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 : 1432173 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 : ['2734610'] with mtr_portfolio_ids : ['22163336'] and first list_photo_ids : [] new path : /proc/1432173/ 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 , BFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 8 ; length of list_pids : 8 ; length of list_args : 8 time to download the photos : 1.256202220916748 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 14:30:29 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 : 10593 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-09 14:30:32.529776: 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 14:30:32.555393: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-09 14:30:32.568844: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7588000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-09 14:30:32.568880: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-09 14:30:32.580456: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-09 14:30:32.839904: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d457fb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-09 14:30:32.839969: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-09 14:30:32.841260: 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 14:30:32.841723: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 14:30:32.844862: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 14:30:32.851381: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 14:30:32.851881: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 14:30:32.854962: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 14:30:32.856300: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 14:30:32.861549: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 14:30:32.863167: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 14:30:32.863263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 14:30:32.864056: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 14:30:32.864072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 14:30:32.864081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 14:30:32.865468: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 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 14:30:33.190748: 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 14:30:33.190880: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 14:30:33.190918: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 14:30:33.190948: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 14:30:33.190973: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 14:30:33.190994: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 14:30:33.191016: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 14:30:33.191034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 14:30:33.192512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 14:30:33.194002: 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 14:30:33.194040: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 14:30:33.194055: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 14:30:33.194070: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 14:30:33.194084: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 14:30:33.194097: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 14:30:33.194110: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 14:30:33.194124: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 14:30:33.195414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 14:30:33.195453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 14:30:33.195462: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 14:30:33.195469: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 14:30:33.196806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9815 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 14:30:42.887807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 14:30:43.092694: 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 : 8 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 2.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 21 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 45 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 50 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 : 85 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 : 78 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 : 62 Detection mask done ! Trying to reset tf kernel 1432891 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 3248 tf kernel not reseted sub process len(results) : 8 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 8 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 : 7365 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 : 0.0012443065643310547 nb_pixel_total : 42700 time to create 1 rle with old method : 0.05065488815307617 length of segment : 161 time for calcul the mask position with numpy : 0.0004546642303466797 nb_pixel_total : 24075 time to create 1 rle with old method : 0.02776789665222168 length of segment : 197 time for calcul the mask position with numpy : 0.00022077560424804688 nb_pixel_total : 14288 time to create 1 rle with old method : 0.01681232452392578 length of segment : 96 time for calcul the mask position with numpy : 0.000148773193359375 nb_pixel_total : 6664 time to create 1 rle with old method : 0.00787210464477539 length of segment : 92 time for calcul the mask position with numpy : 0.00032973289489746094 nb_pixel_total : 17941 time to create 1 rle with old method : 0.0207364559173584 length of segment : 182 time for calcul the mask position with numpy : 0.00027823448181152344 nb_pixel_total : 17065 time to create 1 rle with old method : 0.01938319206237793 length of segment : 193 time for calcul the mask position with numpy : 0.0002810955047607422 nb_pixel_total : 18307 time to create 1 rle with old method : 0.02196335792541504 length of segment : 130 time for calcul the mask position with numpy : 0.0012257099151611328 nb_pixel_total : 68260 time to create 1 rle with old method : 0.07719755172729492 length of segment : 272 time for calcul the mask position with numpy : 0.0002281665802001953 nb_pixel_total : 13775 time to create 1 rle with old method : 0.016176223754882812 length of segment : 131 time for calcul the mask position with numpy : 0.001056671142578125 nb_pixel_total : 47954 time to create 1 rle with old method : 0.05411577224731445 length of segment : 438 time for calcul the mask position with numpy : 0.0012009143829345703 nb_pixel_total : 93506 time to create 1 rle with old method : 0.10673999786376953 length of segment : 249 time for calcul the mask position with numpy : 0.0013339519500732422 nb_pixel_total : 82458 time to create 1 rle with old method : 0.09871196746826172 length of segment : 489 time for calcul the mask position with numpy : 0.000362396240234375 nb_pixel_total : 19526 time to create 1 rle with old method : 0.021895885467529297 length of segment : 237 time for calcul the mask position with numpy : 0.00013709068298339844 nb_pixel_total : 4536 time to create 1 rle with old method : 0.005442142486572266 length of segment : 100 time for calcul the mask position with numpy : 0.0003058910369873047 nb_pixel_total : 18782 time to create 1 rle with old method : 0.0216827392578125 length of segment : 209 time for calcul the mask position with numpy : 0.0002644062042236328 nb_pixel_total : 17366 time to create 1 rle with old method : 0.01981329917907715 length of segment : 126 time for calcul the mask position with numpy : 0.0012595653533935547 nb_pixel_total : 99523 time to create 1 rle with old method : 0.1129767894744873 length of segment : 569 time for calcul the mask position with numpy : 0.0016608238220214844 nb_pixel_total : 72711 time to create 1 rle with old method : 0.0810399055480957 length of segment : 528 time for calcul the mask position with numpy : 0.004621028900146484 nb_pixel_total : 126776 time to create 1 rle with old method : 0.1399228572845459 length of segment : 444 time for calcul the mask position with numpy : 0.0006513595581054688 nb_pixel_total : 14795 time to create 1 rle with old method : 0.016425132751464844 length of segment : 199 time for calcul the mask position with numpy : 0.001130819320678711 nb_pixel_total : 27933 time to create 1 rle with old method : 0.03138136863708496 length of segment : 348 time for calcul the mask position with numpy : 0.0011477470397949219 nb_pixel_total : 33907 time to create 1 rle with old method : 0.038077592849731445 length of segment : 255 time for calcul the mask position with numpy : 0.0045146942138671875 nb_pixel_total : 153966 time to create 1 rle with new method : 0.008510589599609375 length of segment : 464 time for calcul the mask position with numpy : 0.0038671493530273438 nb_pixel_total : 131328 time to create 1 rle with old method : 0.15197014808654785 length of segment : 428 time for calcul the mask position with numpy : 0.00032806396484375 nb_pixel_total : 18003 time to create 1 rle with old method : 0.025414228439331055 length of segment : 122 time for calcul the mask position with numpy : 0.0016603469848632812 nb_pixel_total : 102092 time to create 1 rle with old method : 0.1141355037689209 length of segment : 462 time for calcul the mask position with numpy : 0.0007431507110595703 nb_pixel_total : 37429 time to create 1 rle with old method : 0.046471357345581055 length of segment : 288 time for calcul the mask position with numpy : 0.0018343925476074219 nb_pixel_total : 92442 time to create 1 rle with old method : 0.10869598388671875 length of segment : 315 time for calcul the mask position with numpy : 0.002974987030029297 nb_pixel_total : 141790 time to create 1 rle with old method : 0.18169450759887695 length of segment : 536 time for calcul the mask position with numpy : 0.0011081695556640625 nb_pixel_total : 54928 time to create 1 rle with old method : 0.07952475547790527 length of segment : 219 time for calcul the mask position with numpy : 0.0019066333770751953 nb_pixel_total : 65747 time to create 1 rle with old method : 0.10675883293151855 length of segment : 279 time for calcul the mask position with numpy : 0.0017752647399902344 nb_pixel_total : 71879 time to create 1 rle with old method : 0.09000754356384277 length of segment : 483 time for calcul the mask position with numpy : 0.002789020538330078 nb_pixel_total : 137410 time to create 1 rle with old method : 0.15190458297729492 length of segment : 580 time for calcul the mask position with numpy : 0.005701780319213867 nb_pixel_total : 186364 time to create 1 rle with new method : 0.01674675941467285 length of segment : 860 time for calcul the mask position with numpy : 0.0008504390716552734 nb_pixel_total : 30673 time to create 1 rle with old method : 0.03512763977050781 length of segment : 263 time for calcul the mask position with numpy : 0.0007040500640869141 nb_pixel_total : 38638 time to create 1 rle with old method : 0.044622182846069336 length of segment : 130 time for calcul the mask position with numpy : 0.0011341571807861328 nb_pixel_total : 30586 time to create 1 rle with old method : 0.03556060791015625 length of segment : 232 time for calcul the mask position with numpy : 0.00030541419982910156 nb_pixel_total : 9581 time to create 1 rle with old method : 0.011236906051635742 length of segment : 137 time for calcul the mask position with numpy : 0.0005433559417724609 nb_pixel_total : 17761 time to create 1 rle with old method : 0.021334171295166016 length of segment : 252 time for calcul the mask position with numpy : 0.0003178119659423828 nb_pixel_total : 7783 time to create 1 rle with old method : 0.009592771530151367 length of segment : 99 time for calcul the mask position with numpy : 0.0003600120544433594 nb_pixel_total : 11714 time to create 1 rle with old method : 0.015872478485107422 length of segment : 120 time for calcul the mask position with numpy : 0.0019519329071044922 nb_pixel_total : 98787 time to create 1 rle with old method : 0.12748456001281738 length of segment : 536 time for calcul the mask position with numpy : 0.0003478527069091797 nb_pixel_total : 13348 time to create 1 rle with old method : 0.026409387588500977 length of segment : 182 time for calcul the mask position with numpy : 0.0012755393981933594 nb_pixel_total : 38968 time to create 1 rle with old method : 0.04779553413391113 length of segment : 326 time for calcul the mask position with numpy : 0.0001621246337890625 nb_pixel_total : 5350 time to create 1 rle with old method : 0.006615161895751953 length of segment : 84 time for calcul the mask position with numpy : 0.0003292560577392578 nb_pixel_total : 15357 time to create 1 rle with old method : 0.01822352409362793 length of segment : 220 time for calcul the mask position with numpy : 0.0006957054138183594 nb_pixel_total : 29059 time to create 1 rle with old method : 0.03309202194213867 length of segment : 169 time for calcul the mask position with numpy : 0.0023114681243896484 nb_pixel_total : 98756 time to create 1 rle with old method : 0.10895609855651855 length of segment : 839 time for calcul the mask position with numpy : 0.0007989406585693359 nb_pixel_total : 41109 time to create 1 rle with old method : 0.04885244369506836 length of segment : 240 time for calcul the mask position with numpy : 0.0010921955108642578 nb_pixel_total : 28618 time to create 1 rle with old method : 0.03209257125854492 length of segment : 191 time for calcul the mask position with numpy : 0.001986980438232422 nb_pixel_total : 54554 time to create 1 rle with old method : 0.06137990951538086 length of segment : 187 time for calcul the mask position with numpy : 0.0018038749694824219 nb_pixel_total : 54560 time to create 1 rle with old method : 0.06224226951599121 length of segment : 123 time for calcul the mask position with numpy : 0.009438753128051758 nb_pixel_total : 277524 time to create 1 rle with new method : 0.014928340911865234 length of segment : 664 time for calcul the mask position with numpy : 0.001028299331665039 nb_pixel_total : 8022 time to create 1 rle with old method : 0.009317636489868164 length of segment : 84 time for calcul the mask position with numpy : 0.006181478500366211 nb_pixel_total : 114487 time to create 1 rle with old method : 0.1329646110534668 length of segment : 497 time for calcul the mask position with numpy : 0.0034296512603759766 nb_pixel_total : 69380 time to create 1 rle with old method : 0.07827234268188477 length of segment : 305 time for calcul the mask position with numpy : 0.0004947185516357422 nb_pixel_total : 8973 time to create 1 rle with old method : 0.010442018508911133 length of segment : 170 time for calcul the mask position with numpy : 0.0033483505249023438 nb_pixel_total : 78760 time to create 1 rle with old method : 0.08868956565856934 length of segment : 424 time for calcul the mask position with numpy : 0.0076138973236083984 nb_pixel_total : 196876 time to create 1 rle with new method : 0.01195836067199707 length of segment : 496 time for calcul the mask position with numpy : 0.0008325576782226562 nb_pixel_total : 13138 time to create 1 rle with old method : 0.019252300262451172 length of segment : 217 time for calcul the mask position with numpy : 0.00886678695678711 nb_pixel_total : 151044 time to create 1 rle with new method : 0.015221834182739258 length of segment : 756 time for calcul the mask position with numpy : 0.0011081695556640625 nb_pixel_total : 24271 time to create 1 rle with old method : 0.04398536682128906 length of segment : 129 time for calcul the mask position with numpy : 0.000965118408203125 nb_pixel_total : 11046 time to create 1 rle with old method : 0.01997208595275879 