 1 rle with old method : 0.08423161506652832 time for calcul the mask position with numpy : 0.02922534942626953 nb_pixel_total : 13182 time to create 1 rle with old method : 0.0151824951171875 time for calcul the mask position with numpy : 0.029169321060180664 nb_pixel_total : 8326 time to create 1 rle with old method : 0.009724140167236328 time for calcul the mask position with numpy : 0.029527664184570312 nb_pixel_total : 24093 time to create 1 rle with old method : 0.030277729034423828 time for calcul the mask position with numpy : 0.03504586219787598 nb_pixel_total : 96901 time to create 1 rle with old method : 0.11508703231811523 time for calcul the mask position with numpy : 0.029704809188842773 nb_pixel_total : 18648 time to create 1 rle with old method : 0.021290302276611328 time for calcul the mask position with numpy : 0.030005693435668945 nb_pixel_total : 45379 time to create 1 rle with old method : 0.05411791801452637 time for calcul the mask position with numpy : 0.03656816482543945 nb_pixel_total : 544832 time to create 1 rle with new method : 0.3569314479827881 time for calcul the mask position with numpy : 0.029160261154174805 nb_pixel_total : 16605 time to create 1 rle with old method : 0.019030094146728516 time for calcul the mask position with numpy : 0.029036283493041992 nb_pixel_total : 1982 time to create 1 rle with old method : 0.0024127960205078125 time for calcul the mask position with numpy : 0.02914261817932129 nb_pixel_total : 3625 time to create 1 rle with old method : 0.00416254997253418 time for calcul the mask position with numpy : 0.03097677230834961 nb_pixel_total : 76809 time to create 1 rle with old method : 0.09025835990905762 time for calcul the mask position with numpy : 0.03366661071777344 nb_pixel_total : 43478 time to create 1 rle with old method : 0.060149192810058594 time for calcul the mask position with numpy : 0.029423236846923828 nb_pixel_total : 18691 time to create 1 rle with old method : 0.021243810653686523 time for calcul the mask position with numpy : 0.02909708023071289 nb_pixel_total : 28546 time to create 1 rle with old method : 0.03235793113708496 time for calcul the mask position with numpy : 0.029009580612182617 nb_pixel_total : 34121 time to create 1 rle with old method : 0.039072513580322266 time for calcul the mask position with numpy : 0.02888321876525879 nb_pixel_total : 4359 time to create 1 rle with old method : 0.005005598068237305 time for calcul the mask position with numpy : 0.028799772262573242 nb_pixel_total : 33575 time to create 1 rle with old method : 0.03819131851196289 time for calcul the mask position with numpy : 0.02898693084716797 nb_pixel_total : 9213 time to create 1 rle with old method : 0.010515213012695312 time for calcul the mask position with numpy : 0.029842853546142578 nb_pixel_total : 52816 time to create 1 rle with old method : 0.0597381591796875 time for calcul the mask position with numpy : 0.029221773147583008 nb_pixel_total : 8175 time to create 1 rle with old method : 0.009274005889892578 time for calcul the mask position with numpy : 0.030298233032226562 nb_pixel_total : 293046 time to create 1 rle with new method : 0.3321070671081543 time for calcul the mask position with numpy : 0.030890464782714844 nb_pixel_total : 56184 time to create 1 rle with old method : 0.06728959083557129 time for calcul the mask position with numpy : 0.029223918914794922 nb_pixel_total : 65977 time to create 1 rle with old method : 0.07403850555419922 time for calcul the mask position with numpy : 0.029055118560791016 nb_pixel_total : 11868 time to create 1 rle with old method : 0.07136726379394531 time for calcul the mask position with numpy : 0.029244184494018555 nb_pixel_total : 7000 time to create 1 rle with old method : 0.007915258407592773 time for calcul the mask position with numpy : 0.02920055389404297 nb_pixel_total : 16437 time to create 1 rle with old method : 0.018416643142700195 time for calcul the mask position with numpy : 0.029314756393432617 nb_pixel_total : 47658 time to create 1 rle with old method : 0.05397152900695801 time for calcul the mask position with numpy : 0.028964996337890625 nb_pixel_total : 16691 time to create 1 rle with old method : 0.01894831657409668 time for calcul the mask position with numpy : 0.028832197189331055 nb_pixel_total : 26283 time to create 1 rle with old method : 0.02991008758544922 time for calcul the mask position with numpy : 0.030189990997314453 nb_pixel_total : 262230 time to create 1 rle with new method : 0.9768164157867432 time for calcul the mask position with numpy : 0.031008481979370117 nb_pixel_total : 29189 time to create 1 rle with old method : 0.03518104553222656 time for calcul the mask position with numpy : 0.029145240783691406 nb_pixel_total : 6016 time to create 1 rle with old method : 0.007172107696533203 create new chi : 4.76603627204895 time to delete rle : 0.018036603927612305 batch 1 Loaded 71 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21118 TO DO : save crop sub photo not yet done ! save time : 1.4766383171081543 nb_obj : 36 nb_hashtags : 5 time to prepare the origin masks : 4.1745805740356445 time for calcul the mask position with numpy : 0.37364721298217773 nb_pixel_total : 5632970 time to create 1 rle with new method : 0.8407182693481445 time for calcul the mask position with numpy : 0.02983856201171875 nb_pixel_total : 17340 time to create 1 rle with old method : 0.02040410041809082 time for calcul the mask position with numpy : 0.029571533203125 nb_pixel_total : 25776 time to create 1 rle with old method : 0.029998779296875 time for calcul the mask position with numpy : 0.029038667678833008 nb_pixel_total : 13552 time to create 1 rle with old method : 0.015881776809692383 time for calcul the mask position with numpy : 0.028682947158813477 nb_pixel_total : 16366 time to create 1 rle with old method : 0.019861698150634766 time for calcul the mask position with numpy : 0.030065298080444336 nb_pixel_total : 30159 time to create 1 rle with old method : 0.03917217254638672 time for calcul the mask position with numpy : 0.031479835510253906 nb_pixel_total : 43970 time to create 1 rle with old method : 0.0507352352142334 time for calcul the mask position with numpy : 0.03263354301452637 nb_pixel_total : 44296 time to create 1 rle with old method : 0.0557401180267334 time for calcul the mask position with numpy : 0.03150677680969238 nb_pixel_total : 14056 time to create 1 rle with old method : 0.015955448150634766 time for calcul the mask position with numpy : 0.03174114227294922 nb_pixel_total : 122242 time to create 1 rle with old method : 0.14202260971069336 time for calcul the mask position with numpy : 0.030726194381713867 nb_pixel_total : 52980 time to create 1 rle with old method : 0.06248188018798828 time for calcul the mask position with numpy : 0.0306546688079834 nb_pixel_total : 54746 time to create 1 rle with old method : 0.06337618827819824 time for calcul the mask position with numpy : 0.029250383377075195 nb_pixel_total : 22875 time to create 1 rle with old method : 0.02639174461364746 time for calcul the mask position with numpy : 0.02970099449157715 nb_pixel_total : 28053 time to create 1 rle with old method : 0.033722639083862305 time for calcul the mask position with numpy : 0.031363725662231445 nb_pixel_total : 19183 time to create 1 rle with old method : 0.023203611373901367 time for calcul the mask position with numpy : 0.03038501739501953 nb_pixel_total : 30148 time to create 1 rle with old method : 0.04029273986816406 time for calcul the mask position with numpy : 0.029752254486083984 nb_pixel_total : 123462 time to create 1 rle with old method : 0.16141104698181152 time for calcul the mask position with numpy : 0.030176877975463867 nb_pixel_total : 90681 time to create 1 rle with old method : 0.11501479148864746 time for calcul the mask position with numpy : 0.03117513656616211 nb_pixel_total : 21897 time to create 1 rle with old method : 0.025928497314453125 time for calcul the mask position with numpy : 0.030155181884765625 nb_pixel_total : 6910 time to create 1 rle with old method : 0.00827789306640625 time for calcul the mask position with numpy : 0.031187772750854492 nb_pixel_total : 38941 time to create 1 rle with old method : 0.048476457595825195 time for calcul the mask position with numpy : 0.029378652572631836 nb_pixel_total : 50068 time to create 1 rle with old method : 0.05992913246154785 time for calcul the mask position with numpy : 0.029669761657714844 nb_pixel_total : 41713 time to create 1 rle with old method : 0.04999685287475586 time for calcul the mask position with numpy : 0.030521154403686523 nb_pixel_total : 14505 time to create 1 rle with old method : 0.02305889129638672 time for calcul the mask position with numpy : 0.03316307067871094 nb_pixel_total : 66403 time to create 1 rle with old method : 0.08414483070373535 time for calcul the mask position with numpy : 0.029556751251220703 nb_pixel_total : 112512 time to create 1 rle with old method : 0.16330504417419434 time for calcul the mask position with numpy : 0.02937483787536621 nb_pixel_total : 9969 time to create 1 rle with old method : 0.012282133102416992 time for calcul the mask position with numpy : 0.02945113182067871 nb_pixel_total : 31414 time to create 1 rle with old method : 0.041893959045410156 time for calcul the mask position with numpy : 0.03025531768798828 nb_pixel_total : 19510 time to create 1 rle with old method : 0.023647785186767578 time for calcul the mask position with numpy : 0.030478239059448242 nb_pixel_total : 31452 time to create 1 rle with old method : 0.0366969108581543 time for calcul the mask position with numpy : 0.031148433685302734 nb_pixel_total : 20114 time to create 1 rle with old method : 0.027111291885375977 time for calcul the mask position with numpy : 0.03679656982421875 nb_pixel_total : 66930 time to create 1 rle with old method : 0.09637117385864258 time for calcul the mask position with numpy : 0.030833721160888672 nb_pixel_total : 5102 time to create 1 rle with old method : 0.00688481330871582 time for calcul the mask position with numpy : 0.031299591064453125 nb_pixel_total : 78622 time to create 1 rle with old method : 0.0928032398223877 time for calcul the mask position with numpy : 0.030227184295654297 nb_pixel_total : 16041 time to create 1 rle with old method : 0.019159793853759766 time for calcul the mask position with numpy : 0.02953362464904785 nb_pixel_total : 15746 time to create 1 