python /home/admin/mtr/script_for_cron.py -j default -m 20 -a 'python3 ~/workarea/git/Velours/python/prod/datou.py -j batch_current -C 3772610' -s carac_3318 -M 0 -S 0 -U 100,80,95 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/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', '/home/admin/workarea/git/apy', '/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 : 355232 load datou : 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) 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 : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! 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 ! 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 : (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? load thcls load pdts 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 : ['3772610'] with mtr_portfolio_ids : ['27193186'] and first list_photo_ids : [] new path : /proc/355232/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 22 ; length of list_pids : 22 ; length of list_args : 22 time to download the photos : 4.173298597335815 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 Tue Sep 30 16:56:20 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 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 : 10582 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-30 16:56:24.299334: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-30 16:56:24.332587: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-30 16:56:24.335144: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7c48000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-30 16:56:24.335206: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-30 16:56:24.340466: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-30 16:56:24.518700: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3696fdf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-30 16:56:24.518767: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-30 16:56:24.520739: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-30 16:56:24.522954: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-30 16:56:24.554347: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-30 16:56:24.573686: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-30 16:56:24.577756: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-30 16:56:24.609226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-30 16:56:24.614279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-30 16:56:24.673603: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-30 16:56:24.676695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-30 16:56:24.677202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-30 16:56:24.679310: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-30 16:56:24.679339: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-30 16:56:24.679355: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-30 16:56:24.682132: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-09-30 16:56:25.216520: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-30 16:56:25.217061: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-30 16:56:25.217080: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-30 16:56:25.217094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-30 16:56:25.217108: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-30 16:56:25.217122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-30 16:56:25.217135: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-30 16:56:25.217149: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-30 16:56:25.218345: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-30 16:56:25.219572: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-09-30 16:56:25.219602: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-30 16:56:25.219616: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-30 16:56:25.219629: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-30 16:56:25.219642: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-30 16:56:25.219655: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-30 16:56:25.219667: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-30 16:56:25.219680: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-30 16:56:25.220844: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-30 16:56:25.220878: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-30 16:56:25.220886: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-30 16:56:25.220893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-30 16:56:25.222126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9801 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 : [] 2025-09-30 16:56:33.729688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-30 16:56:34.068164: 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 : 22 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 42.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 48.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 30.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 79.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 45.00000 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: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 46.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 38.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 36.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 43.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 47.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 44.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 20.00000 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: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 23.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 49.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 47.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 42.00000 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: 1920.00000 nb d'objets trouves : 3 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 37.00000 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: 1920.00000 nb d'objets trouves : 4 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 14 Detection mask done ! Trying to reset tf kernel 355414 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5290 tf kernel not reseted sub process len(results) : 22 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 22 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 : 10582 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 : 0.00033092498779296875 nb_pixel_total : 12958 time to create 1 rle with old method : 0.015220880508422852 length of segment : 231 time for calcul the mask position with numpy : 0.00015354156494140625 nb_pixel_total : 7802 time to create 1 rle with old method : 0.009107351303100586 length of segment : 125 time for calcul the mask position with numpy : 0.0004520416259765625 nb_pixel_total : 30234 time to create 1 rle with old method : 0.03461813926696777 length of segment : 162 time for calcul the mask position with numpy : 0.0001399517059326172 nb_pixel_total : 6113 time to create 1 rle with old method : 0.007302045822143555 length of segment : 118 time for calcul the mask position with numpy : 0.00019288063049316406 nb_pixel_total : 11135 time to create 1 rle with old method : 0.01696324348449707 length of segment : 94 time for calcul the mask position with numpy : 0.0021152496337890625 nb_pixel_total : 102269 time to create 1 rle with old method : 0.13005685806274414 length of segment : 518 time for calcul the mask position with numpy : 0.0005688667297363281 nb_pixel_total : 34162 time to create 1 rle with old method : 0.038999080657958984 length of segment : 177 time for calcul the mask position with numpy : 0.00012350082397460938 nb_pixel_total : 6875 time to create 1 rle with old method : 0.008228302001953125 length of segment : 103 time for calcul the mask position with numpy : 0.0004780292510986328 nb_pixel_total : 36689 time to create 1 rle with old method : 0.04192185401916504 length of segment : 195 time for calcul the mask position with numpy : 0.00015115737915039062 nb_pixel_total : 7209 time to create 1 rle with old method : 0.008356094360351562 length of segment : 83 time for calcul the mask position with numpy : 0.00029158592224121094 nb_pixel_total : 21664 time to create 1 rle with old method : 0.02441549301147461 length of segment : 153 time for calcul the mask position with numpy : 0.0010631084442138672 nb_pixel_total : 68268 time to create 1 rle with old method : 0.07535099983215332 length of segment : 511 time for calcul the mask position with numpy : 0.0015134811401367188 nb_pixel_total : 110958 time to create 1 rle with old method : 0.1231379508972168 length of segment : 545 time for calcul the mask position with numpy : 0.0001010894775390625 nb_pixel_total : 3213 time to create 1 rle with old method : 0.0038919448852539062 length of segment : 84 time for calcul the mask position with numpy : 9.083747863769531e-05 nb_pixel_total : 4577 time to create 1 rle with old method : 0.005491018295288086 length of segment : 46 time for calcul the mask position with numpy : 0.0003104209899902344 nb_pixel_total : 17691 time to create 1 rle with old method : 0.020478248596191406 length of segment : 322 time for calcul the mask position with numpy : 0.000568389892578125 nb_pixel_total : 43273 time to create 1 rle with old method : 0.04934501647949219 length of segment : 398 time for calcul the mask position with numpy : 0.00030732154846191406 nb_pixel_total : 21679 time to create 1 rle with old method : 0.024917125701904297 length of segment : 136 time for calcul the mask position with numpy : 0.00014257431030273438 nb_pixel_total : 9583 time to create 1 rle with old method : 0.01149606704711914 length of segment : 85 time for calcul the mask position with numpy : 0.010421991348266602 nb_pixel_total : 615321 time to create 1 rle with new method : 0.04615354537963867 length of segment : 925 time for calcul the mask position with numpy : 0.0002257823944091797 nb_pixel_total : 10267 time to create 1 rle with old method : 0.011999368667602539 length of segment : 170 time for calcul the mask position with numpy : 0.00023221969604492188 