length of segment : 105 time for calcul the mask position with numpy : 0.0009341239929199219 nb_pixel_total : 17524 time to create 1 rle with old method : 0.02020716667175293 length of segment : 156 time for calcul the mask position with numpy : 0.0007154941558837891 nb_pixel_total : 16970 time to create 1 rle with old method : 0.020496606826782227 length of segment : 103 time for calcul the mask position with numpy : 0.001733541488647461 nb_pixel_total : 82198 time to create 1 rle with old method : 0.10263347625732422 length of segment : 279 time for calcul the mask position with numpy : 0.003268003463745117 nb_pixel_total : 32318 time to create 1 rle with old method : 0.040058135986328125 length of segment : 259 time for calcul the mask position with numpy : 0.004430294036865234 nb_pixel_total : 58124 time to create 1 rle with old method : 0.07212018966674805 length of segment : 467 time for calcul the mask position with numpy : 0.003962278366088867 nb_pixel_total : 49334 time to create 1 rle with old method : 0.05682849884033203 length of segment : 282 time for calcul the mask position with numpy : 0.0006830692291259766 nb_pixel_total : 7406 time to create 1 rle with old method : 0.011695384979248047 length of segment : 105 time for calcul the mask position with numpy : 0.001049041748046875 nb_pixel_total : 13205 time to create 1 rle with old method : 0.01686882972717285 length of segment : 143 time for calcul the mask position with numpy : 0.004097938537597656 nb_pixel_total : 41111 time to create 1 rle with old method : 0.050733327865600586 length of segment : 274 time for calcul the mask position with numpy : 0.005885124206542969 nb_pixel_total : 75259 time to create 1 rle with old method : 0.09617185592651367 length of segment : 471 time for calcul the mask position with numpy : 0.0022563934326171875 nb_pixel_total : 26947 time to create 1 rle with old method : 0.03314518928527832 length of segment : 306 time for calcul the mask position with numpy : 0.0011782646179199219 nb_pixel_total : 14520 time to create 1 rle with old method : 0.01682424545288086 length of segment : 228 time for calcul the mask position with numpy : 0.0007376670837402344 nb_pixel_total : 8796 time to create 1 rle with old method : 0.010289907455444336 length of segment : 155 time for calcul the mask position with numpy : 0.0018112659454345703 nb_pixel_total : 31380 time to create 1 rle with old method : 0.05302786827087402 length of segment : 203 time for calcul the mask position with numpy : 0.0016722679138183594 nb_pixel_total : 20149 time to create 1 rle with old method : 0.023164033889770508 length of segment : 220 time for calcul the mask position with numpy : 0.00046443939208984375 nb_pixel_total : 4306 time to create 1 rle with old method : 0.005116701126098633 length of segment : 91 time for calcul the mask position with numpy : 0.002115011215209961 nb_pixel_total : 25584 time to create 1 rle with old method : 0.029710769653320312 length of segment : 251 time for calcul the mask position with numpy : 0.0005335807800292969 nb_pixel_total : 5738 time to create 1 rle with old method : 0.006845712661743164 length of segment : 93 time for calcul the mask position with numpy : 0.0007343292236328125 nb_pixel_total : 9390 time to create 1 rle with old method : 0.01133275032043457 length of segment : 98 time for calcul the mask position with numpy : 0.0008821487426757812 nb_pixel_total : 12740 time to create 1 rle with old method : 0.015209436416625977 length of segment : 173 time for calcul the mask position with numpy : 0.001031637191772461 nb_pixel_total : 16690 time to create 1 rle with old method : 0.02122187614440918 length of segment : 173 time for calcul the mask position with numpy : 0.0009033679962158203 nb_pixel_total : 12372 time to create 1 rle with old method : 0.014539480209350586 length of segment : 139 time for calcul the mask position with numpy : 0.007414817810058594 nb_pixel_total : 127346 time to create 1 rle with old method : 0.1464393138885498 length of segment : 462 time for calcul the mask position with numpy : 0.0005407333374023438 nb_pixel_total : 7876 time to create 1 rle with old method : 0.012893915176391602 length of segment : 149 time for calcul the mask position with numpy : 0.009716272354125977 nb_pixel_total : 111811 time to create 1 rle with old method : 0.13890957832336426 length of segment : 525 time for calcul the mask position with numpy : 0.001630544662475586 nb_pixel_total : 25612 time to create 1 rle with old method : 0.02922654151916504 length of segment : 203 time for calcul the mask position with numpy : 0.0009307861328125 nb_pixel_total : 12051 time to create 1 rle with old method : 0.014143705368041992 length of segment : 136 time for calcul the mask position with numpy : 0.0007040500640869141 nb_pixel_total : 5466 time to create 1 rle with old method : 0.0064182281494140625 length of segment : 176 time for calcul the mask position with numpy : 0.0024459362030029297 nb_pixel_total : 29039 time to create 1 rle with old method : 0.03388381004333496 length of segment : 450 time for calcul the mask position with numpy : 0.0036950111389160156 nb_pixel_total : 52487 time to create 1 rle with old method : 0.06008744239807129 length of segment : 323 time for calcul the mask position with numpy : 0.0015859603881835938 nb_pixel_total : 22641 time to create 1 rle with old method : 0.02603626251220703 length of segment : 252 time for calcul the mask position with numpy : 0.0009469985961914062 nb_pixel_total : 18844 time to create 1 rle with old method : 0.021823644638061523 length of segment : 185 time for calcul the mask position with numpy : 0.002873659133911133 nb_pixel_total : 46940 time to create 1 rle with old method : 0.053754329681396484 length of segment : 285 time for calcul the mask position with numpy : 0.004096269607543945 nb_pixel_total : 76466 time to create 1 rle with old method : 0.08771014213562012 length of segment : 464 time for calcul the mask position with numpy : 0.00031685829162597656 nb_pixel_total : 10577 time to create 1 rle with old method : 0.012296915054321289 length of segment : 91 time for calcul the mask position with numpy : 0.005392789840698242 nb_pixel_total : 86560 time to create 1 rle with old method : 0.09784340858459473 length of segment : 342 time for calcul the mask position with numpy : 0.0012323856353759766 nb_pixel_total : 23409 time to create 1 rle with old method : 0.026806116104125977 length of segment : 381 time for calcul the mask position with numpy : 0.00550532341003418 nb_pixel_total : 78210 time to create 1 rle with old method : 0.08779001235961914 length of segment : 387 time for calcul the mask position with numpy : 0.0072231292724609375 nb_pixel_total : 126383 time to create 1 rle with old method : 0.14175772666931152 length of segment : 479 time for calcul the mask position with numpy : 0.0010516643524169922 nb_pixel_total : 14562 time to create 1 rle with old method : 0.016371488571166992 length of segment : 153 time for calcul the mask position with numpy : 0.003751993179321289 nb_pixel_total : 34695 time to create 1 rle with old method : 0.04038667678833008 length of segment : 348 time for calcul the mask position with numpy : 0.00015115737915039062 nb_pixel_total : 2807 time to create 1 rle with old method : 0.0033953189849853516 length of segment : 99 time for calcul the mask position with numpy : 0.0007119178771972656 nb_pixel_total : 25818 time to create 1 rle with old method : 0.029715299606323242 length of segment : 255 time for calcul the mask position with numpy : 0.00098419189453125 nb_pixel_total : 13758 time to create 1 rle with old method : 0.01731419563293457 length of segment : 189 time for calcul the mask position with numpy : 0.0006146430969238281 nb_pixel_total : 10729 time to create 1 rle with old method : 0.012569904327392578 length of segment : 167 time for calcul the mask position with numpy : 0.02945685386657715 nb_pixel_total : 339468 time to create 1 rle with new method : 0.048736572265625 length of segment : 918 time for calcul the mask position with numpy : 0.0006885528564453125 nb_pixel_total : 10945 time to create 1 rle with old method : 0.01276087760925293 length of segment : 113 time for calcul the mask position with numpy : 0.0008161067962646484 nb_pixel_total : 15743 time to create 1 rle with old method : 0.018570661544799805 length of segment : 221 time for calcul the mask position with numpy : 0.0009362697601318359 nb_pixel_total : 16192 time to create 1 rle with old method : 0.019217729568481445 length of segment : 202 time for calcul the mask position with numpy : 0.0009312629699707031 nb_pixel_total : 14251 time to create 1 rle with old method : 0.016352176666259766 length of segment : 233 time for calcul the mask position with numpy : 0.012386322021484375 nb_pixel_total : 338582 time to create 1 rle with new method : 0.05062747001647949 length of segment : 928 time for calcul the mask position with numpy : 0.0015189647674560547 nb_pixel_total : 25984 time to create 1 rle with old method : 0.027923107147216797 length of segment : 205 time for calcul the mask position with numpy : 0.0017783641815185547 nb_pixel_total : 18205 time to create 1 rle with old method : 0.020717620849609375 length of segment : 314 time for calcul the mask position with numpy : 0.001865386962890625 nb_pixel_total : 33242 time to create 1 rle with old method : 0.03751564025878906 length of segment : 160 time for calcul the mask position with numpy : 0.0029816627502441406 nb_pixel_total : 56339 time to create 1 rle with old method : 0.06198692321777344 length of segment : 295 time for calcul the mask position with numpy : 0.00077056884765625 nb_pixel_total : 7401 time to create 1 rle with old method : 0.012452840805053711 length of segment : 98 time for calcul the mask position with numpy : 0.0029489994049072266 nb_pixel_total : 30174 time to create 1 rle with old method : 0.03782010078430176 length of segment : 214 time for calcul the mask position with numpy : 0.0010132789611816406 nb_pixel_total : 13086 time to create 1 rle with old method : 0.014807462692260742 length of segment : 145 time for calcul the mask position with numpy : 0.0017151832580566406 nb_pixel_total : 25123 time to create 1 rle with old method : 0.02849864959716797 length of segment : 203 time for calcul the mask position with numpy : 0.0025565624237060547 nb_pixel_total : 36010 time to create 1 rle with old method : 0.04126477241516113 length of segment : 200 time for calcul the mask position with numpy : 0.0015156269073486328 nb_pixel_total : 24458 time to create 1 rle with old method : 0.02706146240234375 length of segment : 183 time for calcul the mask position with numpy : 0.003464937210083008 nb_pixel_total : 33989 time to create 1 rle with old method : 0.03891324996948242 length of segment : 275 time for calcul the mask position with numpy : 0.0009138584136962891 nb_pixel_total : 13857 time to create 1 rle with old method : 0.01611804962158203 length of segment : 156 time for calcul the mask position with numpy : 0.0007510185241699219 nb_pixel_total : 12227 time to create 1 rle with old method : 0.014465808868408203 length of segment : 95 time for calcul the mask position with numpy : 0.0005249977111816406 nb_pixel_total : 6560 time to create 1 rle with old method : 0.010811328887939453 length of segment : 104 time for calcul the mask position with numpy : 0.0016596317291259766 nb_pixel_total : 17701 time to create 1 rle with old method : 0.029021739959716797 length of segment : 162 time for calcul the mask position with numpy : 0.0031147003173828125 nb_pixel_total : 47866 time to create 1 rle with old method : 0.05626988410949707 length of segment : 372 time for calcul the mask position with numpy : 0.0006194114685058594 nb_pixel_total : 7644 time to create 1 rle with old method : 0.008912801742553711 length of segment : 95 time for calcul the mask position with numpy : 0.0008146762847900391 nb_pixel_total : 13699 time to create 1 rle with old method : 0.016007184982299805 length of segment : 184 time for calcul the mask position with numpy : 0.002745389938354492 nb_pixel_total : 49365 time to create 1 rle with old method : 0.056036949157714844 length of segment : 343 time for calcul the mask position with numpy : 0.0011150836944580078 nb_pixel_total : 10759 time to create 1 rle with old method : 0.012635231018066406 length of segment : 177 time for calcul the mask position with numpy : 0.004414558410644531 nb_pixel_total : 66268 time to create 1 rle with old method : 0.07575750350952148 length of segment : 457 time for calcul the mask position with numpy : 0.001232147216796875 nb_pixel_total : 22903 time to create 1 rle with old method : 0.02624988555908203 length of segment : 149 time for calcul the mask position with numpy : 0.0006110668182373047 nb_pixel_total : 13483 time to create 1 rle with old method : 0.016887903213500977 length of segment : 120 time for calcul the mask position with numpy : 0.003008127212524414 nb_pixel_total : 61352 time to create 1 rle with old method : 0.06943607330322266 length of segment : 307 time for calcul the mask position with numpy : 0.0005829334259033203 nb_pixel_total : 6835 time to create 1 rle with old method : 0.008157730102539062 length of segment : 96 time for calcul the mask position with numpy : 0.004897594451904297 nb_pixel_total : 66139 time to create 1 rle with old method : 0.09519481658935547 length of segment : 339 time for calcul the mask position with numpy : 0.001027822494506836 nb_pixel_total : 31840 time to create 1 rle with old method : 0.03632831573486328 length of segment : 220 time for calcul the mask position with numpy : 0.0017414093017578125 nb_pixel_total : 28644 time to create 1 rle with old method : 0.032361507415771484 length of segment : 388 time for calcul the mask position with numpy : 0.0006167888641357422 nb_pixel_total : 10866 time to create 1 rle with old method : 0.01294708251953125 length of segment : 103 time for calcul the mask position with numpy : 0.0008955001831054688 nb_pixel_total : 17952 time to create 1 rle with old method : 0.020813703536987305 length of segment : 183 time for calcul the mask position with numpy : 0.0011889934539794922 nb_pixel_total : 20228 time to create 1 rle with old method : 0.023321151733398438 length of segment : 272 time for calcul the mask position with numpy : 0.00017070770263671875 nb_pixel_total : 6714 time to create 1 rle with old method : 0.00834512710571289 length of segment : 79 time for calcul the mask position with numpy : 0.0013566017150878906 nb_pixel_total : 27716 time to create 1 rle with old method : 0.031592607498168945 length of segment : 232 time for calcul the mask position with numpy : 0.009333372116088867 nb_pixel_total : 159561 time to create 1 rle with new method : 0.012382984161376953 length of segment : 531 time for calcul the mask position with numpy : 0.0004401206970214844 nb_pixel_total : 4282 time to create 1 rle with old method : 0.005861759185791016 length of segment : 54 time for calcul the mask position with numpy : 0.005227565765380859 nb_pixel_total : 109455 time to create 1 rle with old method : 0.14563870429992676 length of segment : 490 time for calcul the mask position with numpy : 0.0012145042419433594 nb_pixel_total : 17067 time to create 1 rle with old method : 0.02104783058166504 length of segment : 167 time for calcul the mask position with numpy : 0.0009195804595947266 nb_pixel_total : 13764 time to create 1 rle with old method : 0.01600503921508789 length of segment : 177 time for calcul the mask position with numpy : 0.0005121231079101562 nb_pixel_total : 10840 time to create 1 rle with old method : 0.012807130813598633 length of segment : 128 time for calcul the mask position with numpy : 0.009767770767211914 nb_pixel_total : 82277 time to create 1 rle with old method : 0.1010897159576416 length of segment : 790 time for calcul the mask position with numpy : 0.000942230224609375 nb_pixel_total : 10123 time to create 1 rle with old method : 0.017256975173950195 length of segment : 182 time for calcul the mask position with numpy : 0.002538919448852539 nb_pixel_total : 37577 time to create 1 rle with old method : 0.04506254196166992 length of segment : 192 time for calcul the mask position with numpy : 0.004227638244628906 nb_pixel_total : 53921 time to create 1 rle with old method : 0.06962966918945312 length of segment : 289 time for calcul the mask position with numpy : 0.0008683204650878906 nb_pixel_total : 10859 time to create 1 rle with old method : 0.012635469436645508 length of segment : 135 time for calcul the mask position with numpy : 0.003139495849609375 nb_pixel_total : 40706 time to create 1 rle with old method : 0.04560422897338867 length of segment : 254 time for calcul the mask position with numpy : 0.0009677410125732422 nb_pixel_total : 24572 time to create 1 rle with old method : 0.027971982955932617 length of segment : 220 time for calcul the mask position with numpy : 0.0019102096557617188 nb_pixel_total : 29044 time to create 1 rle with old method : 0.03489995002746582 length of segment : 180 time for calcul the mask position with numpy : 0.009643793106079102 nb_pixel_total : 150482 time to create 1 rle with new method : 0.014223098754882812 length of segment : 630 time for calcul the mask position with numpy : 0.0009486675262451172 nb_pixel_total : 17160 time to create 1 rle with old method : 0.019173860549926758 length of segment : 184 time for calcul the mask position with numpy : 0.0010418891906738281 nb_pixel_total : 8940 time to create 1 rle with old method : 0.010594844818115234 length of segment : 155 time for calcul the mask position with numpy : 0.0013957023620605469 nb_pixel_total : 25535 time to create 1 rle with old method : 0.02963542938232422 length of segment : 177 time for calcul the mask position with numpy : 0.0004646778106689453 nb_pixel_total : 6367 time to create 1 rle with old method : 0.007717609405517578 length of segment : 91 time for calcul the mask position with numpy : 0.00036334991455078125 nb_pixel_total : 6086 time to create 1 rle with old method : 0.0072784423828125 length of segment : 77 time for calcul the mask position with numpy : 0.0006840229034423828 nb_pixel_total : 10065 time to create 1 rle with old method : 0.011442184448242188 length of segment : 127 time for calcul the mask position with numpy : 0.0010025501251220703 nb_pixel_total : 22359 time to create 1 rle with old method : 0.027447938919067383 length of segment : 209 time for calcul the mask position with numpy : 0.003743410110473633 nb_pixel_total : 67862 time to create 1 rle with old method : 0.07544708251953125 length of segment : 389 time for calcul the mask position with numpy : 0.0027959346771240234 nb_pixel_total : 38943 time to create 1 rle with old method : 0.044661760330200195 length of segment : 258 time for calcul the mask position with numpy : 0.003807544708251953 nb_pixel_total : 41226 time to create 1 rle with old method : 0.046718597412109375 length of segment : 364 time for calcul the mask position with numpy : 0.002151966094970703 nb_pixel_total : 16502 time to create 1 rle with old method : 0.019158601760864258 length of segment : 360 time for calcul the mask position with numpy : 0.0010895729064941406 nb_pixel_total : 22719 time to create 1 rle with old method : 0.025728702545166016 length of segment : 169 time for calcul the mask position with numpy : 0.0018191337585449219 nb_pixel_total : 25477 time to create 1 rle with old method : 0.029314041137695312 length of segment : 190 time for calcul the mask position with numpy : 0.0012822151184082031 nb_pixel_total : 10724 time to create 1 rle with old method : 0.013825416564941406 length of segment : 153 time for calcul the mask position with numpy : 0.0010907649993896484 nb_pixel_total : 15886 time to create 1 rle with old method : 0.01838064193725586 length of segment : 134 time for calcul the mask position with numpy : 0.00990152359008789 nb_pixel_total : 183104 time to create 1 rle with new method : 0.014051437377929688 length of segment : 632 time for calcul the mask position with numpy : 0.00028228759765625 nb_pixel_total : 9563 time to create 1 rle with old method : 0.011798858642578125 length of segment : 112 time for calcul the mask position with numpy : 0.0047533512115478516 nb_pixel_total : 110641 time to create 1 rle with old method : 0.12477946281433105 length of segment : 477 time for calcul the mask position with numpy : 0.0014736652374267578 nb_pixel_total : 28049 time to create 1 rle with old method : 0.03169393539428711 length of segment : 228 time for calcul the mask position with numpy : 0.00042748451232910156 nb_pixel_total : 6364 time to create 1 rle with old method : 0.0076541900634765625 length of segment : 70 time for calcul the mask position with numpy : 0.0007727146148681641 nb_pixel_total : 14941 time to create 1 rle with old method : 0.017207860946655273 length of segment : 187 time for calcul the mask position with numpy : 0.001978158950805664 nb_pixel_total : 24296 time to create 1 rle with old method : 0.027405261993408203 length of segment : 226 time for calcul the mask position with numpy : 0.0011358261108398438 nb_pixel_total : 16023 time to create 1 rle with old method : 0.01824355125427246 length of segment : 226 time for calcul the mask position with numpy : 0.0010464191436767578 nb_pixel_total : 25664 time to create 1 rle with old method : 0.029337406158447266 length of segment : 188 time for calcul the mask position with numpy : 0.001245737075805664 nb_pixel_total : 22963 time to create 1 rle with old method : 0.02803802490234375 length of segment : 132 time for calcul the mask position with numpy : 0.0004706382751464844 nb_pixel_total : 6125 time to create 1 rle with old method : 0.0072743892669677734 length of segment : 71 time for calcul the mask position with numpy : 0.00022721290588378906 nb_pixel_total : 2230 time to create 1 rle with old method : 0.002786397933959961 length of segment : 48 time for calcul the mask position with numpy : 0.0006597042083740234 nb_pixel_total : 11819 time to create 1 rle with old method : 0.013534069061279297 length of segment : 194 time for calcul the mask position with numpy : 0.002028942108154297 nb_pixel_total : 32817 time to create 1 rle with old method : 0.03706765174865723 length of segment : 217 time for calcul the mask position with numpy : 0.004988908767700195 nb_pixel_total : 58596 time to create 1 rle with old method : 0.07066202163696289 length of segment : 314 time for calcul the mask position with numpy : 0.0034465789794921875 nb_pixel_total : 38439 time to create 1 rle with old method : 0.04248666763305664 length of segment : 278 time for calcul the mask position with numpy : 0.003375530242919922 nb_pixel_total : 27235 time to create 1 rle with old method : 0.03011345863342285 length of segment : 376 time for calcul the mask position with numpy : 0.0014014244079589844 nb_pixel_total : 23280 time to create 1 rle with old method : 0.026599407196044922 length of segment : 201 time for calcul the mask position with numpy : 0.00037789344787597656 nb_pixel_total : 5149 time to create 1 rle with old method : 0.006262540817260742 length of segment : 75 time for calcul the mask position with numpy : 0.004541635513305664 nb_pixel_total : 85488 time to create 1 rle with old method : 0.10311746597290039 length of segment : 512 time for calcul the mask position with numpy : 0.001922607421875 nb_pixel_total : 44086 time to create 1 rle with old method : 0.04913663864135742 length of segment : 245 time for calcul the mask position with numpy : 0.0009257793426513672 nb_pixel_total : 14695 time to create 1 rle with old method : 0.017083168029785156 length of segment : 124 time for calcul the mask position with numpy : 0.0009584426879882812 nb_pixel_total : 18910 time to create 1 rle with old method : 0.022968292236328125 length of segment : 147 time for calcul the mask position with numpy : 0.001969575881958008 nb_pixel_total : 26241 time to create 1 rle with old method : 0.029757022857666016 length of segment : 330 time for calcul the mask position with numpy : 0.002340555191040039 nb_pixel_total : 48030 time to create 1 rle with old method : 0.05770111083984375 length of segment : 350 time for calcul the mask position with numpy : 0.001750946044921875 nb_pixel_total : 28051 time to create 1 rle with old method : 0.04624819755554199 length of segment : 219 time for calcul the mask position with numpy : 0.002522706985473633 nb_pixel_total : 57551 time to create 1 rle with old method : 0.06784868240356445 length of segment : 323 time for calcul the mask position with numpy : 0.0008144378662109375 nb_pixel_total : 13420 time to create 1 rle with old method : 0.019165992736816406 length of segment : 127 time for calcul the mask position with numpy : 0.001428365707397461 nb_pixel_total : 28117 time to create 1 rle with old method : 0.03184914588928223 length of segment : 238 time for calcul the mask position with numpy : 0.001519918441772461 nb_pixel_total : 28877 time to create 1 rle with old method : 0.03325605392456055 length of segment : 300 time for calcul the mask position with numpy : 0.0026025772094726562 nb_pixel_total : 42667 time to create 1 rle with old method : 0.048387765884399414 length of segment : 302 time for calcul the mask position with numpy : 0.0038394927978515625 nb_pixel_total : 60549 time to create 1 rle with old method : 0.07161569595336914 length of segment : 548 time for calcul the mask position with numpy : 0.0009329319000244141 nb_pixel_total : 16038 time to create 1 rle with old method : 0.018632173538208008 length of segment : 205 time for calcul the mask position with numpy : 0.0008170604705810547 nb_pixel_total : 16159 time to create 1 rle with old method : 0.020351886749267578 length of segment : 144 time for calcul the mask position with numpy : 0.0027976036071777344 nb_pixel_total : 16069 time to create 1 rle with old method : 0.018833160400390625 length of segment : 409 time for calcul the mask position with numpy : 0.001729726791381836 nb_pixel_total : 23414 time to create 1 rle with old method : 0.026867151260375977 length of segment : 263 time for calcul the mask position with numpy : 0.0007154941558837891 nb_pixel_total : 15464 time to create 1 rle with old method : 0.0203092098236084 length of segment : 105 time for calcul the mask position with numpy : 0.0019092559814453125 nb_pixel_total : 34590 time to create 1 rle with old method : 0.052384138107299805 length of segment : 181 time for calcul the mask position with numpy : 0.0011396408081054688 nb_pixel_total : 18352 time to create 1 rle with old method : 0.0215609073638916 length of segment : 219 time for calcul the mask position with numpy : 0.0010733604431152344 nb_pixel_total : 14598 time to create 1 rle with old method : 0.01719045639038086 length of segment : 182 time for calcul the mask position with numpy : 0.0007150173187255859 nb_pixel_total : 17064 time to create 1 rle with old method : 0.02055978775024414 length of segment : 102 time for calcul the mask position with numpy : 0.0017237663269042969 nb_pixel_total : 28088 time to create 1 rle with old method : 0.03266739845275879 length of segment : 348 time for calcul the mask position with numpy : 0.0011420249938964844 nb_pixel_total : 19282 time to create 1 rle with old method : 0.031621456146240234 length of segment : 199 time for calcul the mask position with numpy : 0.0049664974212646484 nb_pixel_total : 97053 time to create 1 rle with old method : 0.10901975631713867 length of segment : 468 time for calcul the mask position with numpy : 0.0008194446563720703 nb_pixel_total : 14492 time to create 1 rle with old method : 0.016914844512939453 length of segment : 162 time for calcul the mask position with numpy : 0.0018744468688964844 nb_pixel_total : 43713 time to create 1 rle with old method : 0.053075551986694336 length of segment : 227 time for calcul the mask position with numpy : 0.0009181499481201172 nb_pixel_total : 20394 time to create 1 rle with old method : 0.023981571197509766 length of segment : 125 time for calcul the mask position with numpy : 0.0012433528900146484 nb_pixel_total : 22162 time to create 1 rle with old method : 0.025585412979125977 length of segment : 175 time for calcul the mask position with numpy : 0.0013358592987060547 nb_pixel_total : 27953 time to create 1 rle with old method : 0.033457279205322266 length of segment : 236 time for calcul the mask position with numpy : 0.0009927749633789062 nb_pixel_total : 19793 time to create 1 rle with old method : 0.0231320858001709 length of segment : 196 time for calcul the mask position with numpy : 0.0003273487091064453 nb_pixel_total : 4764 time to create 1 rle with old method : 0.0057718753814697266 length of segment : 72 time for calcul the mask position with numpy : 0.0006456375122070312 nb_pixel_total : 19034 time to create 1 rle with old method : 0.0247499942779541 length of segment : 147 time for calcul the mask position with numpy : 0.0017275810241699219 nb_pixel_total : 33389 time to create 1 rle with old method : 0.03868508338928223 length of segment : 302 time for calcul the mask position with numpy : 0.0006952285766601562 nb_pixel_total : 11967 time to create 1 rle with old method : 0.014184951782226562 length of segment : 139 time for calcul the mask position with numpy : 0.0009286403656005859 nb_pixel_total : 22206 time to create 1 rle with old method : 0.026030302047729492 length of segment : 154 time spent for convertir_results : 18.391058206558228 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 232 chid ids of type : 3594 Number RLEs to save : 59801 save missing photos in datou_result : time spend for datou_step_exec : 106.00116038322449 time spend to save output : 5.337009906768799 total time spend for step 1 : 111.33817028999329 step2:crop_condition Wed Apr 9 14:32:20 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 : 8 ! batch 1 Loaded 232 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 ! map_result returned by crop_photo_return_map_crop : length : 164 About to insert : list_path_to_insert length 164 new photo from crops ! About to upload 164 photos upload in portfolio : 3736932 init cache_photo without model_param we have 164 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744201970_1432173 we have uploaded 164 photos in the portfolio 3736932 time of upload the photos Elapsed time : 47.90963935852051 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 ! map_result returned by crop_photo_return_map_crop : length : 30 About to insert : list_path_to_insert length 30 new photo from crops ! About to upload 30 photos upload in portfolio : 3736932 init cache_photo without model_param we have 30 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202026_1432173 we have uploaded 30 photos in the portfolio 3736932 time of upload the photos Elapsed time : 14.009040594100952 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 begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 30 About to insert : list_path_to_insert length 30 new photo from crops ! About to upload 30 photos upload in portfolio : 3736932 init cache_photo without model_param we have 30 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202052_1432173 we have uploaded 30 photos in the portfolio 3736932 time