rle with old method : 0.02111029624938965 time for calcul the mask position with numpy : 0.029338836669921875 nb_pixel_total : 19536 time to create 1 rle with old method : 0.022905588150024414 create new chi : 4.133434772491455 time to delete rle : 0.0034894943237304688 batch 1 Loaded 73 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22367 TO DO : save crop sub photo not yet done ! save time : 1.963484525680542 nb_obj : 37 nb_hashtags : 4 time to prepare the origin masks : 4.618661165237427 time for calcul the mask position with numpy : 0.3796510696411133 nb_pixel_total : 5082489 time to create 1 rle with new method : 0.5790910720825195 time for calcul the mask position with numpy : 0.03022599220275879 nb_pixel_total : 28137 time to create 1 rle with old method : 0.03228449821472168 time for calcul the mask position with numpy : 0.030553102493286133 nb_pixel_total : 13017 time to create 1 rle with old method : 0.014952421188354492 time for calcul the mask position with numpy : 0.03081655502319336 nb_pixel_total : 13089 time to create 1 rle with old method : 0.014857769012451172 time for calcul the mask position with numpy : 0.030559778213500977 nb_pixel_total : 21630 time to create 1 rle with old method : 0.025496959686279297 time for calcul the mask position with numpy : 0.03014826774597168 nb_pixel_total : 41733 time to create 1 rle with old method : 0.04881620407104492 time for calcul the mask position with numpy : 0.030408143997192383 nb_pixel_total : 25177 time to create 1 rle with old method : 0.02879047393798828 time for calcul the mask position with numpy : 0.028397560119628906 nb_pixel_total : 32600 time to create 1 rle with old method : 0.03856778144836426 time for calcul the mask position with numpy : 0.03083062171936035 nb_pixel_total : 48501 time to create 1 rle with old method : 0.06158709526062012 time for calcul the mask position with numpy : 0.061278343200683594 nb_pixel_total : 7641 time to create 1 rle with old method : 0.009588003158569336 time for calcul the mask position with numpy : 0.029515504837036133 nb_pixel_total : 56095 time to create 1 rle with old method : 0.07085704803466797 time for calcul the mask position with numpy : 0.03224015235900879 nb_pixel_total : 19688 time to create 1 rle with old method : 0.022240877151489258 time for calcul the mask position with numpy : 0.032550811767578125 nb_pixel_total : 46890 time to create 1 rle with old method : 0.05747485160827637 time for calcul the mask position with numpy : 0.033698081970214844 nb_pixel_total : 13438 time to create 1 rle with old method : 0.021878719329833984 time for calcul the mask position with numpy : 0.03341794013977051 nb_pixel_total : 88712 time to create 1 rle with old method : 0.10731649398803711 time for calcul the mask position with numpy : 0.029085636138916016 nb_pixel_total : 53209 time to create 1 rle with old method : 0.06628632545471191 time for calcul the mask position with numpy : 0.0332491397857666 nb_pixel_total : 39969 time to create 1 rle with old method : 0.04760122299194336 time for calcul the mask position with numpy : 0.03377842903137207 nb_pixel_total : 75302 time to create 1 rle with old method : 0.09029006958007812 time for calcul the mask position with numpy : 0.03170514106750488 nb_pixel_total : 14341 time to create 1 rle with old method : 0.016242265701293945 time for calcul the mask position with numpy : 0.03539228439331055 nb_pixel_total : 190363 time to create 1 rle with new method : 0.6009912490844727 time for calcul the mask position with numpy : 0.029306411743164062 nb_pixel_total : 158554 time to create 1 rle with new method : 0.4492661952972412 time for calcul the mask position with numpy : 0.029204845428466797 nb_pixel_total : 78578 time to create 1 rle with old method : 0.08894991874694824 time for calcul the mask position with numpy : 0.028750896453857422 nb_pixel_total : 10271 time to create 1 rle with old method : 0.012045145034790039 time for calcul the mask position with numpy : 0.030632734298706055 nb_pixel_total : 94291 time to create 1 rle with old method : 0.10639047622680664 time for calcul the mask position with numpy : 0.0290985107421875 nb_pixel_total : 85437 time to create 1 rle with old method : 0.0982518196105957 time for calcul the mask position with numpy : 0.030702590942382812 nb_pixel_total : 21730 time to create 1 rle with old method : 0.024576425552368164 time for calcul the mask position with numpy : 0.030946969985961914 nb_pixel_total : 15013 time to create 1 rle with old method : 0.017278194427490234 time for calcul the mask position with numpy : 0.02946925163269043 nb_pixel_total : 118666 time to create 1 rle with old method : 0.13294744491577148 time for calcul the mask position with numpy : 0.02993011474609375 nb_pixel_total : 155339 time to create 1 rle with new method : 0.46434545516967773 time for calcul the mask position with numpy : 0.030172348022460938 nb_pixel_total : 178225 time to create 1 rle with new method : 0.6238899230957031 time for calcul the mask position with numpy : 0.029007434844970703 nb_pixel_total : 35078 time to create 1 rle with old method : 0.03966522216796875 time for calcul the mask position with numpy : 0.028990507125854492 nb_pixel_total : 25302 time to create 1 rle with old method : 0.028693437576293945 time for calcul the mask position with numpy : 0.02926802635192871 nb_pixel_total : 26652 time to create 1 rle with old method : 0.02992534637451172 time for calcul the mask position with numpy : 0.02915668487548828 nb_pixel_total : 32533 time to create 1 rle with old method : 0.03658747673034668 time for calcul the mask position with numpy : 0.029262542724609375 nb_pixel_total : 55879 time to create 1 rle with old method : 0.0624082088470459 time for calcul the mask position with numpy : 0.028925657272338867 nb_pixel_total : 14772 time to create 1 rle with old method : 0.016736507415771484 time for calcul the mask position with numpy : 0.028858184814453125 nb_pixel_total : 11536 time to create 1 rle with old method : 0.013068199157714844 time for calcul the mask position with numpy : 0.028900146484375 nb_pixel_total : 20363 time to create 1 rle with old method : 0.02297234535217285 create new chi : 5.901472568511963 time to delete rle : 0.005967617034912109 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25647 TO DO : save crop sub photo not yet done ! save time : 2.7353274822235107 nb_obj : 21 nb_hashtags : 3 time to prepare the origin masks : 7.0197272300720215 time for calcul the mask position with numpy : 0.19733166694641113 nb_pixel_total : 5715631 time to create 1 rle with new method : 0.5679688453674316 time for calcul the mask position with numpy : 0.021296024322509766 nb_pixel_total : 66718 time to create 1 rle with old method : 0.07536125183105469 time for calcul the mask position with numpy : 0.021462440490722656 nb_pixel_total : 10015 time to create 1 rle with old method : 0.011446952819824219 time for calcul the mask position with numpy : 0.022357702255249023 nb_pixel_total : 26085 time to create 1 rle with old method : 0.02946186065673828 time for calcul the mask position with numpy : 0.02296614646911621 nb_pixel_total : 7117 time to create 1 rle with old method : 0.008058547973632812 time for calcul the mask position with numpy : 0.02087712287902832 nb_pixel_total : 18352 time to create 1 rle with old method : 0.020885705947875977 time for calcul the mask position with numpy : 0.023722171783447266 nb_pixel_total : 82717 time to create 1 rle with old method : 0.09264302253723145 time for calcul the mask position with numpy : 0.022570133209228516 nb_pixel_total : 9557 time to create 1 rle with old method : 0.010912418365478516 time for calcul the mask position with numpy : 0.02334141731262207 nb_pixel_total : 38504 time to create 1 rle with old method : 0.04367208480834961 time for calcul the mask position with numpy : 0.022891998291015625 nb_pixel_total : 16406 time to create 1 rle with old method : 0.018544673919677734 time for calcul the mask position with numpy : 0.02623271942138672 nb_pixel_total : 208516 time to create 1 rle with new method : 0.44673824310302734 time for calcul the mask position with numpy : 0.02390575408935547 nb_pixel_total : 156560 time to create 1 rle with new method : 0.5194692611694336 time for calcul the mask position with numpy : 0.026520252227783203 nb_pixel_total : 219747 time to create 1 rle with new method : 0.4597799777984619 time for calcul the mask position with numpy : 0.03543829917907715 nb_pixel_total : 75819 time to create 1 rle with old method : 0.09024238586425781 time for calcul the mask position with numpy : 0.03593897819519043 nb_pixel_total : 12229 time to create 1 rle with old method : 0.013881921768188477 time for calcul the mask position with numpy : 0.036492347717285156 nb_pixel_total : 18030 time to create 1 rle with old method : 0.020320892333984375 time for calcul the mask position with numpy : 0.03642582893371582 nb_pixel_total : 66598 time to create 1 rle with old method : 0.07517600059509277 time for calcul the mask position with numpy : 0.03461432456970215 nb_pixel_total : 53069 time to create 1 rle with old method : 0.0679481029510498 time for calcul the mask position with numpy : 0.03894233703613281 nb_pixel_total : 17555 time to create 1 rle with old method : 0.020196199417114258 time for calcul the mask position with numpy : 0.037609100341796875 nb_pixel_total : 113698 time to create 1 rle with old method : 0.13701105117797852 time for calcul the mask position with numpy : 0.03478813171386719 nb_pixel_total : 78967 time to create 1 rle with old method : 0.09231352806091309 time for calcul the mask position with numpy : 0.033562421798706055 nb_pixel_total : 38350 time to create 1 rle with old method : 0.04331350326538086 create new chi : 3.7736880779266357 time to delete rle : 0.0019669532775878906 batch 1 Loaded 43 chid ids of type : 3594 ++++++++++++++++++++++++++++++Number RLEs to save : 14189 TO DO : save crop sub photo not yet done ! save time : 3.0415470600128174 nb_obj : 25 nb_hashtags : 3 time to prepare the origin masks : 10.696697473526001 time for calcul the mask position with numpy : 0.5387561321258545 nb_pixel_total : 5855370 time to create 1 rle with new method : 0.3777647018432617 time for calcul the mask position with numpy : 0.03681015968322754 nb_pixel_total : 53449 time to create 1 rle with old method : 0.06107735633850098 time for calcul the mask position with numpy : 0.037764787673950195 nb_pixel_total : 13197 time to create 1 rle with old method : 0.015604496002197266 time for calcul the mask position with numpy : 0.04135012626647949 nb_pixel_total : 11800 time to create 1 rle with old method : 0.013497114181518555 time for calcul the mask position with numpy : 0.03824019432067871 nb_pixel_total : 114614 time to create 1 rle with old method : 0.13659930229187012 time for calcul the mask position with numpy : 0.03621268272399902 nb_pixel_total : 26029 time to create 1 rle with old method : 0.04015231132507324 time for calcul the mask position with numpy : 0.03451228141784668 nb_pixel_total : 10560 time to create 1 rle with old method : 0.012157440185546875 time for calcul the mask position with numpy : 0.03725004196166992 nb_pixel_total : 4935 time to create 1 rle with old method : 0.005696296691894531 time for calcul the mask position with numpy : 0.03920245170593262 nb_pixel_total : 10462 time to create 1 rle with old method : 0.017542362213134766 time for calcul the mask position with numpy : 0.04164314270019531 nb_pixel_total : 16328 time to create 1 rle with old method : 0.018821239471435547 time for calcul the mask position with numpy : 0.03685712814331055 nb_pixel_total : 12652 time to create 1 rle with old method : 0.014440059661865234 time for calcul the mask position with numpy : 0.03715229034423828 nb_pixel_total : 155515 time to create 1 rle with new method : 0.8023357391357422 time for calcul the mask position with numpy : 0.043473005294799805 nb_pixel_total : 65268 time to create 1 rle with old method : 0.07952570915222168 time for calcul the mask position with numpy : 0.03988981246948242 nb_pixel_total : 17739 time to create 1 rle with old method : 0.02110576629638672 time for calcul the mask position with numpy : 0.038606882095336914 nb_pixel_total : 52562 time to create 1 rle with old method : 0.06509685516357422 time for calcul the mask position with numpy : 0.03875470161437988 nb_pixel_total : 73603 time to create 1 rle with old method : 0.08501863479614258 time for calcul the mask position with numpy : 0.03691458702087402 nb_pixel_total : 6184 time to create 1 rle with old method : 0.0070874691009521484 time for calcul the mask position with numpy : 0.035921573638916016 nb_pixel_total : 15162 time to create 1 rle with old method : 0.017668962478637695 time for calcul the mask position with numpy : 0.03610587120056152 nb_pixel_total : 97917 time to create 1 rle with old method : 0.1127769947052002 time for calcul the mask position with numpy : 0.036437273025512695 nb_pixel_total : 19394 time to create 1 rle with old method : 0.022094249725341797 time for calcul the mask position with numpy : 0.03558230400085449 nb_pixel_total : 19660 time to create 1 rle with old method : 0.022196292877197266 time for calcul the mask position with numpy : 0.036004066467285156 nb_pixel_total : 18540 time to create 1 rle with old method : 0.02623581886291504 time for calcul the mask position with numpy : 0.039067745208740234 nb_pixel_total : 103664 time to create 1 rle with old method : 0.1206197738647461 time for calcul the mask position with numpy : 0.03883695602416992 nb_pixel_total : 26272 time to create 1 rle with old method : 0.03124833106994629 time for calcul the mask position with numpy : 0.03961753845214844 nb_pixel_total : 88023 time to create 1 rle with old method : 0.10934185981750488 time for calcul the mask position with numpy : 0.04086899757385254 nb_pixel_total : 161341 time to create 1 rle with new method : 0.6438536643981934 create new chi : 4.478073358535767 time to delete rle : 0.004715681076049805 batch 1 Loaded 51 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++Number RLEs to save : 15225 TO DO : save crop sub photo not yet done ! save time : 1.0362868309020996 nb_obj : 36 nb_hashtags : 3 time to prepare the origin masks : 4.629690647125244 time for calcul the mask position with numpy : 0.61850905418396 nb_pixel_total : 5060493 time to create 1 rle with new method : 0.9742679595947266 time for calcul the mask position with numpy : 0.030482769012451172 nb_pixel_total : 8686 time to create 1 rle with old method : 0.010151386260986328 time for calcul the mask position with numpy : 0.03030705451965332 nb_pixel_total : 73133 time to create 1 rle with old method : 0.0850675106048584 time for calcul the mask position with numpy : 0.03478407859802246 nb_pixel_total : 267051 time to create 1 rle with new method : 0.7205889225006104 time for calcul the mask position with numpy : 0.03136801719665527 nb_pixel_total : 241534 time to create 1 rle with new method : 0.8054828643798828 time for calcul the mask position with numpy : 0.029501914978027344 nb_pixel_total : 13600 time to create 1 rle with old method : 0.015467405319213867 time for calcul the mask position with numpy : 0.029976844787597656 nb_pixel_total : 71725 time to create 1 rle with old method : 0.08931708335876465 time for calcul the mask position with numpy : 0.03091740608215332 nb_pixel_total : 112406 time to create 1 rle with old method : 0.13030719757080078 time for calcul the mask position with numpy : 0.031191110610961914 nb_pixel_total : 15394 time to create 1 rle with old method : 0.020210981369018555 time for calcul the mask position with numpy : 0.030783414840698242 nb_pixel_total : 17861 time to create 1 rle with old method : 0.02009296417236328 time for calcul the mask position with numpy : 0.02992868423461914 nb_pixel_total : 25978 time to create 1 rle with old method : 0.029886960983276367 time for calcul the mask position with numpy : 0.029764652252197266 nb_pixel_total : 23861 time to create 1 rle with old method : 0.027726411819458008 time for calcul the mask position with numpy : 0.029682397842407227 nb_pixel_total : 21243 time to create 1 rle with old method : 0.02807927131652832 time for calcul the mask position with numpy : 0.03308391571044922 nb_pixel_total : 14961 time to create 1 rle with old method : 0.018787860870361328 time for calcul the mask position with numpy : 0.029778480529785156 nb_pixel_total : 27469 time to create 1 rle with old method : 0.04373598098754883 time for calcul the mask position with numpy : 0.03376579284667969 nb_pixel_total : 6725 time to create 1 rle with old method : 0.00825357437133789 time for calcul the mask position with numpy : 0.02956080436706543 nb_pixel_total : 63451 time to create 1 rle with old method : 0.07460141181945801 time for calcul the mask position with numpy : 0.030057191848754883 nb_pixel_total : 42142 time to create 1 rle with old method : 0.049280405044555664 time for calcul the mask position with numpy : 0.031079769134521484 nb_pixel_total : 116693 time to create 1 rle with old method : 0.13704681396484375 time for calcul the mask position with numpy : 0.029785871505737305 nb_pixel_total : 21008 time to create 1 rle with old method : 0.027286767959594727 time for calcul the mask position with numpy : 0.030065298080444336 nb_pixel_total : 15182 time to create 1 rle with old method : 0.02145218849182129 time for calcul the mask position with numpy : 0.033863067626953125 nb_pixel_total : 41985 time to create 1 rle with old method : 0.051293373107910156 time for calcul the mask position with numpy : 0.03242325782775879 nb_pixel_total : 95910 time to create 1 rle with old method : 0.11114144325256348 time for calcul the mask position with numpy : 0.0294647216796875 nb_pixel_total : 40854 time to create 1 rle with old method : 0.04603981971740723 time for calcul the mask position with numpy : 0.03166389465332031 nb_pixel_total : 34739 time to create 1 rle with old method : 0.0421750545501709 time for calcul the mask position with numpy : 0.029570341110229492 nb_pixel_total : 25411 time to create 1 rle with old method : 0.03202342987060547 time for calcul the mask position with numpy : 0.02940821647644043 nb_pixel_total : 14833 time to create 1 rle with old method : 0.017877817153930664 time for calcul the mask position with numpy : 0.02988409996032715 nb_pixel_total : 65945 time to create 1 rle with old method : 0.07587122917175293 time for calcul the mask position with numpy : 0.02944040298461914 nb_pixel_total : 25077 time to create 1 rle with old method : 0.02869725227355957 time for calcul the mask position with numpy : 0.03149080276489258 nb_pixel_total : 13097 time to create 1 rle with old method : 0.014994382858276367 time for calcul the mask position with numpy : 0.030434131622314453 nb_pixel_total : 197890 time to create 1 rle with new method : 0.32813405990600586 time for calcul the mask position with numpy : 0.02905583381652832 nb_pixel_total : 21204 time to create 1 rle with old method : 0.024029970169067383 time for calcul the mask position with numpy : 0.02899169921875 nb_pixel_total : 45845 time to create 1 rle with old method : 0.07653355598449707 time for calcul the mask position with numpy : 0.03809666633605957 nb_pixel_total : 38162 time to create 1 rle with old method : 0.06924962997436523 time for calcul the mask position with numpy : 0.03934836387634277 nb_pixel_total : 20205 time to create 1 rle with old method : 0.023631572723388672 time for calcul the mask position with numpy : 0.030527830123901367 nb_pixel_total : 90978 time to create 1 rle with old method : 0.10702157020568848 time for calcul the mask position with numpy : 0.029058456420898438 nb_pixel_total : 17509 time to create 1 rle with old method : 0.019629716873168945 create new chi : 6.254166126251221 time to delete rle : 0.003939628601074219 batch 1 Loaded 73 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22900 TO DO : save crop sub photo not yet done ! save time : 2.3847899436950684 nb_obj : 43 nb_hashtags : 3 time to prepare the origin masks : 4.135067462921143 time for calcul the mask position with numpy : 0.571967363357544 nb_pixel_total : 5491421 time to create 1 rle with new method : 0.5243020057678223 time for calcul the mask position with numpy : 0.028985023498535156 nb_pixel_total : 5218 time to create 1 rle with old method : 0.0060389041900634766 time for calcul the mask position with numpy : 0.033162593841552734 nb_pixel_total : 126496 time to create 1 rle with old method : 0.1975719928741455 time for calcul the mask position with numpy : 0.03646492958068848 nb_pixel_total : 53202 time to create 1 rle with old method : 0.06113243103027344 time for calcul the mask position with numpy : 0.030965089797973633 nb_pixel_total : 14102 time to create 1 rle with old method : 0.01722121238708496 time for calcul the mask position with numpy : 0.032421112060546875 