nb_pixel_total : 13438 time to create 1 rle with old method : 0.01583266258239746 length of segment : 158 time for calcul the mask position with numpy : 0.0178678035736084 nb_pixel_total : 919069 time to create 1 rle with new method : 0.03495216369628906 length of segment : 994 time for calcul the mask position with numpy : 0.00029587745666503906 nb_pixel_total : 7321 time to create 1 rle with old method : 0.008360624313354492 length of segment : 122 time for calcul the mask position with numpy : 0.0003998279571533203 nb_pixel_total : 11920 time to create 1 rle with old method : 0.014223814010620117 length of segment : 141 time for calcul the mask position with numpy : 0.0020570755004882812 nb_pixel_total : 102683 time to create 1 rle with old method : 0.1130218505859375 length of segment : 510 time for calcul the mask position with numpy : 0.0002574920654296875 nb_pixel_total : 19800 time to create 1 rle with old method : 0.02311563491821289 length of segment : 85 time for calcul the mask position with numpy : 0.00024390220642089844 nb_pixel_total : 5512 time to create 1 rle with old method : 0.00616765022277832 length of segment : 116 time for calcul the mask position with numpy : 0.0006251335144042969 nb_pixel_total : 39550 time to create 1 rle with old method : 0.04588770866394043 length of segment : 168 time for calcul the mask position with numpy : 0.0019085407257080078 nb_pixel_total : 112318 time to create 1 rle with old method : 0.1289823055267334 length of segment : 532 time for calcul the mask position with numpy : 0.00011610984802246094 nb_pixel_total : 2874 time to create 1 rle with old method : 0.0033426284790039062 length of segment : 44 time for calcul the mask position with numpy : 0.001699209213256836 nb_pixel_total : 76228 time to create 1 rle with old method : 0.08735466003417969 length of segment : 450 time for calcul the mask position with numpy : 0.0015749931335449219 nb_pixel_total : 105994 time to create 1 rle with old method : 0.11780881881713867 length of segment : 530 time for calcul the mask position with numpy : 0.0001494884490966797 nb_pixel_total : 9550 time to create 1 rle with old method : 0.011177301406860352 length of segment : 83 time for calcul the mask position with numpy : 0.0003151893615722656 nb_pixel_total : 11044 time to create 1 rle with old method : 0.013059854507446289 length of segment : 92 time for calcul the mask position with numpy : 0.0003986358642578125 nb_pixel_total : 15382 time to create 1 rle with old method : 0.017680644989013672 length of segment : 115 time for calcul the mask position with numpy : 0.0023393630981445312 nb_pixel_total : 102619 time to create 1 rle with old method : 0.13999080657958984 length of segment : 508 time for calcul the mask position with numpy : 0.0002269744873046875 nb_pixel_total : 9686 time to create 1 rle with old method : 0.010973453521728516 length of segment : 82 time for calcul the mask position with numpy : 0.00019693374633789062 nb_pixel_total : 6910 time to create 1 rle with old method : 0.007940292358398438 length of segment : 81 time for calcul the mask position with numpy : 0.002167224884033203 nb_pixel_total : 104541 time to create 1 rle with old method : 0.11485719680786133 length of segment : 531 time for calcul the mask position with numpy : 0.0014910697937011719 nb_pixel_total : 98412 time to create 1 rle with old method : 0.10912656784057617 length of segment : 337 time for calcul the mask position with numpy : 0.00045108795166015625 nb_pixel_total : 9551 time to create 1 rle with old method : 0.010925531387329102 length of segment : 180 time for calcul the mask position with numpy : 0.00015425682067871094 nb_pixel_total : 4001 time to create 1 rle with old method : 0.004854679107666016 length of segment : 76 time for calcul the mask position with numpy : 0.00022912025451660156 nb_pixel_total : 4817 time to create 1 rle with old method : 0.005707263946533203 length of segment : 88 time for calcul the mask position with numpy : 0.0005366802215576172 nb_pixel_total : 16198 time to create 1 rle with old method : 0.01889204978942871 length of segment : 243 time for calcul the mask position with numpy : 0.00014519691467285156 nb_pixel_total : 3289 time to create 1 rle with old method : 0.004007816314697266 length of segment : 58 time for calcul the mask position with numpy : 0.0004169940948486328 nb_pixel_total : 22414 time to create 1 rle with old method : 0.02516460418701172 length of segment : 150 time for calcul the mask position with numpy : 0.001984834671020508 nb_pixel_total : 105488 time to create 1 rle with old method : 0.11680865287780762 length of segment : 531 time for calcul the mask position with numpy : 0.0002288818359375 nb_pixel_total : 4581 time to create 1 rle with old method : 0.005591869354248047 length of segment : 68 time for calcul the mask position with numpy : 0.0004687309265136719 nb_pixel_total : 21947 time to create 1 rle with old method : 0.0248868465423584 length of segment : 98 time for calcul the mask position with numpy : 0.0009393692016601562 nb_pixel_total : 45197 time to create 1 rle with old method : 0.05088376998901367 length of segment : 174 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1803 time to create 1 rle with old method : 0.0022439956665039062 length of segment : 31 time for calcul the mask position with numpy : 0.0023124217987060547 nb_pixel_total : 104329 time to create 1 rle with old method : 0.11584997177124023 length of segment : 506 time for calcul the mask position with numpy : 0.00016641616821289062 nb_pixel_total : 3261 time to create 1 rle with old method : 0.0037827491760253906 length of segment : 60 time for calcul the mask position with numpy : 0.00035881996154785156 nb_pixel_total : 12152 time to create 1 rle with old method : 0.014076471328735352 length of segment : 97 time spent for convertir_results : 4.346170902252197 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 55 chid ids of type : 3594 Number RLEs to save : 13220 save missing photos in datou_result : time spend for datou_step_exec : 31.974541664123535 time spend to save output : 1.5381007194519043 total time spend for step 1 : 33.51264238357544 step2:crop_condition Tue Sep 30 16:56:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 22 ! batch 1 Loaded 55 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 ! map_result returned by crop_photo_return_map_crop : length : 20 About to insert : list_path_to_insert length 20 new photo from crops ! About to upload 20 photos upload in portfolio : 3736932 init cache_photo without model_param we have 20 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1759244216_355232 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918811_fa3527d2d20e9f4691c96ef94f212c09_rle_crop_3981075822_0.png', 0, 142, 154, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918780_c3ea363b10108853d221ff3a3ee23f27_rle_crop_3981075825_0.png', 0, 85, 116, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075828_0.png', 0, 313, 173, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918730_18de9175283a8b6b19e1667540346363_rle_crop_3981075831_0.png', 0, 132, 83, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075838_0.png', 0, 221, 279, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075835_0.png', 0, 95, 84, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075839_0.png', 0, 229, 117, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075837_0.png', 0, 96, 322, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918720_d0eb5a61d0c6c4993204cf521901b907_rle_crop_3981075842_0.png', 0, 99, 170, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075849_0.png', 0, 71, 116, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075848_0.png', 0, 312, 84, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918674_650d99816a9c17eddaf4f66b1d2f6c06_rle_crop_3981075860_0.png', 0, 118, 79, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918623_11f94f237fcb0f57f381b4e37e3b5b53_rle_crop_3981075863_0.png', 0, 118, 177, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918623_11f94f237fcb0f57f381b4e37e3b5b53_rle_crop_3981075864_0.png', 0, 65, 76, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918619_f4e6c6c38f7dd2ebce3b7d45654781ea_rle_crop_3981075865_0.png', 0, 97, 88, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918619_f4e6c6c38f7dd2ebce3b7d45654781ea_rle_crop_3981075866_0.png', 0, 209, 210, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075870_0.png', 0, 123, 68, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075873_0.png', 0, 80, 31, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075875_0.png', 0, 89, 59, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244220), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075871_0.png', 0, 280, 95, 0, 1759244220,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 20 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.462609052658081 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 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 ! we have both polygon and rles Next one ! 