of upload the photos Elapsed time : 11.67775011062622 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 3736932 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202067_1432173 we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.7970490455627441 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 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 ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202070_1432173 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.8345937728881836 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350770842, 1350769796, 1350769793, 1350769789, 1350769597, 1350769595, 1350769591, 1350769588] Looping around the photos to save general results len do output : 232 /1350784181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784182Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784184Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784186Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784187Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784190Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784191Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784192Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784198Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784199Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784202Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784207Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784212Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784214Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784217Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784218Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784219Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784220Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784221Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784222Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784223Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784224Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784225Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784226Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784227Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784228Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784229Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784230Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784231Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784232Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784233Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784234Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784235Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784236Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784237Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784238Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784239Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784240Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784241Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784242Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784243Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784245Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784246Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784248Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784250Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784251Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784252Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784253Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784254Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784255Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784256Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784259Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784260Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784261Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784262Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784263Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784264Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784265Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784266Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784267Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784268Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784274Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784278Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784279Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784281Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784286Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784305Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784307Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784309Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784311Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784314Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784315Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784318Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784320Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784321Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784323Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784324Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784325Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784326Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784327Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784328Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784329Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784330Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784331Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784332Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784334Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784341Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784356Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784385Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784390Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784397Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784398Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784401Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350784597Didn'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, '2734610') ('3318', '22163336', '1350770842', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769796', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769793', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769789', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769597', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769595', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769591', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769588', None, None, None, None, None, '2734610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 704 time used for this insertion : 0.04538154602050781 save_final save missing photos in datou_result : time spend for datou_step_exec : 130.22775149345398 time spend to save output : 0.05187702178955078 total time spend for step 2 : 130.27962851524353 step3:rle_unique_nms_with_priority Wed Apr 9 14:34:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 232 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 13 nb_hashtags : 3 time to prepare the origin masks : 5.465814590454102 time for calcul the mask position with numpy : 0.7242984771728516 nb_pixel_total : 6591339 time to create 1 rle with new method : 1.3800327777862549 time for calcul the mask position with numpy : 0.04374551773071289 nb_pixel_total : 19526 time to create 1 rle with old method : 0.023148059844970703 time for calcul the mask position with numpy : 0.03902077674865723 nb_pixel_total : 74840 time to create 1 rle with old method : 0.08242368698120117 time for calcul the mask position with numpy : 0.032143354415893555 nb_pixel_total : 93506 time to create 1 rle with old method : 0.11469244956970215 time for calcul the mask position with numpy : 0.029609203338623047 nb_pixel_total : 47954 time to create 1 rle with old method : 0.05343747138977051 time for calcul the mask position with numpy : 0.024326562881469727 nb_pixel_total : 13775 time to create 1 rle with old method : 0.021376609802246094 time for calcul the mask position with numpy : 0.030611276626586914 nb_pixel_total : 68260 time to create 1 rle with old method : 0.08501005172729492 time for calcul the mask position with numpy : 0.022301197052001953 nb_pixel_total : 18307 time to create 1 rle with old method : 0.020397424697875977 time for calcul the mask position with numpy : 0.021435260772705078 nb_pixel_total : 17065 time to create 1 rle with old method : 0.019112825393676758 time for calcul the mask position with numpy : 0.02191329002380371 nb_pixel_total : 17941 time to create 1 rle with old method : 0.02040576934814453 time for calcul the mask position with numpy : 0.022306203842163086 nb_pixel_total : 6664 time to create 1 rle with old method : 0.007541179656982422 time for calcul the mask position with numpy : 0.03514671325683594 nb_pixel_total : 14288 time to create 1 rle with old method : 0.016019344329833984 time for calcul the mask position with numpy : 0.039545536041259766 nb_pixel_total : 24075 time to create 1 rle with old method : 0.026772260665893555 time for calcul the mask position with numpy : 0.03462624549865723 nb_pixel_total : 42700 time to create 1 rle with old method : 0.047925472259521484 create new chi : 3.0766565799713135 time to delete rle : 0.026082992553710938 batch 1 Loaded 27 chid ids of type : 3594 ++++++++++++++++Number RLEs to save : 7692 TO DO : save crop sub photo not yet done ! save time : 0.5286562442779541 nb_obj : 13 nb_hashtags : 3 time to prepare the origin masks : 5.501924276351929 time for calcul the mask position with numpy : 0.47663235664367676 nb_pixel_total : 6245226 time to create 1 rle with new method : 0.47626686096191406 time for calcul the mask position with numpy : 0.034684181213378906 nb_pixel_total : 102092 time to create 1 rle with old method : 0.11772012710571289 time for calcul the mask position with numpy : 0.038164615631103516 nb_pixel_total : 1299 time to create 1 rle with old method : 0.002399921417236328 time for calcul the mask position with numpy : 0.02369213104248047 nb_pixel_total : 131328 time to create 1 rle with old method : 0.1471567153930664 time for calcul the mask position with numpy : 0.021450281143188477 nb_pixel_total : 153966 time to create 1 rle with new method : 0.7895150184631348 time for calcul the mask position with numpy : 0.04392743110656738 nb_pixel_total : 33907 time to create 1 rle with old method : 0.04436206817626953 time for calcul the mask position with numpy : 0.04295086860656738 nb_pixel_total : 27933 time to create 1 rle with old method : 0.03218817710876465 time for calcul the mask position with numpy : 0.033640146255493164 nb_pixel_total : 14795 time to create 1 rle with old method : 0.016387224197387695 time for calcul the mask position with numpy : 0.03421449661254883 nb_pixel_total : 126776 time to create 1 rle with old method : 0.1390516757965088 time for calcul the mask position with numpy : 0.03389263153076172 nb_pixel_total : 72711 time to create 1 rle with old method : 0.09089088439941406 time for calcul the mask position with numpy : 0.03845572471618652 nb_pixel_total : 99523 time to create 1 rle with old method : 0.11881828308105469 time for calcul the mask position with numpy : 0.035500288009643555 nb_pixel_total : 17366 time to create 1 rle with old method : 0.018824338912963867 time for calcul the mask position with numpy : 0.022622346878051758 nb_pixel_total : 18782 time to create 1 rle with old method : 0.02114129066467285 time for calcul the mask position with numpy : 0.025787830352783203 nb_pixel_total : 4536 time to create 1 rle with old method : 0.00522923469543457 create new chi : 2.9909496307373047 time to delete rle : 0.0019199848175048828 batch 1 Loaded 27 chid ids of type : 3594 +++++++++++++Number RLEs to save : 10583 TO DO : save crop sub photo not yet done ! save time : 0.8511581420898438 nb_obj : 25 nb_hashtags : 4 time to prepare the origin masks : 9.546398401260376 time for calcul the mask position with numpy : 0.6672701835632324 nb_pixel_total : 5774138 time to create 1 rle with new method : 0.9572873115539551 time for calcul the mask position with numpy : 0.04165363311767578 nb_pixel_total : 54554 time to create 1 rle with old method : 0.07674193382263184 time for calcul the mask position with numpy : 0.05651116371154785 nb_pixel_total : 28618 time to create 1 rle with old method : 0.04747200012207031 time for calcul the mask position with numpy : 0.04586338996887207 nb_pixel_total : 41109 time to create 1 rle with old method : 0.06193065643310547 time for calcul the mask position with numpy : 0.03596758842468262 nb_pixel_total : 20163 time to create 1 rle with old method : 0.022869110107421875 time for calcul the mask position with numpy : 0.037549734115600586 nb_pixel_total : 29059 time to create 1 rle with old method : 0.032773494720458984 time for calcul the mask position with numpy : 0.02595663070678711 nb_pixel_total : 14710 time to create 1 rle with old method : 0.01664257049560547 time for calcul the mask position with numpy : 0.021650314331054688 nb_pixel_total : 5350 time to create 1 rle with old method : 0.005978107452392578 time for calcul the mask position with numpy : 0.021749258041381836 nb_pixel_total : 38968 time to create 1 rle with old method : 0.04198718070983887 time for calcul the mask position with numpy : 0.020417213439941406 nb_pixel_total : 12131 time to create 1 rle with old method : 0.013049125671386719 time for calcul the mask position with numpy : 0.021059751510620117 nb_pixel_total : 96715 time to create 1 rle with old method : 0.1048283576965332 time for calcul the mask position with numpy : 0.020399808883666992 nb_pixel_total : 11714 time to create 1 rle with old method : 0.01221013069152832 time for calcul the mask position with numpy : 0.02013087272644043 nb_pixel_total : 7783 time to create 1 rle with old method : 0.008168935775756836 time for calcul the mask position with numpy : 0.02048015594482422 nb_pixel_total : 17761 time to create 1 rle with old method : 0.018971920013427734 time for calcul the mask position with numpy : 0.021546125411987305 nb_pixel_total : 9581 time to create 1 rle with old method : 0.010371685028076172 time for calcul the mask position with numpy : 0.021246910095214844 nb_pixel_total : 30586 time to create 1 rle with old method : 0.034291982650756836 time for calcul the mask position with numpy : 0.023076295852661133 nb_pixel_total : 38638 time to create 1 rle with old method : 0.04355001449584961 time for calcul the mask position with numpy : 0.022879838943481445 nb_pixel_total : 30673 time to create 1 rle with old method : 0.0343470573425293 time for calcul the mask position with numpy : 0.02322077751159668 nb_pixel_total : 186364 time to create 1 rle with new method : 0.8322114944458008 time for calcul the mask position with numpy : 0.0230557918548584 nb_pixel_total : 137410 time to create 1 rle with old method : 0.15178442001342773 time for calcul the mask position with numpy : 0.024723291397094727 nb_pixel_total : 71879 time to create 1 rle with old method : 0.08030104637145996 time for calcul the mask position with numpy : 0.023206710815429688 nb_pixel_total : 65747 time to create 1 rle with old method : 0.0718693733215332 time for calcul the mask position with numpy : 0.023407459259033203 nb_pixel_total : 54928 time to create 1 rle with old method : 0.06206679344177246 time for calcul the mask position with numpy : 0.024623870849609375 nb_pixel_total : 141790 time to create 