nb_pixel_total : 11095 time to create 1 rle with old method : 0.012960433959960938 time for calcul the mask position with numpy : 0.029742002487182617 nb_pixel_total : 15191 time to create 1 rle with old method : 0.01751852035522461 time for calcul the mask position with numpy : 0.030452966690063477 nb_pixel_total : 43884 time to create 1 rle with old method : 0.05238151550292969 time for calcul the mask position with numpy : 0.03064703941345215 nb_pixel_total : 9180 time to create 1 rle with old method : 0.010889291763305664 time for calcul the mask position with numpy : 0.0301666259765625 nb_pixel_total : 29901 time to create 1 rle with old method : 0.03383350372314453 time for calcul the mask position with numpy : 0.02805638313293457 nb_pixel_total : 10962 time to create 1 rle with old method : 0.012511968612670898 time for calcul the mask position with numpy : 0.028374910354614258 nb_pixel_total : 38142 time to create 1 rle with old method : 0.04186058044433594 time for calcul the mask position with numpy : 0.02831721305847168 nb_pixel_total : 15429 time to create 1 rle with old method : 0.0173187255859375 time for calcul the mask position with numpy : 0.028098106384277344 nb_pixel_total : 13534 time to create 1 rle with old method : 0.014540672302246094 time for calcul the mask position with numpy : 0.028304576873779297 nb_pixel_total : 25236 time to create 1 rle with old method : 0.02762293815612793 time for calcul the mask position with numpy : 0.02959728240966797 nb_pixel_total : 99304 time to create 1 rle with old method : 0.1079261302947998 time for calcul the mask position with numpy : 0.028477907180786133 nb_pixel_total : 9186 time to create 1 rle with old method : 0.010258197784423828 time for calcul the mask position with numpy : 0.027978181838989258 nb_pixel_total : 10095 time to create 1 rle with old method : 0.01101827621459961 time for calcul the mask position with numpy : 0.029360055923461914 nb_pixel_total : 48419 time to create 1 rle with old method : 0.052252769470214844 time for calcul the mask position with numpy : 0.028273344039916992 nb_pixel_total : 18449 time to create 1 rle with old method : 0.020377635955810547 time for calcul the mask position with numpy : 0.028146028518676758 nb_pixel_total : 26703 time to create 1 rle with old method : 0.02940821647644043 time for calcul the mask position with numpy : 0.028053760528564453 nb_pixel_total : 43273 time to create 1 rle with old method : 0.04752993583679199 time for calcul the mask position with numpy : 0.02872633934020996 nb_pixel_total : 19338 time to create 1 rle with old method : 0.022006750106811523 time for calcul the mask position with numpy : 0.031052350997924805 nb_pixel_total : 44006 time to create 1 rle with old method : 0.05432319641113281 time for calcul the mask position with numpy : 0.029366731643676758 nb_pixel_total : 116330 time to create 1 rle with old method : 0.13175725936889648 time for calcul the mask position with numpy : 0.029185771942138672 nb_pixel_total : 39493 time to create 1 rle with old method : 0.04438018798828125 time for calcul the mask position with numpy : 0.02919173240661621 nb_pixel_total : 48086 time to create 1 rle with old method : 0.05481600761413574 time for calcul the mask position with numpy : 0.029088497161865234 nb_pixel_total : 30651 time to create 1 rle with old method : 0.03467130661010742 time for calcul the mask position with numpy : 0.029265880584716797 nb_pixel_total : 11561 time to create 1 rle with old method : 0.013249874114990234 time for calcul the mask position with numpy : 0.029202938079833984 nb_pixel_total : 32651 time to create 1 rle with old method : 0.03686046600341797 time for calcul the mask position with numpy : 0.030053138732910156 nb_pixel_total : 57504 time to create 1 rle with old method : 0.06458282470703125 time for calcul the mask position with numpy : 0.029608964920043945 nb_pixel_total : 43203 time to create 1 rle with old method : 0.04981708526611328 time for calcul the mask position with numpy : 0.02947854995727539 nb_pixel_total : 52071 time to create 1 rle with old method : 0.05885148048400879 time for calcul the mask position with numpy : 0.02937150001525879 nb_pixel_total : 29142 time to create 1 rle with old method : 0.033133745193481445 time for calcul the mask position with numpy : 0.029334306716918945 nb_pixel_total : 29073 time to create 1 rle with old method : 0.035985469818115234 time for calcul the mask position with numpy : 0.030127763748168945 nb_pixel_total : 70187 time to create 1 rle with old method : 0.0798184871673584 time for calcul the mask position with numpy : 0.029210805892944336 nb_pixel_total : 6667 time to create 1 rle with old method : 0.010719060897827148 time for calcul the mask position with numpy : 0.033463478088378906 nb_pixel_total : 55391 time to create 1 rle with old method : 0.08018875122070312 time for calcul the mask position with numpy : 0.036118268966674805 nb_pixel_total : 33767 time to create 1 rle with old method : 0.03838634490966797 time for calcul the mask position with numpy : 0.03147625923156738 nb_pixel_total : 13099 time to create 1 rle with old method : 0.015189409255981445 time for calcul the mask position with numpy : 0.029613256454467773 nb_pixel_total : 26784 time to create 1 rle with old method : 0.03014540672302246 time for calcul the mask position with numpy : 0.029282808303833008 nb_pixel_total : 15001 time to create 1 rle with old method : 0.01711440086364746 time for calcul the mask position with numpy : 0.030535221099853516 nb_pixel_total : 107372 time to create 1 rle with old method : 0.12068748474121094 time for calcul the mask position with numpy : 0.029197216033935547 nb_pixel_total : 10441 time to create 1 rle with old method : 0.011831998825073242 create new chi : 4.263495445251465 time to delete rle : 0.003815174102783203 batch 1 Loaded 87 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24443 TO DO : save crop sub photo not yet done ! save time : 1.8466672897338867 nb_obj : 48 nb_hashtags : 4 time to prepare the origin masks : 4.508695363998413 time for calcul the mask position with numpy : 0.5451176166534424 nb_pixel_total : 5456812 time to create 1 rle with new method : 0.6017467975616455 time for calcul the mask position with numpy : 0.030636072158813477 nb_pixel_total : 82177 time to create 1 rle with old method : 0.10441970825195312 time for calcul the mask position with numpy : 0.03292965888977051 nb_pixel_total : 15663 time to create 1 rle with old method : 0.018143892288208008 time for calcul the mask position with numpy : 0.030313968658447266 nb_pixel_total : 19251 time to create 1 rle with old method : 0.02427959442138672 time for calcul the mask position with numpy : 0.030494213104248047 nb_pixel_total : 9500 time to create 1 rle with old method : 0.011495113372802734 time for calcul the mask position with numpy : 0.03002333641052246 nb_pixel_total : 15916 time to create 1 rle with old method : 0.018898725509643555 time for calcul the mask position with numpy : 0.030270099639892578 nb_pixel_total : 22830 time to create 1 rle with old method : 0.02638983726501465 time for calcul the mask position with numpy : 0.03281974792480469 nb_pixel_total : 34202 time to create 1 rle with old method : 0.0420684814453125 time for calcul the mask position with numpy : 0.03012847900390625 nb_pixel_total : 13481 time to create 1 rle with old method : 0.016664981842041016 time for calcul the mask position with numpy : 0.0302731990814209 nb_pixel_total : 15493 time to create 1 rle with old method : 0.01985788345336914 time for calcul the mask position with numpy : 0.030059337615966797 nb_pixel_total : 592 time to create 1 rle with old method : 0.0007953643798828125 time for calcul the mask position with numpy : 0.03182840347290039 nb_pixel_total : 102845 time to create 1 rle with old method : 0.12687373161315918 time for calcul the mask position with numpy : 0.03514385223388672 nb_pixel_total : 30044 time to create 1 rle with old method : 0.0398707389831543 time for calcul the mask position with numpy : 0.029949665069580078 nb_pixel_total : 62610 time to create 1 rle with old method : 0.07177472114562988 time for calcul the mask position with numpy : 0.03144359588623047 nb_pixel_total : 63205 time to create 1 rle with old method : 0.07648396492004395 time for calcul the mask position with numpy : 0.029367923736572266 nb_pixel_total : 13417 time to create 1 rle with old method : 0.015219926834106445 time for calcul the mask position with numpy : 0.02972269058227539 nb_pixel_total : 120158 time to create 1 rle with old method : 0.13602304458618164 time for calcul the mask position with numpy : 0.0297698974609375 nb_pixel_total : 56108 time to create 1 rle with old method : 0.06353354454040527 time for calcul the mask position with numpy : 0.029495954513549805 nb_pixel_total : 66859 time to create 1 rle with old method : 0.07541656494140625 time for calcul the mask position with numpy : 0.029915571212768555 nb_pixel_total : 60672 time to create 1 rle with old method : 0.06836295127868652 time for calcul the mask position with numpy : 0.02923440933227539 nb_pixel_total : 31726 time to create 1 rle with old method : 0.03442811965942383 time for calcul the mask position with numpy : 0.031008005142211914 nb_pixel_total : 103615 time to create 1 rle with old method : 0.1525578498840332 time for calcul the mask position with numpy : 0.029488325119018555 nb_pixel_total : 12465 time to create 1 rle with old method : 0.0140533447265625 time for calcul the mask position with numpy : 0.03084421157836914 nb_pixel_total : 64924 time to create 1 rle with old method : 0.0769190788269043 time for calcul the mask position with numpy : 0.029315948486328125 nb_pixel_total : 24456 time to create 1 rle with old method : 0.028453350067138672 time for calcul the mask position with numpy : 0.029776334762573242 nb_pixel_total : 43813 time to create 1 rle with old method : 0.05058908462524414 time for calcul the mask position with numpy : 0.03578448295593262 nb_pixel_total : 17147 time to create 1 rle with old method : 0.019493818283081055 time for calcul the mask position with numpy : 0.029976844787597656 nb_pixel_total : 5739 time to create 1 rle with old method : 0.006665706634521484 time for calcul the mask position with numpy : 0.02932572364807129 nb_pixel_total : 64430 time to create 1 rle with old method : 0.07709789276123047 time for calcul the mask position with numpy : 0.032303810119628906 