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 : 34 About to insert : list_path_to_insert length 34 new photo from crops ! About to upload 34 photos upload in portfolio : 3736932 init cache_photo without model_param we have 34 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1759244236_355232 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918780_c3ea363b10108853d221ff3a3ee23f27_rle_crop_3981075824_0.png', 0, 269, 149, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918780_c3ea363b10108853d221ff3a3ee23f27_rle_crop_3981075823_0.png', 0, 95, 125, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075826_0.png', 0, 183, 91, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075827_0.png', 0, 320, 515, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918732_c800b83bdf2838c2faccee97f049399f_rle_crop_3981075830_0.png', 0, 251, 173, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918730_18de9175283a8b6b19e1667540346363_rle_crop_3981075832_0.png', 0, 219, 152, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918730_18de9175283a8b6b19e1667540346363_rle_crop_3981075833_0.png', 0, 252, 507, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075836_0.png', 0, 128, 44, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075834_0.png', 0, 346, 538, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918720_d0eb5a61d0c6c4993204cf521901b907_rle_crop_3981075841_0.png', 0, 1086, 881, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918720_d0eb5a61d0c6c4993204cf521901b907_rle_crop_3981075840_0.png', 0, 145, 82, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918712_3e87c120eade396223ad499c8ec27a67_rle_crop_3981075843_0.png', 0, 146, 157, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918712_3e87c120eade396223ad499c8ec27a67_rle_crop_3981075844_0.png', 0, 1142, 982, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075845_0.png', 0, 70, 121, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075846_0.png', 0, 120, 138, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075847_0.png', 0, 298, 509, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918706_9cabec5f372296bc0614523fc31561c1_rle_crop_3981075850_0.png', 0, 286, 167, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918706_9cabec5f372296bc0614523fc31561c1_rle_crop_3981075851_0.png', 0, 341, 532, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918703_ab739c97724ce37a9167b355c91e9327_rle_crop_3981075852_0.png', 0, 73, 43, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918703_ab739c97724ce37a9167b355c91e9327_rle_crop_3981075853_0.png', 0, 313, 436, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c_rle_crop_3981075856_0.png', 0, 210, 78, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c_rle_crop_3981075857_0.png', 0, 190, 104, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c_rle_crop_3981075855_0.png', 0, 140, 82, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c_rle_crop_3981075854_0.png', 0, 337, 529, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918674_650d99816a9c17eddaf4f66b1d2f6c06_rle_crop_3981075859_0.png', 0, 143, 82, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918674_650d99816a9c17eddaf4f66b1d2f6c06_rle_crop_3981075858_0.png', 0, 345, 508, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918665_437b2336c929c9110a508b66a22dbcba_rle_crop_3981075862_0.png', 0, 387, 333, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918665_437b2336c929c9110a508b66a22dbcba_rle_crop_3981075861_0.png', 0, 340, 531, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918619_f4e6c6c38f7dd2ebce3b7d45654781ea_rle_crop_3981075867_0.png', 0, 65, 57, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918617_e557312f77940f5d34974394981d45a2_rle_crop_3981075868_0.png', 0, 181, 148, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918617_e557312f77940f5d34974394981d45a2_rle_crop_3981075869_0.png', 0, 351, 525, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075872_0.png', 0, 392, 171, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075876_0.png', 0, 158, 95, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244242), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075874_0.png', 0, 332, 505, 0, 1759244242,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 34 photos in the portfolio 3736932 time of upload the photos Elapsed time : 10.679075956344604 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 ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1759244247_355232 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1759244247), 0.0, 0.0, 14, '', 0, 0, '1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075829_0.png', 0, 81, 101, 0, 1759244247,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.7226095199584961 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 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles 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 [1385918811, 1385918780, 1385918775, 1385918732, 1385918730, 1385918728, 1385918726, 1385918720, 1385918712, 1385918710, 1385918708, 1385918706, 1385918703, 1385918700, 1385918677, 1385918674, 1385918665, 1385918623, 1385918621, 1385918619, 1385918617, 1385918614] Looping around the photos to save general results len do output : 55 /1386979522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1386979657Didn'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, '3772610') ('3318', '27193186', '1385918811', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918780', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918775', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918732', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918730', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918728', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918726', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918720', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918712', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918710', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918708', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918706', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918703', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918700', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918677', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918674', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918665', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918623', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918621', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918619', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918617', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918614', None, None, None, None, None, '3772610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 187 time used for this insertion : 0.04581499099731445 save_final save missing photos in datou_result : time spend for datou_step_exec : 33.99471640586853 time spend to save output : 0.04917025566101074 total time spend for step 2 : 34.04388666152954 step3:rle_unique_nms_with_priority Tue Sep 30 16:57:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 55 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.26155614852905273 time for calcul the mask position with numpy : 0.12148332595825195 nb_pixel_total : 2060642 time to create 1 rle with new method : 0.15268898010253906 time for calcul the mask position with numpy : 0.006417036056518555 nb_pixel_total : 12958 time to create 1 rle with old method : 0.014608383178710938 create new chi : 0.3048574924468994 time to delete rle : 0.12017393112182617 batch 1 Loaded 3 chid ids of type : 3594 ++++++Number RLEs to save : 1542 TO DO : save crop sub photo not yet done ! save time : 0.3431525230407715 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.10621213912963867 time for calcul the mask position with numpy : 0.12871074676513672 nb_pixel_total : 2029451 time to create 1 rle with new method : 0.15991950035095215 time for calcul the mask position with numpy : 0.005739450454711914 nb_pixel_total : 6113 time to create 1 rle with old method : 0.006375312805175781 time for calcul the mask position with numpy : 0.005882978439331055 nb_pixel_total : 30234 time to create 1 rle with old method : 0.03319358825683594 time for calcul the mask position with numpy : 0.006270408630371094 nb_pixel_total : 7802 time to create 1 rle with old method : 0.008165359497070312 create new chi : 0.3640022277832031 time to delete rle : 0.0003304481506347656 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 1890 TO DO : save crop sub photo not yet done ! save time : 0.34896087646484375 nb_obj : 4 nb_hashtags : 3 time to prepare the origin masks : 0.10291814804077148 time for calcul the mask position with numpy : 0.15323638916015625 nb_pixel_total : 1919159 time to create 1 rle with new method : 0.17694950103759766 time for calcul the mask position with numpy : 0.00675511360168457 nb_pixel_total : 6875 time to create 1 rle with old method : 0.011269092559814453 time for calcul the mask position with numpy : 0.006677865982055664 nb_pixel_total : 34162 time to create 1 rle with old method : 0.042619943618774414 time for calcul the mask position with numpy : 0.0067102909088134766 nb_pixel_total : 102269 time to create 1 rle with old method : 0.11154007911682129 time for calcul the mask position with numpy : 0.005980491638183594 nb_pixel_total : 11135 time to create 1 rle with old method : 0.012293338775634766 create new chi : 0.5465493202209473 time to delete rle : 0.0004734992980957031 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2864 TO DO : save crop sub photo not yet done ! save time : 0.4567756652832031 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03311276435852051 time for calcul the mask position with numpy : 0.0185239315032959 nb_pixel_total : 2036911 time to create 1 rle with new method : 0.05002140998840332 time for calcul the mask position with numpy : 0.006018638610839844 nb_pixel_total : 36689 time to create 1 rle with old method : 0.04003787040710449 create new chi : 0.11484003067016602 time to delete rle : 0.00020623207092285156 batch 1 Loaded 3 chid ids of type : 3594 ++Number RLEs to save : 1470 TO DO : save crop sub photo not yet done ! save time : 0.30147480964660645 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.2890815734863281 time for calcul the mask position with numpy : 0.1317138671875 nb_pixel_total : 1976459 time to create 1 rle with new method : 0.08174848556518555 time for calcul the mask position with numpy : 0.006306886672973633 nb_pixel_total : 68268 time to create 1 rle with old method : 0.07510805130004883 time for calcul the mask position with numpy : 0.0060307979583740234 nb_pixel_total : 21664 time to create 1 rle with old method : 0.024221181869506836 time for calcul the mask position with numpy : 0.006307840347290039 nb_pixel_total : 7209 time to create 1 rle with old method : 0.008050918579101562 create new chi : 0.3502633571624756 time to delete rle : 0.0004031658172607422 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2574 TO DO : save crop sub photo not yet done ! save time : 0.4606895446777344 No data in photo_id : 1385918728 nb_obj : 6 nb_hashtags : 2 time to prepare the origin masks : 