1 rle with old method : 0.15528631210327148 time for calcul the mask position with numpy : 0.022779464721679688 nb_pixel_total : 92442 time to create 1 rle with old method : 0.10173702239990234 time for calcul the mask position with numpy : 0.02484917640686035 nb_pixel_total : 37429 time to create 1 rle with old method : 0.04139542579650879 create new chi : 4.448231935501099 time to delete rle : 0.002723217010498047 batch 1 Loaded 51 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++Number RLEs to save : 16429 TO DO : save crop sub photo not yet done ! save time : 1.2553083896636963 nb_obj : 15 nb_hashtags : 2 time to prepare the origin masks : 5.861572980880737 time for calcul the mask position with numpy : 0.42575836181640625 nb_pixel_total : 5925467 time to create 1 rle with new method : 1.5613183975219727 time for calcul the mask position with numpy : 0.024005413055419922 nb_pixel_total : 82198 time to create 1 rle with old method : 0.11315345764160156 time for calcul the mask position with numpy : 0.02651691436767578 nb_pixel_total : 16970 time to create 1 rle with old method : 0.02487802505493164 time for calcul the mask position with numpy : 0.024565458297729492 nb_pixel_total : 17524 time to create 1 rle with old method : 0.019463300704956055 time for calcul the mask position with numpy : 0.026656150817871094 nb_pixel_total : 11046 time to create 1 rle with old method : 0.012347936630249023 time for calcul the mask position with numpy : 0.024455785751342773 nb_pixel_total : 24271 time to create 1 rle with old method : 0.026665687561035156 time for calcul the mask position with numpy : 0.02458930015563965 nb_pixel_total : 151044 time to create 1 rle with new method : 0.4340815544128418 time for calcul the mask position with numpy : 0.022378206253051758 nb_pixel_total : 13138 time to create 1 rle with old method : 0.014495611190795898 time for calcul the mask position with numpy : 0.023692846298217773 nb_pixel_total : 196876 time to create 1 rle with new method : 0.6860935688018799 time for calcul the mask position with numpy : 0.023754596710205078 nb_pixel_total : 78760 time to create 1 rle with old method : 0.0898294448852539 time for calcul the mask position with numpy : 0.02405071258544922 nb_pixel_total : 8973 time to create 1 rle with old method : 0.00957632064819336 time for calcul the mask position with numpy : 0.022588253021240234 nb_pixel_total : 69380 time to create 1 rle with old method : 0.07648491859436035 time for calcul the mask position with numpy : 0.025032758712768555 nb_pixel_total : 114487 time to create 1 rle with old method : 0.12548828125 time for calcul the mask position with numpy : 0.022670745849609375 nb_pixel_total : 8022 time to create 1 rle with old method : 0.008917570114135742 time for calcul the mask position with numpy : 0.03659939765930176 nb_pixel_total : 277524 time to create 1 rle with new method : 1.0727639198303223 time for calcul the mask position with numpy : 0.03821587562561035 nb_pixel_total : 54560 time to create 1 rle with old method : 0.05959200859069824 create new chi : 5.265515327453613 time to delete rle : 0.0017583370208740234 batch 1 Loaded 31 chid ids of type : 3594 +++++++++++++++++Number RLEs to save : 11176 TO DO : save crop sub photo not yet done ! save time : 0.8033134937286377 nb_obj : 51 nb_hashtags : 3 time to prepare the origin masks : 4.368293523788452 time for calcul the mask position with numpy : 0.49228906631469727 nb_pixel_total : 5195399 time to create 1 rle with new method : 0.9143576622009277 time for calcul the mask position with numpy : 0.02902531623840332 nb_pixel_total : 10945 time to create 1 rle with old method : 0.012076139450073242 time for calcul the mask position with numpy : 0.028751134872436523 nb_pixel_total : 29039 time to create 1 rle with old method : 0.034877777099609375 time for calcul the mask position with numpy : 0.028593778610229492 nb_pixel_total : 31380 time to create 1 rle with old method : 0.035646915435791016 time for calcul the mask position with numpy : 0.03168845176696777 nb_pixel_total : 25612 time to create 1 rle with old method : 0.030915498733520508 time for calcul the mask position with numpy : 0.030554771423339844 nb_pixel_total : 422 time to create 1 rle with old method : 0.0006885528564453125 time for calcul the mask position with numpy : 0.028967857360839844 nb_pixel_total : 5466 time to create 1 rle with old method : 0.0062334537506103516 time for calcul the mask position with numpy : 0.029025793075561523 nb_pixel_total : 13585 time to create 1 rle with old method : 0.015435218811035156 time for calcul the mask position with numpy : 0.032387495040893555 nb_pixel_total : 339468 time to create 1 rle with new method : 0.32135510444641113 time for calcul the mask position with numpy : 0.029170989990234375 nb_pixel_total : 7987 time to create 1 rle with old method : 0.009706258773803711 time for calcul the mask position with numpy : 0.02969217300415039 nb_pixel_total : 49334 time to create 1 rle with old method : 0.05456113815307617 time for calcul the mask position with numpy : 0.02982640266418457 nb_pixel_total : 13758 time to create 1 rle with old method : 0.016310453414916992 time for calcul the mask position with numpy : 0.03504204750061035 nb_pixel_total : 15518 time to create 1 rle with old method : 0.025763511657714844 time for calcul the mask position with numpy : 0.035176753997802734 nb_pixel_total : 41111 time to create 1 rle with old method : 0.05907034873962402 time for calcul the mask position with numpy : 0.029146671295166016 nb_pixel_total : 5738 time to create 1 rle with old method : 0.006485939025878906 time for calcul the mask position with numpy : 0.028932809829711914 nb_pixel_total : 34695 time to create 1 rle with old method : 0.03943610191345215 time for calcul the mask position with numpy : 0.03276205062866211 nb_pixel_total : 25584 time to create 1 rle with old method : 0.041085243225097656 time for calcul the mask position with numpy : 0.030537128448486328 nb_pixel_total : 18844 time to create 1 rle with old method : 0.021043777465820312 time for calcul the mask position with numpy : 0.028929948806762695 nb_pixel_total : 14520 time to create 1 rle with old method : 0.01635146141052246 time for calcul the mask position with numpy : 0.029418468475341797 nb_pixel_total : 46940 time to create 1 rle with old method : 0.052922964096069336 time for calcul the mask position with numpy : 0.029352664947509766 nb_pixel_total : 32318 time to create 1 rle with old method : 0.03622579574584961 time for calcul the mask position with numpy : 0.028966188430786133 nb_pixel_total : 12372 time to create 1 rle with old method : 0.013622522354125977 time for calcul the mask position with numpy : 0.0281522274017334 nb_pixel_total : 58124 time to create 1 rle with old method : 0.063873291015625 time for calcul the mask position with numpy : 0.029499053955078125 nb_pixel_total : 7876 time to create 1 rle with old method : 0.00927591323852539 time for calcul the mask position with numpy : 0.031093358993530273 nb_pixel_total : 75931 time to create 1 rle with old method : 0.08234500885009766 time for calcul the mask position with numpy : 0.02886056900024414 nb_pixel_total : 22641 time to create 1 rle with old method : 0.026236534118652344 time for calcul the mask position with numpy : 0.028974294662475586 nb_pixel_total : 75259 time to create 1 rle with old method : 0.08110451698303223 time for calcul the mask position with numpy : 0.027695417404174805 nb_pixel_total : 10577 time to create 1 rle with old method : 0.011317968368530273 time for calcul the mask position with numpy : 0.027437686920166016 nb_pixel_total : 33242 time to create 1 rle with old method : 0.03500533103942871 time for calcul the mask position with numpy : 0.02798151969909668 nb_pixel_total : 26947 time to create 1 rle with old method : 0.029697656631469727 time for calcul the mask position with numpy : 0.0282289981842041 nb_pixel_total : 13205 time to create 1 rle with old method : 0.014745950698852539 time for calcul the mask position with numpy : 0.0293276309967041 nb_pixel_total : 78210 time to create 1 rle with old method : 0.09394478797912598 time for calcul the mask position with numpy : 0.030756235122680664 nb_pixel_total : 8796 time to create 1 rle with old method : 0.009966850280761719 time for calcul the mask position with numpy : 0.02904367446899414 nb_pixel_total : 7406 time to create 1 rle with old method : 0.008385658264160156 time for calcul the mask position with numpy : 0.029323577880859375 nb_pixel_total : 126383 time to create 1 rle with old method : 0.14283394813537598 time for calcul the mask position with numpy : 0.030237913131713867 nb_pixel_total : 12051 time to create 1 rle with old method : 0.014081001281738281 time for calcul the mask position with numpy : 0.030898094177246094 nb_pixel_total : 127346 time to create 1 rle with old method : 0.2659320831298828 time for calcul the mask position with numpy : 0.03872227668762207 nb_pixel_total : 52487 time to create 1 rle with old method : 0.07063031196594238 time for calcul the mask position with numpy : 0.03113412857055664 nb_pixel_total : 15692 time to create 1 rle with old method : 0.018680810928344727 time for calcul the mask position with numpy : 0.031047582626342773 nb_pixel_total : 106805 time to create 1 rle with old method : 0.1304013729095459 time for calcul the mask position with numpy : 0.03006458282470703 nb_pixel_total : 16192 time to create 1 rle with old method : 0.01998734474182129 time for calcul the mask position with numpy : 0.03015923500061035 nb_pixel_total : 25807 time to create 1 rle with old method : 0.028739213943481445 time for calcul the mask position with numpy : 0.029221534729003906 nb_pixel_total : 20149 time to create 1 rle with old method : 0.022521495819091797 time for calcul the mask position with numpy : 0.030779123306274414 nb_pixel_total : 12740 time to create 1 rle with old method : 0.014312505722045898 time for calcul the mask position with numpy : 0.029034137725830078 nb_pixel_total : 4306 time to create 1 rle with old method : 0.0057528018951416016 time for calcul the mask position with numpy : 0.030588626861572266 nb_pixel_total : 2807 time to create 1 rle with old method : 0.003201007843017578 time for calcul the mask position with numpy : 0.028943777084350586 nb_pixel_total : 1606 time to create 1 rle with old method : 0.0019576549530029297 time for calcul the mask position with numpy : 0.028365373611450195 nb_pixel_total : 14251 time to create 1 rle with old method : 0.01598978042602539 time for calcul the mask position with numpy : 0.02923274040222168 nb_pixel_total : 9390 time to create 1 rle with old method : 0.010484457015991211 time for calcul the mask position with numpy : 0.028981447219848633 nb_pixel_total : 10729 time to create 1 rle with old method : 0.011885404586791992 time for calcul the mask position with numpy : 0.02888035774230957 nb_pixel_total : 86560 time to create 1 rle with old method : 0.10123944282531738 time for calcul the mask position with numpy : 0.029723405838012695 nb_pixel_total : 16690 time to create 1 rle with old method : 0.020679950714111328 create new chi : 5.2125403881073 time to delete rle : 0.005156755447387695 batch 1 Loaded 103 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27435 TO DO : save crop sub photo not yet done ! save time : 2.267331600189209 nb_obj : 41 nb_hashtags : 4 time to prepare the origin masks : 3.976982593536377 time for calcul the mask position with numpy : 0.6339333057403564 nb_pixel_total : 5763503 time to create 1 rle with new method : 0.7226920127868652 time for calcul the mask position with numpy : 0.029674053192138672 nb_pixel_total : 159561 time to create 1 rle with new method : 0.5810730457305908 time for calcul the mask position with numpy : 0.02936553955078125 nb_pixel_total : 7644 time to create 1 rle with old method : 0.008631706237792969 time for calcul the mask position with numpy : 0.029002666473388672 nb_pixel_total : 17067 time to create 1 rle with old method : 0.01942753791809082 time for calcul the mask position with numpy : 0.02882981300354004 nb_pixel_total : 17701 time to create 1 rle with old method : 0.01997661590576172 time for calcul the mask position with numpy : 0.02877640724182129 nb_pixel_total : 31840 time to create 1 rle with old method : 0.036212921142578125 time for calcul the mask position with numpy : 0.0291900634765625 nb_pixel_total : 66139 time to create 1 rle with old method : 0.07347631454467773 time for calcul the mask position with numpy : 0.029124021530151367 nb_pixel_total : 47245 time to create 1 rle with old method : 0.05264544486999512 time for calcul the mask position with numpy : 0.028879880905151367 nb_pixel_total : 33989 time to create 1 rle with old method : 0.03886771202087402 time for calcul the mask position with numpy : 0.0289003849029541 nb_pixel_total : 10759 time to create 1 rle with old method : 0.01214456558227539 time for calcul the mask position with numpy : 0.028966903686523438 nb_pixel_total : 48483 time to create 1 rle with old method : 0.0546870231628418 time for calcul the mask position with numpy : 0.02890491485595703 nb_pixel_total : 24458 time to create 1 rle with old method : 0.030785560607910156 time for calcul the mask position with numpy : 0.029252290725708008 nb_pixel_total : 109455 time to create 1 rle with old method : 0.1219632625579834 time for calcul the mask position with numpy : 0.028670310974121094 nb_pixel_total : 49365 time to create 1 rle with old method : 0.054955244064331055 time for calcul the mask position with numpy : 0.028903484344482422 nb_pixel_total : 13764 time to create 1 rle with old method : 0.015477657318115234 time for calcul the mask position with numpy : 0.028849124908447266 nb_pixel_total : 13857 time to create 1 rle with old method : 0.015564203262329102 time for calcul the mask position with numpy : 0.028956174850463867 nb_pixel_total : 13086 time to create 1 rle with old method : 0.014684677124023438 time for calcul the mask position with numpy : 0.028878211975097656 nb_pixel_total : 22903 time to create 1 rle with old method : 0.02543926239013672 time for calcul the mask position with numpy : 0.028850317001342773 nb_pixel_total : 37577 time to create 1 rle with old method : 0.04164004325866699 time for calcul the mask position with numpy : 0.029125690460205078 nb_pixel_total : 20228 time to create 1 rle with old method : 0.026581525802612305 time for calcul the mask position with numpy : 0.03510093688964844 nb_pixel_total : 61352 time to create 1 rle with old method : 0.07320594787597656 time for calcul the mask position with numpy : 0.03015446662902832 nb_pixel_total : 66268 time to create 1 rle with old method : 0.07447481155395508 time for calcul the mask position with numpy : 0.029368162155151367 nb_pixel_total : 10123 time to create 1 rle with old method : 0.011560916900634766 time for calcul the mask position with numpy : 0.029766321182250977 nb_pixel_total : 17952 time to create 1 rle with old method : 0.02031564712524414 time for calcul the mask position with numpy : 0.02970600128173828 nb_pixel_total : 82277 time to create 1 rle with old method : 0.09334111213684082 time for calcul the mask position with numpy : 0.02964043617248535 nb_pixel_total : 6712 time to create 1 rle with old method : 0.007635593414306641 time for calcul the mask position with numpy : 0.02927994728088379 nb_pixel_total : 28644 time to create 1 rle with old method : 0.03193521499633789 time for calcul the mask position with numpy : 0.028951406478881836 nb_pixel_total : 25123 time to create 1 rle with old method : 0.02812361717224121 time for calcul the mask