nb_pixel_total : 47335 time to create 1 rle with old method : 0.05443549156188965 time for calcul the mask position with numpy : 0.029803037643432617 nb_pixel_total : 50849 time to create 1 rle with old method : 0.06300139427185059 time for calcul the mask position with numpy : 0.029938220977783203 nb_pixel_total : 35592 time to create 1 rle with old method : 0.06899380683898926 time for calcul the mask position with numpy : 0.033216238021850586 nb_pixel_total : 11810 time to create 1 rle with old method : 0.01469111442565918 time for calcul the mask position with numpy : 0.03255605697631836 nb_pixel_total : 30488 time to create 1 rle with old method : 0.03774237632751465 time for calcul the mask position with numpy : 0.03221750259399414 nb_pixel_total : 36246 time to create 1 rle with old method : 0.04315900802612305 time for calcul the mask position with numpy : 0.029742002487182617 nb_pixel_total : 4079 time to create 1 rle with old method : 0.005219936370849609 time for calcul the mask position with numpy : 0.03427314758300781 nb_pixel_total : 7205 time to create 1 rle with old method : 0.0125579833984375 time for calcul the mask position with numpy : 0.06187129020690918 nb_pixel_total : 25426 time to create 1 rle with old method : 0.07026815414428711 time for calcul the mask position with numpy : 0.034005165100097656 nb_pixel_total : 20857 time to create 1 rle with old method : 0.02574300765991211 time for calcul the mask position with numpy : 0.030817031860351562 nb_pixel_total : 37174 time to create 1 rle with old method : 0.04422354698181152 time for calcul the mask position with numpy : 0.02937030792236328 nb_pixel_total : 4353 time to create 1 rle with old method : 0.0050716400146484375 time for calcul the mask position with numpy : 0.030116796493530273 nb_pixel_total : 26850 time to create 1 rle with old method : 0.030699968338012695 time for calcul the mask position with numpy : 0.029434919357299805 nb_pixel_total : 6579 time to create 1 rle with old method : 0.007590055465698242 time for calcul the mask position with numpy : 0.029308557510375977 nb_pixel_total : 14267 time to create 1 rle with old method : 0.01619553565979004 time for calcul the mask position with numpy : 0.029242515563964844 nb_pixel_total : 31941 time to create 1 rle with old method : 0.03595590591430664 time for calcul the mask position with numpy : 0.02975296974182129 nb_pixel_total : 8477 time to create 1 rle with old method : 0.013703346252441406 time for calcul the mask position with numpy : 0.033258676528930664 nb_pixel_total : 6161 time to create 1 rle with old method : 0.010339498519897461 time for calcul the mask position with numpy : 0.0325319766998291 nb_pixel_total : 5117 time to create 1 rle with old method : 0.005954742431640625 time for calcul the mask position with numpy : 0.029068708419799805 nb_pixel_total : 9284 time to create 1 rle with old method : 0.010799884796142578 create new chi : 4.699614524841309 time to delete rle : 0.004514217376708984 batch 1 Loaded 97 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27481 TO DO : save crop sub photo not yet done ! save time : 1.943695306777954 nb_obj : 31 nb_hashtags : 5 time to prepare the origin masks : 4.259156703948975 time for calcul the mask position with numpy : 0.5067439079284668 nb_pixel_total : 6212425 time to create 1 rle with new method : 0.9003756046295166 time for calcul the mask position with numpy : 0.029276132583618164 nb_pixel_total : 7266 time to create 1 rle with old method : 0.00832056999206543 time for calcul the mask position with numpy : 0.02937626838684082 nb_pixel_total : 31668 time to create 1 rle with old method : 0.03632164001464844 time for calcul the mask position with numpy : 0.030023574829101562 nb_pixel_total : 18874 time to create 1 rle with old method : 0.03034496307373047 time for calcul the mask position with numpy : 0.0332486629486084 nb_pixel_total : 33692 time to create 1 rle with old method : 0.04034543037414551 time for calcul the mask position with numpy : 0.0294950008392334 nb_pixel_total : 26266 time to create 1 rle with old method : 0.03330111503601074 time for calcul the mask position with numpy : 0.03359818458557129 nb_pixel_total : 33589 time to create 1 rle with old method : 0.03909158706665039 time for calcul the mask position with numpy : 0.029369115829467773 nb_pixel_total : 7191 time to create 1 rle with old method : 0.00899195671081543 time for calcul the mask position with numpy : 0.03406071662902832 nb_pixel_total : 45921 time to create 1 rle with old method : 0.05760908126831055 time for calcul the mask position with numpy : 0.030406475067138672 nb_pixel_total : 27699 time to create 1 rle with old method : 0.031529903411865234 time for calcul the mask position with numpy : 0.02939748764038086 nb_pixel_total : 19803 time to create 1 rle with old method : 0.02295994758605957 time for calcul the mask position with numpy : 0.031893014907836914 nb_pixel_total : 16932 time to create 1 rle with old method : 0.020517826080322266 time for calcul the mask position with numpy : 0.03262472152709961 nb_pixel_total : 66141 time to create 1 rle with old method : 0.08058476448059082 time for calcul the mask position with numpy : 0.032232046127319336 nb_pixel_total : 49737 time to create 1 rle with old method : 0.059600830078125 time for calcul the mask position with numpy : 0.029623746871948242 nb_pixel_total : 62793 time to create 1 rle with old method : 0.07571172714233398 time for calcul the mask position with numpy : 0.029660701751708984 nb_pixel_total : 18911 time to create 1 rle with old method : 0.021712779998779297 time for calcul the mask position with numpy : 0.029355287551879883 nb_pixel_total : 8635 time to create 1 rle with old method : 0.009889602661132812 time for calcul the mask position with numpy : 0.029390811920166016 nb_pixel_total : 52706 time to create 1 rle with old method : 0.06224417686462402 time for calcul the mask position with numpy : 0.03160834312438965 nb_pixel_total : 28772 time to create 1 rle with old method : 0.03320169448852539 time for calcul the mask position with numpy : 0.029288291931152344 nb_pixel_total : 30375 time to create 1 rle with old method : 0.03515791893005371 time for calcul the mask position with numpy : 0.029377460479736328 nb_pixel_total : 17121 time to create 1 rle with old method : 0.019319534301757812 time for calcul the mask position with numpy : 0.029495954513549805 nb_pixel_total : 63339 time to create 1 rle with old method : 0.0743868350982666 time for calcul the mask position with numpy : 0.03321218490600586 nb_pixel_total : 22987 time to create 1 rle with old method : 0.037140607833862305 time for calcul the mask position with numpy : 0.03137922286987305 nb_pixel_total : 30988 time to create 1 rle with old method : 0.03889203071594238 time for calcul the mask position with numpy : 0.031169652938842773 nb_pixel_total : 42243 time to create 1 rle with old method : 0.05188941955566406 time for calcul the mask position with numpy : 0.029468059539794922 nb_pixel_total : 19925 time to create 1 rle with old method : 0.022507429122924805 time for calcul the mask position with numpy : 0.02942514419555664 nb_pixel_total : 10334 time to create 1 rle with old method : 0.011883735656738281 time for calcul the mask position with numpy : 0.02934575080871582 nb_pixel_total : 6527 time to create 1 rle with old method : 0.007566690444946289 time for calcul the mask position with numpy : 0.02954864501953125 nb_pixel_total : 12261 time to create 1 rle with old method : 0.014138221740722656 time for calcul the mask position with numpy : 0.029763460159301758 nb_pixel_total : 13247 time to create 1 rle with old method : 0.015468597412109375 time for calcul the mask position with numpy : 0.031493425369262695 nb_pixel_total : 5777 time to create 1 rle with old method : 0.006655693054199219 time for calcul the mask position with numpy : 0.029336929321289062 nb_pixel_total : 6095 time to create 1 rle with old method : 0.006978750228881836 create new chi : 3.406071662902832 time to delete rle : 0.0024204254150390625 batch 1 Loaded 63 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 15207 TO DO : save crop sub photo not yet done ! save time : 0.9121949672698975 map_output_result : {1350336436: (0.0, 'Should be the crop_list due to order', 0), 1350336421: (0.0, 'Should be the crop_list due to order', 0), 1350336284: (0.0, 'Should be the crop_list due to order', 0), 1350336226: (0.0, 'Should be the crop_list due to order', 0), 1350287350: (0.0, 'Should be the crop_list due to order', 0), 1350287140: (0.0, 'Should be the crop_list due to order', 0), 1350287110: (0.0, 'Should be the crop_list due to order', 0), 1350287098: (0.0, 'Should be the crop_list due to order', 0), 1350287073: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1350336436, 1350336421, 1350336284, 1350336226, 1350287350, 1350287140, 1350287110, 1350287098, 1350287073] Looping around the photos to save general results len do output : 9 /1350336436.Didn't retrieve data . /1350336421.Didn't retrieve data . /1350336284.Didn't retrieve data . /1350336226.Didn't retrieve data . /1350287350.Didn't retrieve data . /1350287140.Didn't retrieve data . /1350287110.Didn't retrieve data . /1350287098.Didn't retrieve data . /1350287073.Didn't retrieve data . before output type Used above Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336436', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336421', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336284', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336226', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287350', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287140', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287110', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287098', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287073', None, None, None, None, None, '2733653') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 27 time used for this insertion : 0.01463460922241211 save_final save missing photos in datou_result : time spend for datou_step_exec : 110.08439993858337 time spend to save output : 0.015288352966308594 total time spend for step 3 : 110.09968829154968 step4:ventilate_hashtags_in_portfolio Wed Apr 9 11:08:35 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 22153573 get user id for portfolio 22153573 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`=22153573 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','papier','flou','mal_croppe','metal','background','pehd','pet_fonce','environnement','autre')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153573 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','papier','flou','mal_croppe','metal','background','pehd','pet_fonce','environnement','autre')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153573 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','papier','flou','mal_croppe','metal','background','pehd','pet_fonce','environnement','autre')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22158561,22158562,22158563,22158564,22158565,22158566,22158567,22158568,22158569,22158570,22158571?tags=carton,pet_clair,papier,flou,mal_croppe,metal,background,pehd,pet_fonce,environnement,autre Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350336436, 1350336421, 1350336284, 1350336226, 1350287350, 1350287140, 1350287110, 1350287098, 1350287073] Looping around the photos to save general results len do output : 1 /22153573. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336436', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336421', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336284', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336226', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287350', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287140', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287110', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287098', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287073', None, None, None, None, None, '2733653') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.013495683670043945 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.1228389739990234 time spend to save output : 0.013831138610839844 total time spend for step 4 : 2.1366701126098633 step5:final Wed Apr 9 11:08:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : {1350336436: ('0.21942126162450581',), 1350336421: ('0.21942126162450581',), 1350336284: ('0.21942126162450581',), 1350336226: ('0.21942126162450581',), 1350287350: ('0.21942126162450581',), 1350287140: ('0.21942126162450581',), 1350287110: ('0.21942126162450581',), 1350287098: ('0.21942126162450581',), 1350287073: ('0.21942126162450581',)} new output for save of step final : {1350336436: ('0.21942126162450581',), 1350336421: ('0.21942126162450581',), 1350336284: ('0.21942126162450581',), 1350336226: ('0.21942126162450581',), 1350287350: ('0.21942126162450581',), 1350287140: ('0.21942126162450581',), 1350287110: ('0.21942126162450581',), 1350287098: ('0.21942126162450581',), 1350287073: ('0.21942126162450581',)} [1350336436, 1350336421, 1350336284, 1350336226, 1350287350, 1350287140, 1350287110, 1350287098, 1350287073] Looping around the photos to save general results len do output : 9 /1350336436.Didn't retrieve data . /1350336421.Didn't retrieve data . /1350336284.Didn't retrieve data . /1350336226.Didn't retrieve data . /1350287350.Didn't retrieve data . /1350287140.Didn't retrieve data . /1350287110.Didn't retrieve data . /1350287098.Didn't retrieve data . /1350287073.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336436', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336421', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336284', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336226', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287350', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287140', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287110', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287098', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287073', None, None, None, None, None, '2733653') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 27 time used for this insertion : 0.013558387756347656 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.11110830307006836 time spend to save output : 0.014069795608520508 total time spend for step 5 : 0.12517809867858887 step6:blur_detection Wed Apr 9 11:08:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf.jpg resize: (2160, 3264) 1350336436 -3.2486934496713795 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac.jpg resize: (2160, 3264) 1350336421 -4.4866412167798675 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca.jpg resize: (2160, 3264) 1350336284 -3.2124925290789315 treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7.jpg resize: (2160, 3264) 1350336226 -3.969992850521878 treat image : temp/1744189230_624923_1350287350_bd7c0d53df58aefad086deffcc6f10a3.jpg resize: (2160, 3264) 1350287350 -4.452861945907975 treat image : temp/1744189230_624923_1350287140_d13d7930e3687fa0924f6fe0d918e21a.jpg resize: (2160, 3264) 1350287140 -3.9293101788030382 treat image : temp/1744189230_624923_1350287110_ae791c7a7744e15dd86caefc34a57ddf.jpg resize: (2160, 3264) 1350287110 -5.003461839301694 treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79.jpg resize: (2160, 3264) 1350287098 -4.834185732652523 treat image : temp/1744189230_624923_1350287073_8d5d7772383d77a3b010bfcd9ab4de4d.jpg resize: (2160, 3264) 1350287073 -4.837823667238723 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326439_0.png resize: (430, 391) 1350737582 -1.8645438482271888 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326464_0.png resize: (237, 357) 1350737587 -1.3897825563492514 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326456_0.png resize: (366, 669) 1350737592 -1.8867590440436883 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326447_0.png resize: (208, 302) 1350737596 -1.1302363831209326 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326435_0.png resize: (244, 203) 1350737601 -2.6624993654819127 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326438_0.png resize: (151, 214) 1350737607 -1.5491094451490721 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326459_0.png resize: (124, 264) 1350737613 -2.4159295357104034 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326458_0.png resize: (217, 237) 1350737618 -2.0986683258886125 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326448_0.png resize: (124, 302) 1350737623 -1.2965725274002426 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326436_0.png resize: (132, 99) 1350737627 -1.389736817646961 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326433_0.png resize: (314, 376) 1350737632 -1.743940292458885 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326445_0.png resize: (302, 232) 1350737638 -1.7035245359153475 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326446_0.png resize: (226, 111) 1350737643 -2.2199030302172136 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326442_0.png resize: (66, 170) 1350737648 -2.5700400339893217 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326440_0.png resize: (315, 278) 1350737653 -1.9743370987423619 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326452_0.png resize: (218, 236) 1350737658 -1.9517173709725448 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326467_0.png resize: (182, 154) 1350737661 -1.3153995321750203 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326434_0.png resize: (157, 173) 1350737664 -2.3784076268645573 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326437_0.png resize: (259, 190) 1350737666 -1.1995822445238415 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326453_0.png resize: (985, 1076) 1350737667 -1.5007137773748616 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326451_0.png resize: (78, 106) 1350737669 -0.4441839646197725 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326455_0.png resize: (269, 372) 1350737670 -2.273801906914681 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326443_0.png resize: (352, 355) 1350737671 -2.063510672011687 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326465_0.png resize: (132, 180) 1350737673 -0.7511956083034469 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326461_0.png resize: (170, 126) 1350737674 -1.7393790439397392 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326449_0.png resize: (414, 354) 1350737676 -2.6754299603967193 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326462_0.png resize: (144, 135) 1350737677 -0.3617012670390473 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326460_0.png resize: (545, 636) 1350737679 -2.798294519049844 treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326450_0.png resize: (140, 133) 1350737680 -2.586213807779615 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326495_0.png resize: (184, 191) 1350737682 -2.296077048474829 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326486_0.png resize: (125, 208) 1350737683 -1.5578211934672177 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326497_0.png resize: (412, 582) 1350737685 -2.225044458725516 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326482_0.png resize: (380, 326) 1350737686 -2.8634066087603207 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326493_0.png resize: (79, 172) 1350737688 -2.5536197834146606 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326494_0.png resize: (153, 309) 1350737689 -3.2634323557285945 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326472_0.png resize: (317, 296) 1350737691 -2.1388723894895785 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326468_0.png resize: (160, 147) 1350737692 -1.6541931717117666 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326475_0.png resize: (223, 237) 1350737693 -2.6478588825600546 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326479_0.png resize: (270, 243) 1350737695 -3.5729738816287537 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326473_0.png resize: (154, 188) 1350737696 -3.433230829350646 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326492_0.png resize: (175, 143) 1350737698 -1.4587931086135384 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326501_0.png resize: (246, 331) 1350737699 -3.3723595227552337 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326474_0.png resize: (165, 281) 1350737700 -2.8647083329692498 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326471_0.png resize: (178, 119) 1350737702 -1.4565187364392753 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326478_0.png resize: (213, 139) 1350737703 -3.2590167213723564 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326496_0.png resize: (105, 92) 1350737706 -3.229740860900423 