0.20400238037109375 time for calcul the mask position with numpy : 0.12500309944152832 nb_pixel_total : 1872209 time to create 1 rle with new method : 0.08315062522888184 time for calcul the mask position with numpy : 0.006249189376831055 nb_pixel_total : 21679 time to create 1 rle with old method : 0.024029016494750977 time for calcul the mask position with numpy : 0.0062444210052490234 nb_pixel_total : 43273 time to create 1 rle with old method : 0.046878814697265625 time for calcul the mask position with numpy : 0.0058896541595458984 nb_pixel_total : 17691 time to create 1 rle with old method : 0.01964879035949707 time for calcul the mask position with numpy : 0.0063266754150390625 nb_pixel_total : 4577 time to create 1 rle with old method : 0.005078792572021484 time for calcul the mask position with numpy : 0.006027698516845703 nb_pixel_total : 3213 time to create 1 rle with old method : 0.003658771514892578 time for calcul the mask position with numpy : 0.006757259368896484 nb_pixel_total : 110958 time to create 1 rle with old method : 0.1225435733795166 create new chi : 0.4781932830810547 time to delete rle : 0.0005960464477539062 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 4141 TO DO : save crop sub photo not yet done ! save time : 0.6118202209472656 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.05049419403076172 time for calcul the mask position with numpy : 0.015354394912719727 nb_pixel_total : 1438429 time to create 1 rle with new method : 0.13621187210083008 time for calcul the mask position with numpy : 0.0062558650970458984 nb_pixel_total : 10267 time to create 1 rle with old method : 0.01156926155090332 time for calcul the mask position with numpy : 0.012786626815795898 nb_pixel_total : 615321 time to create 1 rle with new method : 0.051422119140625 time for calcul the mask position with numpy : 0.005838632583618164 nb_pixel_total : 9583 time to create 1 rle with old method : 0.010698318481445312 create new chi : 0.25730180740356445 time to delete rle : 0.0005147457122802734 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 3440 TO DO : save crop sub photo not yet done ! save time : 0.4958343505859375 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.045510053634643555 time for calcul the mask position with numpy : 0.013362646102905273 nb_pixel_total : 1141093 time to create 1 rle with new method : 0.14931368827819824 time for calcul the mask position with numpy : 0.012081146240234375 nb_pixel_total : 919069 time to create 1 rle with new method : 0.027550697326660156 time for calcul the mask position with numpy : 0.005777120590209961 nb_pixel_total : 13438 time to create 1 rle with old method : 0.014621973037719727 create new chi : 0.22319984436035156 time to delete rle : 0.0005178451538085938 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 3384 TO DO : save crop sub photo not yet done ! save time : 0.5118825435638428 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.21578097343444824 time for calcul the mask position with numpy : 0.1355452537536621 nb_pixel_total : 1926455 time to create 1 rle with new method : 0.08382129669189453 time for calcul the mask position with numpy : 0.010492324829101562 nb_pixel_total : 5512 time to create 1 rle with old method : 0.005965709686279297 time for calcul the mask position with numpy : 0.005957603454589844 nb_pixel_total : 19709 time to create 1 rle with old method : 0.021732330322265625 time for calcul the mask position with numpy : 0.00695490837097168 nb_pixel_total : 102683 time to create 1 rle with old method : 0.1111154556274414 time for calcul the mask position with numpy : 0.006583690643310547 nb_pixel_total : 11920 time to create 1 rle with old method : 0.015003681182861328 time for calcul the mask position with numpy : 0.005623817443847656 nb_pixel_total : 7321 time to create 1 rle with old method : 0.007707118988037109 create new chi : 0.42671799659729004 time to delete rle : 0.0004942417144775391 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 3014 TO DO : save crop sub photo not yet done ! save time : 0.4895451068878174 No data in photo_id : 1385918708 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.07960104942321777 time for calcul the mask position with numpy : 0.025399446487426758 nb_pixel_total : 1921732 time to create 1 rle with new method : 0.143402099609375 time for calcul the mask position with numpy : 0.006628990173339844 nb_pixel_total : 112318 time to create 1 rle with old method : 0.12525629997253418 time for calcul the mask position with numpy : 0.006215095520019531 nb_pixel_total : 39550 time to create 1 rle with old method : 0.04394245147705078 create new chi : 0.3512108325958252 time to delete rle : 0.00035834312438964844 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2480 TO DO : save crop sub photo not yet done ! save time : 0.4159250259399414 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.04775714874267578 time for calcul the mask position with numpy : 0.019620180130004883 nb_pixel_total : 1994498 time to create 1 rle with new method : 0.15434718132019043 time for calcul the mask position with numpy : 0.006444692611694336 nb_pixel_total : 76228 time to create 1 rle with old method : 0.0828695297241211 time for calcul the mask position with numpy : 0.005738258361816406 nb_pixel_total : 2874 time to create 1 rle with old method : 0.0030748844146728516 create new chi : 0.2788853645324707 time to delete rle : 0.00035381317138671875 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2068 TO DO : save crop sub photo not yet done ! save time : 0.37867188453674316 No data in photo_id : 1385918700 nb_obj : 4 nb_hashtags : 1 time to prepare the origin masks : 0.05894947052001953 time for calcul the mask position with numpy : 0.06001591682434082 nb_pixel_total : 1931630 time to create 1 rle with new method : 0.14918208122253418 time for calcul the mask position with numpy : 0.0061571598052978516 nb_pixel_total : 15382 time to create 1 rle with old method : 0.016660690307617188 time for calcul the mask position with numpy : 0.0055751800537109375 nb_pixel_total : 11044 time to create 1 rle with old method : 0.012466669082641602 time for calcul the mask position with numpy : 0.005648136138916016 nb_pixel_total : 9550 time to create 1 rle with old method : 0.01022195816040039 time for calcul the mask position with numpy : 0.006310462951660156 nb_pixel_total : 105994 time to create 1 rle with old method : 0.11331915855407715 create new chi : 0.3959674835205078 time to delete rle : 0.00025200843811035156 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2720 TO DO : save crop sub photo not yet done ! save time : 0.4744088649749756 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.047246694564819336 time for calcul the mask position with numpy : 0.14027786254882812 nb_pixel_total : 1954385 time to create 1 rle with new method : 0.08160638809204102 time for calcul the mask position with numpy : 0.0059697628021240234 nb_pixel_total : 6910 time to create 1 rle with old method : 0.0076904296875 time for calcul the mask position with numpy : 0.0056819915771484375 nb_pixel_total : 9686 time to create 1 rle with old method : 0.01108694076538086 time for calcul the mask position with numpy : 0.006052494049072266 nb_pixel_total : 102619 time to create 1 rle with old method : 0.11342668533325195 create new chi : 0.382382869720459 time to delete rle : 0.0003838539123535156 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2422 TO DO : save crop sub photo not yet done ! save time : 0.41338562965393066 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.05265545845031738 time for calcul the mask position with numpy : 0.0199429988861084 nb_pixel_total : 1870647 time to create 1 rle with new method : 0.09065747261047363 time for calcul the mask position with numpy : 0.006482124328613281 nb_pixel_total : 98412 time to create 1 rle with old method : 0.10827159881591797 time for calcul the mask position with numpy : 0.0066661834716796875 nb_pixel_total : 104541 time to create 1 rle with old method : 0.1152048110961914 create new chi : 0.354541540145874 time to delete rle : 0.00036454200744628906 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2816 TO DO : save crop sub photo not yet done ! save time : 0.4809610843658447 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.04152703285217285 time for calcul the mask position with numpy : 0.018177509307861328 nb_pixel_total : 2060048 time to create 1 rle with new method : 0.159928560256958 time for calcul the mask position with numpy : 0.00604557991027832 nb_pixel_total : 4001 time to create 1 rle with old method : 0.004525661468505859 time for calcul the mask position with numpy : 0.0055811405181884766 nb_pixel_total : 9551 time to create 1 rle with old method : 0.010605573654174805 create new chi : 0.2152998447418213 time to delete rle : 0.0002987384796142578 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1592 TO DO : save crop sub photo not yet done ! save time : 0.32832884788513184 No data in photo_id : 1385918621 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.09296941757202148 time for calcul the mask position with numpy : 0.14442157745361328 nb_pixel_total : 2049296 time to create 1 rle with new method : 0.17936277389526367 time for calcul the mask position with numpy : 0.006050825119018555 nb_pixel_total : 3289 time to create 1 rle with old method : 0.0038514137268066406 time for calcul the mask position with numpy : 0.006085395812988281 nb_pixel_total : 16198 time to create 1 rle with old method : 0.020480632781982422 time for calcul the mask position with numpy : 0.006159543991088867 nb_pixel_total : 4817 time to create 1 rle with old method : 0.0055942535400390625 create new chi : 0.38250207901000977 time to delete rle : 0.00032591819763183594 batch 1 Loaded 7 chid ids of type : 3594 +++++Number RLEs to save : 1858 TO DO : save crop sub photo not yet done ! save time : 0.370664119720459 nb_obj : 2 nb_hashtags : 1 time to prepare the origin masks : 0.04282188415527344 time for calcul the mask position with numpy : 0.018012285232543945 nb_pixel_total : 1945698 time to create 1 rle with new