position with numpy : 0.029226303100585938 nb_pixel_total : 27716 time to create 1 rle with old method : 0.03134417533874512 time for calcul the mask position with numpy : 0.029440641403198242 nb_pixel_total : 7401 time to create 1 rle with old method : 0.008474111557006836 time for calcul the mask position with numpy : 0.029442310333251953 nb_pixel_total : 30174 time to create 1 rle with old method : 0.03402876853942871 time for calcul the mask position with numpy : 0.030179738998413086 nb_pixel_total : 13483 time to create 1 rle with old method : 0.015146493911743164 time for calcul the mask position with numpy : 0.02931499481201172 nb_pixel_total : 56339 time to create 1 rle with old method : 0.06297945976257324 time for calcul the mask position with numpy : 0.029320716857910156 nb_pixel_total : 13699 time to create 1 rle with old method : 0.01531982421875 time for calcul the mask position with numpy : 0.029169321060180664 nb_pixel_total : 36010 time to create 1 rle with old method : 0.04030656814575195 time for calcul the mask position with numpy : 0.02981257438659668 nb_pixel_total : 10859 time to create 1 rle with old method : 0.017260313034057617 time for calcul the mask position with numpy : 0.03300809860229492 nb_pixel_total : 6560 time to create 1 rle with old method : 0.010468721389770508 time for calcul the mask position with numpy : 0.03102564811706543 nb_pixel_total : 12227 time to create 1 rle with old method : 0.013849020004272461 time for calcul the mask position with numpy : 0.029015302658081055 nb_pixel_total : 6835 time to create 1 rle with old method : 0.007672309875488281 time for calcul the mask position with numpy : 0.029036283493041992 nb_pixel_total : 10866 time to create 1 rle with old method : 0.012120485305786133 time for calcul the mask position with numpy : 0.028995513916015625 nb_pixel_total : 6714 time to create 1 rle with old method : 0.007482767105102539 time for calcul the mask position with numpy : 0.028997182846069336 nb_pixel_total : 4282 time to create 1 rle with old method : 0.004872560501098633 create new chi : 4.491126537322998 time to delete rle : 0.003231048583984375 batch 1 Loaded 83 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20678 TO DO : save crop sub photo not yet done ! save time : 1.313683032989502 nb_obj : 39 nb_hashtags : 4 time to prepare the origin masks : 3.866051435470581 time for calcul the mask position with numpy : 0.2704145908355713 nb_pixel_total : 5741338 time to create 1 rle with new method : 1.1225776672363281 time for calcul the mask position with numpy : 0.028855562210083008 nb_pixel_total : 16502 time to create 1 rle with old method : 0.01862645149230957 time for calcul the mask position with numpy : 0.028847932815551758 nb_pixel_total : 16023 time to create 1 rle with old method : 0.018201828002929688 time for calcul the mask position with numpy : 0.02934575080871582 nb_pixel_total : 22719 time to create 1 rle with old method : 0.025846481323242188 time for calcul the mask position with numpy : 0.029250621795654297 nb_pixel_total : 40706 time to create 1 rle with old method : 0.04615950584411621 time for calcul the mask position with numpy : 0.029640913009643555 nb_pixel_total : 24572 time to create 1 rle with old method : 0.02716374397277832 time for calcul the mask position with numpy : 0.028947830200195312 nb_pixel_total : 67862 time to create 1 rle with old method : 0.07480025291442871 time for calcul the mask position with numpy : 0.02891373634338379 nb_pixel_total : 10724 time to create 1 rle with old method : 0.01207113265991211 time for calcul the mask position with numpy : 0.028987407684326172 nb_pixel_total : 25535 time to create 1 rle with old method : 0.028617382049560547 time for calcul the mask position with numpy : 0.02890634536743164 nb_pixel_total : 10065 time to create 1 rle with old method : 0.011283636093139648 time for calcul the mask position with numpy : 0.028852462768554688 nb_pixel_total : 6367 time to create 1 rle with old method : 0.007169246673583984 time for calcul the mask position with numpy : 0.029112815856933594 nb_pixel_total : 24296 time to create 1 rle with old method : 0.026998519897460938 time for calcul the mask position with numpy : 0.02923107147216797 nb_pixel_total : 110641 time to create 1 rle with old method : 0.12311434745788574 time for calcul the mask position with numpy : 0.029458045959472656 nb_pixel_total : 15886 time to create 1 rle with old method : 0.01805853843688965 time for calcul the mask position with numpy : 0.029824256896972656 nb_pixel_total : 27235 time to create 1 rle with old method : 0.030553102493286133 time for calcul the mask position with numpy : 0.029382944107055664 nb_pixel_total : 23280 time to create 1 rle with old method : 0.026160478591918945 time for calcul the mask position with numpy : 0.029752731323242188 nb_pixel_total : 25477 time to create 1 rle with old method : 0.029254436492919922 time for calcul the mask position with numpy : 0.03031158447265625 nb_pixel_total : 85142 time to create 1 rle with old method : 0.09517335891723633 time for calcul the mask position with numpy : 0.029429912567138672 nb_pixel_total : 25664 time to create 1 rle with old method : 0.028691530227661133 time for calcul the mask position with numpy : 0.02919769287109375 nb_pixel_total : 38943 time to create 1 rle with old method : 0.043565988540649414 time for calcul the mask position with numpy : 0.029631614685058594 nb_pixel_total : 38439 time to create 1 rle with old method : 0.0431668758392334 time for calcul the mask position with numpy : 0.02944493293762207 nb_pixel_total : 22963 time to create 1 rle with old method : 0.02586960792541504 time for calcul the mask position with numpy : 0.02926182746887207 nb_pixel_total : 14941 time to create 1 rle with old method : 0.016705751419067383 time for calcul the mask position with numpy : 0.029738187789916992 nb_pixel_total : 150482 time to create 1 rle with new method : 1.1771721839904785 time for calcul the mask position with numpy : 0.029277563095092773 nb_pixel_total : 32817 time to create 1 rle with old method : 0.03650403022766113 time for calcul the mask position with numpy : 0.029464006423950195 nb_pixel_total : 5370 time to create 1 rle with old method : 0.006110191345214844 time for calcul the mask position with numpy : 0.029262542724609375 nb_pixel_total : 183104 time to create 1 rle with new method : 0.8108806610107422 time for calcul the mask position with numpy : 0.030895233154296875 nb_pixel_total : 6364 time to create 1 rle with old method : 0.0076122283935546875 time for calcul the mask position with numpy : 0.029037952423095703 nb_pixel_total : 29044 time to create 1 rle with old method : 0.0323028564453125 time for calcul the mask position with numpy : 0.029941082000732422 nb_pixel_total : 41226 time to create 1 rle with old method : 0.04567241668701172 time for calcul the mask position with numpy : 0.02955341339111328 nb_pixel_total : 58596 time to create 1 rle with old method : 0.06534719467163086 time for calcul the mask position with numpy : 0.02921295166015625 nb_pixel_total : 17160 time to create 1 rle with old method : 0.019435882568359375 time for calcul the mask position with numpy : 0.029203414916992188 nb_pixel_total : 5149 time to create 1 rle with old method : 0.006130218505859375 time for calcul the mask position with numpy : 0.028543949127197266 nb_pixel_total : 8940 time to create 1 rle with old method : 0.010113954544067383 time for calcul the mask position with numpy : 0.028651952743530273 nb_pixel_total : 28049 time to create 1 rle with old method : 0.03089618682861328 time for calcul the mask position with numpy : 0.028722286224365234 nb_pixel_total : 11819 time to create 1 rle with old method : 0.013223409652709961 time for calcul the mask position with numpy : 0.028773069381713867 nb_pixel_total : 22359 time to create 1 rle with old method : 0.024911165237426758 time for calcul the mask position with numpy : 0.02864384651184082 nb_pixel_total : 2230 time to create 1 rle with old method : 0.002479076385498047 time for calcul the mask position with numpy : 0.03128480911254883 nb_pixel_total : 6125 time to create 1 rle with old method : 0.0068132877349853516 time for calcul the mask position with numpy : 0.02895832061767578 nb_pixel_total : 6086 time to create 1 rle with old method : 0.007676601409912109 create new chi : 5.701024293899536 time to delete rle : 0.0054187774658203125 batch 1 Loaded 79 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20077 TO DO : save crop sub photo not yet done ! save time : 1.3181259632110596 nb_obj : 35 nb_hashtags : 4 time to prepare the origin masks : 3.70890212059021 time for calcul the mask position with numpy : 0.8647561073303223 nb_pixel_total : 6083008 time to create 1 rle with new method : 0.7070302963256836 time for calcul the mask position with numpy : 0.03103041648864746 nb_pixel_total : 44086 time to create 1 rle with old method : 0.05014204978942871 time for calcul the mask position with numpy : 0.030434131622314453 nb_pixel_total : 57551 time to create 1 rle with old method : 0.07944536209106445 time for calcul the mask position with numpy : 0.03235363960266113 nb_pixel_total : 15464 time to create 1 rle with old method : 0.018081188201904297 time for calcul the mask position with numpy : 0.03021717071533203 nb_pixel_total : 26241 time to create 1 rle with old method : 0.036188602447509766 time for calcul the mask position with numpy : 0.03427743911743164 nb_pixel_total : 48030 time to create 1 rle with old method : 0.07602906227111816 time for calcul the mask position with numpy : 0.03238677978515625 nb_pixel_total : 28051 time to create 1 rle with old method : 0.03237342834472656 time for calcul the mask position with numpy : 0.029494762420654297 nb_pixel_total : 28877 time to create 1 rle with old method : 0.032312870025634766 time for calcul the mask position with numpy : 0.02908015251159668 nb_pixel_total : 4764 time to create 1 rle with old method : 0.005453824996948242 time for calcul the mask position with numpy : 0.029999971389770508 nb_pixel_total : 97053 time to create 1 rle with old method : 0.11505961418151855 time for calcul the mask position with numpy : 0.03150343894958496 nb_pixel_total : 16038 time to create 1 rle with old method : 0.017885208129882812 time for calcul the mask position with numpy : 0.0291290283203125 nb_pixel_total : 28088 time to create 1 rle with old method : 0.0313725471496582 time for calcul the mask position with numpy : 0.029561281204223633 nb_pixel_total : 43713 time to create 1 rle with old method : 0.049112796783447266 time for calcul the mask position with numpy : 0.03376054763793945 nb_pixel_total : 22206 time to create 1 rle with old method : 0.03850889205932617 time for calcul the mask position with numpy : 0.0320887565612793 nb_pixel_total : 14598 time to create 1 rle with old method : 0.016363859176635742 time for calcul the mask position with numpy : 0.029389381408691406 nb_pixel_total : 28117 time to create 1 rle with old method : 0.03177690505981445 time for calcul the mask position with numpy : 0.029547929763793945 nb_pixel_total : 17064 time to create 1 rle with old method : 0.01935887336730957 time for calcul the mask position with numpy : 0.029389142990112305 nb_pixel_total : 18910 time to create 1 rle with old method : 0.021063804626464844 time for calcul the mask position with numpy : 0.0375213623046875 nb_pixel_total : 27953 time to create 1 rle with old method : 0.034543514251708984 time for calcul the mask position with numpy : 0.030281543731689453 nb_pixel_total : 33389 time to create 1 rle with old method : 0.03747749328613281 time for calcul the mask position with numpy : 0.029686450958251953 nb_pixel_total : 20394 time to create 1 rle with old method : 0.022611618041992188 time for calcul the mask position with numpy : 0.029718637466430664 nb_pixel_total : 34590 time to create 1 rle with old method : 0.039542436599731445 time for calcul the mask position with numpy : 0.0298309326171875 nb_pixel_total : 11967 time to create 1 rle with old method : 0.01700305938720703 time for calcul the mask position with numpy : 0.030918121337890625 nb_pixel_total : 60549 time to create 1 rle with old method : 0.0751185417175293 time for calcul the mask position with numpy : 0.03146696090698242 nb_pixel_total : 14695 time to create 1 rle with old method : 0.016597747802734375 time for calcul the mask position with numpy : 0.03235936164855957 nb_pixel_total : 19793 time to create 1 rle with old method : 0.022567272186279297 time for calcul the mask position with numpy : 0.029897451400756836 nb_pixel_total : 22162 time to create 1 rle with old method : 0.024977445602416992 time for calcul the mask position with numpy : 0.029207944869995117 nb_pixel_total : 18352 time to create 1 rle with old method : 0.02044677734375 time for calcul the mask position with numpy : 0.029909372329711914 nb_pixel_total : 13420 time to create 1 rle with old method : 0.015596151351928711 time for calcul the mask position with numpy : 0.03125119209289551 nb_pixel_total : 14492 time to create 1 rle with old method : 0.01631307601928711 time for calcul the mask position with numpy : 0.03621697425842285 nb_pixel_total : 19034 time to create 1 rle with old method : 0.02252197265625 time for calcul the mask position with numpy : 0.02936267852783203 nb_pixel_total : 16159 time to create 1 rle with old method : 0.01795816421508789 time for calcul the mask position with numpy : 0.029836416244506836 nb_pixel_total : 42667 time to create 1 rle with old method : 0.05423402786254883 time for calcul the mask position with numpy : 0.03336620330810547 nb_pixel_total : 16069 time to create 1 rle with old method : 0.024927377700805664 time for calcul the mask position with numpy : 0.03265523910522461 nb_pixel_total : 23414 time to create 1 rle with old method : 0.02625417709350586 time for calcul the mask position with numpy : 0.02952122688293457 nb_pixel_total : 19282 time to create 1 rle with old method : 0.021585464477539062 create new chi : 3.883105754852295 time to delete rle : 0.00315093994140625 batch 1 Loaded 71 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18186 TO DO : save crop sub photo not yet done ! save time : 1.4860751628875732 map_output_result : {1350770842: (0.0, 'Should be the crop_list due to order', 0), 1350769796: (0.0, 'Should be the crop_list due to order', 0), 1350769793: (0.0, 'Should be the crop_list due to order', 0), 1350769789: (0.0, 'Should be the crop_list due to order', 0), 1350769597: (0.0, 'Should be the crop_list due to order', 0), 1350769595: (0.0, 'Should be the crop_list due to order', 0), 1350769591: (0.0, 'Should be the crop_list due to order', 0), 1350769588: (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 [1350770842, 1350769796, 1350769793, 1350769789, 1350769597, 1350769595, 1350769591, 1350769588] Looping around the photos to save general results len do output : 8 /1350770842.Didn't retrieve data . /1350769796.Didn't retrieve data . /1350769793.Didn't retrieve data . /1350769789.Didn't retrieve data . /1350769597.Didn't retrieve data . /1350769595.Didn't retrieve data . /1350769591.Didn't retrieve data . /1350769588.