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326503_0.png resize: (134, 247) 1350737709 -3.747380409502068 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326470_0.png resize: (175, 474) 1350737712 -4.159550552542748 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326477_0.png resize: (363, 615) 1350737716 -3.4022801837364147 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326498_0.png resize: (191, 155) 1350737719 -2.861029308053407 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326499_0.png resize: (420, 339) 1350737721 -3.0955286933762354 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326489_0.png resize: (94, 79) 1350737725 -0.4792017912799623 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326487_0.png resize: (548, 447) 1350737727 -3.60052735912318 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326484_0.png resize: (169, 188) 1350737730 -2.004547610430614 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326469_0.png resize: (280, 171) 1350737732 -3.3086798754591276 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326491_0.png resize: (647, 278) 1350737734 -3.3322313659792444 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326485_0.png resize: (219, 272) 1350737735 -4.5112369102690035 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326529_0.png resize: (243, 125) 1350737737 -1.8908497933075596 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326512_0.png resize: (225, 212) 1350737739 -2.1182327559918774 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326522_0.png resize: (270, 335) 1350737741 -2.3811948869548334 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326528_0.png resize: (116, 212) 1350737743 -1.041523371399433 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326538_0.png resize: (382, 416) 1350737744 -1.691555120159337 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326521_0.png resize: (298, 204) 1350737746 -0.21805962663056253 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326534_0.png resize: (214, 94) 1350737747 -0.9256484533036197 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326540_0.png resize: (133, 83) 1350737749 -0.4816886358661552 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326516_0.png resize: (288, 247) 1350737753 -3.364174852056327 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326513_0.png resize: (338, 373) 1350737754 -0.8603305997917172 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326507_0.png resize: (243, 161) 1350737756 -1.725046257984746 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326525_0.png resize: (256, 105) 1350737757 -2.123379242304898 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326506_0.png resize: (217, 265) 1350737759 -1.3323552871058757 treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326518_0.png resize: (377, 249) 1350737760 -1.728126368584428 treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326552_0.png resize: (479, 732) 1350737762 -0.6296356813297654 treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326556_0.png resize: (343, 448) 1350737763 -3.4133550153173853 treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326554_0.png resize: (294, 185) 1350737765 -3.228601576608156 treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326559_0.png resize: (216, 178) 1350737770 -3.405890995145309 treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326541_0.png resize: (236, 235) 1350737771 -4.5268917654091005 treat image : 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temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79_rle_crop_3751326679_0.png resize: (485, 218) 1350738396 -2.157939305068694 treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79_rle_crop_3751326691_0.png resize: (190, 199) 1350738432 -0.5266402340587392 treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79_rle_crop_3751326705_0.png resize: (164, 126) 1350738433 -0.7149025462939284 treat image : temp/1744189230_624923_1350287073_8d5d7772383d77a3b010bfcd9ab4de4d_rle_crop_3751326735_0.png resize: (210, 195) 1350738435 -1.7051540944775723 treat image : temp/1744189230_624923_1350287073_8d5d7772383d77a3b010bfcd9ab4de4d_rle_crop_3751326742_0.png resize: (107, 103) 1350738456 -1.1741116336805597 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326490_0.png resize: (164, 161) 1350738467 -2.087854994935234 treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326502_0.png resize: (130, 159) 1350738468 -2.6020370825106403 treat image : temp/1744189230_624923_1350287073_8d5d7772383d77a3b010bfcd9ab4de4d_rle_crop_3751326741_0.png resize: (115, 171) 1350738469 -1.4732011175651034 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 : 321 time used for this insertion : 0.11885523796081543 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 321 time used for this insertion : 0.05903124809265137 save missing photos in datou_result : time spend for datou_step_exec : 37.80546808242798 time spend to save output : 0.18521666526794434 total time spend for step 6 : 37.99068474769592 step7:brightness Wed Apr 9 11:09:15 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/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf.jpg treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac.jpg treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca.jpg treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7.jpg treat image : temp/1744189230_624923_1350287350_bd7c0d53df58aefad086deffcc6f10a3.jpg treat image : temp/1744189230_624923_1350287140_d13d7930e3687fa0924f6fe0d918e21a.jpg treat image : temp/1744189230_624923_1350287110_ae791c7a7744e15dd86caefc34a57ddf.jpg treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79.jpg treat image : temp/1744189230_624923_1350287073_8d5d7772383d77a3b010bfcd9ab4de4d.jpg treat image : temp/1744189230_624923_1350336436_e4500461023fc20b9f82f38c3f6fc2cf_rle_crop_3751326439_0.png treat image : 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temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326527_0.png treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326530_0.png treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326524_0.png treat image : temp/1744189230_624923_1350336284_eb9d735f3d3acbf0195968574f90c4ca_rle_crop_3751326515_0.png treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326543_0.png treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326550_0.png treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326549_0.png treat image : temp/1744189230_624923_1350336226_ccd06a4f4c3bc3330c414cad6857a8b7_rle_crop_3751326561_0.png treat image : temp/1744189230_624923_1350287350_bd7c0d53df58aefad086deffcc6f10a3_rle_crop_3751326576_0.png treat image : temp/1744189230_624923_1350287350_bd7c0d53df58aefad086deffcc6f10a3_rle_crop_3751326565_0.png treat image : temp/1744189230_624923_1350287140_d13d7930e3687fa0924f6fe0d918e21a_rle_crop_3751326622_0.png treat image : temp/1744189230_624923_1350287140_d13d7930e3687fa0924f6fe0d918e21a_rle_crop_3751326593_0.png treat image : temp/1744189230_624923_1350287110_ae791c7a7744e15dd86caefc34a57ddf_rle_crop_3751326662_0.png treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79_rle_crop_3751326683_0.png treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79_rle_crop_3751326694_0.png treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79_rle_crop_3751326679_0.png treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79_rle_crop_3751326691_0.png treat image : temp/1744189230_624923_1350287098_1e1a48580294ca2ade40be3909dacb79_rle_crop_3751326705_0.png treat image : temp/1744189230_624923_1350287073_8d5d7772383d77a3b010bfcd9ab4de4d_rle_crop_3751326735_0.png treat image : temp/1744189230_624923_1350287073_8d5d7772383d77a3b010bfcd9ab4de4d_rle_crop_3751326742_0.png treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326490_0.png treat image : temp/1744189230_624923_1350336421_7776258914cfb38be12685ee151c61ac_rle_crop_3751326502_0.png treat image : temp/1744189230_624923_1350287073_8d5d7772383d77a3b010bfcd9ab4de4d_rle_crop_3751326741_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 321 time used for this insertion : 0.11623692512512207 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 321 time used for this insertion : 0.06604290008544922 save missing photos in datou_result : time spend for datou_step_exec : 10.027194499969482 time spend to save output : 0.18936657905578613 total time spend for step 7 : 10.216561079025269 step8:velours_tree Wed Apr 9 11:09:25 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.12801790237426758 time spend to save output : 4.076957702636719e-05 total time spend for step 8 : 0.12805867195129395 step9:send_mail_cod Wed Apr 9 11:09:25 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P22153573_09-04-2025_11_09_25.pdf 22158561 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 .imagette221585611744189765 22158562 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 .imagette221585621744189767 22158563 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 .imagette221585631744189769 22158564 imagette221585641744189770 22158565 imagette221585651744189770 22158566 change filename to text .change filename to text .change filename to text .change filename to text .imagette221585661744189770 22158567 imagette221585671744189770 22158568 change filename to text .imagette221585681744189770 22158569 change filename to text .change filename to text .change filename to text .imagette221585691744189770 22158571 change filename to text .change filename to text .change filename to text .imagette221585711744189771 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22153573 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22158561,22158562,22158563,22158564,22158565,22158566,22158567,22158568,22158569,22158570,22158571?tags=carton,pet_clair,papier,flou,mal_croppe,metal,background,pehd,pet_fonce,environnement,autre args[1350336436] : ((1350336436, -3.2486934496713795, 492609224), (1350336436, -0.0841729705195978, 496442774), '0.21942126162450581') We are sending mail with results at report@fotonower.com args[1350336421] : ((1350336421, -4.4866412167798675, 492609224), (1350336421, 0.053075268401159725, 2107752395), '0.21942126162450581') We are sending mail with results at report@fotonower.com args[1350336284] : ((1350336284, -3.2124925290789315, 492609224), (1350336284, 0.06685218045013368, 2107752395), '0.21942126162450581') We are sending mail with results at report@fotonower.com