method : 0.08148956298828125 time for calcul the mask position with numpy : 0.00634765625 nb_pixel_total : 105488 time to create 1 rle with old method : 0.11461210250854492 time for calcul the mask position with numpy : 0.00576329231262207 nb_pixel_total : 22414 time to create 1 rle with old method : 0.02413463592529297 create new chi : 0.2577197551727295 time to delete rle : 0.00045108795166015625 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 2442 TO DO : save crop sub photo not yet done ! save time : 0.40029454231262207 nb_obj : 7 nb_hashtags : 2 time to prepare the origin masks : 0.5036218166351318 time for calcul the mask position with numpy : 0.05692267417907715 nb_pixel_total : 1881617 time to create 1 rle with new method : 0.2551248073577881 time for calcul the mask position with numpy : 0.006141185760498047 nb_pixel_total : 12152 time to create 1 rle with old method : 0.014174461364746094 time for calcul the mask position with numpy : 0.005964517593383789 nb_pixel_total : 3261 time to create 1 rle with old method : 0.003690481185913086 time for calcul the mask position with numpy : 0.0066950321197509766 nb_pixel_total : 104329 time to create 1 rle with old method : 0.11391663551330566 time for calcul the mask position with numpy : 0.006636381149291992 nb_pixel_total : 849 time to create 1 rle with old method : 0.0010182857513427734 time for calcul the mask position with numpy : 0.006420612335205078 nb_pixel_total : 44864 time to create 1 rle with old method : 0.04868745803833008 time for calcul the mask position with numpy : 0.0064105987548828125 nb_pixel_total : 21947 time to create 1 rle with old method : 0.0241696834564209 time for calcul the mask position with numpy : 0.005974292755126953 nb_pixel_total : 4581 time to create 1 rle with old method : 0.005040645599365234 create new chi : 0.5772807598114014 time to delete rle : 0.0006136894226074219 batch 1 Loaded 15 chid ids of type : 3594 +++++++Number RLEs to save : 3077 TO DO : save crop sub photo not yet done ! save time : 0.5462174415588379 map_output_result : {1385918811: (0.0, 'Should be the crop_list due to order', 0), 1385918780: (0.0, 'Should be the crop_list due to order', 0), 1385918775: (0.0, 'Should be the crop_list due to order', 0), 1385918732: (0.0, 'Should be the crop_list due to order', 0), 1385918730: (0.0, 'Should be the crop_list due to order', 0), 1385918728: (0.0, 'Should be the crop_list due to order', 0.0), 1385918726: (0.0, 'Should be the crop_list due to order', 0), 1385918720: (0.0, 'Should be the crop_list due to order', 0), 1385918712: (0.0, 'Should be the crop_list due to order', 0), 1385918710: (0.0, 'Should be the crop_list due to order', 0), 1385918708: (0.0, 'Should be the crop_list due to order', 0.0), 1385918706: (0.0, 'Should be the crop_list due to order', 0), 1385918703: (0.0, 'Should be the crop_list due to order', 0), 1385918700: (0.0, 'Should be the crop_list due to order', 0.0), 1385918677: (0.0, 'Should be the crop_list due to order', 0), 1385918674: (0.0, 'Should be the crop_list due to order', 0), 1385918665: (0.0, 'Should be the crop_list due to order', 0), 1385918623: (0.0, 'Should be the crop_list due to order', 0), 1385918621: (0.0, 'Should be the crop_list due to order', 0.0), 1385918619: (0.0, 'Should be the crop_list due to order', 0), 1385918617: (0.0, 'Should be the crop_list due to order', 0), 1385918614: (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 [1385918811, 1385918780, 1385918775, 1385918732, 1385918730, 1385918728, 1385918726, 1385918720, 1385918712, 1385918710, 1385918708, 1385918706, 1385918703, 1385918700, 1385918677, 1385918674, 1385918665, 1385918623, 1385918621, 1385918619, 1385918617, 1385918614] Looping around the photos to save general results len do output : 22 /1385918811.Didn't retrieve data . /1385918780.Didn't retrieve data . /1385918775.Didn't retrieve data . /1385918732.Didn't retrieve data . /1385918730.Didn't retrieve data . /1385918728.Didn't retrieve data . /1385918726.Didn't retrieve data . /1385918720.Didn't retrieve data . /1385918712.Didn't retrieve data . /1385918710.Didn't retrieve data . /1385918708.Didn't retrieve data . /1385918706.Didn't retrieve data . /1385918703.Didn't retrieve data . /1385918700.Didn't retrieve data . /1385918677.Didn't retrieve data . /1385918674.Didn't retrieve data . /1385918665.Didn't retrieve data . /1385918623.Didn't retrieve data . /1385918621.Didn't retrieve data . /1385918619.Didn't retrieve data . /1385918617.Didn't retrieve data . /1385918614.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, '3772610') ('3318', '27193186', '1385918811', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918780', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918775', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918732', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918730', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918728', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918726', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918720', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918712', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918710', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918708', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918706', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918703', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918700', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918677', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918674', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918665', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918623', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918621', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918619', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918617', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918614', None, None, None, None, None, '3772610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 66 time used for this insertion : 0.03753662109375 save_final save missing photos in datou_result : time spend for datou_step_exec : 17.267397165298462 time spend to save output : 0.038460493087768555 total time spend for step 3 : 17.30585765838623 step4:ventilate_hashtags_in_portfolio Tue Sep 30 16:57:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 27193186 get user id for portfolio 27193186 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`=27193186 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','flou','metal','background','pehd','pet_fonce','pet_clair','mal_croppe','papier','carton','environnement')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=27193186 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','flou','metal','background','pehd','pet_fonce','pet_clair','mal_croppe','papier','carton','environnement')) AND mptpi.`min_score`=0.5 To do 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`=27193186 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('autre','flou','metal','background','pehd','pet_fonce','pet_clair','mal_croppe','papier','carton','environnement')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/27356587,27356588,27356589,27356590,27356591,27356592,27356593,27356594,27356595,27356596,27356597?tags=autre,flou,metal,background,pehd,pet_fonce,pet_clair,mal_croppe,papier,carton,environnement Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1385918811, 1385918780, 1385918775, 1385918732, 1385918730, 1385918728, 1385918726, 1385918720, 1385918712, 1385918710, 1385918708, 1385918706, 1385918703, 1385918700, 1385918677, 1385918674, 1385918665, 1385918623, 1385918621, 1385918619, 1385918617, 1385918614] Looping around the photos to save general results len do output : 1 /27193186. 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, '3772610') ('3318', '27193186', '1385918811', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918780', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918775', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918732', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918730', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918728', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918726', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918720', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918712', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918710', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918708', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918706', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918703', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918700', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918677', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918674', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918665', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918623', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918621', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918619', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918617', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918614', None, None, None, None, None, '3772610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 23 time used for this insertion : 0.03959226608276367 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.377352714538574 time spend to save output : 0.03992509841918945 total time spend for step 4 : 2.4172778129577637 step5:final Tue Sep 30 16:57:47 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 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 : {1385918811: ('0.07265451827300783',), 1385918780: ('0.07265451827300783',), 1385918775: ('0.07265451827300783',), 1385918732: ('0.07265451827300783',), 1385918730: ('0.07265451827300783',), 1385918728: ('0.07265451827300783',), 1385918726: ('0.07265451827300783',), 1385918720: ('0.07265451827300783',), 1385918712: ('0.07265451827300783',), 1385918710: ('0.07265451827300783',), 1385918708: ('0.07265451827300783',), 1385918706: ('0.07265451827300783',), 1385918703: ('0.07265451827300783',), 1385918700: ('0.07265451827300783',), 1385918677: ('0.07265451827300783',), 