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, '2734610') ('3318', '22163336', '1350770842', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769796', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769793', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769789', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769597', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769595', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769591', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769588', None, None, None, None, None, '2734610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 3.480426549911499 save_final save missing photos in datou_result : time spend for datou_step_exec : 88.13665437698364 time spend to save output : 3.480841636657715 total time spend for step 3 : 91.61749601364136 step4:ventilate_hashtags_in_portfolio Wed Apr 9 14:36:02 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 : 22163336 get user id for portfolio 22163336 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`=22163336 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','carton','mal_croppe','flou','pehd','papier','background','pet_clair','metal','environnement','pet_fonce')) 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`=22163336 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','carton','mal_croppe','flou','pehd','papier','background','pet_clair','metal','environnement','pet_fonce')) 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`=22163336 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','carton','mal_croppe','flou','pehd','papier','background','pet_clair','metal','environnement','pet_fonce')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22163844,22163845,22163846,22163847,22163848,22163849,22163850,22163851,22163852,22163853,22163854?tags=autre,carton,mal_croppe,flou,pehd,papier,background,pet_clair,metal,environnement,pet_fonce Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350770842, 1350769796, 1350769793, 1350769789, 1350769597, 1350769595, 1350769591, 1350769588] Looping around the photos to save general results len do output : 1 /22163336. 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, '2734610') ('3318', '22163336', '1350770842', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769796', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769793', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769789', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769597', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769595', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769591', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769588', None, None, None, None, None, '2734610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.01714038848876953 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.7554833889007568 time spend to save output : 0.017421722412109375 total time spend for step 4 : 1.7729051113128662 step5:final Wed Apr 9 14:36:04 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 : {1350770842: ('0.16103178757035222',), 1350769796: ('0.16103178757035222',), 1350769793: ('0.16103178757035222',), 1350769789: ('0.16103178757035222',), 1350769597: ('0.16103178757035222',), 1350769595: ('0.16103178757035222',), 1350769591: ('0.16103178757035222',), 1350769588: ('0.16103178757035222',)} new output for save of step final : {1350770842: ('0.16103178757035222',), 1350769796: ('0.16103178757035222',), 1350769793: ('0.16103178757035222',), 1350769789: ('0.16103178757035222',), 1350769597: ('0.16103178757035222',), 1350769595: ('0.16103178757035222',), 1350769591: ('0.16103178757035222',), 1350769588: ('0.16103178757035222',)} [1350770842, 1350769796, 1350769793, 1350769789, 1350769597, 1350769595, 1350769591, 1350769588] Looping around the photos to save general results len do output : 8 /1350770842.Didn't retrieve data . /1350769796.Didn't retrieve data . /1350769793.Didn't retrieve data . /1350769789.Didn't retrieve data . /1350769597.Didn't retrieve data . /1350769595.Didn't retrieve data . /1350769591.Didn't retrieve data . /1350769588.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, '2734610') ('3318', '22163336', '1350770842', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769796', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769793', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769789', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769597', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769595', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769591', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769588', None, None, None, None, None, '2734610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.022121667861938477 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10177278518676758 time spend to save output : 0.022646665573120117 total time spend for step 5 : 0.1244194507598877 step6:blur_detection Wed Apr 9 14:36:04 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/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42.jpg resize: (2160, 3264) 1350770842 -1.9121510312586458 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6.jpg resize: (2160, 3264) 1350769796 -2.621694387897446 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287.jpg resize: (2160, 3264) 1350769793 -2.9038640955668087 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1.jpg resize: (2160, 3264) 1350769789 -3.4798681125294104 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40.jpg resize: (2160, 3264) 1350769597 -4.3998643860474544 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9.jpg resize: (2160, 3264) 1350769595 -4.427362913097076 treat image : temp/1744201828_1432173_1350769591_8baf55f43eac6182a4210942e2fd4689.jpg resize: (2160, 3264) 1350769591 -4.559674776811736 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18.jpg resize: (2160, 3264) 1350769588 -2.6318205312736054 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587893_0.png resize: (160, 316) 1350784181 -3.758405365563439 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587895_0.png resize: (95, 188) 1350784182 -1.1531549344075924 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587897_0.png resize: (139, 205) 1350784183 -2.0192830423642025 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587905_0.png resize: (232, 111) 1350784184 -2.015472111275271 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587894_0.png resize: (195, 225) 1350784186 0.419788070133977 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587899_0.png resize: (129, 165) 1350784187 -2.0505521118274204 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587902_0.png resize: (374, 259) 1350784188 -1.3448724555091027 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587896_0.png resize: (92, 103) 1350784190 -1.7099421580995122 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587909_0.png resize: (569, 235) 1350784191 -0.21736140059937203 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587917_0.png resize: (121, 162) 1350784192 -0.5825167668171456 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587907_0.png resize: (209, 119) 1350784194 -2.9755136554996473 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587906_0.png resize: (98, 79) 1350784195 -1.3596680938307133 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587912_0.png resize: (199, 82) 1350784196 1.6898521591766857 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587915_0.png resize: (461, 422) 1350784198 4.20100049122393 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587914_0.png resize: (255, 214) 1350784199 -0.9395851792291448 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587919_0.png resize: (272, 178) 1350784200 -0.38393893600533024 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587922_0.png resize: (219, 337) 1350784201 -0.90229930102912 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587933_0.png resize: (118, 180) 1350784202 -0.8557996023524991 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587927_0.png resize: (261, 190) 1350784203 -0.8661526350810078 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587932_0.png resize: (93, 186) 1350784204 -2.7015259263950817 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587931_0.png resize: (190, 179) 1350784205 0.6223478139780424 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587938_0.png resize: (211, 110) 1350784206 -1.7134858069376608 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587940_0.png resize: (663, 300) 1350784207 -2.2928196691048015 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587950_0.png resize: (424, 279) 1350784208 -0.9019170933046597 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587953_0.png resize: (725, 436) 1350784209 -1.0212424420411308 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587954_0.png resize: (129, 241) 1350784210 -1.4992714498223165 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587949_0.png resize: (170, 89) 1350784211 -1.851342425166613 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587946_0.png resize: (84, 121) 1350784212 -0.09208005457710304 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587952_0.png resize: (217, 106) 1350784213 -2.6248443193025994 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587957_0.png resize: (100, 217) 1350784214 0.8465392064418635 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587960_0.png resize: (457, 193) 1350784215 -1.7011773191951174 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751588008_0.png resize: (266, 139) 1350784216 -1.9245914452935133 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587987_0.png resize: (163, 173) 1350784217 -2.052701864147807 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587970_0.png resize: (185, 155) 1350784218 -2.4452722943307137 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587991_0.png resize: (297, 506) 1350784219 -2.560518012985731 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587979_0.png resize: (149, 71) 1350784220 -2.52014672094821 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587967_0.png resize: (218, 95) 1350784221 -0.4283274677376275 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587969_0.png resize: (199, 191) 1350784222 1.3556545771869608 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587978_0.png resize: (398, 455) 1350784223 -2.8611275220530756 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587977_0.png resize: (136, 130) 1350784224 -2.6926054817036733 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587999_0.png resize: (189, 98) 1350784225 -2.3608050645622622 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587983_0.png resize: (143, 66) 1350784226 -3.071493098691097 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751588009_0.png resize: (154, 274) 1350784227 -3.0255709004106506 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587995_0.png resize: (125, 155) 1350784228 -2.84725814865185 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587982_0.png resize: (136, 135) 1350784229 -2.7355441168444297 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587996_0.png resize: (273, 254) 1350784230 -1.9093001119902233 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751588007_0.png resize: (205, 200) 1350784231 -3.2975960081153883 treat image : 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temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588105_0.png resize: (263, 131) 1350784334 -2.193173184585426 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588103_0.png resize: (120, 167) 1350784335 -0.4945699353625137 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588097_0.png resize: (125, 154) 1350784336 -1.1395991788303528 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588112_0.png resize: (198, 126) 1350784337 -0.6644677975138509 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588094_0.png resize: (303, 275) 1350784338 -3.3395004323337503 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588115_0.png resize: (214, 314) 1350784339 -1.3183787925910515 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588108_0.png resize: (199, 139) 1350784340 -2.129050167836239 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588093_0.png resize: (330, 184) 1350784341 -2.561953261967587 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588113_0.png resize: (455, 295) 1350784342 -0.9566324715585657 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588117_0.png resize: (173, 172) 1350784343 -1.2041363430230698 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588107_0.png resize: (181, 278) 1350784344 0.08402541802229471 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588102_0.png resize: (202, 118) 1350784345 -0.38597489343913083 treat image : 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temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588121_0.png resize: (143, 195) 1350784354 -2.740207242026011 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588111_0.png resize: (260, 163) 1350784355 -2.476534274361058 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588091_0.png resize: (123, 205) 1350784356 -0.4405278985209789 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587900_0.png resize: (254, 374) 1350784373 -1.6289827268146089 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587904_0.png resize: (378, 390) 1350784374 -2.3454280626844284 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587901_0.png resize: (131, 137) 1350784375 -2.442273871582184 treat image : 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temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587981_0.png resize: (203, 197) 1350784382 -3.2608398214540135 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587973_0.png resize: (93, 88) 1350784383 -1.5991355429934404 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751588006_0.png resize: (917, 718) 1350784384 -1.2317533705265067 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587984_0.png resize: (329, 169) 1350784385 -2.666167617729617 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587966_0.png resize: (306, 178) 1350784386 -2.075832277770779 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587985_0.png resize: (298, 268) 1350784387 -3.283186771343709 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587994_0.png resize: (479, 403) 1350784389 -2.208126434437212 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588021_0.png resize: (158, 166) 1350784390 -2.6575563699257483 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588046_0.png resize: (521, 422) 1350784392 -2.365953337682837 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588015_0.png resize: (176, 339) 1350784393 -2.2365546702452086 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588028_0.png resize: (147, 193) 1350784394 -2.9071223512523665 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588018_0.png resize: (132, 141) 1350784395 -2.6227750448962692 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588043_0.png resize: (167, 146) 1350784396 -2.140312649144163 treat image : temp/1744201828_1432173_1350769591_8baf55f43eac6182a4210942e2fd4689_rle_crop_3751588076_0.png resize: (217, 167) 1350784397 -3.217027822060575 treat image : temp/1744201828_1432173_1350769591_8baf55f43eac6182a4210942e2fd4689_rle_crop_3751588069_0.png resize: (133, 173) 1350784398 -2.0300112734019407 treat image : temp/1744201828_1432173_1350769591_8baf55f43eac6182a4210942e2fd4689_rle_crop_3751588060_0.png resize: (127, 117) 1350784399 -1.0450624574433003 treat image : temp/1744201828_1432173_1350769591_8baf55f43eac6182a4210942e2fd4689_rle_crop_3751588064_0.png resize: (273, 331) 1350784400 -2.9793517618685708 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588101_0.png resize: (528, 152) 1350784401 -1.5011072154193381 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588119_0.png resize: (196, 129) 1350784402 -0.5132240036988194 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588100_0.png resize: (256, 299) 1350784403 -2.36862522275051 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588120_0.png