args[1350336226] : ((1350336226, -3.969992850521878, 492609224), (1350336226, -0.22689593116444645, 496442774), '0.21942126162450581') We are sending mail with results at report@fotonower.com args[1350287350] : ((1350287350, -4.452861945907975, 492609224), (1350287350, 0.0709156077020542, 2107752395), '0.21942126162450581') We are sending mail with results at report@fotonower.com args[1350287140] : ((1350287140, -3.9293101788030382, 492609224), (1350287140, 0.08559010606774207, 2107752395), '0.21942126162450581') We are sending mail with results at report@fotonower.com args[1350287110] : ((1350287110, -5.003461839301694, 492609224), (1350287110, 0.09191936371141868, 2107752395), '0.21942126162450581') We are sending mail with results at report@fotonower.com args[1350287098] : ((1350287098, -4.834185732652523, 492609224), (1350287098, 0.17625349962518586, 2107752395), '0.21942126162450581') We are sending mail with results at report@fotonower.com args[1350287073] : ((1350287073, -4.837823667238723, 492609224), (1350287073, -0.08670042048595807, 496442774), '0.21942126162450581') We are sending mail with results at report@fotonower.com refus_total : 0.21942126162450581 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=22153573 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350287073,1350287098,1350287110,1350336284,1350336436,1350287140,1350287350,1350336226,1350336421) Found this number of photos: 9 begin to download photo : 1350287073 begin to download photo : 1350287110 begin to download photo : 1350336436 begin to download photo : 1350287350 begin to download photo : 1350336421 download finish for photo 1350287073 begin to download photo : 1350287098 download finish for photo 1350336421 download finish for photo 1350336436 begin to download photo : 1350287140 download finish for photo 1350287350 begin to download photo : 1350336226 download finish for photo 1350287110 begin to download photo : 1350336284 download finish for photo 1350287098 download finish for photo 1350336226 download finish for photo 1350287140 download finish for photo 1350336284 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153573_09-04-2025_11_09_25.pdf results_Auto_P22153573_09-04-2025_11_09_25.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153573_09-04-2025_11_09_25.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3318','22153573','results_Auto_P22153573_09-04-2025_11_09_25.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153573_09-04-2025_11_09_25.pdf','pdf','','0.91','0.21942126162450581') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22153573

https://www.fotonower.com/image?json=false&list_photos_id=1350336436
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350336421
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350336284
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350336226
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350287350
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350287140
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350287110
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350287098
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350287073
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 21.94%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/22158561?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22158562?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22158563?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22158566?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22158568?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22158569?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22158571?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153573_09-04-2025_11_09_25.pdf.

Lien vers velours :https://www.fotonower.com/velours/22158561,22158562,22158563,22158564,22158565,22158566,22158567,22158568,22158569,22158570,22158571?tags=carton,pet_clair,papier,flou,mal_croppe,metal,background,pehd,pet_fonce,environnement,autre.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 09:09:34 GMT Content-Length: 0 Connection: close X-Message-Id: MsMAe0P4QW6tBJhCtad1FQ Access-Control-Allow-Origin: https://sendgrid.api-docs.io Access-Control-Allow-Methods: POST Access-Control-Allow-Headers: Authorization, Content-Type, On-behalf-of, x-sg-elas-acl Access-Control-Max-Age: 600 X-No-CORS-Reason: https://sendgrid.com/docs/Classroom/Basics/API/cors.html Strict-Transport-Security: max-age=31536000; includeSubDomains Content-Security-Policy: frame-ancestors 'none' Cache-Control: no-cache X-Content-Type-Options: no-sniff Referrer-Policy: strict-origin-when-cross-origin Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1350336436, 1350336421, 1350336284, 1350336226, 1350287350, 1350287140, 1350287110, 1350287098, 1350287073] Looping around the photos to save general results len do output : 0 before output type Used above Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336436', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336421', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336284', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336226', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287350', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287140', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287110', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287098', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287073', None, None, None, None, None, '2733653') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.013089179992675781 save_final save missing photos in datou_result : time spend for datou_step_exec : 8.603764772415161 time spend to save output : 0.01331186294555664 total time spend for step 9 : 8.617076635360718 step10:split_time_score Wed Apr 9 11:09:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('11', 9),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 07042025 22153573 Nombre de photos uploadées : 9 / 23040 (0%) 07042025 22153573 Nombre de photos taguées (types de déchets): 0 / 9 (0%) 07042025 22153573 Nombre de photos taguées (volume) : 0 / 9 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 4.0531158447265625e-06 ????????? elapsed_time : fill_and_build_computed_from_old_data 0.0004038810729980469 elapsed_time : insert_dashboard_record_day_entry 0.02440333366394043 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153527 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153533 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153536 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153537 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153567 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153572 order by id desc limit 1 Qualite : 0.21942126162450581 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153573_09-04-2025_11_09_25.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153573 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153573 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153575 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153579 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153585 order by id desc limit 1 Qualite : 0.1808327092411039 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153590_09-04-2025_11_04_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153590 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153590 AND mptpi.`type`=3594 To do Qualite : 0.2245747095134351 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153594_09-04-2025_10_39_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153594 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153594 AND mptpi.`type`=3594 To do Qualite : 0.18817508340141612 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153599_09-04-2025_10_31_18.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153599 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153599 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153631 order by id desc limit 1 Qualite : 0.2387647538497724 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153644_09-04-2025_10_11_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153644 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153644 AND mptpi.`type`=3594 To do Qualite : 0.20050398026424382 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153647_09-04-2025_10_19_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153647 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153647 AND mptpi.`type`=3594 To do Qualite : 0.16284528966389794 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153651_09-04-2025_09_56_05.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153651 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153651 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153655 order by id desc limit 1 Qualite : 0.13395730297527286 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153656_09-04-2025_09_56_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153656 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153656 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'07042025': {'nb_upload': 9, '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 [1350336436, 1350336421, 1350336284, 1350336226, 1350287350, 1350287140, 1350287110, 1350287098, 1350287073] Looping around the photos to save general results len do output : 1 /22153573Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336436', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336421', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336284', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350336226', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287350', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287140', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287110', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287098', None, None, None, None, None, '2733653') ('3318', None, None, None, None, None, None, None, '2733653') ('3318', '22153573', '1350287073', None, None, None, None, None, '2733653') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.013375997543334961 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.066312551498413 time spend to save output : 0.01356959342956543 total time spend for step 10 : 10.079882144927979 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 9 set_done_treatment 270.25user 134.21system 9:18.36elapsed 72%CPU (0avgtext+0avgdata 8372480maxresident)k 3173688inputs+187352outputs (52198major+24990591minor)pagefaults 0swaps