1385918674: ('0.07265451827300783',), 1385918665: ('0.07265451827300783',), 1385918623: ('0.07265451827300783',), 1385918621: ('0.07265451827300783',), 1385918619: ('0.07265451827300783',), 1385918617: ('0.07265451827300783',), 1385918614: ('0.07265451827300783',)} new output for save of step final : {1385918811: ('0.07265451827300783',), 1385918780: ('0.07265451827300783',), 1385918775: ('0.07265451827300783',), 1385918732: ('0.07265451827300783',), 1385918730: ('0.07265451827300783',), 1385918728: ('0.07265451827300783',), 1385918726: ('0.07265451827300783',), 1385918720: ('0.07265451827300783',), 1385918712: ('0.07265451827300783',), 1385918710: ('0.07265451827300783',), 1385918708: ('0.07265451827300783',), 1385918706: ('0.07265451827300783',), 1385918703: ('0.07265451827300783',), 1385918700: ('0.07265451827300783',), 1385918677: ('0.07265451827300783',), 1385918674: ('0.07265451827300783',), 1385918665: ('0.07265451827300783',), 1385918623: ('0.07265451827300783',), 1385918621: ('0.07265451827300783',), 1385918619: ('0.07265451827300783',), 1385918617: ('0.07265451827300783',), 1385918614: ('0.07265451827300783',)} [1385918811, 1385918780, 1385918775, 1385918732, 1385918730, 1385918728, 1385918726, 1385918720, 1385918712, 1385918710, 1385918708, 1385918706, 1385918703, 1385918700, 1385918677, 1385918674, 1385918665, 1385918623, 1385918621, 1385918619, 1385918617, 1385918614] Looping around the photos to save general results len do output : 22 /1385918811.Didn't retrieve data . /1385918780.Didn't retrieve data . /1385918775.Didn't retrieve data . /1385918732.Didn't retrieve data . /1385918730.Didn't retrieve data . /1385918728.Didn't retrieve data . /1385918726.Didn't retrieve data . /1385918720.Didn't retrieve data . /1385918712.Didn't retrieve data . /1385918710.Didn't retrieve data . /1385918708.Didn't retrieve data . /1385918706.Didn't retrieve data . /1385918703.Didn't retrieve data . /1385918700.Didn't retrieve data . /1385918677.Didn't retrieve data . /1385918674.Didn't retrieve data . /1385918665.Didn't retrieve data . /1385918623.Didn't retrieve data . /1385918621.Didn't retrieve data . /1385918619.Didn't retrieve data . /1385918617.Didn't retrieve data . /1385918614.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, '3772610') ('3318', '27193186', '1385918811', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918780', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918775', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918732', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918730', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918728', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918726', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918720', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918712', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918710', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918708', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918706', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918703', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918700', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918677', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918674', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918665', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918623', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918621', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918619', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918617', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918614', None, None, None, None, None, '3772610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 66 time used for this insertion : 0.03861498832702637 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.3757443428039551 time spend to save output : 0.03969097137451172 total time spend for step 5 : 0.4154353141784668 step6:blur_detection Tue Sep 30 16:57:48 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/1759244176_355232_1385918811_fa3527d2d20e9f4691c96ef94f212c09.jpg resize: (1080, 1920) 1385918811 -2.9232143230470746 treat image : temp/1759244176_355232_1385918780_c3ea363b10108853d221ff3a3ee23f27.jpg resize: (1080, 1920) 1385918780 -0.06692886180466057 treat image : temp/1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088.jpg resize: (1080, 1920) 1385918775 -2.963594456437328 treat image : temp/1759244176_355232_1385918732_c800b83bdf2838c2faccee97f049399f.jpg resize: (1080, 1920) 1385918732 -0.727537769645004 treat image : temp/1759244176_355232_1385918730_18de9175283a8b6b19e1667540346363.jpg resize: (1080, 1920) 1385918730 -0.5275303321157556 treat image : temp/1759244176_355232_1385918728_5462e46aea8ea9b49ffab79a9d0dc645.jpg resize: (1080, 1920) 1385918728 -0.07307825870394025 treat image : temp/1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a.jpg resize: (1080, 1920) 1385918726 -3.0097905153465216 treat image : temp/1759244176_355232_1385918720_d0eb5a61d0c6c4993204cf521901b907.jpg resize: (1080, 1920) 1385918720 0.043581180351700076 treat image : temp/1759244176_355232_1385918712_3e87c120eade396223ad499c8ec27a67.jpg resize: (1080, 1920) 1385918712 1.4911450399338464 treat image : temp/1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17.jpg resize: (1080, 1920) 1385918710 -1.8116458050942432 treat image : temp/1759244176_355232_1385918708_0c5b582f7ecc888c75602ddfc84f5693.jpg resize: (1080, 1920) 1385918708 0.5607612647802612 treat image : temp/1759244176_355232_1385918706_9cabec5f372296bc0614523fc31561c1.jpg resize: (1080, 1920) 1385918706 -3.915667796348709 treat image : temp/1759244176_355232_1385918703_ab739c97724ce37a9167b355c91e9327.jpg resize: (1080, 1920) 1385918703 -0.2991500675901506 treat image : temp/1759244176_355232_1385918700_361e31e6e037012417feb273c4523fcf.jpg resize: (1080, 1920) 1385918700 -0.9676412443106854 treat image : temp/1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c.jpg resize: (1080, 1920) 1385918677 -3.421231426121388 treat image : temp/1759244176_355232_1385918674_650d99816a9c17eddaf4f66b1d2f6c06.jpg resize: (1080, 1920) 1385918674 -4.3452520899426474 treat image : temp/1759244176_355232_1385918665_437b2336c929c9110a508b66a22dbcba.jpg resize: (1080, 1920) 1385918665 4.320113925596685 treat image : temp/1759244176_355232_1385918623_11f94f237fcb0f57f381b4e37e3b5b53.jpg resize: (1080, 1920) 1385918623 -1.1070237424854177 treat image : temp/1759244176_355232_1385918621_5f32b5cd2e1c30e68575479019446262.jpg resize: (1080, 1920) 1385918621 -0.39474228751321805 treat image : temp/1759244176_355232_1385918619_f4e6c6c38f7dd2ebce3b7d45654781ea.jpg resize: (1080, 1920) 1385918619 1.348300647793795 treat image : temp/1759244176_355232_1385918617_e557312f77940f5d34974394981d45a2.jpg resize: (1080, 1920) 1385918617 0.9915207411549152 treat image : temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761.jpg resize: (1080, 1920) 1385918614 -3.5334570827520078 treat image : temp/1759244176_355232_1385918811_fa3527d2d20e9f4691c96ef94f212c09_rle_crop_3981075822_0.png resize: (154, 142) 1386979522 -3.6694472604132384 treat image : temp/1759244176_355232_1385918780_c3ea363b10108853d221ff3a3ee23f27_rle_crop_3981075825_0.png resize: (116, 85) 1386979523 -1.211837389253384 treat image : temp/1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075828_0.png resize: (173, 313) 1386979524 -3.5486051060806894 treat image : temp/1759244176_355232_1385918730_18de9175283a8b6b19e1667540346363_rle_crop_3981075831_0.png resize: (83, 132) 1386979525 -0.9791968417284387 treat image : temp/1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075838_0.png resize: (279, 221) 1386979526 -3.079559057373173 treat image : temp/1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075835_0.png resize: (84, 95) 1386979527 -1.9185011377746026 treat image : temp/1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075839_0.png resize: (117, 229) 1386979528 -2.031723603659375 treat image : temp/1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075837_0.png resize: (322, 96) 1386979529 -1.828296036666523 treat image : temp/1759244176_355232_1385918720_d0eb5a61d0c6c4993204cf521901b907_rle_crop_3981075842_0.png resize: (170, 99) 1386979530 -0.5770267498077858 treat image : temp/1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075849_0.png resize: (116, 71) 1386979538 -0.7455171049870104 treat image : temp/1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075848_0.png resize: (84, 312) 1386979539 -0.7677473507112658 treat image : temp/1759244176_355232_1385918674_650d99816a9c17eddaf4f66b1d2f6c06_rle_crop_3981075860_0.png resize: (79, 118) 1386979540 -4.01050370492122 treat image : temp/1759244176_355232_1385918623_11f94f237fcb0f57f381b4e37e3b5b53_rle_crop_3981075863_0.png resize: (177, 118) 1386979541 -1.7058739322225795 treat image : temp/1759244176_355232_1385918623_11f94f237fcb0f57f381b4e37e3b5b53_rle_crop_3981075864_0.png resize: (76, 65) 1386979543 0.926927904603111 treat image : temp/1759244176_355232_1385918619_f4e6c6c38f7dd2ebce3b7d45654781ea_rle_crop_3981075865_0.png resize: (88, 97) 1386979544 -0.37054953444471267 treat image : temp/1759244176_355232_1385918619_f4e6c6c38f7dd2ebce3b7d45654781ea_rle_crop_3981075866_0.png resize: (210, 209) 1386979545 -1.025771481634719 treat image : temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075870_0.png resize: (68, 123) 1386979546 -1.9292138059932387 treat image : temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075873_0.png resize: (31, 80) 1386979547 -1.9601693761654013 treat image : temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075875_0.png resize: (59, 89) 1386979548 -1.337500490733212 treat image : temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075871_0.png resize: (95, 280) 1386979549 -3.3507991736746523 treat image : temp/1759244176_355232_1385918780_c3ea363b10108853d221ff3a3ee23f27_rle_crop_3981075824_0.png resize: (149, 269) 1386979614 0.08862998015595452 treat image : temp/1759244176_355232_1385918780_c3ea363b10108853d221ff3a3ee23f27_rle_crop_3981075823_0.png resize: (125, 95) 1386979615 -2.1786115055408106 treat image : temp/1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075826_0.png resize: (91, 183) 1386979616 -1.7597066585899899 treat image : temp/1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075827_0.png resize: (515, 320) 1386979617 0.45421680441441786 treat image : temp/1759244176_355232_1385918732_c800b83bdf2838c2faccee97f049399f_rle_crop_3981075830_0.png resize: (173, 251) 1386979618 -1.3720131568542062 treat image : temp/1759244176_355232_1385918730_18de9175283a8b6b19e1667540346363_rle_crop_3981075832_0.png resize: (152, 219) 1386979623 -0.316752909395429 treat image : temp/1759244176_355232_1385918730_18de9175283a8b6b19e1667540346363_rle_crop_3981075833_0.png resize: (507, 252) 1386979624 -0.4142589002950002 treat image : temp/1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075836_0.png resize: (44, 128) 1386979625 -4.175811860218376 treat image : temp/1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a_rle_crop_3981075834_0.png resize: (538, 346) 1386979626 0.5466010576709954 treat image : temp/1759244176_355232_1385918720_d0eb5a61d0c6c4993204cf521901b907_rle_crop_3981075841_0.png resize: (881, 1086) 1386979627 0.35200348604386134 treat image : temp/1759244176_355232_1385918720_d0eb5a61d0c6c4993204cf521901b907_rle_crop_3981075840_0.png resize: (82, 145) 1386979628 -2.018072911810471 treat image : temp/1759244176_355232_1385918712_3e87c120eade396223ad499c8ec27a67_rle_crop_3981075843_0.png resize: (157, 146) 1386979629 -1.7446962553054497 treat image : temp/1759244176_355232_1385918712_3e87c120eade396223ad499c8ec27a67_rle_crop_3981075844_0.png resize: (982, 1142) 1386979630 0.8285668408987884 treat image : temp/1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075845_0.png resize: (121, 70) 1386979631 0.12702878291480507 treat image : temp/1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075846_0.png resize: (138, 120) 1386979632 -3.0731100263443287 treat image : temp/1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17_rle_crop_3981075847_0.png resize: (509, 298) 1386979633 0.3749271479679858 treat image : temp/1759244176_355232_1385918706_9cabec5f372296bc0614523fc31561c1_rle_crop_3981075850_0.png resize: (167, 286) 1386979634 -1.5659665675972307 treat image : temp/1759244176_355232_1385918706_9cabec5f372296bc0614523fc31561c1_rle_crop_3981075851_0.png resize: (532, 341) 1386979639 0.3280294653306857 treat image : temp/1759244176_355232_1385918703_ab739c97724ce37a9167b355c91e9327_rle_crop_3981075852_0.png resize: (43, 73) 1386979640 -0.49417587261441936 treat image : temp/1759244176_355232_1385918703_ab739c97724ce37a9167b355c91e9327_rle_crop_3981075853_0.png resize: (436, 313) 1386979641 -0.22597592103469147 treat image : temp/1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c_rle_crop_3981075856_0.png resize: (78, 210) 1386979643 -4.374597684091811 treat image : temp/1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c_rle_crop_3981075857_0.png resize: (104, 190) 1386979644 -4.054679326139368 treat image : temp/1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c_rle_crop_3981075855_0.png resize: (82, 140) 1386979645 -3.7551759037397243 treat image : temp/1759244176_355232_1385918677_68d35589977cc262a550e7766864ca5c_rle_crop_3981075854_0.png resize: (529, 337) 1386979646 0.10311106734330337 treat image : temp/1759244176_355232_1385918674_650d99816a9c17eddaf4f66b1d2f6c06_rle_crop_3981075859_0.png resize: (82, 143) 1386979647 -3.862683365783987 treat image : temp/1759244176_355232_1385918674_650d99816a9c17eddaf4f66b1d2f6c06_rle_crop_3981075858_0.png resize: (508, 345) 1386979648 0.10826443178382142 treat image : temp/1759244176_355232_1385918665_437b2336c929c9110a508b66a22dbcba_rle_crop_3981075862_0.png resize: (333, 387) 1386979649 -0.2873458625736705 treat image : temp/1759244176_355232_1385918665_437b2336c929c9110a508b66a22dbcba_rle_crop_3981075861_0.png resize: (531, 340) 1386979650 0.43036381851191274 treat image : temp/1759244176_355232_1385918619_f4e6c6c38f7dd2ebce3b7d45654781ea_rle_crop_3981075867_0.png resize: (57, 65) 1386979651 0.37774859700083974 treat image : temp/1759244176_355232_1385918617_e557312f77940f5d34974394981d45a2_rle_crop_3981075868_0.png resize: (148, 181) 1386979652 -0.23712645489654846 treat image : temp/1759244176_355232_1385918617_e557312f77940f5d34974394981d45a2_rle_crop_3981075869_0.png resize: (525, 351) 1386979653 0.5196698800778093 treat image : temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075872_0.png resize: (171, 392) 1386979654 -2.8663944455501844 treat image : temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075876_0.png resize: (95, 158) 1386979655 -2.046300473678472 treat image : temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075874_0.png resize: (505, 332) 1386979656 0.38615186923289185 treat image : temp/1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075829_0.png resize: (101, 81) 1386979657 2.3147775426813975 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 : 77 time used for this insertion : 0.04195713996887207 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 77 time used for this insertion : 0.0400393009185791 save missing photos in datou_result : time spend for datou_step_exec : 18.906311750411987 time spend to save output : 0.09953045845031738 total time spend for step 6 : 19.005842208862305 step7:brightness Tue Sep 30 16:58:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1759244176_355232_1385918811_fa3527d2d20e9f4691c96ef94f212c09.jpg treat image : temp/1759244176_355232_1385918780_c3ea363b10108853d221ff3a3ee23f27.jpg treat image : temp/1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088.jpg treat image : temp/1759244176_355232_1385918732_c800b83bdf2838c2faccee97f049399f.jpg treat image : temp/1759244176_355232_1385918730_18de9175283a8b6b19e1667540346363.jpg treat image : temp/1759244176_355232_1385918728_5462e46aea8ea9b49ffab79a9d0dc645.jpg treat image : temp/1759244176_355232_1385918726_c2005d63bec8ccf88d0d6e5d1d10113a.jpg treat image : temp/1759244176_355232_1385918720_d0eb5a61d0c6c4993204cf521901b907.jpg treat image : temp/1759244176_355232_1385918712_3e87c120eade396223ad499c8ec27a67.jpg treat image : temp/1759244176_355232_1385918710_902731c3c3d0e8f7e8f9a1aa7f7dae17.jpg treat image : 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temp/1759244176_355232_1385918614_19d9e9a4f0a8b638c276c9d187b9f761_rle_crop_3981075874_0.png treat image : temp/1759244176_355232_1385918775_1010e9664b1168ca916a31fffc70c088_rle_crop_3981075829_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 77 time used for this insertion : 0.04196310043334961 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 77 time used for this insertion : 0.03958010673522949 save missing photos in datou_result : time spend for datou_step_exec : 5.497380018234253 time spend to save output : 0.10010838508605957 total time spend for step 7 : 5.5974884033203125 step8:velours_tree Tue Sep 30 16:58:12 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 : 0.1867671012878418 time spend to save output : 4.267692565917969e-05 total time spend for step 8 : 0.18680977821350098 step9:send_mail_cod Tue Sep 30 16:58:12 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 in order to get the selector url, please entre the license of selector results_Auto_P27193186_30-09-2025_16_58_12.pdf 27356587 change filename to text .imagette273565871759244292 27356588 imagette273565881759244293 27356589 imagette273565891759244293 27356590 imagette273565901759244293 27356591 imagette273565911759244293 27356592 imagette273565921759244293 27356593 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette273565931759244293 27356594 imagette273565941759244295 27356595 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette273565951759244295 27356596 imagette273565961759244296 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=27193186 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/27356587,27356588,27356589,27356590,27356591,27356592,27356593,27356594,27356595,27356596,27356597?tags=autre,flou,metal,background,pehd,pet_fonce,pet_clair,mal_croppe,papier,carton,environnement args[1385918811] : ((1385918811, -2.9232143230470746, 492609224), (1385918811, 0.35129257352479243, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918780] : ((1385918780, -0.06692886180466057, 492688767), (1385918780, 0.9117413061762477, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918775] : ((1385918775, -2.963594456437328, 492609224), (1385918775, 0.5018246297444406, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918732] : ((1385918732, -0.727537769645004, 492688767), (1385918732, 0.2814752193250679, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918730] : ((1385918730, -0.5275303321157556, 492688767), (1385918730, 1.7021606468070236, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918728] : ((1385918728, -0.07307825870394025, 492688767), (1385918728, 0.6858332315871167, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918726] : ((1385918726, -3.0097905153465216, 492609224), (1385918726, 0.5117801942692801, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918720] : ((1385918720, 0.043581180351700076, 492688767), (1385918720, 0.43927294549954593, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918712] : ((1385918712, 1.4911450399338464, 492688767), (1385918712, 0.7784545889809115, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918710] : ((1385918710, -1.8116458050942432, 492688767), (1385918710, 0.5107596550351742, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918708] : ((1385918708, 0.5607612647802612, 492688767), (1385918708, 0.5479959652227085, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918706] : ((1385918706, -3.915667796348709, 492609224), (1385918706, 0.4157086255018543, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918703] : ((1385918703, -0.2991500675901506, 492688767), (1385918703, 0.995847231915602, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918700] : ((1385918700, -0.9676412443106854, 492688767), (1385918700, 0.8194096417110891, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918677] : ((1385918677, -3.421231426121388, 492609224), (1385918677, 0.5351431793664349, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918674] : ((1385918674, -4.3452520899426474, 492609224), (1385918674, 0.520847875960946, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918665] : ((1385918665, 4.320113925596685, 492688767), (1385918665, 0.49623399654478734, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918623] : ((1385918623, -1.1070237424854177, 492688767), (1385918623, 1.036044732080799, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918621] : ((1385918621, -0.39474228751321805, 492688767), (1385918621, 0.6314687979064396, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918619] : ((1385918619, 1.348300647793795, 492688767), (1385918619, 0.565823233094061, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918617] : ((1385918617, 0.9915207411549152, 492688767), (1385918617, 0.7133413656759493, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com args[1385918614] : ((1385918614, -3.5334570827520078, 492609224), (1385918614, 0.4510899245753016, 2107752395), '0.07265451827300783') We are sending mail with results at report@fotonower.com refus_total : 0.07265451827300783 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=27193186 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27193186_30-09-2025_16_58_12.pdf results_Auto_P27193186_30-09-2025_16_58_12.