resize: (68, 91) 1350784404 2.1423582447465797 treat image : temp/1744201828_1432173_1350770842_3bdb475f217eaf66641ccc1fce0acd42_rle_crop_3751587903_0.png resize: (248, 511) 1350784511 -1.1375160708716499 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587910_0.png resize: (523, 193) 1350784513 -1.8516308817220528 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587918_0.png resize: (462, 270) 1350784517 -3.58655269376743 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587911_0.png resize: (423, 474) 1350784520 -4.051525829554587 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587916_0.png resize: (412, 471) 1350784523 -0.28119123652933187 treat image : temp/1744201828_1432173_1350769796_edbb918d0f6d80869c1106ea8b9a47b6_rle_crop_3751587913_0.png resize: (348, 159) 1350784527 -3.399522071120284 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587943_0.png resize: (184, 391) 1350784530 -2.217761000735299 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587942_0.png resize: (187, 187) 1350784533 -2.904986051681944 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587924_0.png resize: (472, 195) 1350784536 -2.0173261955984296 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587923_0.png resize: (279, 291) 1350784539 -1.6540505834208399 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587920_0.png resize: (315, 502) 1350784540 -3.3598908937216523 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587935_0.png resize: (160, 101) 1350784541 -1.7617904994234415 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587928_0.png resize: (130, 366) 1350784542 -1.4641489961134229 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587921_0.png resize: (536, 324) 1350784543 -3.0299175379728664 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587926_0.png resize: (739, 424) 1350784544 -3.3151722179982217 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587939_0.png resize: (159, 262) 1350784545 -2.247800820601322 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587925_0.png resize: (441, 527) 1350784546 -2.7760874243501883 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587934_0.png resize: (524, 293) 1350784547 -1.9257061783893188 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587958_0.png resize: (279, 375) 1350784552 -1.3645945701520825 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587948_0.png resize: (245, 482) 1350784554 -2.6457012411061385 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587944_0.png resize: (119, 549) 1350784555 -2.3695333718160034 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587951_0.png resize: (491, 584) 1350784556 -2.3317887307960565 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587947_0.png resize: (371, 531) 1350784557 -1.4548445065933444 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587955_0.png resize: (98, 143) 1350784558 0.9563201981246358 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587945_0.png resize: (664, 512) 1350784559 -2.454918714551959 treat image : temp/1744201828_1432173_1350769789_dbb723d232dd1b89d87f650e016bdcf1_rle_crop_3751587956_0.png resize: (156, 134) 1350784560 -3.408574094465624 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588027_0.png resize: (406, 242) 1350784561 -3.0258397252012137 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588049_0.png resize: (283, 325) 1350784562 -3.961645624165558 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588048_0.png resize: (192, 291) 1350784563 -3.3870850840563693 treat image : temp/1744201828_1432173_1350769591_8baf55f43eac6182a4210942e2fd4689_rle_crop_3751588066_0.png resize: (165, 174) 1350784564 -4.9614618627124605 treat image : temp/1744201828_1432173_1350769793_76a28a6c8b3d0b23b88e47ad7cb8a287_rle_crop_3751587937_0.png resize: (84, 76) 1350784581 0.8994240945257552 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751587986_0.png resize: (251, 120) 1350784582 -0.616204447034934 treat image : temp/1744201828_1432173_1350769597_9d6a69980d96bc6a8a154308aa1f6d40_rle_crop_3751588001_0.png resize: (915, 675) 1350784583 -1.277686901257804 treat image : temp/1744201828_1432173_1350769595_890c79b83a624c0e4602287369ced6c9_rle_crop_3751588033_0.png resize: (218, 193) 1350784584 -1.238577134276834 treat image : temp/1744201828_1432173_1350769591_8baf55f43eac6182a4210942e2fd4689_rle_crop_3751588074_0.png resize: (65, 115) 1350784585 3.188143245067501 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588106_0.png resize: (96, 203) 1350784586 -1.2734128399333509 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588118_0.png resize: (235, 142) 1350784596 -0.24628585998256539 treat image : temp/1744201828_1432173_1350769588_bac1a8f95b717b8c30c0777d110a4f18_rle_crop_3751588116_0.png resize: (119, 226) 1350784597 -4.004761396960472 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 : 240 time used for this insertion : 0.02479243278503418 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 240 time used for this insertion : 0.04853558540344238 save missing photos in datou_result : time spend for datou_step_exec : 33.80190563201904 time spend to save output : 0.07940196990966797 total time spend for step 6 : 33.88130760192871 step7:brightness Wed Apr 9 14:36:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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spend to save output : 0.08513283729553223 total time spend for step 7 : 8.409333229064941 step8:velours_tree Wed Apr 9 14:36:47 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 : 0.16198253631591797 time spend to save output : 4.7206878662109375e-05 total time spend for step 8 : 0.16202974319458008 step9:send_mail_cod Wed Apr 9 14:36:47 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_P22163336_09-04-2025_14_36_47.pdf 22163844 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221638441744202207 22163845 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221638451744202207 22163846 imagette221638461744202208 22163847 imagette221638471744202208 22163848 imagette221638481744202208 22163849 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221638491744202208 22163850 imagette221638501744202210 22163851 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221638511744202210 22163852 imagette221638521744202211 22163854 change filename to text .change filename to text .imagette221638541744202211 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22163336 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22163844,22163845,22163846,22163847,22163848,22163849,22163850,22163851,22163852,22163853,22163854?tags=autre,carton,mal_croppe,flou,pehd,papier,background,pet_clair,metal,environnement,pet_fonce args[1350770842] : ((1350770842, -1.9121510312586458, 492688767), (1350770842, 0.3469889249836792, 2107752395), '0.16103178757035222') We are sending mail with results at report@fotonower.com args[1350769796] : ((1350769796, -2.621694387897446, 492609224), (1350769796, -0.09465480798625558, 496442774), '0.16103178757035222') We are sending mail with results at report@fotonower.com args[1350769793] : ((1350769793, -2.9038640955668087, 492609224), (1350769793, -0.09078881712566303, 496442774), '0.16103178757035222') We are sending mail with results at report@fotonower.com args[1350769789] : ((1350769789, -3.4798681125294104, 492609224), (1350769789, 0.06703632839869152, 2107752395), '0.16103178757035222') We are sending mail with results at report@fotonower.com args[1350769597] : ((1350769597, -4.3998643860474544, 492609224), (1350769597, -0.1319907576679306, 496442774), '0.16103178757035222') We are sending mail with results at report@fotonower.com args[1350769595] : ((1350769595, -4.427362913097076, 492609224), (1350769595, 0.02982121399112533, 2107752395), '0.16103178757035222') We are sending mail with results at report@fotonower.com args[1350769591] : ((1350769591, -4.559674776811736, 492609224), (1350769591, -0.10269982762592589, 496442774), '0.16103178757035222') We are sending mail with results at report@fotonower.com args[1350769588] : ((1350769588, -2.6318205312736054, 492609224), (1350769588, -0.1062726526284525, 496442774), '0.16103178757035222') We are sending mail with results at report@fotonower.com refus_total : 0.16103178757035222 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=22163336 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350769588,1350769591,1350769595,1350769597,1350769789,1350769793,1350769796,1350770842) Found this number of photos: 8 begin to download photo : 1350769588 begin to download photo : 1350769595 begin to download photo : 1350769789 begin to download photo : 1350769796 download finish for photo 1350769588 begin to download photo : 1350769591 download finish for photo 1350769796 begin to download photo : 1350770842 download finish for photo 1350769595 begin to download photo : 1350769597 download finish for photo 1350769789 begin to download photo : 1350769793 download finish for photo 1350770842 download finish for photo 1350769793 download finish for photo 1350769597 download finish for photo 1350769591 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163336_09-04-2025_14_36_47.pdf results_Auto_P22163336_09-04-2025_14_36_47.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163336_09-04-2025_14_36_47.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','22163336','results_Auto_P22163336_09-04-2025_14_36_47.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163336_09-04-2025_14_36_47.pdf','pdf','','0.7','0.16103178757035222') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22163336

https://www.fotonower.com/image?json=false&list_photos_id=1350770842
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350769796
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350769793
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350769789
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350769597
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350769595
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350769591
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350769588
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 16.10%
Veuillez trouver les photos des contaminants.

exemples de contaminants: autre: https://www.fotonower.com/view/22163844?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22163845?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22163849?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22163851?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22163854?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163336_09-04-2025_14_36_47.pdf.

Lien vers velours :https://www.fotonower.com/velours/22163844,22163845,22163846,22163847,22163848,22163849,22163850,22163851,22163852,22163853,22163854?tags=autre,carton,mal_croppe,flou,pehd,papier,background,pet_clair,metal,environnement,pet_fonce.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 12:36:54 GMT Content-Length: 0 Connection: close X-Message-Id: wz1cl0HjR4mIldo94GILYg 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 [1350770842, 1350769796, 1350769793, 1350769789, 1350769597, 1350769595, 1350769591, 1350769588] 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, '2734610') ('3318', '22163336', '1350770842', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769796', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769793', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769789', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769597', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769595', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769591', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769588', None, None, None, None, None, '2734610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.016577720642089844 save_final save missing photos in datou_result : time spend for datou_step_exec : 7.4475226402282715 time spend to save output : 0.016834497451782227 total time spend for step 9 : 7.464357137680054 step10:split_time_score Wed Apr 9 14:36:54 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'}] (('13', 8),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 09042025 22163336 Nombre de photos uploadées : 8 / 23040 (0%) 09042025 22163336 Nombre de photos taguées (types de déchets): 0 / 8 (0%) 09042025 22163336 Nombre de photos taguées (volume) : 0 / 8 (0%) elapsed_time : load_data_split_time_score 3.5762786865234375e-06 elapsed_time : order_list_meta_photo_and_scores 8.344650268554688e-06 ???????? elapsed_time : fill_and_build_computed_from_old_data 0.0006797313690185547 elapsed_time : insert_dashboard_record_day_entry 0.026676654815673828 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.24394142078851233 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161064_09-04-2025_12_55_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161064 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`=22161064 AND mptpi.`type`=3594 To do Qualite : 0.04609045052441947 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161067_09-04-2025_12_22_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161067 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`=22161067 AND mptpi.`type`=3726 To do Qualite : 0.1886047378056162 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161073_09-04-2025_12_52_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161073 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`=22161073 AND mptpi.`type`=3594 To do Qualite : 0.22379874656749282 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161075_09-04-2025_12_27_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161075 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`=22161075 AND mptpi.`type`=3594 To do Qualite : 0.0944780203179495 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161654_09-04-2025_13_05_02.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161654 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`=22161654 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22163334 order by id desc limit 1 Qualite : 0.16103178757035222 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163336_09-04-2025_14_36_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22163336 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`=22163336 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'09042025': {'nb_upload': 8, '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 [1350770842, 1350769796, 1350769793, 1350769789, 1350769597, 1350769595, 1350769591, 1350769588] Looping around the photos to save general results len do output : 1 /22163336Didn'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, '2734610') ('3318', '22163336', '1350770842', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769796', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769793', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769789', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769597', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769595', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769591', None, None, None, None, None, '2734610') ('3318', None, None, None, None, None, None, None, '2734610') ('3318', '22163336', '1350769588', None, None, None, None, None, '2734610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.013623714447021484 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.5505480766296387 time spend to save output : 0.013908863067626953 total time spend for step 10 : 1.5644569396972656 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 8 set_done_treatment 189.43user 106.27system 6:30.66elapsed 75%CPU (0avgtext+0avgdata 6323004maxresident)k 1150304inputs+125296outputs (28871major+16686729minor)pagefaults 0swaps