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27193186_30-09-2025_16_58_12.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','27193186','results_Auto_P27193186_30-09-2025_16_58_12.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27193186_30-09-2025_16_58_12.pdf','pdf','','0.36','0.07265451827300783') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/27193186

https://www.fotonower.com/image?json=false&list_photos_id=1385918811
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918780
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918775
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918732
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918730
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918728
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918726
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918720
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918712
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.4911450399338464)
https://www.fotonower.com/image?json=false&list_photos_id=1385918710
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918708
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918706
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918703
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918700
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918677
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918674
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918665
La photo est trop floue, merci de reprendre une photo.(avec le score = 4.320113925596685)
https://www.fotonower.com/image?json=false&list_photos_id=1385918623
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918621
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385918619
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.348300647793795)
https://www.fotonower.com/image?json=false&list_photos_id=1385918617
La photo est trop floue, merci de reprendre une photo.(avec le score = 0.9915207411549152)
https://www.fotonower.com/image?json=false&list_photos_id=1385918614
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 7.27%
Veuillez trouver les photos des contaminants.

exemples de contaminants: autre: https://www.fotonower.com/view/27356587?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/27356593?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/27356595?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27193186_30-09-2025_16_58_12.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/27356587,27356588,27356589,27356590,27356591,27356592,27356593,27356594,27356595,27356596,27356597?tags=autre,flou,metal,background,pehd,pet_fonce,pet_clair,mal_croppe,papier,carton,environnement.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 30 Sep 2025 14:58:19 GMT Content-Length: 0 Connection: close X-Message-Id: oPwJ8S1zT3iK7361_OqpTA 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 [1385918811, 1385918780, 1385918775, 1385918732, 1385918730, 1385918728, 1385918726, 1385918720, 1385918712, 1385918710, 1385918708, 1385918706, 1385918703, 1385918700, 1385918677, 1385918674, 1385918665, 1385918623, 1385918621, 1385918619, 1385918617, 1385918614] 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, '3772610') ('3318', '27193186', '1385918811', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918780', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918775', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918732', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918730', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918728', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918726', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918720', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918712', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918710', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918708', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918706', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918703', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918700', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918677', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918674', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918665', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918623', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918621', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918619', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918617', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918614', None, None, None, None, None, '3772610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 22 time used for this insertion : 0.04171609878540039 save_final save missing photos in datou_result : time spend for datou_step_exec : 6.712412118911743 time spend to save output : 0.04201006889343262 total time spend for step 9 : 6.754422187805176 step10:split_time_score Tue Sep 30 16:58:19 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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'}] (('16', 22),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 24092025 27193186 Nombre de photos uploadées : 22 / 23040 (0%) 24092025 27193186 Nombre de photos taguées (types de déchets): 0 / 22 (0%) 24092025 27193186 Nombre de photos taguées (volume) : 0 / 22 (0%) elapsed_time : load_data_split_time_score 2.384185791015625e-06 elapsed_time : order_list_meta_photo_and_scores 7.3909759521484375e-06 ?????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0015869140625 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.6413776874542236 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 : 0.0 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27176479_24-09-2025_10_11_17.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27176479 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`=27176479 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27193183 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27193184 order by id desc limit 1 Qualite : 0.07265451827300783 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27193186_30-09-2025_16_58_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27193186 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`=27193186 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27195478 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'24092025': {'nb_upload': 22, '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 [1385918811, 1385918780, 1385918775, 1385918732, 1385918730, 1385918728, 1385918726, 1385918720, 1385918712, 1385918710, 1385918708, 1385918706, 1385918703, 1385918700, 1385918677, 1385918674, 1385918665, 1385918623, 1385918621, 1385918619, 1385918617, 1385918614] Looping around the photos to save general results len do output : 1 /27193186Didn'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, '3772610') ('3318', '27193186', '1385918811', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918780', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918775', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918732', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918730', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918728', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918726', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918720', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918712', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918710', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918708', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918706', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918703', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918700', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918677', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918674', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918665', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918623', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918621', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918619', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918617', None, None, None, None, None, '3772610') ('3318', None, None, None, None, None, None, None, '3772610') ('3318', '27193186', '1385918614', None, None, None, None, None, '3772610') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 23 time used for this insertion : 0.04201555252075195 save_final save missing photos in datou_result : time spend for datou_step_exec : 3.5915584564208984 time spend to save output : 0.04232954978942871 total time spend for step 10 : 3.633888006210327 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 22 set_done_treatment 65.25user 24.68system 2:11.62elapsed 68%CPU (0avgtext+0avgdata 3058348maxresident)k 2385584inputs+32152outputs (8657major+2091183minor)pagefaults 0swaps